Spike-Based Bayesian-Hebbian Learning of Temporal Sequences.
Many cognitive and motor functions are enabled by the temporal representation and processing of stimuli, but it remains an open issue how neocortical microcircuits can reliably encode and replay such sequences of information. To better understand this, a modular attractor memory network is proposed...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2016-05-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1004954&type=printable |
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| author | Philip J Tully Henrik Lindén Matthias H Hennig Anders Lansner |
| author_facet | Philip J Tully Henrik Lindén Matthias H Hennig Anders Lansner |
| author_sort | Philip J Tully |
| collection | DOAJ |
| description | Many cognitive and motor functions are enabled by the temporal representation and processing of stimuli, but it remains an open issue how neocortical microcircuits can reliably encode and replay such sequences of information. To better understand this, a modular attractor memory network is proposed in which meta-stable sequential attractor transitions are learned through changes to synaptic weights and intrinsic excitabilities via the spike-based Bayesian Confidence Propagation Neural Network (BCPNN) learning rule. We find that the formation of distributed memories, embodied by increased periods of firing in pools of excitatory neurons, together with asymmetrical associations between these distinct network states, can be acquired through plasticity. The model's feasibility is demonstrated using simulations of adaptive exponential integrate-and-fire model neurons (AdEx). We show that the learning and speed of sequence replay depends on a confluence of biophysically relevant parameters including stimulus duration, level of background noise, ratio of synaptic currents, and strengths of short-term depression and adaptation. Moreover, sequence elements are shown to flexibly participate multiple times in the sequence, suggesting that spiking attractor networks of this type can support an efficient combinatorial code. The model provides a principled approach towards understanding how multiple interacting plasticity mechanisms can coordinate hetero-associative learning in unison. |
| format | Article |
| id | doaj-art-5634f8a41be9463c8367baa90e1008f1 |
| institution | Kabale University |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2016-05-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-5634f8a41be9463c8367baa90e1008f12025-08-20T03:24:36ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582016-05-01125e100495410.1371/journal.pcbi.1004954Spike-Based Bayesian-Hebbian Learning of Temporal Sequences.Philip J TullyHenrik LindénMatthias H HennigAnders LansnerMany cognitive and motor functions are enabled by the temporal representation and processing of stimuli, but it remains an open issue how neocortical microcircuits can reliably encode and replay such sequences of information. To better understand this, a modular attractor memory network is proposed in which meta-stable sequential attractor transitions are learned through changes to synaptic weights and intrinsic excitabilities via the spike-based Bayesian Confidence Propagation Neural Network (BCPNN) learning rule. We find that the formation of distributed memories, embodied by increased periods of firing in pools of excitatory neurons, together with asymmetrical associations between these distinct network states, can be acquired through plasticity. The model's feasibility is demonstrated using simulations of adaptive exponential integrate-and-fire model neurons (AdEx). We show that the learning and speed of sequence replay depends on a confluence of biophysically relevant parameters including stimulus duration, level of background noise, ratio of synaptic currents, and strengths of short-term depression and adaptation. Moreover, sequence elements are shown to flexibly participate multiple times in the sequence, suggesting that spiking attractor networks of this type can support an efficient combinatorial code. The model provides a principled approach towards understanding how multiple interacting plasticity mechanisms can coordinate hetero-associative learning in unison.https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1004954&type=printable |
| spellingShingle | Philip J Tully Henrik Lindén Matthias H Hennig Anders Lansner Spike-Based Bayesian-Hebbian Learning of Temporal Sequences. PLoS Computational Biology |
| title | Spike-Based Bayesian-Hebbian Learning of Temporal Sequences. |
| title_full | Spike-Based Bayesian-Hebbian Learning of Temporal Sequences. |
| title_fullStr | Spike-Based Bayesian-Hebbian Learning of Temporal Sequences. |
| title_full_unstemmed | Spike-Based Bayesian-Hebbian Learning of Temporal Sequences. |
| title_short | Spike-Based Bayesian-Hebbian Learning of Temporal Sequences. |
| title_sort | spike based bayesian hebbian learning of temporal sequences |
| url | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1004954&type=printable |
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