Computational differences between spiking neural networks and previous ANNs This is an AI question regarding &quot;3rd generation neural networks&quot; - spiking neural networks (SNN).
I hve been studying this concept online from various papers, mainly Maass (1997). I and am not entirely sure I understand why SNN's are considered pulse-code in contrast to earlier ANN's which are rate-code.
I have background in neuroscience so I understand the terms and ratio, I am asking regarding the actual implementation.
Is the practical difference in the fact that when each neuron updates its current state in an SNN it deals with the entire history of every pre-synaptic neuron and not only the last step? Is that what gives it temporal characteristics which previous generation ANN's lack? What is the key computational difference between SNN and earlier approaches?
Notes
This question was migrated from SO, and a duplicate was asked on cstheory that was migrated to CS.SE.
Rate-based-network is a specific implementation of the more general spiking-networks. One may see a rate-based-network as a spiking-network in which the inputs from each neuron are accumulated over a short time-period (think of "one second") and are used to update the state of their target neuron only once in each time-period.

For example, if an input neuron has an output of '40' in a rate-based-network, you should think about it as if it fire 40 times in the time-period, and only once in each time-period it's target neuron 'read' this input.

On the other hand, with spiking-neurons the output is limited to binary and hence the time-period used must be shorter than the maximal rate. In each time-period the target neuron updates its state based on it's (binary) input. 

So to summarize:
* Spiking networks describe a more general phenomena.
* Spiking networks can react to inputs with fine temporal structure and act in time-periods which are order of magnitude smaller than rate-based-models.

Also one may add that spiking networks are likely to be more accurate biological description of the brain (but still a very rough account).