CSULA EE585, Won

EE585 Spring 2015: Neural Computation

Announcements
Assignments	Problem Set 1: Neural communication and biomedical applications Journal Paper Presentation Problem Set 2: Sensory encoding and neuron firing Problem Set 3: Decoding motor information from neural signals, population vectors. Problem Set 4: Linear filtering Problem Set 5: Kalman filtering filtering
Lectures	Download the DyKnow Client onto your personal Windows machine. Lecture 1 (Apr. 2): Defining neural computation. Lecture 2 (Apr. 7): Electrical signalling in the nervous system + characterizing neural firing. Lecture 3 (Apr. 9): Characterizing neural firing. Lecture 4 (Apr. 14): Sensory mapping Lecture 5 (Apr. 21): Sensory encoding / receptive fields. Lecture 6 (Apr 23): Tuning curves and population vector. Lecture 7 (Apr 30): Population vector. Lecture 8 (May 5): Optimal linear filter. Lecture 9 (May 12): Optimal linear filter + midterm review. Lecture 10 (May 14): Optimal linear filter Lecture 11 (May 19): Optimal linear filter Lecture 12 (May 21): Kalman filter overview Lecture 13 (May 26): Kalman filter derivation and implementation Lecture 14 (May 28): Principal component analysis Lecture 15 (June 2): Principal component analysis
Supplementary	Class Syllabus Perspective on BMIs Nicolelis 2003 Biomimetic robotics Giszter et al. BMI neuromotor prosthesisSimeral et al. Neuron simulation: Phet simulation of a neuron Purves et al. Electrophysiology section 1 Purves et al. Electrophysiology section 2 Purves et al. Electrophysiology section 3 Paper presentation example signup. Retinal prosthesis image processing Asher et al. Memory prosthesis Berger et al. More on BMI decoders Sample papers from the Nicolelis, Donoghue, Schwartz, and Wolpaw groups Visual encoding part I: Purves et al. pp. 179-186 Visual encoding part II: Purves et al. pp. 199-211 Visual encoding part III: Kuffler 1953 Somatosensory encoding: Zhu & Connors 1999 Somatosensory encoding + information theory: von Heimendah et al. 2007 Temporal encoding in visual system + information theory: Reich et al. 2001 (temporal coding of contrast) Temporal encoding: Fellous et al. 2004 Motor encoding: Georgopoulos et al. 1988 Motor decoding: Wessberg et al. 2004 Motor decoding: Schiff et al. 2008 BMI Kalman filtering decoder Kim et al. Memory prosthesis Berger et al. 2011 Code for tuning curves: Tuning curve / population vector MATLAB code Supplement on PCA: J. Schlens Muscle synergies: Kutch JJ & Valero-Cuevas FJ 2012 Muscle redundancy: Kutch JJ & Valero-Cuevas FJ 2011
Data	Population Vector (PS 3) Data: motorcx_data_forPS3.mat Population Vector (PS 3) Data: continuous_tracking.mat Linear Filter (PS 4) Data: au032003b_bin100.mat Linear Filter (PS 4) Code: linmodel_blanks.m Linear Filter (PS 4) Code: linpred_blanks.m Kalman Filter (PS 5) Data: kalmanNeuralData.mat
Other useful links	MATLAB Tutorial Under "Tutorial Options", go through the following MATLAB examples -> Getting Started with MATLAB MATLAB examples -> Working in The Development Environment MATLAB examples -> Writing a MATLAB program MATLAB examples -> Basic Matrix Operations Getting Started with MATLAB (User's Guide)

Course outline

Date	Topic	Reading	Related Assignment
Th Apr 2	Defining neural computation and biomedical applications of neural computation	Nicolelis 2003 - skim sections; Giszter et al. - abstract + skim over section headings; Simeral et al. - abstract + skim over section headings and figures; Supplement on Electrophysiology (Purves et al.);	PS 1
T Apr 7	Electrical signalling in the nervous system; Characterizing neuron firing - part I	Supplement on Electrophysiology (Purves et al.); Dayan and Abbott 5.1 - 5.3; Dayan and Abbott 1.1-1.4, 2.2, 7.2	PS 1
Th Apr 9	Characterizing neuron firing	Dayan and Abbott 1.1 - 1.4, 2.2, 7.2	PS 2
T Apr 14	Modelling neuron output; Anatomical mapping of sensory spaces	Somatosensory encoding supplement; Visual encoding supplement	PS 2
Th Apr 16	Journal article selection, partner selection for end-of-quarter presentation		Journal article presentation
T Apr 21	Anatomical mapping of sensory spaces; tuning curves.	Dayan and Abbott 1.1-1.4, 2.2, 7.2; Georgopoulos et al	PS 2
Th Apr 23	Tuning curves and population vectors.	Dayan and Abbott 1.2, 3.3; Georgopoulos et al	PS 3
T Apr 28	Population vectors.	Dayan and Abbott 1.2, 3.3; Georgopoulos et al	PS 3
Th May 3	Population vectors.	Dayan and Abbott 1.2, 3.3; Georgopoulos et al	PS 3
T May 5	Optimal linear filtering	Dayan and Abbott 2.2, 2.9; Wessberg and Nicolelis	PS 4
Th May 7	Midterm exam
T May 12	Optimal linear filtering	Dayan and Abbott 2.2, 2.9; Wessberg and Nicolelis	PS 4
Th May 14	Optimal linear filtering	Dayan and Abbott 2.2, 2.9; Wessberg and Nicolelis	PS 4
T May 19	Optimal linear filtering	Dayan and Abbott 2.2, 2.9; Wessberg and Nicolelis	PS 4
Th May 21	Kalman filtering	Kim et al. 2011; Schiff et al. 2008	PS 5
T May 26	Kalman filtering	Kim et al. 2011; Schiff et al. 2008	PS 5
Th May 28	Principal component analysis	J. Schlens
T June 2	Principal component analysis; information theory	J. Schlens; Fellous et al. 2004; Reich et al. 2001