Learning and Prospective Recall of Noisy Spike Pattern Episodes
Spike patterns in vivo are often incomplete or corrupted with noise that makes inputs to neuronal networks appear to vary although they may, in fact, be samples of a single underlying pattern or repeated presentation. Here we present a recurrent spiking neural network (SNN) model that learns noisy p...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2013-06-01
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| Series: | Frontiers in Computational Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fncom.2013.00080/full |
| _version_ | 1818993357001588736 |
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| author | Karl eDockendorf Narayan eSrinivasa |
| author_facet | Karl eDockendorf Narayan eSrinivasa |
| author_sort | Karl eDockendorf |
| collection | DOAJ |
| description | Spike patterns in vivo are often incomplete or corrupted with noise that makes inputs to neuronal networks appear to vary although they may, in fact, be samples of a single underlying pattern or repeated presentation. Here we present a recurrent spiking neural network (SNN) model that learns noisy pattern sequences through the use of homeostasis and spike-timing dependent plasticity (STDP). We find that the changes in the synaptic weight vector during learning of patterns of random ensembles are approximately orthogonal in a reduced dimension space when the patterns are constructed to minimize overlap in representations. Using this model, representations of sparse patterns maybe associated through co-activated firing and integrated into ensemble representations. While the model is tolerant to noise, prospective activity and pattern completion differ in their ability to adapt in the presence of noise. One version of the model is able to demonstrate the recently discovered phenomena of preplay and replay reminiscent of hippocampal like behaviors. |
| first_indexed | 2024-12-20T20:40:46Z |
| format | Article |
| id | doaj.art-456e2bae4eec42cfa60de94decd39275 |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-12-20T20:40:46Z |
| publishDate | 2013-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-456e2bae4eec42cfa60de94decd392752022-12-21T19:27:07ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882013-06-01710.3389/fncom.2013.0008048868Learning and Prospective Recall of Noisy Spike Pattern EpisodesKarl eDockendorf0Narayan eSrinivasa1HRL Laboratories LLCHRL Laboratories LLCSpike patterns in vivo are often incomplete or corrupted with noise that makes inputs to neuronal networks appear to vary although they may, in fact, be samples of a single underlying pattern or repeated presentation. Here we present a recurrent spiking neural network (SNN) model that learns noisy pattern sequences through the use of homeostasis and spike-timing dependent plasticity (STDP). We find that the changes in the synaptic weight vector during learning of patterns of random ensembles are approximately orthogonal in a reduced dimension space when the patterns are constructed to minimize overlap in representations. Using this model, representations of sparse patterns maybe associated through co-activated firing and integrated into ensemble representations. While the model is tolerant to noise, prospective activity and pattern completion differ in their ability to adapt in the presence of noise. One version of the model is able to demonstrate the recently discovered phenomena of preplay and replay reminiscent of hippocampal like behaviors.http://journal.frontiersin.org/Journal/10.3389/fncom.2013.00080/fullHippocampusLearningMemorySTDPSequencesSpiking Neural network |
| spellingShingle | Karl eDockendorf Narayan eSrinivasa Learning and Prospective Recall of Noisy Spike Pattern Episodes Frontiers in Computational Neuroscience Hippocampus Learning Memory STDP Sequences Spiking Neural network |
| title | Learning and Prospective Recall of Noisy Spike Pattern Episodes |
| title_full | Learning and Prospective Recall of Noisy Spike Pattern Episodes |
| title_fullStr | Learning and Prospective Recall of Noisy Spike Pattern Episodes |
| title_full_unstemmed | Learning and Prospective Recall of Noisy Spike Pattern Episodes |
| title_short | Learning and Prospective Recall of Noisy Spike Pattern Episodes |
| title_sort | learning and prospective recall of noisy spike pattern episodes |
| topic | Hippocampus Learning Memory STDP Sequences Spiking Neural network |
| url | http://journal.frontiersin.org/Journal/10.3389/fncom.2013.00080/full |
| work_keys_str_mv | AT karledockendorf learningandprospectiverecallofnoisyspikepatternepisodes AT narayanesrinivasa learningandprospectiverecallofnoisyspikepatternepisodes |