Population Encoding With Hodgkin-Huxley Neurons
The recovery of (weak) stimuli encoded with a population of Hodgkin-Huxley neurons is investigated. In the absence of a stimulus, the Hodgkin-Huxley neurons are assumed to be tonically spiking. The methodology employed calls for 1) finding an input-output (I/O) equivalent description of the Hodgkin-...
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description | The recovery of (weak) stimuli encoded with a population of Hodgkin-Huxley neurons is investigated. In the absence of a stimulus, the Hodgkin-Huxley neurons are assumed to be tonically spiking. The methodology employed calls for 1) finding an input-output (I/O) equivalent description of the Hodgkin-Huxley neuron and 2) devising a recovery algorithm for stimuli encoded with the I/O equivalent neuron(s). A Hodgkin-Huxley neuron with multiplicative coupling is I/O equivalent with an Integrate-and-Fire neuron with a variable threshold sequence. For bandlimited stimuli a perfect recovery of the stimulus can be achieved provided that a Nyquist-type rate condition is satisfied. A Hodgkin-Huxley neuron with additive coupling and deterministic conductances is first-order I/O equivalent with a Project-Integrate-and-Fire neuron that integrates a projection of the stimulus on the phase response curve. The stimulus recovery is formulated as a spline interpolation problem in the space of finite length bounded energy signals. A Hodgkin-Huxley neuron with additive coupling and stochastic conductances is shown to be first-order I/O equivalent with a Project-Integrate-and-Fire neuron with random thresholds. For stimuli modeled as elements of Sobolev spaces the reconstruction algorithm minimizes a regularized quadratic optimality criterion. Finally, all previous recovery results of stimuli encoded with Hodgkin-Huxley neurons with multiplicative and additive coupling, and deterministic and stochastic conductances are extended to stimuli encoded with a population of Hodgkin-Huxley neurons. |
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In the absence of a stimulus, the Hodgkin-Huxley neurons are assumed to be tonically spiking. The methodology employed calls for 1) finding an input-output (I/O) equivalent description of the Hodgkin-Huxley neuron and 2) devising a recovery algorithm for stimuli encoded with the I/O equivalent neuron(s). A Hodgkin-Huxley neuron with multiplicative coupling is I/O equivalent with an Integrate-and-Fire neuron with a variable threshold sequence. For bandlimited stimuli a perfect recovery of the stimulus can be achieved provided that a Nyquist-type rate condition is satisfied. A Hodgkin-Huxley neuron with additive coupling and deterministic conductances is first-order I/O equivalent with a Project-Integrate-and-Fire neuron that integrates a projection of the stimulus on the phase response curve. The stimulus recovery is formulated as a spline interpolation problem in the space of finite length bounded energy signals. A Hodgkin-Huxley neuron with additive coupling and stochastic conductances is shown to be first-order I/O equivalent with a Project-Integrate-and-Fire neuron with random thresholds. For stimuli modeled as elements of Sobolev spaces the reconstruction algorithm minimizes a regularized quadratic optimality criterion. Finally, all previous recovery results of stimuli encoded with Hodgkin-Huxley neurons with multiplicative and additive coupling, and deterministic and stochastic conductances are extended to stimuli encoded with a population of Hodgkin-Huxley neurons.</description><identifier>ISSN: 0018-9448</identifier><identifier>EISSN: 1557-9654</identifier><identifier>DOI: 10.1109/TIT.2009.2037040</identifier><identifier>PMID: 24194625</identifier><identifier>CODEN: IETTAW</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Additives ; Applied sciences ; Artificial intelligence ; Bioinformatics ; Biomedical signal processing ; Biostatistics ; Coding, codes ; Computer science; control theory; systems ; Connectionism. Neural networks ; Encoding ; Exact sciences and technology ; Genetic algorithms ; Hodgkin-Huxley neurons ; Information theory ; Information, signal and communications theory ; input-output (I/O) equivalence ; Interpolation ; neural encoding ; Neurons ; Neuroscience ; population encoding ; reproducing Kernel Hilbert spaces ; Signal and communications theory ; Signal processing ; Signal processing algorithms ; Spline ; splines ; stimulus reconstruction ; Stochastic processes ; Telecommunications and information theory</subject><ispartof>IEEE transactions on information theory, 2010-02, Vol.56 (2), p.821-837</ispartof><rights>2015 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Feb 2010</rights><rights>2010 IEEE 2010</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c537t-bffe57997c491492a6dd62bad228579117bb3d401ef9db9551cf63cea56a0dc23</citedby><cites>FETCH-LOGICAL-c537t-bffe57997c491492a6dd62bad228579117bb3d401ef9db9551cf63cea56a0dc23</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5420291$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>230,314,778,782,794,883,27911,27912,54745</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5420291$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=22485914$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/24194625$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Lazar, A.A.</creatorcontrib><title>Population Encoding With Hodgkin-Huxley Neurons</title><title>IEEE transactions on information theory</title><addtitle>TIT</addtitle><addtitle>IEEE Trans Inf Theory</addtitle><description>The recovery of (weak) stimuli encoded with a population of Hodgkin-Huxley neurons is investigated. In the absence of a stimulus, the Hodgkin-Huxley neurons are assumed to be tonically spiking. The methodology employed calls for 1) finding an input-output (I/O) equivalent description of the Hodgkin-Huxley neuron and 2) devising a recovery algorithm for stimuli encoded with the I/O equivalent neuron(s). A Hodgkin-Huxley neuron with multiplicative coupling is I/O equivalent with an Integrate-and-Fire neuron with a variable threshold sequence. For bandlimited stimuli a perfect recovery of the stimulus can be achieved provided that a Nyquist-type rate condition is satisfied. A Hodgkin-Huxley neuron with additive coupling and deterministic conductances is first-order I/O equivalent with a Project-Integrate-and-Fire neuron that integrates a projection of the stimulus on the phase response curve. The stimulus recovery is formulated as a spline interpolation problem in the space of finite length bounded energy signals. A Hodgkin-Huxley neuron with additive coupling and stochastic conductances is shown to be first-order I/O equivalent with a Project-Integrate-and-Fire neuron with random thresholds. For stimuli modeled as elements of Sobolev spaces the reconstruction algorithm minimizes a regularized quadratic optimality criterion. Finally, all previous recovery results of stimuli encoded with Hodgkin-Huxley neurons with multiplicative and additive coupling, and deterministic and stochastic conductances are extended to stimuli encoded with a population of Hodgkin-Huxley neurons.</description><subject>Additives</subject><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Bioinformatics</subject><subject>Biomedical signal processing</subject><subject>Biostatistics</subject><subject>Coding, codes</subject><subject>Computer science; control theory; systems</subject><subject>Connectionism. Neural networks</subject><subject>Encoding</subject><subject>Exact sciences and technology</subject><subject>Genetic algorithms</subject><subject>Hodgkin-Huxley neurons</subject><subject>Information theory</subject><subject>Information, signal and communications theory</subject><subject>input-output (I/O) equivalence</subject><subject>Interpolation</subject><subject>neural encoding</subject><subject>Neurons</subject><subject>Neuroscience</subject><subject>population encoding</subject><subject>reproducing Kernel Hilbert spaces</subject><subject>Signal and communications theory</subject><subject>Signal processing</subject><subject>Signal processing algorithms</subject><subject>Spline</subject><subject>splines</subject><subject>stimulus reconstruction</subject><subject>Stochastic processes</subject><subject>Telecommunications and information theory</subject><issn>0018-9448</issn><issn>1557-9654</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNqNkc1rHCEYh6U0NNu090IhLIGQXCZ5dfwYL4ESkm4gtD1s6VEcdTams7rRmZL893XZ7fbjEHpR9H3eH_o-CL3DcIYxyPP5zfyMAMiy1AIovEATzJioJGf0JZoA4KaSlDb76HXO9-VIGSav0D6hWFJO2ASdf4mrsdeDj2F6FUy0Piym3_xwN51Fu_juQzUbH3v3NP3kxhRDfoP2Ot1n93a7H6Cv11fzy1l1-_njzeWH28qwWgxV23WOCSmFoRJTSTS3lpNWW0Kaco-xaNvaUsCuk7aVjGHT8do4zbgGa0h9gC42uauxXTprXBiS7tUq-aVOTypqr_6uBH-nFvGHqhvMQeIScLINSPFhdHlQS5-N63sdXByzEpSDoIyy_yBrQTgV68zTZ0ncEM4k0GYdevQPeh_HFMrIFJZM1rIRUCDYQCbFnJPrdv_DoNaCVRGs1oLVVnBpOfxzLruGX0YLcLwFdDa675IOxuffHCmvK0oK937DeefcrswoAVKm9xPZKbU_</recordid><startdate>20100201</startdate><enddate>20100201</enddate><creator>Lazar, A.A.</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>IQODW</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>7X8</scope><scope>F28</scope><scope>FR3</scope><scope>7T5</scope><scope>7TK</scope><scope>H94</scope><scope>5PM</scope></search><sort><creationdate>20100201</creationdate><title>Population Encoding With Hodgkin-Huxley Neurons</title><author>Lazar, A.A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c537t-bffe57997c491492a6dd62bad228579117bb3d401ef9db9551cf63cea56a0dc23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Additives</topic><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Bioinformatics</topic><topic>Biomedical signal processing</topic><topic>Biostatistics</topic><topic>Coding, codes</topic><topic>Computer science; control theory; systems</topic><topic>Connectionism. Neural networks</topic><topic>Encoding</topic><topic>Exact sciences and technology</topic><topic>Genetic algorithms</topic><topic>Hodgkin-Huxley neurons</topic><topic>Information theory</topic><topic>Information, signal and communications theory</topic><topic>input-output (I/O) equivalence</topic><topic>Interpolation</topic><topic>neural encoding</topic><topic>Neurons</topic><topic>Neuroscience</topic><topic>population encoding</topic><topic>reproducing Kernel Hilbert spaces</topic><topic>Signal and communications theory</topic><topic>Signal processing</topic><topic>Signal processing algorithms</topic><topic>Spline</topic><topic>splines</topic><topic>stimulus reconstruction</topic><topic>Stochastic processes</topic><topic>Telecommunications and information theory</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lazar, A.A.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Pascal-Francis</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>MEDLINE - Academic</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Immunology Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>IEEE transactions on information theory</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Lazar, A.A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Population Encoding With Hodgkin-Huxley Neurons</atitle><jtitle>IEEE transactions on information theory</jtitle><stitle>TIT</stitle><addtitle>IEEE Trans Inf Theory</addtitle><date>2010-02-01</date><risdate>2010</risdate><volume>56</volume><issue>2</issue><spage>821</spage><epage>837</epage><pages>821-837</pages><issn>0018-9448</issn><eissn>1557-9654</eissn><coden>IETTAW</coden><abstract>The recovery of (weak) stimuli encoded with a population of Hodgkin-Huxley neurons is investigated. In the absence of a stimulus, the Hodgkin-Huxley neurons are assumed to be tonically spiking. The methodology employed calls for 1) finding an input-output (I/O) equivalent description of the Hodgkin-Huxley neuron and 2) devising a recovery algorithm for stimuli encoded with the I/O equivalent neuron(s). A Hodgkin-Huxley neuron with multiplicative coupling is I/O equivalent with an Integrate-and-Fire neuron with a variable threshold sequence. For bandlimited stimuli a perfect recovery of the stimulus can be achieved provided that a Nyquist-type rate condition is satisfied. A Hodgkin-Huxley neuron with additive coupling and deterministic conductances is first-order I/O equivalent with a Project-Integrate-and-Fire neuron that integrates a projection of the stimulus on the phase response curve. The stimulus recovery is formulated as a spline interpolation problem in the space of finite length bounded energy signals. 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subjects | Additives Applied sciences Artificial intelligence Bioinformatics Biomedical signal processing Biostatistics Coding, codes Computer science control theory systems Connectionism. Neural networks Encoding Exact sciences and technology Genetic algorithms Hodgkin-Huxley neurons Information theory Information, signal and communications theory input-output (I/O) equivalence Interpolation neural encoding Neurons Neuroscience population encoding reproducing Kernel Hilbert spaces Signal and communications theory Signal processing Signal processing algorithms Spline splines stimulus reconstruction Stochastic processes Telecommunications and information theory |
title | Population Encoding With Hodgkin-Huxley Neurons |
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