<p>This is an AI question regarding &quot;3rd generation neural networks&quot; - spiking neural networks (SNN).</p>
<p>I hve been studying this concept online from various papers, mainly <a href="http://mathsci.kaist.ac.kr/%7Enipl/am621/lecturenotes/spiking_neurons_2.pdf" rel="noreferrer">Maass (1997)</a>. 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.</p>
<p>I have background in neuroscience so I understand the terms and ratio, I am asking regarding the actual implementation.</p>
<p>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?</p>
<h3>Notes</h3>
<p>This question was migrated from SO, and a duplicate was asked on cstheory that was <a href="https://cs.stackexchange.com/q/3195/55">migrated to CS.SE</a>.</p>

CC BY-SA 3.0
2012-08-15T13:19:31.440Z
Apparently [this question has been cross-posted on our Computer Science site](http://cs.stackexchange.com/questions/3195).
Josh
CC BY-SA 3.0
2012-08-15T13:19:36.497Z
Generally at Stack Exchange this is heavily frowned upon; questions should exist on one site only. Amir, please make sure to keep *both* questions updated, and when you accept an answer on one site, be sure to answer the question yourself on the other site, and link the two together. And please avoid cross-posting in the future. Thank you!
Josh
CC BY-SA 3.0
2012-08-16T08:08:50.683Z
Sorry, I wasn't aware of the policy. I just thought the question was in the gray area between the two fields. I will maintain both and link a final answer if I reach it.
Amir
<computational-modeling><neural-network><artificial-intelligence>
Computational differences between spiking neural networks and previous ANNs
Amir
# Computational differences between spiking neural networks and previous ANNs

(Asked by: username_0 on 2012-03-10)

This is an AI question regarding "3rd generation neural networks" - spiking neural networks (SNN).

I hve been studying this concept online from various papers, mainly [Maass (1997)](http://mathsci.kaist.ac.kr/%7Enipl/am621/lecturenotes/spiking_neurons_2.pdf). 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](https://cs.stackexchange.com/q/3195/55).

**Tags:** computational-modeling, neural-network, artificial-intelligence

> **Comments:**
> **username_1**: Apparently [this question has been cross-posted on our Computer Science site](http://cs.stackexchange.com/questions/3195).  
> **username_1**: Generally at Stack Exchange this is heavily frowned upon; questions should exist on one site only. username_0, please make sure to keep *both* questions updated, and when you accept an answer on one site, be sure to answer the question yourself on the other site, and link the two together. And please avoid cross-posting in the future. Thank you!  
> **username_0**: Sorry, I wasn't aware of the policy. I just thought the question was in the gray area between the two fields. I will maintain both and link a final answer if I reach it.  

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