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9.29J Introduction to Computational Neuroscience (MIT) 9.29J Introduction to Computational Neuroscience (MIT)

Description

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. 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.

Subjects

neural coding | neural coding | dynamics | dynamics | convolution | convolution | correlation | correlation | linear systems | linear systems | Fourier analysis | Fourier analysis | signal detection theory | signal detection theory | probability theory | probability theory | information theory | information theory | neural excitability | neural excitability | stochastic models | stochastic models | ion channels | ion channels | cable theory | cable theory | 9.29 | 9.29 | 8.261 | 8.261

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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9.29J Introduction to Computational Neuroscience (MIT)

Description

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.

Subjects

neural coding | dynamics | convolution | correlation | linear systems | Fourier analysis | signal detection theory | probability theory | information theory | neural excitability | stochastic models | ion channels | cable theory | 9.29 | 8.261

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

Site sourced from

https://ocw.mit.edu/rss/all/mit-allarchivedcourses.xml

Attribution

Click to get HTML | Click to get attribution | Click to get URL

All metadata

See all metadata