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MIT 9.40 Introduction to Neural Computation, Spring 2018
About this film
Instructor: Michale Fee, Daniel Zysman View the complete course: https://ocw.mit.edu/9-40S18 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP61I4aI5T6OaFfRK2gihjiMm This course introduces quantitative approaches to understanding brain and cognitive functions. License: Creative Commons BY-NC-SA More information at https://ocw.mit.edu/terms More courses at https://ocw.mit.edu
Directors & creators
MIT OpenCourseWare
Subjects
Fick's First Law, Ohm's Law and resistivity, charge drift, neurons, injected currents, membrane, RC model, Integrate and Fire model, batteries of neuron, circuit diagram, Hidgkin-Huxley model, spike train, peri-stimulus time histogram, PSTH, Spatio-temporal Receptive Field, STRF, Fano Factor Interspike Interval, ISI, Fast Fourier Transform, FFT, Gaussian Noise, Spectral estimation, Shannon-Nyquist Theorem, zero paddingline noise removal, multi-taper spectral analysis, DPSS, vector algebra, perceptrons, neuronal logic, linear separability, invariance, two-layer feedforward networks, matrix algebra, matrix transformations, linear independence, eigenvectors, eigenvalues, covariance matrix, Principal Components Analysis, PCA, recurrent networks, autapse networks, storing memories, decision-making, winner-take-all, Hopfield network capacity, long-term memory, short-term memory, energy landscape
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