Storage of phase-coded patterns via STDP in fully-connected and sparse network: a study of the network capacity
We study the storage and retrieval of phase-coded patterns as stable dynamical attractors in recurrent neural networks, for both an analog and a integrate-and-fire spiking model. The synaptic strength is determined by a learning rule based on spike-time-dependent plasticity, with an asymmetric time...
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| creator | Scarpetta, S de Candia, A Giacco, F |
| description | We study the storage and retrieval of phase-coded patterns as stable dynamical attractors in recurrent neural networks, for both an analog and a integrate-and-fire spiking model. The synaptic strength is determined by a learning rule based on spike-time-dependent plasticity, with an asymmetric time window depending on the relative timing between pre- and post-synaptic activity. We store multiple patterns and study the network capacity. For the analog model, we find that the network capacity scales linearly with the network size, and that both capacity and the oscillation frequency of the retrieval state depend on the asymmetry of the learning time window. In addition to fully-connected networks, we study sparse networks, where each neuron is connected only to a small number z |
| doi_str_mv | 10.48550/arxiv.1009.1286 |
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The synaptic strength is determined by a learning rule based on spike-time-dependent plasticity, with an asymmetric time window depending on the relative timing between pre- and post-synaptic activity. We store multiple patterns and study the network capacity. For the analog model, we find that the network capacity scales linearly with the network size, and that both capacity and the oscillation frequency of the retrieval state depend on the asymmetry of the learning time window. In addition to fully-connected networks, we study sparse networks, where each neuron is connected only to a small number z << N of other neurons. Connections can be short range, between neighboring neurons placed on a regular lattice, or long range, between randomly chosen pairs of neurons. We find that a small fraction of long range connections is able to amplify the capacity of the network. This imply that a small-world-network topology is optimal, as a compromise between the cost of long range connections and the capacity increase. Also in the spiking integrate and fire model the crucial result of storing and retrieval of multiple phase-coded patterns is observed. The capacity of the fully-connected spiking network is investigated, together with the relation between oscillation frequency of retrieval state and window asymmetry.</description><identifier>EISSN: 2331-8422</identifier><identifier>DOI: 10.48550/arxiv.1009.1286</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Asymmetry ; Network topologies ; Neural networks ; Neurons ; Quantitative Biology - Neurons and Cognition ; Recurrent neural networks ; Retrieval ; Spiking ; Storage ; Time dependence ; Windows (intervals)</subject><ispartof>arXiv.org, 2010-09</ispartof><rights>2010. 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This imply that a small-world-network topology is optimal, as a compromise between the cost of long range connections and the capacity increase. Also in the spiking integrate and fire model the crucial result of storing and retrieval of multiple phase-coded patterns is observed. The capacity of the fully-connected spiking network is investigated, together with the relation between oscillation frequency of retrieval state and window asymmetry.</description><subject>Asymmetry</subject><subject>Network topologies</subject><subject>Neural networks</subject><subject>Neurons</subject><subject>Quantitative Biology - Neurons and Cognition</subject><subject>Recurrent neural networks</subject><subject>Retrieval</subject><subject>Spiking</subject><subject>Storage</subject><subject>Time dependence</subject><subject>Windows (intervals)</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GOX</sourceid><recordid>eNo9kF1LwzAYhYMgOObuvZKA1515k6bJvJP5CQOF9b68bRPXOZuapNP-ezsnXh04PBwODyEXwOaplpJdo_9u9nNgbDEHrrMTMuFCQKJTzs_ILIQtY4xnikspJsSto_P4ZqiztNtgMEnlalPTDmM0vg103yBd53evtGmp7Xe7YQTa1lRxhLCtaejQB0NbE7-cf7-hSEPs6-GwFzf_Pa2ww6qJwzk5tbgLZvaXU5I_3OfLp2T18vi8vF0lKEEmWcpsmsoaQPNSMkh5aeyCKaiFVSVDYQUohNJCCrXSWQVSq0UlS6GwMtKKKbk8zv7KKDrffKAfioOU4iBlBK6OQOfdZ29CLLau9-14qeBMK2BSMSl-AJxtZLk</recordid><startdate>20100907</startdate><enddate>20100907</enddate><creator>Scarpetta, S</creator><creator>de Candia, A</creator><creator>Giacco, F</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>ALC</scope><scope>GOX</scope></search><sort><creationdate>20100907</creationdate><title>Storage of phase-coded patterns via STDP in fully-connected and sparse network: a study of the network capacity</title><author>Scarpetta, S ; de Candia, A ; Giacco, F</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a515-640f445d1182b50142bef9071d3f7b0a3f317a1bf141d786c15879c5b37ace5f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Asymmetry</topic><topic>Network topologies</topic><topic>Neural networks</topic><topic>Neurons</topic><topic>Quantitative Biology - Neurons and Cognition</topic><topic>Recurrent neural networks</topic><topic>Retrieval</topic><topic>Spiking</topic><topic>Storage</topic><topic>Time dependence</topic><topic>Windows (intervals)</topic><toplevel>online_resources</toplevel><creatorcontrib>Scarpetta, S</creatorcontrib><creatorcontrib>de Candia, A</creatorcontrib><creatorcontrib>Giacco, F</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Access via ProQuest (Open Access)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>arXiv Quantitative Biology</collection><collection>arXiv.org</collection><jtitle>arXiv.org</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Scarpetta, S</au><au>de Candia, A</au><au>Giacco, F</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Storage of phase-coded patterns via STDP in fully-connected and sparse network: a study of the network capacity</atitle><jtitle>arXiv.org</jtitle><date>2010-09-07</date><risdate>2010</risdate><eissn>2331-8422</eissn><abstract>We study the storage and retrieval of phase-coded patterns as stable dynamical attractors in recurrent neural networks, for both an analog and a integrate-and-fire spiking model. The synaptic strength is determined by a learning rule based on spike-time-dependent plasticity, with an asymmetric time window depending on the relative timing between pre- and post-synaptic activity. We store multiple patterns and study the network capacity. For the analog model, we find that the network capacity scales linearly with the network size, and that both capacity and the oscillation frequency of the retrieval state depend on the asymmetry of the learning time window. In addition to fully-connected networks, we study sparse networks, where each neuron is connected only to a small number z << N of other neurons. Connections can be short range, between neighboring neurons placed on a regular lattice, or long range, between randomly chosen pairs of neurons. We find that a small fraction of long range connections is able to amplify the capacity of the network. This imply that a small-world-network topology is optimal, as a compromise between the cost of long range connections and the capacity increase. Also in the spiking integrate and fire model the crucial result of storing and retrieval of multiple phase-coded patterns is observed. The capacity of the fully-connected spiking network is investigated, together with the relation between oscillation frequency of retrieval state and window asymmetry.</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><doi>10.48550/arxiv.1009.1286</doi><oa>free_for_read</oa></addata></record> |
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| source | Freely Accessible Journals; arXiv.org |
| subjects | Asymmetry Network topologies Neural networks Neurons Quantitative Biology - Neurons and Cognition Recurrent neural networks Retrieval Spiking Storage Time dependence Windows (intervals) |
| title | Storage of phase-coded patterns via STDP in fully-connected and sparse network: a study of the network capacity |
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