Using a Linear Non-linear Cascade with a Non-Random (deterministic) Stimulus I'm attempting to use a computational model, in particular a linear non-linear cascade (LNP) to encode the spiking of a neuron responding to a thermal stimulus. In a LNP, it assumes that the stimulus is 'radially symmetric' [1], for example having a Gaussian probability distribution. However, is it possible to use a LNP model (or another well known encoding model) when the stimulus is deterministic? The stimulus I'm using is a ramping thermal stimulus. 

[1] Chichilnisky EJ (2001) A simple white noise analysis of neuronal light responses. Network (Bristol, England) 12: 199–213.
The short answer is yes. The longer answer involves a more precise meaning of deterministic and a number of research considerations.

In the strict sense of deterministic, which means that given the same input, the same output will always occur, any probability distribution modeled on a computer is deterministic, since most digital computers have a deterministic instruction set. However, this isn't quite relevant for your task, since the samples from the distribution that your model sees are largely uncorrelated with the model properties, making them appear random to the model. 

Researchers often use probabilistic or stochastic stimuli because they are the simplest option and because they do not want to introduce a number of potential biases that occur when the model interacts with or entrains to specific patterns in deterministic stimuli. Such patterns would produce effects that are not representative of the general properties of the model, which may be misleading. Introducing a probability distribution therefore becomes a simple way to explore more of the state space of the model. Additional reasons why people introduce noise (through a stochastic stimulus) include the fact that real neurons tend to exist in noisy environments (modeling considerations), and the fact that some kinds of proofs about model performance are easier to perform for probabilistic stimuli. 

Your choice as to whether you want to use a deterministic stimulus depends largely on your goals in this research project. Do you want to know what happens when the model sees a specific pattern? Do you care if this pattern has noise in it? Do you want to perform mathematical proofs? Do you care about perturbation stability (some deterministic models may falsely appear stable because they are not moved off of their tiny equilibrium point by noise)? What process are you modeling?

Given these considerations, you should be able to choose what kind of stimulus to use. Your model will accept many kinds of stimuli as input, but the choice of stimulus affects what you will learn from the specific simulation.