Effects of Firing Variability on Network Structures with Spike-Timing-Dependent Plasticity
Synaptic plasticity is believed to be the biological substrate underlying learning and memory. One of the most widespread forms of synaptic plasticity, spike-timing-dependent plasticity (STDP), uses the spike timing information of presynaptic and postsynaptic neurons to induce synaptic potentiation...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2018-01-01
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| Series: | Frontiers in Computational Neuroscience |
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| Online Access: | http://journal.frontiersin.org/article/10.3389/fncom.2018.00001/full |
| _version_ | 1819067086828208128 |
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| author | Bin Min Bin Min Douglas Zhou David Cai David Cai David Cai |
| author_facet | Bin Min Bin Min Douglas Zhou David Cai David Cai David Cai |
| author_sort | Bin Min |
| collection | DOAJ |
| description | Synaptic plasticity is believed to be the biological substrate underlying learning and memory. One of the most widespread forms of synaptic plasticity, spike-timing-dependent plasticity (STDP), uses the spike timing information of presynaptic and postsynaptic neurons to induce synaptic potentiation or depression. An open question is how STDP organizes the connectivity patterns in neuronal circuits. Previous studies have placed much emphasis on the role of firing rate in shaping connectivity patterns. Here, we go beyond the firing rate description to develop a self-consistent linear response theory that incorporates the information of both firing rate and firing variability. By decomposing the pairwise spike correlation into one component associated with local direct connections and the other associated with indirect connections, we identify two distinct regimes regarding the network structures learned through STDP. In one regime, the contribution of the direct-connection correlations dominates over that of the indirect-connection correlations in the learning dynamics; this gives rise to a network structure consistent with the firing rate description. In the other regime, the contribution of the indirect-connection correlations dominates in the learning dynamics, leading to a network structure different from the firing rate description. We demonstrate that the heterogeneity of firing variability across neuronal populations induces a temporally asymmetric structure of indirect-connection correlations. This temporally asymmetric structure underlies the emergence of the second regime. Our study provides a new perspective that emphasizes the role of high-order statistics of spiking activity in the spike-correlation-sensitive learning dynamics. |
| first_indexed | 2024-12-21T16:12:40Z |
| format | Article |
| id | doaj.art-3e7a9de83d6d48a98c833889e6f0f9ae |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-12-21T16:12:40Z |
| publishDate | 2018-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-3e7a9de83d6d48a98c833889e6f0f9ae2022-12-21T18:57:46ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882018-01-011210.3389/fncom.2018.00001303788Effects of Firing Variability on Network Structures with Spike-Timing-Dependent PlasticityBin Min0Bin Min1Douglas Zhou2David Cai3David Cai4David Cai5Center for Neural Science, Courant Institute of Mathematical Sciences, New York University, New York, NY, United StatesNYUAD Institute, New York University Abu Dhabi, Abu Dhabi, United Arab EmiratesSchool of Mathematical Sciences, MOE-LSC, Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, ChinaCenter for Neural Science, Courant Institute of Mathematical Sciences, New York University, New York, NY, United StatesNYUAD Institute, New York University Abu Dhabi, Abu Dhabi, United Arab EmiratesSchool of Mathematical Sciences, MOE-LSC, Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, ChinaSynaptic plasticity is believed to be the biological substrate underlying learning and memory. One of the most widespread forms of synaptic plasticity, spike-timing-dependent plasticity (STDP), uses the spike timing information of presynaptic and postsynaptic neurons to induce synaptic potentiation or depression. An open question is how STDP organizes the connectivity patterns in neuronal circuits. Previous studies have placed much emphasis on the role of firing rate in shaping connectivity patterns. Here, we go beyond the firing rate description to develop a self-consistent linear response theory that incorporates the information of both firing rate and firing variability. By decomposing the pairwise spike correlation into one component associated with local direct connections and the other associated with indirect connections, we identify two distinct regimes regarding the network structures learned through STDP. In one regime, the contribution of the direct-connection correlations dominates over that of the indirect-connection correlations in the learning dynamics; this gives rise to a network structure consistent with the firing rate description. In the other regime, the contribution of the indirect-connection correlations dominates in the learning dynamics, leading to a network structure different from the firing rate description. We demonstrate that the heterogeneity of firing variability across neuronal populations induces a temporally asymmetric structure of indirect-connection correlations. This temporally asymmetric structure underlies the emergence of the second regime. Our study provides a new perspective that emphasizes the role of high-order statistics of spiking activity in the spike-correlation-sensitive learning dynamics.http://journal.frontiersin.org/article/10.3389/fncom.2018.00001/fullSTDPlinear response theorycorrelation structurefiring variabilitysynaptic plasticity |
| spellingShingle | Bin Min Bin Min Douglas Zhou David Cai David Cai David Cai Effects of Firing Variability on Network Structures with Spike-Timing-Dependent Plasticity Frontiers in Computational Neuroscience STDP linear response theory correlation structure firing variability synaptic plasticity |
| title | Effects of Firing Variability on Network Structures with Spike-Timing-Dependent Plasticity |
| title_full | Effects of Firing Variability on Network Structures with Spike-Timing-Dependent Plasticity |
| title_fullStr | Effects of Firing Variability on Network Structures with Spike-Timing-Dependent Plasticity |
| title_full_unstemmed | Effects of Firing Variability on Network Structures with Spike-Timing-Dependent Plasticity |
| title_short | Effects of Firing Variability on Network Structures with Spike-Timing-Dependent Plasticity |
| title_sort | effects of firing variability on network structures with spike timing dependent plasticity |
| topic | STDP linear response theory correlation structure firing variability synaptic plasticity |
| url | http://journal.frontiersin.org/article/10.3389/fncom.2018.00001/full |
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