network profile
| bio | website | |
|---|---|---|
| location | ||
| age | ||
| visits | member for | 10 months |
| seen | Aug 25 '12 at 10:11 | |
| stats | profile views | 0 |
|
Aug 16 |
comment |
Computational differences between spiking neural networks and previous ANNs 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. |
|
Jul 20 |
comment |
How can STDP fit with reciprocal connectivity? Yes I just got that answer from a friend in the field as well. It seems to be the solution - aligning the STDP function not on time 0 but rather on a certain -Xms to the left. Do you have any idea what transmission delay is reasonable? |
|
Jul 19 |
awarded | Student |
|
Jul 19 |
asked | How can STDP fit with reciprocal connectivity? |
|
Mar 19 |
comment |
Computational differences between spiking neural networks and previous ANNs OK, I think I understand that. But lets take that network, with the exact same structure, and swap the neuron implementation from IAF to perceptron. Have 2 copies of the same topology and running time (M time, N inputs) with a different implementation for the neurons. Why would one be pulse code and the other rate code? |
|
Mar 14 |
comment |
Computational differences between spiking neural networks and previous ANNs Let's take a simple perceptron also with a binary output. From your description it sounds like he too can be interpreted as pulse-code, it's all a question of which time-scale you attribute each iteration of the net (each time-period). However, IAF neurons are considered to have different abilities than perceptrons, namely the ability to find temporal patterns in data. So regardless of interpretation, there should be some essential difference in implementation to account for that. Am I missing something? Thanks a lot Uri |
|
Mar 10 |
asked | Computational differences between spiking neural networks and previous ANNs |
