Refractory pulse counting Processes in stochastic neural computers
This letter quantitatively investigates the effect of a temporary refractory period or dead time in the ability of a stochastic Bernoulli processor to record subsequent pulse events, following the arrival of a pulse. These effects can arise in either the input detectors of a stochastic neural networ...
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| Veröffentlicht in: | IEEE transaction on neural networks and learning systems 2005-03, Vol.16 (2), p.505-508 |
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| creator | McNeill, D.K. Card, H.C. |
| description | This letter quantitatively investigates the effect of a temporary refractory period or dead time in the ability of a stochastic Bernoulli processor to record subsequent pulse events, following the arrival of a pulse. These effects can arise in either the input detectors of a stochastic neural network or in subsequent processing. A transient period is observed, which increases with both the dead time and the Bernoulli probability of the dead-time free system, during which the system reaches equilibrium. Unless the Bernoulli probability is small compared to the inverse of the dead time, the mean and variance of the pulse count distributions are both appreciably reduced. |
| doi_str_mv | 10.1109/TNN.2005.844089 |
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These effects can arise in either the input detectors of a stochastic neural network or in subsequent processing. A transient period is observed, which increases with both the dead time and the Bernoulli probability of the dead-time free system, during which the system reaches equilibrium. Unless the Bernoulli probability is small compared to the inverse of the dead time, the mean and variance of the pulse count distributions are both appreciably reduced.</description><identifier>ISSN: 1045-9227</identifier><identifier>ISSN: 2162-237X</identifier><identifier>EISSN: 1941-0093</identifier><identifier>EISSN: 2162-2388</identifier><identifier>DOI: 10.1109/TNN.2005.844089</identifier><identifier>PMID: 15787159</identifier><identifier>CODEN: ITNNEP</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Applied sciences ; Artificial intelligence ; Clocks ; Computer science; control theory; systems ; Connectionism. 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These effects can arise in either the input detectors of a stochastic neural network or in subsequent processing. A transient period is observed, which increases with both the dead time and the Bernoulli probability of the dead-time free system, during which the system reaches equilibrium. Unless the Bernoulli probability is small compared to the inverse of the dead time, the mean and variance of the pulse count distributions are both appreciably reduced.</description><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Clocks</subject><subject>Computer science; control theory; systems</subject><subject>Connectionism. Neural networks</subject><subject>Detectors</subject><subject>Digital signal processing</subject><subject>Exact sciences and technology</subject><subject>Neural networks</subject><subject>Neural Networks (Computer)</subject><subject>Neural pulse coding</subject><subject>Optical refraction</subject><subject>Robots</subject><subject>Signal processing</subject><subject>stochastic arithmetic</subject><subject>Stochastic Processes</subject><subject>stochastic signal processing</subject><subject>Stochastic systems</subject><subject>Very large scale integration</subject><issn>1045-9227</issn><issn>2162-237X</issn><issn>1941-0093</issn><issn>2162-2388</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2005</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>EIF</sourceid><recordid>eNqF0c9L5DAUB_AgLv4-exCkLOit48uvJjmqqLswzC6i55Ckr1rptGPSHvzvNzoDA172lJB83nskX0JOKcwoBXP1tFjMGICcaSFAmx1yQI2gJYDhu3kPQpaGMbVPDlN6A6BCQrVH9qlUWlFpDsjNIzbRhXGIH8Vq6hIWYZj6se1fir9xCJgSpqLtizQO4dWlsQ1Fj1N0XXbL1TRiTMfkR-Ny5clmPSLP93dPt7_K-Z-H37fX8zII4GNZaVbVUjaUuZobYQKXHqCmioKTwiM470PAKp-gZ543SjXemaquhNcIDT8il-u-qzi8T5hGu2xTwK5zPQ5TspWSQmoO_4VMg5Gaygx_foNvwxT7_AhrGHCqzRe6WqMQh5QiNnYV26WLH5aC_QzB5hDsZwh2HUKuON-0nfwS663f_HoGFxvgUnBdDqAPbdq6SirFv0afrV2LiNtrAUxSzv8BumeXtA</recordid><startdate>20050301</startdate><enddate>20050301</enddate><creator>McNeill, D.K.</creator><creator>Card, H.C.</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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Neural networks</topic><topic>Detectors</topic><topic>Digital signal processing</topic><topic>Exact sciences and technology</topic><topic>Neural networks</topic><topic>Neural Networks (Computer)</topic><topic>Neural pulse coding</topic><topic>Optical refraction</topic><topic>Robots</topic><topic>Signal processing</topic><topic>stochastic arithmetic</topic><topic>Stochastic Processes</topic><topic>stochastic signal processing</topic><topic>Stochastic systems</topic><topic>Very large scale integration</topic><toplevel>online_resources</toplevel><creatorcontrib>McNeill, D.K.</creatorcontrib><creatorcontrib>Card, H.C.</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>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Chemoreception Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</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>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>IEEE transaction on neural networks and learning systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>McNeill, D.K.</au><au>Card, H.C.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Refractory pulse counting Processes in stochastic neural computers</atitle><jtitle>IEEE transaction on neural networks and learning systems</jtitle><stitle>TNN</stitle><addtitle>IEEE Trans Neural Netw</addtitle><date>2005-03-01</date><risdate>2005</risdate><volume>16</volume><issue>2</issue><spage>505</spage><epage>508</epage><pages>505-508</pages><issn>1045-9227</issn><issn>2162-237X</issn><eissn>1941-0093</eissn><eissn>2162-2388</eissn><coden>ITNNEP</coden><abstract>This letter quantitatively investigates the effect of a temporary refractory period or dead time in the ability of a stochastic Bernoulli processor to record subsequent pulse events, following the arrival of a pulse. 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| subjects | Applied sciences Artificial intelligence Clocks Computer science control theory systems Connectionism. Neural networks Detectors Digital signal processing Exact sciences and technology Neural networks Neural Networks (Computer) Neural pulse coding Optical refraction Robots Signal processing stochastic arithmetic Stochastic Processes stochastic signal processing Stochastic systems Very large scale integration |
| title | Refractory pulse counting Processes in stochastic neural computers |
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