The influence of synaptic weight distribution on neuronal population dynamics.
The manner in which different distributions of synaptic weights onto cortical neurons shape their spiking activity remains open. To characterize a homogeneous neuronal population, we use the master equation for generalized leaky integrate-and-fire neurons with shot-noise synapses. We develop fast se...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2013-10-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24204219/?tool=EBI |
| _version_ | 1828940196443324416 |
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| author | Ramakrishnan Iyer Vilas Menon Michael Buice Christof Koch Stefan Mihalas |
| author_facet | Ramakrishnan Iyer Vilas Menon Michael Buice Christof Koch Stefan Mihalas |
| author_sort | Ramakrishnan Iyer |
| collection | DOAJ |
| description | The manner in which different distributions of synaptic weights onto cortical neurons shape their spiking activity remains open. To characterize a homogeneous neuronal population, we use the master equation for generalized leaky integrate-and-fire neurons with shot-noise synapses. We develop fast semi-analytic numerical methods to solve this equation for either current or conductance synapses, with and without synaptic depression. We show that its solutions match simulations of equivalent neuronal networks better than those of the Fokker-Planck equation and we compute bounds on the network response to non-instantaneous synapses. We apply these methods to study different synaptic weight distributions in feed-forward networks. We characterize the synaptic amplitude distributions using a set of measures, called tail weight numbers, designed to quantify the preponderance of very strong synapses. Even if synaptic amplitude distributions are equated for both the total current and average synaptic weight, distributions with sparse but strong synapses produce higher responses for small inputs, leading to a larger operating range. Furthermore, despite their small number, such synapses enable the network to respond faster and with more stability in the face of external fluctuations. |
| first_indexed | 2024-12-14T03:15:17Z |
| format | Article |
| id | doaj.art-7804ba7fc2c44fadb56d3609e4765000 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-14T03:15:17Z |
| publishDate | 2013-10-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-7804ba7fc2c44fadb56d3609e47650002022-12-21T23:19:10ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582013-10-01910e100324810.1371/journal.pcbi.1003248The influence of synaptic weight distribution on neuronal population dynamics.Ramakrishnan IyerVilas MenonMichael BuiceChristof KochStefan MihalasThe manner in which different distributions of synaptic weights onto cortical neurons shape their spiking activity remains open. To characterize a homogeneous neuronal population, we use the master equation for generalized leaky integrate-and-fire neurons with shot-noise synapses. We develop fast semi-analytic numerical methods to solve this equation for either current or conductance synapses, with and without synaptic depression. We show that its solutions match simulations of equivalent neuronal networks better than those of the Fokker-Planck equation and we compute bounds on the network response to non-instantaneous synapses. We apply these methods to study different synaptic weight distributions in feed-forward networks. We characterize the synaptic amplitude distributions using a set of measures, called tail weight numbers, designed to quantify the preponderance of very strong synapses. Even if synaptic amplitude distributions are equated for both the total current and average synaptic weight, distributions with sparse but strong synapses produce higher responses for small inputs, leading to a larger operating range. Furthermore, despite their small number, such synapses enable the network to respond faster and with more stability in the face of external fluctuations.https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24204219/?tool=EBI |
| spellingShingle | Ramakrishnan Iyer Vilas Menon Michael Buice Christof Koch Stefan Mihalas The influence of synaptic weight distribution on neuronal population dynamics. PLoS Computational Biology |
| title | The influence of synaptic weight distribution on neuronal population dynamics. |
| title_full | The influence of synaptic weight distribution on neuronal population dynamics. |
| title_fullStr | The influence of synaptic weight distribution on neuronal population dynamics. |
| title_full_unstemmed | The influence of synaptic weight distribution on neuronal population dynamics. |
| title_short | The influence of synaptic weight distribution on neuronal population dynamics. |
| title_sort | influence of synaptic weight distribution on neuronal population dynamics |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24204219/?tool=EBI |
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