Phase diagram of spiking neural networks
In computer simulations of spiking neural networks, often it is assumed that every two neurons of the network are connected by a probability of 2%, 20% of neurons are inhibitory and 80% are excitatory. These common values are based on experiments, observations, and trials and errors, but here, I tak...
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Frontiers Media S.A.
2015
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4349167/ |
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pubmed-43491672015-03-18 Phase diagram of spiking neural networks Seyed-allaei, Hamed Neuroscience In computer simulations of spiking neural networks, often it is assumed that every two neurons of the network are connected by a probability of 2%, 20% of neurons are inhibitory and 80% are excitatory. These common values are based on experiments, observations, and trials and errors, but here, I take a different perspective, inspired by evolution, I systematically simulate many networks, each with a different set of parameters, and then I try to figure out what makes the common values desirable. I stimulate networks with pulses and then measure their: dynamic range, dominant frequency of population activities, total duration of activities, maximum rate of population and the occurrence time of maximum rate. The results are organized in phase diagram. This phase diagram gives an insight into the space of parameters – excitatory to inhibitory ratio, sparseness of connections and synaptic weights. This phase diagram can be used to decide the parameters of a model. The phase diagrams show that networks which are configured according to the common values, have a good dynamic range in response to an impulse and their dynamic range is robust in respect to synaptic weights, and for some synaptic weights they oscillates in α or β frequencies, independent of external stimuli. Frontiers Media S.A. 2015-03-04 /pmc/articles/PMC4349167/ /pubmed/25788885 http://dx.doi.org/10.3389/fncom.2015.00019 Text en Copyright © 2015 Seyed-allaei. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
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Open Access Journal |
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Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Seyed-allaei, Hamed |
| spellingShingle |
Seyed-allaei, Hamed Phase diagram of spiking neural networks |
| author_facet |
Seyed-allaei, Hamed |
| author_sort |
Seyed-allaei, Hamed |
| title |
Phase diagram of spiking neural networks |
| title_short |
Phase diagram of spiking neural networks |
| title_full |
Phase diagram of spiking neural networks |
| title_fullStr |
Phase diagram of spiking neural networks |
| title_full_unstemmed |
Phase diagram of spiking neural networks |
| title_sort |
phase diagram of spiking neural networks |
| description |
In computer simulations of spiking neural networks, often it is assumed that every two neurons of the network are connected by a probability of 2%, 20% of neurons are inhibitory and 80% are excitatory. These common values are based on experiments, observations, and trials and errors, but here, I take a different perspective, inspired by evolution, I systematically simulate many networks, each with a different set of parameters, and then I try to figure out what makes the common values desirable. I stimulate networks with pulses and then measure their: dynamic range, dominant frequency of population activities, total duration of activities, maximum rate of population and the occurrence time of maximum rate. The results are organized in phase diagram. This phase diagram gives an insight into the space of parameters – excitatory to inhibitory ratio, sparseness of connections and synaptic weights. This phase diagram can be used to decide the parameters of a model. The phase diagrams show that networks which are configured according to the common values, have a good dynamic range in response to an impulse and their dynamic range is robust in respect to synaptic weights, and for some synaptic weights they oscillates in α or β frequencies, independent of external stimuli. |
| publisher |
Frontiers Media S.A. |
| publishDate |
2015 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4349167/ |
| _version_ |
1613195037940645888 |