Learning and executing goal-directed choices by internally generated sequences in spiking neural circuits.
Recent neural ensemble recordings have established a link between goal-directed spatial decision making and internally generated neural sequences in the hippocampus of rats. To elucidate the synaptic mechanisms of these sequences underlying spatial decision making processes, we develop and investiga...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2017-07-01
|
| Series: | PLoS Computational Biology |
| Online Access: | http://europepmc.org/articles/PMC5552356?pdf=render |
| _version_ | 1818555612488794112 |
|---|---|
| author | John Palmer Adam Keane Pulin Gong |
| author_facet | John Palmer Adam Keane Pulin Gong |
| author_sort | John Palmer |
| collection | DOAJ |
| description | Recent neural ensemble recordings have established a link between goal-directed spatial decision making and internally generated neural sequences in the hippocampus of rats. To elucidate the synaptic mechanisms of these sequences underlying spatial decision making processes, we develop and investigate a spiking neural circuit model endowed with a combination of two synaptic plasticity mechanisms including spike-timing dependent plasticity (STDP) and synaptic scaling. In this model, the interplay of the combined synaptic plasticity mechanisms and network dynamics gives rise to neural sequences which propagate ahead of the animals' decision point to reach goal locations. The dynamical properties of these forward-sweeping sequences and the rates of correct binary choices executed by these sequences are quantitatively consistent with experimental observations; this consistency, however, is lost in our model when only one of STDP or synaptic scaling is included. We further demonstrate that such sequence-based decision making in our network model can adaptively respond to time-varying and probabilistic associations of cues and goal locations, and that our model performs as well as an optimal Kalman filter model. Our results thus suggest that the combination of plasticity phenomena on different timescales provides a candidate mechanism for forming internally generated neural sequences and for implementing adaptive spatial decision making. |
| first_indexed | 2024-12-12T09:56:00Z |
| format | Article |
| id | doaj.art-90539227704b4b47b8110a3d3643f093 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-12T09:56:00Z |
| publishDate | 2017-07-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-90539227704b4b47b8110a3d3643f0932022-12-22T00:28:07ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582017-07-01137e100566910.1371/journal.pcbi.1005669Learning and executing goal-directed choices by internally generated sequences in spiking neural circuits.John PalmerAdam KeanePulin GongRecent neural ensemble recordings have established a link between goal-directed spatial decision making and internally generated neural sequences in the hippocampus of rats. To elucidate the synaptic mechanisms of these sequences underlying spatial decision making processes, we develop and investigate a spiking neural circuit model endowed with a combination of two synaptic plasticity mechanisms including spike-timing dependent plasticity (STDP) and synaptic scaling. In this model, the interplay of the combined synaptic plasticity mechanisms and network dynamics gives rise to neural sequences which propagate ahead of the animals' decision point to reach goal locations. The dynamical properties of these forward-sweeping sequences and the rates of correct binary choices executed by these sequences are quantitatively consistent with experimental observations; this consistency, however, is lost in our model when only one of STDP or synaptic scaling is included. We further demonstrate that such sequence-based decision making in our network model can adaptively respond to time-varying and probabilistic associations of cues and goal locations, and that our model performs as well as an optimal Kalman filter model. Our results thus suggest that the combination of plasticity phenomena on different timescales provides a candidate mechanism for forming internally generated neural sequences and for implementing adaptive spatial decision making.http://europepmc.org/articles/PMC5552356?pdf=render |
| spellingShingle | John Palmer Adam Keane Pulin Gong Learning and executing goal-directed choices by internally generated sequences in spiking neural circuits. PLoS Computational Biology |
| title | Learning and executing goal-directed choices by internally generated sequences in spiking neural circuits. |
| title_full | Learning and executing goal-directed choices by internally generated sequences in spiking neural circuits. |
| title_fullStr | Learning and executing goal-directed choices by internally generated sequences in spiking neural circuits. |
| title_full_unstemmed | Learning and executing goal-directed choices by internally generated sequences in spiking neural circuits. |
| title_short | Learning and executing goal-directed choices by internally generated sequences in spiking neural circuits. |
| title_sort | learning and executing goal directed choices by internally generated sequences in spiking neural circuits |
| url | http://europepmc.org/articles/PMC5552356?pdf=render |
| work_keys_str_mv | AT johnpalmer learningandexecutinggoaldirectedchoicesbyinternallygeneratedsequencesinspikingneuralcircuits AT adamkeane learningandexecutinggoaldirectedchoicesbyinternallygeneratedsequencesinspikingneuralcircuits AT pulingong learningandexecutinggoaldirectedchoicesbyinternallygeneratedsequencesinspikingneuralcircuits |