统计代写代考

MA 568 – Statistical Analysis of Point Process Data Solutions to Problem Set 2 % Question 1 load M1_spikes plot(T,spikes); plot(X,Y,X(spiketimes),Y(spiketimes),’r.’); plot(phi(spiketimes),V(spiketimes),’r.’); 1 0.5 0 -75 -80 -85 0 1000 2000 3000 4000 5000 6000 7000 8000 Time (ms) 15 10 5 0.05 0.045 0.04 0.035 0.03 0.025 0.02 0.015 0.01 0.005 0 This occupancy

MA 568 – Statistical Analysis of Point Process Data Solutions to Problem Set #1 % Question 1 load Retinal_ISIs.mat; hist(ISIs,50); spiketimes = cumsum(ISIs); N = length(spiketimes); for i = 1:N, line([spiketimes(i) spiketimes(i)],[0 1]); end; 1 0.5 0 200 180 160 140 120 100 80 60 40 20 0 0 20 40 60 80 100 120

Objective ISE 3614 – Introduction to Human Factors Engineering 2018 Fall Lab 1: Information Theory – Hick’s Law Providing practical experience of applying information theory to quantify information processing demanded of an operator. Specifically, this laboratory requires completion of a choice-reaction task to illustrate Hick’s Law. This is an individual assignment. Equipment for Laboratory •

CA1 Region of Rat Hippocampus Spike Time Series Time (sec) Position Specific Spiking Inhomogeneous Rate Model: Gaussian Place Field ⎧1 ′-1 ⎫ λ(t)=exp α- (x(t)-μ)W (x(t)-μ) Model covariate: position x(t) = (x1(t),x2 (t)) Model parameters: maximum field height scale matrix e x p {α } center ⎡ σ 12 ⎢σ σ2⎥ μ = (μ1 ,μ2