| dc.contributor.author | Seung, H. Sebastian | en_US |
| dc.coverage.temporal | Spring 2002 | en_US |
| dc.date.issued | 2002-06 | |
| dc.identifier | 9.29J-Spring2002 | |
| dc.identifier | local: 9.29J | |
| dc.identifier | local: 8.261J | |
| dc.identifier | local: IMSCP-MD5-871dd32a126b2a044a2605ea539dd83e | |
| dc.identifier.uri | http://hdl.handle.net/1721.1/35859 | |
| dc.description.abstract | Mathematical introduction to neural coding and dynamics. Convolution, correlation, linear systems, Fourier analysis, signal detection theory, probability theory, and information theory. Applications to neural coding, focusing on the visual system. Hodgkin-Huxley and related models of neural excitability, stochastic models of ion channels, cable theory, and models of synaptic transmission. | en_US |
| dc.format.extent | 15152 bytes | en_US |
| dc.format.extent | 13190 bytes | en_US |
| dc.format.extent | 37498 bytes | en_US |
| dc.format.extent | 14008 bytes | en_US |
| dc.format.extent | 13033 bytes | en_US |
| dc.format.extent | 12317 bytes | en_US |
| dc.format.extent | 15276 bytes | en_US |
| dc.format.extent | 11 bytes | en_US |
| dc.format.extent | 4586 bytes | en_US |
| dc.format.extent | 21366 bytes | en_US |
| dc.format.extent | 11602 bytes | en_US |
| dc.format.extent | 38351 bytes | en_US |
| dc.format.extent | 4755 bytes | en_US |
| dc.format.extent | 27322 bytes | en_US |
| dc.format.extent | 25313 bytes | en_US |
| dc.format.extent | 4039 bytes | en_US |
| dc.format.extent | 301 bytes | en_US |
| dc.format.extent | 354 bytes | en_US |
| dc.format.extent | 339 bytes | en_US |
| dc.format.extent | 180 bytes | en_US |
| dc.format.extent | 285 bytes | en_US |
| dc.format.extent | 67 bytes | en_US |
| dc.format.extent | 17685 bytes | en_US |
| dc.format.extent | 49 bytes | en_US |
| dc.format.extent | 143 bytes | en_US |
| dc.format.extent | 247 bytes | en_US |
| dc.format.extent | 19283 bytes | en_US |
| dc.format.extent | 262 bytes | en_US |
| dc.format.extent | 77934 bytes | en_US |
| dc.format.extent | 294993 bytes | en_US |
| dc.format.extent | 117363 bytes | en_US |
| dc.format.extent | 155742 bytes | en_US |
| dc.format.extent | 206719 bytes | en_US |
| dc.format.extent | 172825 bytes | en_US |
| dc.format.extent | 60158 bytes | en_US |
| dc.format.extent | 64276 bytes | en_US |
| dc.format.extent | 66802 bytes | en_US |
| dc.format.extent | 19283 bytes | en_US |
| dc.format.extent | 3486 bytes | en_US |
| dc.format.extent | 811 bytes | en_US |
| dc.format.extent | 813 bytes | en_US |
| dc.format.extent | 830 bytes | en_US |
| dc.format.extent | 571 bytes | en_US |
| dc.format.extent | 2097 bytes | en_US |
| dc.format.extent | 23140 bytes | en_US |
| dc.format.extent | 7910 bytes | en_US |
| dc.format.extent | 8221 bytes | en_US |
| dc.format.extent | 7728 bytes | en_US |
| dc.format.extent | 7754 bytes | en_US |
| dc.format.extent | 8185 bytes | en_US |
| dc.format.extent | 8210 bytes | en_US |
| dc.format.extent | 8204 bytes | en_US |
| dc.format.extent | 9562 bytes | en_US |
| dc.format.extent | 8194 bytes | en_US |
| dc.format.extent | 7539 bytes | en_US |
| dc.format.extent | 7563 bytes | en_US |
| dc.format.extent | 7649 bytes | en_US |
| dc.format.extent | 8210 bytes | en_US |
| dc.format.extent | 7655 bytes | en_US |
| dc.format.extent | 8274 bytes | en_US |
| dc.format.extent | 8197 bytes | en_US |
| dc.language | en-US | en_US |
| dc.rights.uri | Usage Restrictions: This site (c) Massachusetts Institute of Technology 2003. Content within individual courses is (c) by the individual authors unless otherwise noted. The Massachusetts Institute of Technology is providing this Work (as defined below) under the terms of this Creative Commons public license ("CCPL" or "license"). The Work is protected by copyright and/or other applicable law. Any use of the work other than as authorized under this license is prohibited. By exercising any of the rights to the Work provided here, You (as defined below) accept and agree to be bound by the terms of this license. The Licensor, the Massachusetts Institute of Technology, grants You the rights contained here in consideration of Your acceptance of such terms and conditions. | en_US |
| dc.subject | neural coding | en_US |
| dc.subject | dynamics | en_US |
| dc.subject | convolution | en_US |
| dc.subject | correlation | en_US |
| dc.subject | linear systems | en_US |
| dc.subject | Fourier analysis | en_US |
| dc.subject | signal detection theory | en_US |
| dc.subject | probability theory | en_US |
| dc.subject | information theory | en_US |
| dc.subject | neural excitability | en_US |
| dc.subject | stochastic models | en_US |
| dc.subject | ion channels | en_US |
| dc.subject | cable theory | en_US |
| dc.subject | 9.29J | en_US |
| dc.subject | 8.261J | en_US |
| dc.subject | 9.29 | en_US |
| dc.subject | 8.261 | en_US |
| dc.title | 9.29J / 8.261J Introduction to Computational Neuroscience, Spring 2002 | en_US |
| dc.title.alternative | Introduction to Computational Neuroscience | en_US |
| dc.type | Learning Object | |
| dc.contributor.department | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences | |
| dc.contributor.department | Massachusetts Institute of Technology. Department of Physics | |
