Optimal Localist and Distributed Coding of Spatiotemporal Spike Patterns Through STDP and Coincidence Detection
Repeating spatiotemporal spike patterns exist and carry information. Here we investigated how a single spiking neuron can optimally respond to one given pattern (localist coding), or to either one of several patterns (distributed coding, i.e., the neuron's response is ambiguous but the identity...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2018-09-01
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| Series: | Frontiers in Computational Neuroscience |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fncom.2018.00074/full |
| _version_ | 1819054896454828032 |
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| author | Timothée Masquelier Timothée Masquelier Saeed R. Kheradpisheh |
| author_facet | Timothée Masquelier Timothée Masquelier Saeed R. Kheradpisheh |
| author_sort | Timothée Masquelier |
| collection | DOAJ |
| description | Repeating spatiotemporal spike patterns exist and carry information. Here we investigated how a single spiking neuron can optimally respond to one given pattern (localist coding), or to either one of several patterns (distributed coding, i.e., the neuron's response is ambiguous but the identity of the pattern could be inferred from the response of multiple neurons), but not to random inputs. To do so, we extended a theory developed in a previous paper (Masquelier, 2017), which was limited to localist coding. More specifically, we computed analytically the signal-to-noise ratio (SNR) of a multi-pattern-detector neuron, using a threshold-free leaky integrate-and-fire (LIF) neuron model with non-plastic unitary synapses and homogeneous Poisson inputs. Surprisingly, when increasing the number of patterns, the SNR decreases slowly, and remains acceptable for several tens of independent patterns. In addition, we investigated whether spike-timing-dependent plasticity (STDP) could enable a neuron to reach the theoretical optimal SNR. To this aim, we simulated a LIF equipped with STDP, and repeatedly exposed it to multiple input spike patterns, embedded in equally dense Poisson spike trains. The LIF progressively became selective to every repeating pattern with no supervision, and stopped discharging during the Poisson spike trains. Furthermore, tuning certain STDP parameters, the resulting pattern detectors were optimal. Tens of independent patterns could be learned by a single neuron using a low adaptive threshold, in contrast with previous studies, in which higher thresholds led to localist coding only. Taken together these results suggest that coincidence detection and STDP are powerful mechanisms, fully compatible with distributed coding. Yet we acknowledge that our theory is limited to single neurons, and thus also applies to feed-forward networks, but not to recurrent ones. |
| first_indexed | 2024-12-21T12:58:54Z |
| format | Article |
| id | doaj.art-54ff0e88b0a541379047dfbdc5066d1b |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-12-21T12:58:54Z |
| publishDate | 2018-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-54ff0e88b0a541379047dfbdc5066d1b2022-12-21T19:03:16ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882018-09-011210.3389/fncom.2018.00074410212Optimal Localist and Distributed Coding of Spatiotemporal Spike Patterns Through STDP and Coincidence DetectionTimothée Masquelier0Timothée Masquelier1Saeed R. Kheradpisheh2Centre de Recherche Cerveau et Cognition, UMR5549 CNRS—Université Toulouse 3, Toulouse, FranceInstituto de Microelectrónica de Sevilla (IMSE-CNM), CSIC, Universidad de Sevilla, Sevilla, SpainDepartment of Computer Science, Faculty of Mathematical Sciences and Computer, Kharazmi University, Tehran, IranRepeating spatiotemporal spike patterns exist and carry information. Here we investigated how a single spiking neuron can optimally respond to one given pattern (localist coding), or to either one of several patterns (distributed coding, i.e., the neuron's response is ambiguous but the identity of the pattern could be inferred from the response of multiple neurons), but not to random inputs. To do so, we extended a theory developed in a previous paper (Masquelier, 2017), which was limited to localist coding. More specifically, we computed analytically the signal-to-noise ratio (SNR) of a multi-pattern-detector neuron, using a threshold-free leaky integrate-and-fire (LIF) neuron model with non-plastic unitary synapses and homogeneous Poisson inputs. Surprisingly, when increasing the number of patterns, the SNR decreases slowly, and remains acceptable for several tens of independent patterns. In addition, we investigated whether spike-timing-dependent plasticity (STDP) could enable a neuron to reach the theoretical optimal SNR. To this aim, we simulated a LIF equipped with STDP, and repeatedly exposed it to multiple input spike patterns, embedded in equally dense Poisson spike trains. The LIF progressively became selective to every repeating pattern with no supervision, and stopped discharging during the Poisson spike trains. Furthermore, tuning certain STDP parameters, the resulting pattern detectors were optimal. Tens of independent patterns could be learned by a single neuron using a low adaptive threshold, in contrast with previous studies, in which higher thresholds led to localist coding only. Taken together these results suggest that coincidence detection and STDP are powerful mechanisms, fully compatible with distributed coding. Yet we acknowledge that our theory is limited to single neurons, and thus also applies to feed-forward networks, but not to recurrent ones.https://www.frontiersin.org/article/10.3389/fncom.2018.00074/fullneural codinglocalist codingdistributed codingcoincidence detectionleaky integrate-and-fire neuronspatiotemporal spike pattern |
| spellingShingle | Timothée Masquelier Timothée Masquelier Saeed R. Kheradpisheh Optimal Localist and Distributed Coding of Spatiotemporal Spike Patterns Through STDP and Coincidence Detection Frontiers in Computational Neuroscience neural coding localist coding distributed coding coincidence detection leaky integrate-and-fire neuron spatiotemporal spike pattern |
| title | Optimal Localist and Distributed Coding of Spatiotemporal Spike Patterns Through STDP and Coincidence Detection |
| title_full | Optimal Localist and Distributed Coding of Spatiotemporal Spike Patterns Through STDP and Coincidence Detection |
| title_fullStr | Optimal Localist and Distributed Coding of Spatiotemporal Spike Patterns Through STDP and Coincidence Detection |
| title_full_unstemmed | Optimal Localist and Distributed Coding of Spatiotemporal Spike Patterns Through STDP and Coincidence Detection |
| title_short | Optimal Localist and Distributed Coding of Spatiotemporal Spike Patterns Through STDP and Coincidence Detection |
| title_sort | optimal localist and distributed coding of spatiotemporal spike patterns through stdp and coincidence detection |
| topic | neural coding localist coding distributed coding coincidence detection leaky integrate-and-fire neuron spatiotemporal spike pattern |
| url | https://www.frontiersin.org/article/10.3389/fncom.2018.00074/full |
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