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Natural visual stimuli have highly structured spatial and temporal properties which influence the way visual information is encoded in the visual pathway. In response to natural scene stimuli, neurons in the lateral geniculate nucleus (LGN) are temporally precise – on a time scale of 10–25ms – both within single cells and across cells within a population. This time scale, established by non stimulus-driven elements of neuronal firing, is significantly shorter than that of natural scenes, yet is critical for the neural representation of the spatial and temporal structure of the scene. Here, a generalized linear model (GLM) that combines stimulus-driven elements with spike-history dependence associated with intrinsic cellular dynamics is shown to predict the fine timing precision of LGN responses to natural scene stimuli, the corresponding correlation structure across nearby neurons in the population, and the continuous modulation of spike timing precision and latency across neurons. A single model captured the experimentally observed neural response, across different levels of contrasts and different classes of visual stimuli, through interactions between the stimulus correlation structure and the nonlinearity in spike generation and spike history dependence. Given the sensitivity of the thalamocortical synapse to closely timed spikes and the importance of fine timing precision for the faithful representation of natural scenes, the modulation of thalamic population timing over these time scales is likely important for cortical representations of the dynamic natural visual environment.
Natural visual stimuli have highly structured spatial and temporal properties, which influence the way visual information is encoded in the visual pathway (Field, 1987; Atick and Redlich, 1992; van Hateren, 1992; Dong and Atick, 1995; Simoncelli and Olshausen, 2001). Early visual neurons exhibit episodic activity, with firing "events" (i.e., small clusters of spikes) clearly separated by periods of silence (Dan et al., 1996; Berry et al., 1997; Reinagel and Reid, 2000; Keat et al., 2001). We recently showed that, in the case of natural visual stimuli, the response of neurons in the lateral geniculate nucleus (LGN) is temporally precise – on a time scale of 10–25ms – both within single cells and across cells within a population (Desbordes et al., 2008). Given the sensitivity of the thalamocortical synapse to closely timed spikes, both from single neurons and from several neurons projecting to a common cortical target (Alonso et al., 1996; Dan et al., 1998; Usrey and Reid, 1999; Kara and Reid, 2003; Bruno and Sakmann, 2006; Kumbhani et al., 2007), thalamic precision at this time scale is likely important for cortical computation in the natural visual environment.
The timing of thalamic activity is significantly more precise than the time scale of the more slowly varying natural scene would dictate (Butts et al., 2007). This suggests that some elements of neuronal firing which are not stimulus-driven, such as the recent history of spiking (due to refractoriness, burstiness, fast adaptation, etc.), are crucial in shaping the fine temporal precision of early visual neurons (Berry and Meister, 1998; Keat et al., 2001). However, conventional models of the early visual system (reviewed in Carandini et al., 2005) do not capture this level of temporal precision because, by construction, these models predict the neural response based solely on the visual input and their temporal resolution is thus limited to the time scale of the visual stimulus. Paradoxically, the fast time scale of the fine temporal precision of the neural response is necessary for the faithful representation of the more slowly varying natural scene (Butts et al., 2007), and is thus an important element of population models.
Here, LGN neurons were fit with a generalized linear model (GLM) (Paninski, 2004; Truccolo et al., 2005; Paninski et al., 2007; Pillow et al., 2008) designed to predict the neural response to spatiotemporal visual stimuli while capturing the non stimulus-driven influence of spike-history dependence. The model was able to match the fine timing precision of LGN responses to natural scene stimuli, not only on single trials, but also on individual firing events. The model was then used to analyze in detail the timing of spikes across neurons in the LGN population, demonstrating the degree to which the timing of spikes in one neuron relative to another is continuously modulated in response to natural scenes, in terms of both latency and precision. These fluctuations in temporal precision across LGN cells likely play an important role in the neural population code used in the early visual system to represent the dynamically varying properties of natural scenes.
Single-cell activity was recorded extracellularly in layer A of the Lateral Geniculate Nucleus (LGN) of anesthetized and paralyzed cats using a multielectrode matrix of seven electrodes. Two animals were used for a total of four electrode penetrations. In the first animal 7 cells were recorded simultaneously in the first electrode penetration, 9 cells in the second, 13 in the third, and in the second animal 8 cells were recorded simultaneously in a single electrode penetration, for a total of 37 cells. Surgical and experimental procedures were performed in accordance with United States Department of Agriculture guidelines and were approved by the Institutional Animal Care and Use Committee at the State University of New York, State College of Optometry. As described in Weng et al. (2005), cats were initially anesthetized with ketamine (10mg/kg intramuscular) followed by thiopental sodium (20mg/kg, intravenous, supplemented as needed during surgery; and at a continuous rate of 1–2mg/kg/h intravenous during recording). A craniotomy and duratomy were made to introduce recording electrodes into the LGN (anterior: 5.5; lateral 10.5). Animals were paralyzed with atracurium besylate (0.6–1mg/kg/hr intravenous) to minimize eye movements, and were artificially ventilated. The multielectrode array was introduced in the brain with an angle that was precisely adjusted (25–30 degrees antero-posterior, 2–5 degrees lateral–central) to record from iso-retinotopic lines across the depth of the LGN. A glass guide tube with an inner diameter of ≈300μm at the tip was attached to the shaft probe of the multi-electrode to reduce the inter-electrode distances to approximately 80–300 μm. Layer A of LGN was physiologically identified by performing several electrode penetrations to map the retinotopic organization of the LGN and center the multielectrode array at the retinotopic location selected for this study (5–10 degrees eccentricity). Recorded voltage signals were conventionally amplified, filtered, and passed to a computer running the RASPUTIN software package (Plexon). For each cell, spike waveforms were identified initially during the experiment and were verified carefully off-line by spike-sorting analysis. Cells were classified as X or Y according to their responses to counterphase sinusoidal gratings. Cells were eliminated from this study if they did not have at least 2Hz mean firing rates in response to all stimulus conditions, or if the maximum amplitude of their spike-triggered average in response to spatiotemporal white noise stimuli was not at least five times greater than the amplitude outside of the receptive field area. All cells in the final dataset had space–time separable receptive fields.
Visual stimuli included four different classes of movies: spatiotemporal binary white noise (WN, or WR if repeated multiple times) or natural scenes (NS, or NR if repeated). The natural scenes movies were taken from "cat-cam" movies recorded from a small camera mounted on top of a cat's head while roaming in grasslands and forests (Kayser et al., 2003). As in Lesica et al. (2007), to improve temporal resolution, these movies were interpolated by a factor of two (from 25 to 50 Hz) using commercial software (MotionPerfect, Dynapel Systems Inc.) and then presented at 60 frames per second, i.e., at 1.2× speed. Following interpolation, the intensities of each movie frame were rescaled to have a mean value of 125 (where the full range of intensity values was 0–255). Each movie (either WN, WR or NS, NR) spanned 48×48 pixels at an angular resolution of 0.2 degree per pixel.
The model was fitted with spatiotemporal white noise at high contrast (0.55 RMS contrast). The WN movie used for fitting the model was 9600 frames long and 2.7min in duration. The first NR movie (presented to 28 of the cells) was 750 frames long (12.5s), while the second NR movie (presented to the remaining 5 cells) was 600 frames long (10s). The stimuli were presented at 60 frames per second with a 120-Hz monitor refresh rate, such that each frame was displayed twice. Each movie was repeated 64–120 times at each of two global levels of luminance contrast, HC and LC (Lesica et al., 2007; Desbordes et al., 2008).
The responses of LGN neurons to those various visual stimuli were analyzed with a Generalized Linear Model approach recently applied to in-vitro recordings of retinal ganglion cells (Pillow et al., 2008). This class of model is a generalization of the well-known linear–nonlinear Poisson (LNP) cascade model (Paninski, 2004; Simoncelli et al., 2004; Truccolo et al., 2005). The LNP model cannot capture elements of the neural response that give rise to the high degree of temporal precision found in the LGN (Alonso et al., 1996; Dan et al., 1998; Kara et al., 2000; Usrey, 2002; Butts et al., 2007; Kumbhani et al., 2007; Desbordes et al., 2008). In this study, we show that the GLM can capture this fine precision.
The present GLM is an encoding spiking model whose input is a spatiotemporal visual stimulus and whose output consists of the times of spikes emitted by each cell in response to the visual input. The model for each cell i included a spatiotemporal filter ki, a constant μi specifying the logarithm of the baseline firing rate, a static exponential nonlinearity, a Poisson spike generator, and a re-entrant post-spike filter hi which captured the overall influence of spike history (caused by refractoriness, burstiness, fast adaptation in the local population of neurons, etc.). The model output is therefore a conditionally Poisson process in which the conditional intensity function (a.k.a. instantaneous firing rate) depends on both the visual stimulus and the recent spiking activity (Barbieri et al., 2001). It should be noted that the re-entrant spike history component hi is a functional representation of the combination of intrinsic voltage-gated ion channel conductances and external influences from the local neural circuitry.
The model consisted of two separate pathways (roughly corresponding to the center and surround of the classical receptive field) where each pathway had its own spatial filter (25 parameters, one per pixel) and temporal filter (5 parameters), for a total of 60 parameters. The number of spatial and temporal parameters were determined empirically in preliminary simulations. Using less than 25 spatial parameters and 5 temporal parameters significantly degraded the prediction results, while increasing these numbers did not improve the model predictions for single cell responses. The spatial receptive field encompassed 25 pixels (arranged in a square), where the length of one pixel spanned 0.2 degree of visual angle. The temporal filter was 300-ms long and was parametrized by a linear combination of five basis functions, using a basis of raised cosine "bumps" of the form
for t such that a log(t+ c)[j− π,j+ π] and 0 elsewhere, with π/2 spacing between the j. The constants a and c were free parameters which could be adjusted to improve model fits. This basis allowed for the representation of fine temporal structure near the time of a spike and coarser (smoother) dependency at later times (Pillow et al., 2005).
The re-entrant post-spike filter hi was parametrized by a linear combination of seven raised cosine basis functions of the same form as those for the temporal filter in ki, but with different values for the a and c parameters. Again, these free parameters could be adjusted to improve model fits – in particular, to match the structure observed in auto- and cross-correlation functions.
The model was fitted to the responses of LGN cells recorded extracellularly in anesthetized cats presented with a movie stimulus which consisted of spatiotemporal binary white noise (see Visual stimuli above). The model was fitted at 0.1-ms resolution using Maximum Likelihood estimation (Paninski, 2004). In short, the fitting procedure involved maximizing the probability of the observed neural spike train as a function of the model parameters at the times when the actual cell produced a spike, while minimizing it at the times when the cell was silent. This probability, also known as spiking conditional intensity (or spike rate) was given for each individual cell i by
where x(t) is the spatiotemporal stimulus, yi(t) is the cell's own spike train history at time t, and μi is the logarithm of the cell's baseline firing rate. The log-likelihood for each cell was
where tsp denotes the set of (actual) spike times. The population log-likelihood was the sum over single-cell log-likelihoods. The optimization procedure used to maximize this function was implemented in Matlab (Mathworks, Natick, MA) using the native function "fminunc" from the Optimization toolbox.
The model was then used to simulate the response of each cell to new stimuli (not used for model fitting). The spikes predicted by the model were compared to the actual spikes from the corresponding LGN cells in response to new short movie sequences repeated 64 to 120 times, either of spatiotemporal binary white noise (White noise Repeated, WR) or of "cat-cam" natural scenes (Natural scenes Repeated, NR).
Both the model fitting and the simulations were implemented for parallel processing on a computer grid (n=50 processors) using Matlab Parallel Computing Toolbox.
For each cell at each level of contrast (HC or LC), the peri-stimulus time histogram (PSTH) was computed as the cumulative response of the cell over all 64–120 repeats of the same short movie. Each PSTH was therefore 10 or 12.5 s long, depending on the duration of the stimulus presented to the cell. The PSTH was computed with a bin size of 16.7ms (the duration of one movie frame).
The goodness-of-fit of the model was estimated by r, the correlation coefficient between the experimental and predicted PSTH, which is equal to the square root of the percentage of variance in the PSTH across trials that could be explained by the model (r2). The r statistics was computed between the cell's actual PSTH and the model simulated PSTH in response to 64 repeats (in the case of the 8 cells from one of the electrodes penetrations) or 120 repeats (in all other penetrations) of the same visual stimulus, at a time resolution of one frame (16.7ms) using Matlab Statistics Toolbox (function "regstats").
Previous studies typically defined temporal precision of single neurons as the standard deviation of the spike times within an identified event across trials (Mainen and Sejnowski, 1995; Berry et al., 1997; Kara et al., 2000; Uzzell and Chichilnisky, 2004; Avissar et al., 2007; Kumbhani et al., 2007). In this study, as in Desbordes et al. (2008), we first defined a related measure which is the (temporal) width of the central peak in the PSTH autocorrelation (Brody, 1999). The width of PSTH events and the width of the PSTH autocorrelation function are directly related, by a factor of in the Gaussian approximation. In computing the PSTH (and its autocorrelation), all spike trains that the cell produced in response to multiple repeats of an identical stimulus were collapsed into one "lumped" spike train (i.e., a PSTH with a 1-ms bin size, of the same duration as a single presentation of the movie, i.e., 10 or 12.5s). In the PSTH autocorrelation measure, the relative timings of spikes within a given trial or across all trials were confounded. To investigate within-trial temporal precision, we therefore computed a different measure: the width of the central broad peak in the spike autocorrelation, which we defined as the autocorrelation function of the full (several minutes long) spike train without collapsing the trials together (Perkel et al., 1967; Brillinger et al., 1976).
We similarly defined two types of cross-correlation: spike cross-correlation (Perkel et al., 1967; Brillinger et al., 1976; see also Park et al., 2008) and PSTH cross-correlation, which is the cross-correlation between two PSTHs. Spike cross-correlation width gives the spike timing variability across cells within each trial. PSTH cross-correlation has a different meaning: it is approximately equivalent to the "shuffled" or "shifted" spike correlation, in which each spike train of one cell is paired with a spike train of the other cell recorded during a different repeat of the same stimulus. The PSTH cross-correlation averages correlations from all possible pairwise combinations of repeats (actually including the non-shuffled one, which is only one in thousands of combinations and therefore has a negligible contribution).
All four types of correlation functions (spike or PSTH, auto- or cross-correlation) were made analogous to Pearson's correlation coefficient by (i) subtracting the product of the average firing rates, and (ii) dividing by a normalization factor (see below), such that correlation could take values between −1 and 1. To determine the existence of a central peak or trough in a correlation function, we found the Gaussian function that best fitted the central ±100ms, in a least-mean-square sense. The standard deviation of this Gaussian provides a measure of the correlation width. In the case of autocorrelation, the height Ai of the best-fitting Gaussian was measured for each cell i and was subsequently set to 1 to normalize the autocorrelation function. In the case of cross-correlation between cells i and j, the best-fitting Gaussian was normalized by a factor of , where Ai and Aj are the heights of each respective autocorrelation function before normalization. The area under the Gaussian curve after normalization was used to define the strength of the cross-correlation between two neurons.
The analysis of firing "events" in the model-simulated data was similar to that performed on the neural data recorded extracellularly (Desbordes et al., 2008; Butts et al., 2010). The time of each event was identified in the neural data and used to assign the simulated spikes to their respective events.
PSTH events were first defined in the experimentally-recorded PSTH at high contrast (HC) as times of firing interspersed with periods of silence lasting at least 20ms. If the standard deviation of all spike times constituting an event was less than 20ms, an attempt was made to break up the event into several events, a procedure in which the spikes were fitted to a mixture-of-Gaussians model using the Expectation Maximization (EM) algorithm for maximum likelihood (Dempster et al., 1977). PSTH events at low contrast (LC) were then defined by aligning LC spikes to existing HC events if possible, with a preference for an HC event that occurred earlier rather than after the LC spike (since it is known that spikes tend to be more delayed at LC than HC). If no corresponding HC event was found, a new event was created at LC, with a corresponding empty event at HC. The timing of an event on a given repeat was defined as the average time across repeats of the first spike belonging to this event. Event time variability was the standard deviation of the timing of the event across repeats. For each event, spike autocorrelation was computed as described above, on the simulated data.
For each cell pair, starting from the single-cell event analysis above, each event from the first cell was matched to one or several events in the second cell with which it overlapped in time. If several events in one cell could be matched to a single event in the other cell, these events were merged into one. The list of all events that could be matched across the two cells constituted the list of "shared events." For each shared event, the event time variability was the standard deviation of the timing of the event across repeats and across both cells. For each shared event, spike cross-correlation was computed as described above, on the simulated data.
We investigated the properties of the neural population code in the LGN in response to a natural visual stimulus consisting of a movie recorded from a small camera mounted on top of a cat's head while roaming in grasslands and forests ("cat-cam" movie; Kayser et al., 2003). Extracellular single-unit activity was recorded simultaneously from multiple cells in the LGN of anesthetized cats in response to repeated presentations of the movie (Figure (Figure1A).1A). Figure Figure1B1B shows a typical LGN cell response to repeated stimulation for a short segment of the movie (at HC, i.e., 40% RMS contrast). While the spiking events were fairly reproducible from trial to trial, neural activity exhibited some variability at a finer time scale. As shown at the bottom of the panel, the variability in spiking activity that gives rise to the characteristic width of the peri-stimulus time histogram (PSTH) events can be decomposed into two distinct sources: (i) variability in the timing of a firing event from trial to trial and (ii) variability of spike times within each event, which we have described in detail previously (Desbordes et al., 2008), both of which having implications for the neural code.
Previous modeling studies of early visual neurons have suggested that non stimulus-driven elements of neuronal firing are crucial in shaping the trial-averaged fine temporal precision of single cell response (Berry and Meister, 1998; Keat et al., 2001; Uzzell and Chichilnisky, 2004), which may be critical in faithfully encoding features of the more slowly varying natural scene (Butts et al., 2007). To systematically explore spiking precision in the context of event timing across trials and across neurons within a population, we turned to a generalized linear model (GLM) which incorporates not only stimulus-driven elements captured in the classical spatiotemporal receptive field but also non-stimulus elements captured in spike-history dependence (Paninski, 2004; Paninski et al., 2007; Pillow et al., 2008). In this framework, the firing activity is modeled as a conditionally Poisson process with a rate that depends on both the visual stimulus and the recent spiking activity. Note that this history dependence ensures that the response itself is strongly non-Poisson, with the global spike count variability lower than expected from Poisson statistics, as reported in neurons from the early visual system (Berry et al., 1997; Kara et al., 2000; Keat et al., 2001; Uzzell and Chichilnisky, 2004). The model fitting is then cast as a maximum likelihood estimation problem, a well-posed optimization problem for this framework (Paninski et al., 2007).
Figure Figure22 shows the model framework, which transforms the visual input (in the form of a spatiotemporal signal) into a series of spikes. The visual stimulus is first passed through a spatiotemporal filter corresponding to the classical receptive field of the neuron, which yields a temporal signal that we refer to as the filtered stimulus. This signal is then passed through a static exponential nonlinearity, resulting in the conditional intensity function which drives the Poisson spike generator. In the absence of spike-history dependence, the model reduces to the classical linear non-linear Poisson (LNP) model structure (Chichilnisky, 2001; Dayan and Abbott, 2001). In the full GLM, however, each generated spike is fed back through a spike history temporal filter and sums with the filtered stimulus, thereby modifying the conditional intensity function and potentially influencing the probability of future spiking. See Methods for more details on the model structure and fitting procedure.
The model was fitted to LGN activity recorded extracellularly during presentation of a non-repeating spatiotemporal white noise movie. An example of a GLM fit to a typical LGN X ON cell is shown in Figure Figure3A,3A, while the fits on all cells (n=37) are summarized in Figure Figure3B.3B. The spatiotemporal receptive fields were consistent with previous findings with a center-dominant, weak-surround spatial component and a biphasic temporal component. All spike history terms exhibited a short refractory period following the occurrence of a spike followed by an increase in probability of firing, as shown in Figure Figure3A3A (right panel and corresponding inset).
After fitting, the model was used to predict the neural response to the natural scene stimulus (not used for fitting), for comparison with the actual recorded responses of the cells. Figure Figure4A4A illustrates the response of a typical LGN X OFF cell to a short segment of the natural movie. The "event-like" patterns observed in the experimentally recorded cell response (first panel from the top) were captured by the GLM (second panel). They were also captured by the LNP model (third panel), here implemented as a GLM without the spike history component (h; see Materials and Methods) – consistent with the notion that the occurrence of events is tied to the visual stimulus (Butts et al., 2010).
The cell responses during multiple repeats of this visual stimulus were summarized in the peri-stimulus time histogram (PSTH) which sums the responses across trials into a measure of instantaneous firing rate, here computed with a 17-ms bin (which is the duration of one frame of the visual stimulus). As illustrated at the bottom of Figure Figure4A,4A, the PSTHs of the predicted cell response (green) and the experimentally recorded response (black) largely overlapped, indicating that the model was able to predict the occurrence of these events and their timing. For this cell, the correlation coefficient between the experimental and predicted PSTHs across 120 repeats was r=0.39 for the GLM and r=0.28 for the LNP model. Across all cells (n=37), the correlation coefficient was 0.38±0.18 (mean±standard deviation) for the GLM and 0.36±0.17 for the LNP model in the case of natural scenes stimuli, and 0.48±0.19 for the GLM and 0.48±0.19 for the LNP model in the case of spatiotemporal white noise. With this measure, both models gave similar performances that were consistent with the performances of previous models of LGN response designed at this relatively coarse time scale (Dan et al., 1996; Lesica and Stanley, 2004; Carandini et al., 2005; Lesica et al., 2007).
Given the episodic nature of the observed neural response, the recorded activity was parsed into discrete events (see Methods). We then computed for all events in all cells the difference between the average event time in the experimental data and that in the simulated data (where event time is defined as the time of the first spike in the event on a given trial). This time difference was less than 10ms in 86% of the total number of non-empty events from all 37 cells (i.e., the events that contained at least one spike in at least one trial in both the experimental data and the simulated data, n=5646), suggesting that the GLM could predict the average timing of an event with good accuracy. However, the LNP model performed just as well, with a time difference less than 10ms in 86% of the total number of non-empty events (n=5705). The variability of event time from trial to trial was also well captured by the models, as shown in Figure Figure4B.4B. Defining event time variability as the standard deviation of the timing of the first spike in an event (across all trials in which the event contained at least one spike), we found that the distribution of event time variability was similar in the experimentally recorded cells (top panel) and in the GLM simulations (middle panel). In both cases it ranged between 0 and 25ms, with a mean±standard deviation of 8.3±4.5ms in the experimental data (n=3086 non-empty events from 37 cells) and 13.3±7.0ms in the GLM prediction (n=4026 non-empty events from 37 cells). The classic LNP model (bottom panel) gave a similar prediction of event time variability (12.8±6.8ms, n=4087 non-empty events from 37 cells). These measurements suggest that both model structures could predict the timing of firing events and their variability across trials with a precision of approximately 10–20ms.
Taken together, the existence of event-like response structure, the timing of the response events, and the variations in event times across trials were reasonably well predicted by the stimulus-driven element of the modeling framework (i.e., the model part shared by the LNP model and the GLM). The importance of the non-stimulus driven component of the response emerged, however, in the analysis of the spike autocorrelation function (Figures (Figures4C,D).4C,D). The autocorrelation function quantifies the temporal relationship between neighboring spikes on a trial-by-trial basis by indicating, given a spike at time 0, the probability of other spikes occurring shortly before (negative lags) or after (positive lags) (see Methods). On the left panel of Figure Figure4C4C are the actual (black) and GLM-predicted (green) spike autocorrelation functions for the event highlighted in Figure Figure4A,4A, illustrating that the GLM captured the fine temporal precision of this event (6.6ms predicted width versus 7.8ms experimentally measured width). The lack of short inter-spike intervals, indicative of neural refractoriness, was also well captured through the spike-history dependence embedded in the model, as explained below. Note that in addition to the spike autocorrelation function for the GLM prediction, superimposed is the best Gaussian fit to the autocorrelation function. In contrast, the LNP model (right panel of Figure Figure4C)4C) significantly under-estimated the local timing precision (or over-estimated the spread of spiking on a particular trial, 10.7ms predicted width versus 7.8ms experimentally measured width) due to the absence of the spike-history dependent element. These results were consistent when the measures for this cell were repeated across all events within the sequence, as shown in Figure Figure44D.
When applied across a larger sample of neurons (n=37), the GLM captured the precision of spike timing, as shown in the scatter plot of observed versus predicted autocorrelation widths in Figure Figure4E4E (green triangles). When the spike-history dependence was removed, however, the LNP model prediction (red squares) significantly differed from the experimental observation. Note that both models captured the mean firing rates of the actual neurons (paired t-test, p>0.1 for all pairwise comparisons of the mean firing rate of experiment versus model). We also tested the model on a different stimulus class, spatiotemporal white noise, and found similar results (see Figures Figures55A,B).
The GLM, therefore, with a specific mechanism for capturing spike-history dependence, was more predictive of the fine timing precision in LGN cells when not obscured by averaging across trials. This mechanism is illustrated in Figure Figure6,6, where the conditional intensity functions for both the GLM and LNP model are shown for several trials. For the LNP, the conditional intensity function is solely a function of the visual stimulus, and therefore is identical from trial to trial (Figure (Figure6A).6A). The variability in the timing of events is simply due to the intrinsic variability of the Poisson spike generator. In contrast, the GLM conditional intensity function is modified in each trial by the presence and timing of each generated spike (Figure (Figure6B).6B). Every time a spike occurs, the conditional intensity function is updated to a much lower value, reflecting the fact that an immediately subsequent spike is very unlikely (due to short term refractoriness). After a few milliseconds, however, the probability rises to a new level which is higher than before the spike, making it more likely that another spike will soon follow. This is all consistent with the shape of the spike history function showing a less-than-unity gain shortly after 0 followed by a greater-than-one rebound (Figure (Figure6C).6C). This rebound facilitates the generation of spikes that follow each other quickly, and hence is consistent with a narrower spike autocorrelation function (Figures (Figures44C–E).
Perhaps even more interestingly – since the model was fit on a different stimulus class (spatiotemporal white noise) at high contrast – the model was able to predict the cell responses for natural scenes at low (15%) contrast as well as or even better than at high (40%) contrast. Across all cells (n=37), the correlation coefficient at low contrast was 0.44±0.21 (mean±standard deviation), compared to 0.38±0.18 at high contrast. The model was able to predict the contrast-invariant nature of spike timing precision within events (Figure (Figure7,7, green triangles), which we previously reported (Desbordes et al., 2008). This was not the case for the LNP model, which not only failed dramatically at capturing fine spike timing precision, but incorrectly predicted a degraded precision at low contrast (Figure (Figure7,7, red squares). The failure of the LNP model was expected since it could only match the PSTH of the cell response, and not the finer details of spike timing within events. As we previously showed, PSTH events have a longer duration at low contrast than high contrast because the timing of events is more variable (across repeats) at low contrast, even though the timing of spikes within events is not (Desbordes et al., 2008). Therefore, it is not surprising that the LNP model would incorrectly predict a degraded precision at low contrast. This result suggests that the differences in the geniculate response at different contrast levels can be well captured through the nonlinear interactions between the natural scene and the spike-history dependence, even when fitting the model at a single contrast level.
We now turn to the question of the relative timing of firing activity across neurons within the population. Figure Figure8A8A shows the simultaneously recorded responses of two neighboring neurons to the natural movie (denoted as "Exp." in the raster), below which are the corresponding rasters of the GLM simulation for each neuron. Timing precision in the population code can be estimated by the width of the cross-correlation between the activity of pairs of cells (Desbordes et al., 2008), which gives the probability of neuron 2 spiking at various time lags relative to a spike in neuron 1. Note that this quantity is typically measured as an average across the full duration of a visual stimulus, with the assumption of stationarity. In natural scenes, however, stimulus-driven correlation (and thus spike timing precision) across cells is not constant, but rather varies continually as the visual stimulus changes.
In Figure Figure8A,8A, both cells had neighboring receptive fields and tended to respond at similar times to the stimulus features, making it possible to identify events that were aligned across both cells (see Methods). Four particular events are highlighted here. The local spike cross-correlation was computed for each of these four events (Figure (Figure8B),8B), based on model-generated spikes as explained below. It can be qualitatively seen that the local cross-correlations varied in several ways, perhaps most importantly in terms of the mean local latency (μ) between the firing of the two neurons (e.g., in event #7, cell 1 tended to consistently fire before cell 2), and of the width (σ) of the cross-correlation function which captures the temporal precision of synchronous firing across the cells. The correlation function width (σ), peak value (α), and mean latency (μ) took different values in these four events, highlighting the degree of modulation in the relative firing across cells in the population.
The reliable estimation of temporally localized correlation structure requires a very large number of spikes, often unattainable experimentally. For a small number of events, however, the localized cross-correlation could be measured in the experimental data, and was reliably predicted by the GLM (Figure (Figure9).9). Given the ability of the GLM to predict local cross-correlations for these examples and to predict the activity of each neuron individually, we used the GLM to generate spike trains from each cell in response to 10,000 repetitions of the visual stimulus (see Methods), a number largely sufficient to yield smooth estimates of the local cross-correlation between cells on an event-by-event basis, over all events shared by a pair of cells.
A summary of the variability in the shape of local cross-correlation functions is shown in Figure Figure10A10A for the same pair of cells as in Figure Figure8.8. The local cross-correlation functions for all events (black curves) are superimposed with the global cross-correlation computed on the full stimulus duration (dashed blue curve). Across all 38 pairs, there was a wide distribution of event-by-event cross-correlation widths (σ; Figure Figure10B)10B) and of mean latencies (μ; Figure Figure10C).10C). See figure caption for statistics. As a validation, the global temporal precision across cell pairs averaged across the entire natural scene movie was estimated using the GLM, resulting in predictions that were consistent with our previously reported experimental measurements (Desbordes et al., 2008), as shown in Figure Figure11A.11A. Even though the cross-correlation widths in the model prediction and in the experimental data slightly differed (paired t-test, p=0.006, n=37 cells), the best-fitting ratio between model prediction and experimental measurement was 1.10 (Figure (Figure11A),11A), corresponding to an error of only 10%, suggesting that the GLM was indeed a good predictor of correlations across geniculate neurons in response to natural scene stimuli. Not surprisingly, the GLM performed even better in the case of spatiotemporal white noise (Figure (Figure11B),11B), where the model predictions were not significantly different from the experimental measurements (paired t-test: p=0.36), with a ratio of 0.96.
In response to natural scenes, early visual neurons tend to fire in a characteristic manner, with firing "events" (i.e., small clusters of spikes) clearly separated by periods of silence (Dan et al., 1996; Berry et al., 1997; Reinagel and Reid, 2000; Keat et al., 2001). We recently showed that in the case of natural visual stimuli the responses of neurons in the LGN exhibit a temporal precision of 10–25ms within and across cells in the geniculate population (Desbordes et al., 2008), a time scale significantly faster than would be predicted by the more slowly varying natural scene (Butts et al., 2007). Here, we used a spiking model of neural encoding based on a generalized linear model framework with spike history dependence to fit the responses of LGN neurons. With this model, we investigated variations in temporal precision of thalamic population responses to natural scene stimuli. We showed that the model could predict the experimental measurements of geniculate responses to natural stimuli, not only capturing the global correlation structure measured across the entire stimulus duration, but also the local correlation structure at the scale of individual firing events. The model was able to capture the occurrence of firing events, the timing of these events and their variability, and the finer details of spikes within an event. Measurements across the population revealed the continuous modulation of the relative timing of activity across neurons in a 5–25ms range.
Functional models have been used previously to predict early visual neuron response to natural scene stimuli (Carandini et al., 2005; Lesica et al., 2007; Mante et al., 2008). In the case of the standard LNP model, the cell response is solely due to modulations by the visual stimulus, thus the time scale of the neural activity is limited to the time scale of the input. The LNP model does capture the coarse nature of the observed response to natural scene inputs (Lesica et al., 2007), but requires fitting on natural scene data and does not generalize well across different stimulus conditions. We previously showed that within the LNP modeling framework, the spatiotemporal filtering and the threshold properties of the nonlinearity differ across stimulus classes such that LNP models fit to spatiotemporal white noise data do not predict well the geniculate response to natural scenes (Lesica et al., 2007). Here, by fitting the GLM on the neural response to spatiotemporal white noise stimuli and testing it on natural scene stimuli, we show that the expansion of the framework to include the non stimulus-driven spike-history dependence embodies an important element missing from the conventional LNP framework in the context of natural scene stimuli. This suggests that interactions between the natural scene inputs and the nonlinearity of the spike-history dependence make it possible to capture the important differences in cell response that would manifest as threshold shifts in the simpler LNP model framework. Importantly, fitting the GLM to high-contrast spatiotemporal white noise data generalized to natural scenes at different contrasts, exhibiting the contrast-invariance in the relative timing of geniculate neurons that we have previously shown experimentally (Desbordes et al., 2008). The LNP model, however, showed more dependence on contrast and was thus unable to capture the observed phenomena with a single set of model parameters. Taken together, these results suggest that the augmentation of the classical (LNP) modeling framework with non stimulus-driven spike-history dependence expands the generality of the model to allow complex interactions between properties of the stimulus and the nonlinear dependence of spiking upon both visual stimulation and past activity.
The importance of spike history (or recovery) in shaping a cell's fine temporal precision was previously emphasized in models of retinal firing (Berry and Meister, 1998; Keat et al., 2001; Uzzell and Chichilnisky, 2004; Pillow et al., 2005). In contrast to the GLM presented here, in these models, the visual stimulus consisted of a one-dimensional time series and was passed through a linear (or linear–nonlinear) temporal filter, whose output (after driving an integrate-and-fire mechanism in the case of Keat et al., 2001) was fed back through a recovery function to influence the subsequent integration and thus future firing. These models were fitted to the activity of individual retinal ganglion cells and LGN neurons in response to temporally-white noise stimulus (often referred to as "full-field flicker"), and predicted the narrow PSTH events observed experimentally. Here, in a more general spatiotemporal modeling framework, we showed the interactions between spike history and the complex spatial and temporal structure of natural scenes across a population of LGN cells.
In the retina, previous studies suggest the existence of a significant degree of correlated (or synchronous) firing, often referred to as "noise correlations" because they cannot be explained solely by the visual input to each cell independently (Mastronarde, 1983a,1983b; Meister et al., 1995; DeVries, 1999). Synchronized firing in the retina varies with cell types and appears to be mediated by a combination of mechanisms: direct gap junction coupling between neighboring retinal ganglion cells, gap junction coupling through interneurons (e.g., amacrine cells), and chemical synapses providing common input to retinal ganglion cells from bipolar or amacrine cells (reviewed in Baccus, 2007; Demb, 2007; Field and Chichilnisky, 2007). Common synaptic input seems to be the dominant source of synchronous firing in the retina – probably due to a single noise source, namely photoreceptor noise (Trong and Rieke, 2008). However, while the existence of these noise correlations in the retina is well documented, their importance for neural encoding and decoding of the visual input has been controversial (Meister et al., 1995; Meister, 1996; Nirenberg et al., 2001; Nirenberg and Latham, 2003; Schneidman et al., 2003; Latham and Nirenberg, 2005; Puchalla et al., 2005). In a recent study, a generalized linear model accounting for pairwise coupling among neighboring retinal ganglion cells could better fit cross-correlation functions and could extract ≈20% more information about the visual stimulus than the uncoupled version of the model (Pillow et al., 2008), favoring the hypothesis that noise correlations in the retina contribute at least some information about the visual stimulus.
In the next visual processing stage – the LGN – which is the focus of this study, neighboring neurons typically do not exhibit high noise correlations (unless they share a retinal afferent, as detailed below), and the relevance of these correlations for the representation of natural visual scenes is unclear. Our present and previous results suggest that noise correlations are weaker overall in the LGN than in the retina, especially in the presence of natural scene stimulation (Desbordes et al., 2008), with the exception of cell pairs sharing a retinal afferent which are instead very strongly correlated on a short (1-ms) time scale (Alonso et al., 1996, 2008; Yeh et al., 2009). The lower amount of noise correlation in the LGN may be due to the fact that the local neural circuitry in the LGN is very different from that in the retina. There is no evidence of direct excitatory connections between LGN cells and it is likely that noise correlations only exist between cells that receive some amount of common retinal input from the same afferent (Alonso et al., 1996; Usrey et al., 1998). In summary, direct coupling between neighboring cells, modeled in the retina with pairwise reciprocal spike history terms (Pillow et al., 2008), was not relevant in the LGN population activity described here.
Fine temporal precision in early visual neurons is necessary for the faithful representation of the more slowly varying natural visual scene (Butts et al., 2007). The results presented here point to the importance of spiking history in shaping temporal precision of the neural code and thus in maintaining a high fidelity of scene representation. Modulation of the relative timing of spikes across the population was found both in the mean latency between one cell and another and in the variability of firing around this mean. Previous studies have shown the sensitivity of the thalamocortical synapse for synchronous activity in the thalamic input (Alonso et al., 1996; Dan et al., 1998; Usrey and Reid, 1999; Kara and Reid, 2003; Bruno and Sakmann, 2006; Kumbhani et al., 2007), hinting at the importance of precise timing for cortical selectivity to particular patterns of luminance changes associated with motion in natural visual scenes.
Since fine precision in spike timing may be crucial to neural coding in other brain areas (Rieke et al., 1996; Lestienne, 2001), we hypothesize that the relative timing of spikes is similarly modulated by natural stimuli in other brain areas, along the visual pathway and in other modalities. Given the limited predictive power of the LNP model, the GLM framework may offer an improved approach to this question for future studies.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
We would like to thank Jonathan Pillow for sharing code and for helpful comments on model fitting, Dan Butts for help with the event analysis, and members of the Stanley group for useful comments on the manuscript. This work was supported by NSF CRCNS Grant IIS-0904630 (GBS, JMA), and the National Eye Institute EY005253 (JMA).
PubMed Central Canada is a service of the Canadian Institutes of Health Research (CIHR) working in partnership with the National Research Council's national science library in cooperation with the National Center for Biotechnology Information at the U.S. National Library of Medicine(NCBI/NLM). It includes content provided to the PubMed Central International archive by participating publishers.


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(using the same template as danielle's post right here)
watching: portlandia. i just started and i do the most unattractive snort-laugh over and over again while i'm watching. i also watched pitch perfect when it came to the redbox (twice) and LOVED it! "i'm going to finish them like a cheesecake!"
thinking about: my talk this sunday! our church invites members of the congregation, rather than a paid minister or pastor, to speak every sunday. david and i are speaking together on goals, with my focus being spiritual goals. i'm excited (/terrified) to speak and i'm enjoying preparing. any resources you have on the topic would be mucho mucho appreciated!
reading: too many things at once. mere christianity by c.s. lewis, fairest by gail carson levine, and a million miles in a thousand years by donald miller. oh...and i'm open to suggestions if you've read anything un-put-down-able (it's a thing) lately.
celebrating: everything joony does. sitting up, standing up, rolling everywhere, babbling. it seems like every day calls for celebration with a baby in the house.
making me happy: this mug that my mom found at a thrift store and gave me for christmas:
other bits and pieces composing our "currently:"

subset: commoncrawl


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*still trying to figure this part out.
Georgia. Digital Marketing Executive. 20. UK.
Despite the highly misleading title, I actually live in a UK city with the ultimate life goal of having my feet (preferably in Louboutins) firmly placed in New York, being a girl boss and ruling the digital marketing world. I'm a way off from that point yet though, so feel free to stick with me on my journey to getting there – which is currently in Leeds wearing New Look loafers. *sigh*
I work in digital marketing, which I adore. I'm super lucky to have a job which I love, when realistically at this age I thought I'd be living in student accommodation spending 80% of my life drunk. I live in a lovely house, have a great social life and only spend 70% of my life drunk, which I feel is far more acceptable.
Confession time: I have an unhealthy obsession with make-up. Nurtured by my teen years spent working on various cosmetic counters part-time whilst at school/college, I am a self confessed makeup geek with zero shame.
Confession no.2: I spend
a little too much time on Pinterest and pretty much all types of social media. Good job I work in digital, right?! I'm such a Snapchat geek I could pass a degree with flying colours, if there was one – and there totally should be. That also brings me on to my next point… I did things a little differently to how I expected I would, as I decided not to go to uni and to go head-first into the world of work. When an oppportunity comes knocking, so they say!
Still intrigued? Wow, I'm flattered! Here are some completely useless things about me that you may (or may not) have wanted to know:
Likes: Autumn, Chai Lattes, 80's films, good eyeliner days.
Dislikes: Rude people, bad eyeliner days, diets and mushrooms. Really dislikes mushrooms. Update: I now actually like mushrooms, after 20 years of detesting them (enough so to write it in my 'about me' section) – anything is possible.
Fun facts: I'm vegetarian and have been all of my life, I'm double jointed in both of my thumbs, I've had 8 piercings and 2 tattoos and I was 2nd choice to play Blair Waldorf in Gossip Girl. Ok, so one of those may be a fib.
So, now you know me probably better than I know myself. Congrats!
Join me on my ramblings of day to day life, beauty tips and trying to make my life as Carrie Bradshaw-esque as possible.
Thanks for reading!
Georgia xo
P.S. Whilst you're here, why not check me out on Instagram:


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The textbook Lending Program wants to hear from you! request your books for winter term to have them available at the HSRC!
Come to our next scheduled Food Pantry!
We are now offering training for HSRC and OSU Emergency Food Pantry volunteers right in the office between 9am and 4pm!
Office of Student Life Corvallis, OR 97331
Please see information at right to make direct contact with department.
ASOSU Office of Advocacy 325 Student Experience Center (541)737-9200Send EmailCenter for Fraternity & Sorority Life (CFSL) 306 Student Experience Center (541)737-5432Send EmailChildcare & Family Resources (CFR) 211 & 213 Avery Lodge (541)737-4906Send EmailCorvallis Community Relations (CCR) 150 Snell Hall (541)737-8606Send Email
Disability Access Services (DAS) A200 Kerr Administration Building (541)737-4098Send EmailHuman Services Resource Center (HSRC) Avery Lodge, 1030 SW Madison (541)737-3747Send EmailMilitary & Veteran Resources 137 Snell Hall (541)737-7662Send Email
New Student Programs & Family Outreach (NSPFO) A150 Kerr Administration Building (541)737-7627Send EmailOffice of Student Life 150 Snell Hall (541)737-8748Send EmailStudent Conduct & Community Standards (SCCS) 150 Snell Hall (541)737-3656Send Email


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Upcoming Events › Local Events
Events List Navigation
Pocatello Raceway
June 24th, August 5th, 12th, 26th, September 16th are days to remember for the Pocatello Raceway featuring Midgets, Project Filter Pro 4 Series, Lawn Mowers, Driver Appreciation, Night of Destruction, Pro Trucks, and Fan Appreciation.…Find out more »
Rigby Farmers market
Rigby Farmers Market at Crown of Life Church. Produce, artisan food, crafts, soaps, and more. Bounce house and entertainment for the family. Every Wednesday from 4 p.m. to 8 p.m.Find out more »
Monthly Reiki Open House
Healing Hands Reiki and Massage Spiritual Center would like to invite you to their monthly open house every first Saturday of the month.Find out more »
Live Horse Racing
Live Horse Racing at Sandy Downs. July 4th & 22nd and September 16th & 30th. 4 p.m. - 10 p.m.Find out more »
Great Snake River Greenbelt Duck Race
"27th Annual Great Snake River Greenbelt duck race. The ducks hit the water for a good cause: raising funds to expand and enhance the Idaho Falls greenbelt."Find out more »
Eastern Idaho Eclipse 2017
To see all the events and updates of the 2017 Total Solar Eclipse. Please visit http://590kid.com/eclipse/ "Idaho Falls is a prime location for viewing the 2017 total eclipse. The city is directly in the path and…Find out more »


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New York Times bestselling author Katy Regnery has given her modern fairytale collection a whole new look! They're so gorgeous…go take a peek!
Amazon US → http://amzn.to/2vyRjSq
iBooks → http://apple.co/2us4TJA
Nook → http://bit.ly/2valxee
Kobo → http://bit.ly/2uvzBkL
Available now (coming to KU on Aug 1):
~ THE VIXEN AND THE VET (Beauty & the Beast)
~ NEVER LET YOU GO (Hansel & Gretel)
~ GINGER'S HEART (Little Red Riding Hood)
~ DARK SEXY KNIGHT (Camelot)
~ DON'T SPEAK (The Little Mermaid) - Only 99¢ until July 30!
Coming in December 2017:
~ SHEAR HEAVEN (Rapunzel)
Coming in 2018:
~ FRAGMENTS OF ASH (Cinderella)
~ SWAN SONG (The Ugly Duckling)
About the Author
New York Times and USA Today bestselling author Katy Regnery started her writing career by enrolling in a short story class in January 2012. One year later, she signed her first contract and Katy's first novel was published in September 2013.
Twenty-five books later, Katy claims authorship of the multi-titled, New York Times and USA Today Blueberry Lane Series, which follows the English, Winslow, Rousseau, Story, and Ambler families of Philadelphia; the six-book, bestselling ~a modern fairytale~ series; and several other standalone novels and novellas.
Katy's first modern fairytale romance, The Vixen and the Vet, was nominated for a RITA® in 2015 and won the 2015 Kindle Book Award for romance. Katy's boxed set, The English Brothers Boxed Set, Books #1–4, hit the USA Today bestseller list in 2015, and her Christmas story, Marrying Mr. English, appeared on the list a week later. In May 2016, Katy's Blueberry Lane collection, The Winslow Brothers Boxed Set, Books #1-4, became a New York Times E-book bestseller.
In 2016, Katy signed a print-only agreement with Spencerhill Press. As a result, her Blueberry Lane paperback books will now be distributed to brick and mortar bookstores all over the United States.
Katy lives in the relative wilds of northern Fairfield County, Connecticut, where her writing room looks out at the woods, and her husband, two young children, two dogs, and one Blue Tonkinese kitten create just enough cheerful chaos to remind her that the very best love stories begin at home.
Author Links:
Website/Newsletter Signup: http://katyregnery.com/
Goodreads: https://www.goodreads.com/author/show/7211470.Katy_Regnery
Amazon: http://amzn.to/2dqySsy
Facebook: https://www.facebook.com/KatyRegnery
Twitter: https://twitter.com/KatyRegnery
Pinterest: http://pinterest.com/katharineregner/
#Excerpt ~ The Forbidden by Jodi Ellen Malpas + #ARCgiveaway @JodiEllenMalpas @ForeverRomance
Keeping his eyes on mine, he calls to the barman. "Two tequilas, please."
"Tequila," I muse, looking over my shoulder when the salt and lemon land behind me. "Is that my challenge?"
"Crying off?" he goads, reaching into his pocket and pulling out some notes.
"Never," I scoff, turning into the bar. I don't know what his game is, but I want to play. With him. "You're asking me to prove I'm sober by doing a shot?" I narrow my eyes on him, teasing. "Or is your plan to get me drunk and take advantage of me?"
He smiles to himself as he pays the barman. "You don't look like the kind of woman who could be taken advantage of."
"What kind of woman do I look like, then?" I challenge quietly.
He turns into me, watching me for a few moments. "I don't know, but I think I'd like to find out."
I hold his gaze for a few seconds, no retort coming to me. I think I want him to find out, too, just as much as I want to find out what kind of man he is. My eyes drop from his sparkling greys, down his tall, lean frame to his feet.
"Let's play," he says, moving in closer and pulling one of the glasses forward. I don't mean to, but I yank my arm away abruptly when he brushes against me, startled by the tiny stabs of pleasure that pitter-patter all over my skin. The fleeting touch tells me he would feel as good as he looks, and—give me strength—he smells divine, all manly and earthy and fucking edible.
The sudden lapse in movement and talking from both of us becomes slightly awkward. I can feel him looking down at me.
"What do I have to do?" I ask again quietly, almost on a breathy gasp.
He clears his throat. "You're not drunk?"
"Not even the slightest bit." I raise my nose in the air.
"Good. Then you'll smash this challenge first time." He places a finger on the brim of one of the shot glasses. "Brace your palms on the edge of the bar," he orders, firm but softly. I look at him, finding a serious face. "Go on."
Frowning, I place my hands on the edge of the bar. "Okay?"
He takes my hips. He takes my fucking hips! I freeze from top to toe and swallow hard, waiting. My insides are quickly furling, my mind in chaos. "Move back a bit," he says, pulling at them a little until I step back.
Oh, Jesus. I'm on fire. I have a strange man bending me over a bar in public, and me, Annie I'm-immune-to-men Ryan, isn't fighting him off. It's like he has me under a spell. What gives? I dare not look behind me. I'm not stupid enough to think Lizzy isn't currently watching a man manipulate my body to where he wants it.
"You feel tense," he observes, releasing me and moving back to my side.
I don't deny it; neither do I confirm it. His big hands felt so good resting on my hips, so much so I have to resist not claiming them and putting them back where they were. "What now?" I ask, evidently struggling for air, damn me.
"Now." He picks up his beer and grins. "I get to gloat that I had you bent over a bar within five minutes of meeting you." He takes a swig, still grinning, and I hear the roar of a man down the bar laughing his head off.
Oh, the fucker! Part of me has admiration. Another part of me wants to slap him stupid; I don't care how beautiful he is. And another part of me wants to rip his clothes from his body and ravish the sly bastard.
I cannot believe I fell for it! How many women has he played like a fiddle? I drop my head, shaking it to myself.
I knew that smile was dangerous. A man who can bend a woman to his will so easily and so soon couldn't be anything less than lethal. And the fact that he got me with his wicked game means hats off to him. I can't possibly take that away from him, and since I'm lacking in the dignity department right now, I decide not to slap him. Nor will I chuck a drink over his head, or fire a load of verbal abuse at him.
I'll do what he least expects.
I push myself up and turn to face him, unable to stop myself from smiling at his half-grin. Holding his gaze, I slowly lick the back of my hand, blindly take the salt off the bar, sprinkle a bit, and take one of the shots of tequila. But as I'm taking my hand to my mouth to lick the salt up, he seizes my wrist and takes the shot from my other hand. My heartbeat accelerates, our eyes glued to each other as he moves into me and slowly brings my hand to his mouth. I watch, gripped, as he lazily licks up the salt from the back of my hand, eyes on mine, and then knocks the tequila back. Kill me now, for I will certainly die a happy woman. His tongue on my skin. His eyes boring into mine. His hold of my wrist. I must look like a statue—unable to talk, move, or think clearly.
"There's one more tequila," he says, cocking his head toward the bar but keeping me in his sights. "And it's yours."
Oh good lord. My heart is speeding up by the second as I watch him lick the back of his hand and sprinkle some salt. Then he offers it to me. I stare at his hand, and then slowly look up at him. I could get lost in those grey glittery eyes.
"I taste good," he whispers.
About The Author
Jodi Ellen Malpas wrote her debut series, The This Man Trilogy, in secret, worried about what people might think if they knew what her imagination was capable of. She was shocked herself. But she finally found the courage to unleash her creative streak and self-published THIS MAN – the first book of the This Man Trilogy – in October 2012. She took a chance on the story with a hero who soon became one of modern day fictions most prolific alpha male characters. Jesse Ward – also affectionately known as The Lord of The Manor, sparked incredible reactions from women across the globe and catapulted Jodi into the world of women's fiction.
Jodi went on to self-publish the second book in the trilogy, Beneath This Man in Jan 2013, just before Grand Central Publishing, part of the Hachette Book Group, picked up the popular trilogy and the unknown Jodi. The third book of the trilogy, This Man Confessed, took Jodi to the top of the bestsellers lists, earning her the proud title of #1 New York Times Bestselling Author. This Man has since been voted one of the top romance novels of all time.
With so much love, enthusiasm and a thirst for her words from her readers, Jodi suddenly wasn't afraid of her imagination anymore. She went on to write The One Night Trilogywith the delectable and mysterious Miller Hart, and stole the hearts of her readers once again. Her first stand-alone novel, THE PROTECTOR, released in September 2016 and has since been nominated for a Reviewers' Choice Best Book Award. All I Am, a This Man Novella featuring Drew from the trilogy, releases June 18th, and Jodi's next stand-alone novel. THE FORBIDDEN, is set for release in August 2017.
All seven of her published novels have hit the New York Times best sellers list – as well as the Sunday Times list and various international best seller lists. Her work is now published in over 24 languages across the world.
Jodi was born and raised in the Midlands town of Northampton, England, where she lives with her two boys and a beagle. She is a self-professed daydreamer, a mojito addict, is famous for her obsession with Converse, and has a terrible weak spot for Alpha Males. Writing powerful love stories and creating addictive characters has become her passion – a passion she now shares with her devoted readers.
Cover Reveal! A Little Bit Like Love by Brooke Blaine, Coming July 27th! #MMRomance #ComingSoon @BrookeBlaine1
A Little Bit Like Love, an all-new sexy STANDALONE MM romance from Brooke Blaine is coming July 27th!
Publication Date: July 27th, 2017
Genre: Contemporary Romance/MM Romance
Cover Design: © By Hang Le
Photography by: Eric Battershell Photography
Model: David Wills
What if you had everything in the world you wanted…except the man you'd left behind?
Jackson Davenport, the charismatic, strait-laced heir to the Davenport fortune, has a secret. One he's been hiding since he graduated from South Haven all-boys academy—and that secret's name is Lucas.
When a work trip takes Jackson back to his old stomping grounds, memories of the year he shared with Lucas come crashing to the surface. With growing pressure from his father to settle down and take over the family business, Jackson knows he's on borrowed time, and sets out to find the free-spirited daredevil he once knew.
But Lucas isn't the same man he was eight years ago.
One night. A shattered heart. And an endless parade of nameless faces. Lucas Sullivan is South Haven's ultimate playboy, a reputation he's honed since the only boy he ever loved left without a trace. To the world, he's brash and confident, an in-demand artist who spends his days designing one-of-a-kind pieces and his nights as king of the downtown scene.
Many have tried and failed to get past the barrier he's carefully constructed, but it's the shy, studious boy he once coaxed out of his shell who still haunts him.
Maybe it was a mistake. Maybe it was lust. Or maybe…it was a little bit like love.
About The Author
You could say Brooke Blaine was a book-a-holic from the time she knew how to read; she used to tell her mother that curling up with one at 4 a.m. before elementary school was her 'quiet time.' Not much has changed except for the espresso I.V. pump she now carries around and the size of her onesie pajamas.
She is the author of Flash Point, a romantic suspense standalone, as well as the co-author of the erotic series, A Desperate Man, with Ella Frank. The latter has scarred her conservative southern family for life, bless their hearts. Licked, a romantic comedy, will be released November 11th, 2015 and is the first in the L.A. Liaisons series.
If you'd like to get in touch with her, she's easy to find - just keep an ear out for the Rick Astley ringtone that's dominated her cell phone for ten years.
Connect with Brooke:
Website- http://www.brookeblaine.com/home
Twitter- @BrookeBlaine1
FB- https://www.facebook.com/BrookeBlaine.Writer/
FB Street Team- www.facebook.com/groups/TheBrellas/
Blog Tour: The Darkest Sunrise by Aly Martinez, Stacy's review and Pre-order for Book 2! @AlyMartinezAuth
My Review
Well thanks a lot Aly Martinez!!! I'm dead now. Omg WHAT JUST HAPPENED!?!?!????????? I NEED THE SECOND BOOK ASAP!
Ok. So, after A heartbreaking prologue I had a feeling that this book was going to get to me, plus I've seen a lot of buzz about this series on Facebook and Twitter and Goodreads. I was worried though because books that have a lot of buzz sometimes fall flat and this book definitely did not fall flat! In fact, it's just the opposite. Full of intrigue, and mysterious characters, and one HUGE plot twist, this book is just what you need, especially if you're a sucker for cliffhangers, because WATCH OUT.
If I'm honest it took a while for me to get into the first 35% or so of the story. It was just a little slow for me, which is totally understandable considering we needed to get to know the characters and back stories etc... but once all those technicalities got out of the way it was like BAM, you're on a roller coaster ride of this beautifully difficult courtship between two people who are so obviously damaged. But, at the same time they desperately need something to hang on to. Something to take them out of their darkness while at the same time, remaining in the darkness. Their relationship is short and choppy, and some might call it insta-love-ish, but I didn't see it that way at all. I think Aly Martinez did a great job of making their short term relationship seem deeply rooted and they had a very close emotional connection. Both grieving different types of loss, but dealing with insurmountable pain.
Charlotte and Porter have this invisible connection. It's magnetic and sweet and awkward, and it's hard not to fall in love with them out of sheer agony for what they're going through. But neither of them want to force their baggage on each other, so it's interesting to see how they get around that while at the same time trying to slowly integrate their lives.
There's not really much more I can say, because practically everything is a spoiler. All I know is that I need book 2 immediately because Aly Martinez left like the biggest cliffhanger EVER!! In all seriousness, what a great start, this series is going to be super popular!
THE BRIGHTEST SUNSET (The Darkest Sunrise Duet, #2) Coming July 27th
Sticks and stones will break my bones, but words will never harm me.
Words destroyed me.
"I'm sorry. She didn't make it."
"Daddy, he can't breathe!"
"There's nothing more we can do for your son."
Sticks and stones will break my bones, but words will never harm me.
Those syllables and letters became my executioner. I told myself that, if I didn't acknowledge the pain and the fear, they would have no power over me. But, as the years passed, the hate and the anger left behind began to control me.
Two words—that was all it took to plunge my life into darkness.
"He's gone."
In the end, it was four soft, silky words that gave me hope of another sunrise.
"Hi. I'm Charlotte Mills."
Pre-order Now:
Amazon US: http://amzn.to/2sX0Yo9
Amazon UK: http://amzn.to/2tG4oJ9
Amazon CA: http://amzn.to/2u9VhU1
Amazon AU: http://amzn.to/2u0aVAI
About The Author
Originally from Savannah, Georgia, USA Today bestselling author Aly Martinez now lives in South Carolina with her four young children.
Never one to take herself too seriously, she enjoys cheap wine, mystery leggings, and baked feta. It should be known, however, that she hates pizza and ice cream, almost as much as writing her bio in the third person.
She passes what little free time she has reading anything and everything she can get her hands on, preferably with a super-sized tumbler of wine by her side.
AUTHOR LINKS:Website: www.alymartinez.com
Facebook: https://www.facebook.com/AuthorAlyMartinez/
Twitter: https://twitter.com/AlyMartinezAuth
Instagram: https://www.instagram.com/authoralymartinez/
Newsletter: https://app.mailerlite.com/webforms/landing/t3w3q3
Goodreads: https://www.goodreads.com/AlyMartinez
Amazon: http://bit.ly/AlyMartinezAmazon
#ReleaseBlitz ~ The Darkest Sunrise by Aly Martinez is NOW LIVE! #Teasers and #Preorder for Book 2! @AlyMartinezAuth
Today we are sharing the release for The Darkest Sunrise by Aly Martinez. It is the first book in a contemporary romance duet, and is it available to read for FREE on KindleUnlimited!
Be sure to follow Aly's newsletter for exclusive news about the duet.
The Darkest Sunrise (The Darkest Sunrise Duet, #1)
Buy it Now
Amazon US | Amazon UK | Amazon CA | Amazon AU
Synopsis:Sticks and stones will break my bones, but words will never harm me. Whoever coined that phrase is a bald-faced liar. Words are often the sharpest weapon of all, triggering some of the most powerful emotions a human can experience. "You're pregnant." "It's a boy." "Your son needs a heart transplant." Sticks and stones will break my bones, but words will never harm me. Lies. Syllables and letters may not be tangible, but they can still destroy your entire life faster than a bullet from a gun. Two words—that was all it took to extinguish the sun from my sky. "He's gone." For ten years, the darkness consumed me. In the end, it was four deep, gravelly words that gave me hope of another sunrise. "Hi. I'm Porter Reese."
Add The Darkest Sunrise to Goodreads
The Brightest Sunset (The Darkest Sunrise Duet, #2)
Coming July 27th
Pre-order it Now
Amazon US | Amazon UK | Amazon CA | Amazon AU
You won't want to miss this amazing series!
AUTHOR INFORMATION:Originally from Savannah, Georgia, USA Today bestselling author Aly Martinez now lives in South Carolina with her four young children. Never one to take herself too seriously, she enjoys cheap wine, mystery leggings, and baked feta. It should be known, however, that she hates pizza and ice cream, almost as much as writing her bio in the third person. She passes what little free time she has reading anything and everything she can get her hands on, preferably with a super-sized tumbler of wine by her side.
Website | Twitter | Facebook | Goodreads | Instagram | Amazon
Hate to Love You, an all-new standalone from New York Times Bestseller Tijan is coming August 28th!
Hate to Love You by Tijan
Genre: Contemporary Romance
Publication Date: August 28th
Rule #1: No hot guys.
It might sound ridiculous. I get it. It kind of is, but college was supposed to be my sanctuary. It was my place to start over. The rumors, the whispers, and the jealousy I endured through high school would all be gone.
No one would know me at college.
Rule #2: No drama.
I'd major in pre-law. I'd make a few loyal friends. Everything would be easy breezy. No one was going to use me or hurt me. I wouldn't let them.
Rule #3: New year. New place. New me.
And all because of Shay Coleman.
Football captain and quarterback, he was the big guy on campus. The cocky guy in my political science class with a smirk. I hated him on sight . . .
. . . and he was about to break all my rules.
About The Author
Tijan is a New York Times Bestselling author that writes suspenseful and unpredictable novels. Her characters are strong, intense, and gut-wrenchingly real with a little bit of sass on the side. Tijan began writing later in life and once she started, she was hooked. She's written multi-bestsellers including the Carter Reed Series, the Fallen Crest Series, and the Broken and Screwed Series among others. She is currently writing a new YA series along with so many more from north Minnesota where she lives with a man she couldn't be without and an English Cocker she adores.
To find what to read next of hers, go to www.tijansbooks.com
Connect with Tijan:
Facebook: https://www.facebook.com/Tijansbooks/
Twitter: https://twitter.com/TijansBooks
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Reader Group: https://www.facebook.com/groups/TijansFanPage/
Stay up to date with Tijan by signing up for her newsletter here: https://goo.gl/9GxnKU
Release Blitz: The Unrequited by Saffron A. Kent! #5StarReview #Excerpt #Teasers @SaffronAKent @GiveMeBooksBlog
My Review
When I read the blurb for this book I knew I had to have it, anything forbidden that has a warning with cheating in it, and I'm already sold. And right from the beginning of the book the angst has already started because you have no clue what's up with this girl. She's naturally mysterious and that transfers over well in to the writing, I was immediately smitten with this book because the story was very magnetic and full of curiosity right from the start. The Unrequited forces you to hunker down and get cozy, because once you start you won't come up for air until you reach the end.
Thomas Abrams and Layla Robinson are two scorching souls who radiate from their forbidden desires and beg you to come along on their deviant journey. It's relentless, exhausting and so deliciously addictive, I WANT MORE. I NEED MORE.
Thomas is a dark poet of sorts, a college professor who is as mysterious as he is broody, and Layla is a young inquisitive college student who is immediately drawn to this man, it's almost as if she took some sort of potion because she is under his spell whether he wants her to be or not. And he is definitely unavailable.
These are two lost and lonely souls who see their own pain in each other, and when the slow burn of their depravity comes in to fruition, it's one of the most erotically torturous things I've read in a long time! There's this one scene that will leave you breathless, and I'm not even kidding, It's one of my favorite scenes. The way Thomas talks to Layla is dirty, so dirty, and he teases her relentlessly. This author knows how to build you up slowly and slap you in the face when she's done with you. My heart was beating faster and faster right along with these characters and I wanted to stay lost in their world for as long as possible. You are mean, Saffron Kent! But I loved every minute.
I should feel guilty for liking this, because Layla is all kinds of naive and makes rash decisions like they're going out of style, and Thomas is knowingly taking advantage of her but, I DON'T CARE. Why? Because it's fiction and I'm throwing out the rule book because I like it. I like how these two make decisions based on their insecurities, I like how they play little games with each other to take away the pain. I like it all!
This story was so many things to me, it's erotic, rude, mesmerizing, hot and cold, fascinating and dark, and bright, so bright! If you need a book to wake you up THIS is the book. I fell in love with these characters harder than I thought I would, and I loved the originality in the writing. This was my first book by this author but it won't be the last!
Read An Excerpt
I'm hit by a storm of desire to kiss him better. It's a tornado, an avalanche in my body, and in one breathless moment, I decide to go for it. It's okay. I can take the blame for it later.
I break the rules and reach up and kiss him. A feathery peck on his plump lips, it's a kiss of solidarity, a kiss that intends to tell him I understand—but one isn't enough. It only manages to ratchet up my lust. So I give him another, this time on the corner of his mouth, and then another one on his jaw.
It's not enough, these small, barely-there touches. I want more, but I won't take it. I'll be good; I'll only give.
Abruptly, he fists my curls and stops me. I look at him fearfully, ready to apologize—not for the kiss, but for being the kisser. His gaze reflects passion, stark, raving need, and I shiver, despite wearing layers and sweating with his heat.
"Are you trying to kiss me, Layla?" he rasps, flexing his fingers on my makeshift ponytail.
He couldn't tell? Blush rises to the surface and I know I'm glowing like a neon sign. Swallowing, I nod. "Yes."
He inches closer to me, still not touching—as impossible as that is—but infinitely closer. "You want to kiss me, Miss Robinson, you do it right."
Oh God, does he have to call me that? Now, here? My spine arches on its own and my heavy tits graze the contours of his shuddering chest.
"H-How?" I ask innocently, belying the daring action of my body. His stern, professor-y voice is doing things to me, making me wild, uncontrolled.
For a second, he's silent, just watching. I'm afraid he'll back out from whatever this is, whatever insanity we're about to commit—but then I sense the shift in the liquor-laced air as he opens his mouth and growls, "Like this."
About The Author
Writer of bad romances. Coffee Addict. White Russian Drinker. Imaginary Ballet Dancer and poetess. Aspiring Lana Del Ray of the book world.
I'm a big believer in love (obviously). I believe in happily ever after, the butterflies and the tingling. But I also believe in edgy, rough and gutsy kind of love. I believe in pushing the boundaries, darker (sometimes morally ambiguous) emotions and imperfections.
The kind of love I write about is flawed just like my characters. And I hope by the end of it, you'll come to root for them just as much as me. Because love, no matter where it comes from, is always pure and beautiful.
Website | Facebook | Facebook Group | Newsletter
#ReleaseBlitz ~ Jilted by Sawyer Bennett + #Review & #Teasers @InkSlingerPR @BennettBooks
My Review
This was a really cute second chance romance, Eden and Coop were together at the age of 18 but, couldn't keep up with a long-distance relationship so they mutually ended things. Eden was just getting into the business of being a model/actress in Hollywood, and Coop wanted to stay home in Georgia and grow his landscape architecture business. Both weren't willing to give up on their dreams so that's why it didn't work. But, this is first-love stuff we're talking about here, and that doesn't just go away. Not even after 14 years.
Eden decides to go back home to Georgia for some R&R and to get away from paparazzi and the media surrounding her break-up with her cheating fiancé, and when she gets there she realizes that the estate that she thought her grandmother left solely to her, is in fact a shared property with Coop and now they must find a way to live in the same house without killing each other.
I love the premise of this trope and it was entertaining, but in the beginning, there were some things that bothered me about the Eden's behavior, like acting childish about their arrangement. In the grand scheme of things, I was pleased with her character overall but, I found myself shaking my head at her more than once. By the end I really liked Eden, but there was just something about her character that I couldn't connect to. I'm not sure if it was her sense of entitlement in the beginning or not, because I thought I got over that. Maybe my subconscious couldn't see past it, I guess I'll never know!
I really loved Coop and the way he handled Eden's attitude. That, combined with the chemistry these two had made the banter very cute and snarky. And when it came to the sexy times I was pleasantly surprised that Coop has such a dirty mouth and a kinky side. No too much though, it was just right.
This was my first book by this author and I was very impressed by her writing, I just think something was missing for me personally. But if you're looking for a quick, sexy, summer read, I think this book would be great!
About The Author
Since the release of her debut contemporary romance novel, Off Sides, in January 2013, Sawyer Bennett has released more than 30 books and has been featured on both the USA Today and New York Times bestseller lists on multiple occasions.
A reformed trial lawyer from North Carolina, Sawyer uses real life experience to create relatable, sexy stories that appeal to a wide array of readers. From new adult to erotic contemporary romance, Sawyer writes something for just about everyone.
Sawyer likes her Bloody Marys strong, her martinis dirty, and her heroes a combination of the two. When not bringing fictional romance to life, Sawyer is a chauffeur, stylist, chef, maid, and personal assistant to a very active toddler, as well as full-time servant to two adorably naughty dogs. She believes in the good of others, and that a bad day can be cured with a great work-out, cake, or a combination of the two.
Big News! The official movie trailer for Hollywood Dirt is here! #TrailerReveal #AlessandraTorre #Book2Screen
BIG NEWS! The official movie trailer for Hollywood Dirt is here!
When Hollywood comes to a small Georgia town, the cautious locals don't know wh at to expect. Feathers ruffle, secrets bloom, and sparks fly in this funny and se xy romance, inspired by the New York Times Bestseller.
Read the book: </ span>www.alessandratorre .com/hollywood- dirt/
Get more movie info: www.hollywooddirtmovie.com
Watch the official trailer: https://vimeo.com/ 223724733/a38c372e7d
PassionFlix is bringing your favorite romance novels to the TV screen! Want to become a founding member with all kinds of perks?
Find out more at https://www.passionflix.com/#atorre</
Release Blitz: Savage (The Kingwood Duet, #1) is Live! #Excerpt #Teasers & #PreOrder for Savior! #1Click #MustRead
Title: Savage
Series: The Kingwood Duet
Author: S.L. Scott
Genre: Mystery, Romantic Suspense
Release Date: July 9, 2017
Welcome to the mysterious world of the rich and the damned in this gritty, modern day fairy tale. Two star-crossed lovers will either find their destiny or meet their fate in a world where demons come in the form of familiar faces and pawns aren't just players, but deadly.
She was my destiny.
I was her downfall.
We were a match made in hell.
But when we were together, that hell was pure heaven.
The moment I laid eyes on her, I knew she would pay the price for my sins. I wasn't much older than she was, but old enough to know better. Old enough to know she would be good for me and I was bad for her. But I pursued her anyway. Back then I had hope that maybe she could change my future.
Maybe together we could change our fate . . .
US: http://amzn.to/2uKJOqj
UK: http://amzn.eu/1RL6jRi
CA: http://a.co/5rmBmyk
AU: https://www.amazon.com.au/dp/B073QYC9C8/
Paperback: http://amzn.to/2tM9QMw
~ Free in Kindle Unlimited ~
SAVAGE GOODREADS LINK: http://bit.ly/SAVAGEGR
Spotify Playlist: https://open.spotify.com/user/slscottauthor/playlist/52en9UjRyIGsPjDZ1aNgij
He moves with assurance, every step taken with purpose. When I met him, he oozed confidence, but I discovered that he didn't know who he was. It was as if his mother's death had caused him to lose track of himself. He made me feel safe and secure, taken care of, and loved. He was never unsteady around me. But I would catch him lost in his own thoughts when he assumed I was studying. I remember a night when music played softly in the background, a fire flamed in the fireplace, my books were scattered on the coffee table, and his room was dim with the heaviness of his mood, he stood in the open doorway that led to the balcony. The anguish seemed to cover him like a cloak, his shoulders sagging and his head seeming too cumbersome to hold up. Alexander leaned against the railing and closed his eyes . . .
"Kingwood Enterprises is the only thing left of my mother."
"You're left of her."
"No." He laughed, looking up at the night sky. "I'm all bad, just like my dad." Glancing over his shoulder back at me, he sighed. "When are you going to see the real me, Sara Jane?"
"I see the real you every time I see you. This is who you are. I don't know why you think you're so bad when all I see is the good."
With a smile on his face, he turns around, and crosses his arms over his chest. "You're the smartest decision I ever made. You know that?"
"Yes," I say, feeling sassy. "Now come and kiss me."
"You're so demanding when you get compliments."
"You like it."
Hopping over the arm of the couch, he lands with a thud next to me sending my textbooks to the floor. "I do like it. A lot."
That was a good kiss. A kiss that went from chaste to more in seconds. I still remember it so clearly.
Alexander may not have known who he was at the time, but he knew who he was going to be. I often wonder if it was a self-fulfilling prophecy or falling into a trap set long before he was born. There are a lot of ghosts haunting this manor and I don't intend to let him be the next.
Tonight, his secrets are protected by a solid wave of muscle that rolls over broad shoulders, crashing down strong arms. I turn over to get a better look at him before he disappears outside. He's been working out a lot more lately. He says it's to get rid of pent-up energy. I think it might be preparing for battle.
SAVIOR coming August 13th, 2017
SAVIOR GOODREADS LINK: http://bit.ly/SAVIORGR
SAVIOR Amazon Preorder Link: http://amzn.to/2sk8ftD
Living in the capital of Texas with her family, Scott loves traveling and avocados, beaches, and cooking with her kids. She's obsessed with epic romances and loves a good plot twist. Her favorite color is blue, but she likens it more toward the sky than the emotion. Her home is filled with the welcoming symbol of the pineapple and finds surfing a challenge though she likes to think she's a pro.
Huffington Post Contributor Page:http://www.huffingtonpost.com/sl-scott/
About Us...
Three book besties who love to read and discuss books. We are addicted to all kinds of romance with Alpha Hero's, Kick-ass Heroine's and plenty of angst.
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April 2017


text:
Tabbert Vivaldi 650, twin axle 2010 model with dinette, shower and toilet. Built in microwave.
In platinum condition inside and out, first to see guaranteed to buy.
Price: £7,750 ono
Location: Kent
Contact: Miley
Phone: 07770 367238
Aeroplane for sale, Practavia sprite G-Ross. Project build no. PFA 005-10404, project needs finishing also includes Lycoming engine rebuilt by Ronaldson engineering Oxford.
Work order ref no. R/L00214, loads of paper work and plans for project.
Price: £8,500 ono
Location: Wingfield Green, Norfolk Suffolk border
Contact: Lee Smith
Phone: 07778 747275
Roma Gt640, 2002, 1 of the last made by Baz. Oven and hob, cupboard and hook up for a small fridge. Cream leather upholstery with black arm rests, buttons and piping. Crystal door knobs throughout.
Plenty of display cupboards and lots of storage. 3 double pull out beds, sliding bedroom doors. Slight crack on 1 window (maybe by door blowing back, was like when we got it and is water tight) Dry trailer. No damp. Venetian blinds and nets.
Lights everywhere inside and out, dimmer switch. Blow air heating. Everything works as it should. Bedroom use only for last 5 years. Very good condition for a trailer this age, extremely hard to come by now. Cash on collection only. No time wasters.
Price: £8,000 ovno
Location: Lowestoft
Contact: Frank
Phone: 07788 266809
2011 Bailey Olympus 462, 2 berth, end bathroom with full awning and cassette toilet, shower hot and cold water. Full oven.
Fly nets and blinds, blow air central heating. TV aerial. Alco sway hitch. 3 way fridge. CD player. Spare wheel, elec cable, gas bottle, led lights all round.
All paper work and log book, 1 owner from new with full services from new. Call me, px considered.
Price: £6,250
Location: Stone
Contact: John
Phone: 07733 094186
Willerby Solara mobile home, 2 bedrooms, 28x10, 2012 in very good condition.
Price: £7,500 ono
Location: Reading
Contact: Tommy
Phone: 07407 164644
2010 Swift Fairway 540 in immaculate condition inside and out. End bedroom. For more information contact me.
Price: £6,800 ono
Location: Chester
Contact: Shane
Phone: 07426 796984
2009 Abbey twin wheel caravan in excellent condition inside and out, immaculately clean inside and out.
Good layout with end bedroom. Cris registration, all paperwork. 1st to see will buy.
£8,250 £8,000 ono
Location: Kent
Contact: Tommy
Phone: 07547 819695
Brand new 2017 Datejust Rolex watch, ring Martin.
Price: £7,750
Location: Scotland
Contact: Martin
Phone: 07379 561031
Here I have for sale my 2009 Dethleffs DL 550. 4 berth with fixed bed. Toilet & power shower & separate hand basin. Full cooker with 4 ring hob, grill & full oven. Fly nets, black out blinds & matching curtains to all windows. In immaculate condition throughout, no damp whatsoever, floor solid.
Not long had full service so everything works exactly as it should. Fridge with mini freezer. Full blow air central heating runs off gas or electric. Removable carpet mats. This caravan as been very well cared for, no marks or dings to the inside or outside. Seats are as new with no rips or stains.
Comes with full awning, gas cylinder, water waste, water pump, hook up cable end dining table. First to see will buy, can deliver anywhere in the UK. For more info you can contact me. There are 2 sets of keys and all paper work present. We are non smokers & no pets.
Price: £6,500 ono
Location: Stoke on Trent
Contact: Jim
Phone: 07833 217042
Elddis Avante Club 556, 6 berth, 2008, extremely good condition, used for the family holidays.
Bunk beds, full oven, microwave, extractor fan, CD player. Full awning. Any questions ring Barry.
Price: £7,500 ono
Location: Harrogate
Contact: Barry
Phone: 07788 674352
Fantastic Reading type gypsy wagon built by the Gypsy Caravan Company.
In vgc, used for Airbnb. Pull out double bed, propane cooker and all kitchen items. No wood burner.
NB: not horse drawn, has a draw bar to move around or trailer.
Price: £7,500 ono
Location: Marlborough, Wiltshire
Contact: Sarah Brown
Phone: 07501 159060


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text: I discovered a tasty treat yesterday that needs a companion. This treat is Sweet Potato chips by Food Should Taste Good. The companion it needs is Apple Butter. Do any of you have an apple butter recipe that you enjoy and would be willing to share? I would love to add it and give you credit for it.