Programming

Question 1

what is length() used for

Answer 1

returns the longest dimension of a vector or array

Question 2

what does histcounts(data, bins) do

Answer 2

bins the data into discrete intervals and returns counts per bin

Question 3

how do you calculate mean firing rate from spike times and time range?

Answer 3

mean firing rate from spike times and time range:
length(spike_times)/(end_time - start_time)

Question 4

what is a cell array

Answer 4

a container that stores data of varying types and sizes using {}

Question 5

how do you index a cell array to get the content

Answer 5

{row, col} instead of ()

Question 6

what is the function of namean()

Answer 6

calculates the mean while ignoring NaN values

Question 7

what is zscore(data) used for

Answer 7

standardizes data by substracting the mean and dividing by standard deviation

Question 8

how do you plot a histogram of neuron activity with 5 second bins

Answer 8

how do you plot a histogram of neuron activity with 5 second bins
bin_edges = min(times):5:max(times);
histogram(times, bin_edges);

Question 9

what does range(x) return

Answer 9

max(x) - min(x)

Question 10

what is the difference between a matrix and a cell array?

Answer 10

matrices store only numeric data; cell arrays can store mixed types

Question 11

how would you check if neuron 3 fired more than 1000 times?

Answer 11

if length(active_times{3}) > 1000

Question 12

what function is used for PCA in matlab

Answer 12

pca()

Question 13

what does subplot(1,2,1) do

Answer 13

creates a 1-row, 2-column subplot and activates the first one

Question 14

what does sign(x) return

Answer 14

1 if x>0 and -1 if x<0 and 0 if x==0

Question 15

how do you loop through all neurons in a dataset stored in a cell array

Answer 15

for i = 1:length(active_times)

Question 16

how do you define a figure with multiple subplots

Answer 16

subplot(rows, cols, index) inside a for loop

Question 17

whats a common mistake when using && in a vector comparison

Answer 17

&& only works on scalars, use & for vectors

Question 18

what is the purpose of smoothdata()

Answer 18

applies a smoothing algorithm to reduce noise in data

Question 19

what does fprintf(‘Rate: %f’, rate) do

Answer 19

printe the variable rate with formatting

Question 20

why initialize vectors with nan()

Answer 20

to reserve space and handle missing values during loops

Question 21

function to convert radians to degrees

Answer 21

rad2deg()

Question 22

how do you find spikes within a time window

Answer 22

spikes(spikes > t1 & spikes < t2)

Question 23

how do you apply a function to each cell in a cell array

Answer 23

cellfun(@function, cell_array)

Question 24

what function do you use to generate random integers without replacement

Answer 24

datasample(vector, N, ‘Replace’, false)

Question 25

code to calculate peak firing rate uring binnin

Answer 25

max(histcounts(spikes, bin_edges))/bin_width

Question 26

how to make a raster plot with time on x-axis and neuron index on y-axis

Answer 26

plot(spike_times, neuron_ids, '.')

Question 27

when to use for loop vs while loop

Answer 27

use for when you know the number of iterations: while when you dont

Question 28

whats the difference between plot() and scatter()

Answer 28

plot() is for lines; scatter() is for individual points

Question 29

what are gcf and gca good for

Answer 29

get current figure gcf and current axis gca

Question 30

how do you check for missing data

Answer 30

isnan()

Question 31

what is keyboard used for

Answer 31

pauses execution and enters debug mode

Question 32

explain how to bin spike times for mutual information calculation

Answer 32

use histcounts(spike_times, tracking_time_edges)

Question 33

whats the role of omitnan in sum(..., 'omitnan')

Answer 33

ignores NaNs during summation

Question 34

how do you update a hopfield network state in matlab

Answer 34

S(i) = sign(W(i,:) * S')

Question 35

function to combine matrices vertically

Answer 35

vertcat(a,b) or [a;b]

Question 36

why might a tuning curve be flat even if a neuron is active

Answer 36

the neuron fires uniformly across conditions, indicating no selectivity

Question 37

how would you compute the mean firing rate for each neuron over the session

Answer 37

Loop over each neuron Count spikes using length(spike_times) Compute total duration using range(frame_times) Divide spikes by duration Store result in a vector

Question 38

How would you build a tuning curve from spike times and a continuous stimulus (like head direction)

Answer 38

Bin head direction data (e.g. 36 bins across 0–2π) Bin spike times using same time bins Match spike bins to stimulus bins Count spikes per stimulus bin Normalize by occupancy (time spent per bin) Plot firing rate vs. stimulus

Question 39

how would you calculate mutual information between firing and a stimulus variable

Answer 39

calculate tuning curve normalize firing rate to Hz calculate probability of each stimulus compute mean firing rate across all conditions Apply: sum(f_i * log2(f_i / f_mean) * p_i) Store result for each neuron

Question 40

How would you write a function to sort neurons by their firing rate

Answer 40

Compute firing rate vector Use [~, idx] = sort(rates, 'descend') Return sorted neuron indices

Question 41

How would you make a raster plot for motor cortex neurons only

Answer 41

Identify motor neurons (e.g. brain_area == 1) Loop over those neurons Plot each spike time at y = neuron index Use hold on to layer all neurons

Question 42

How would you bin spikes using histcounts to calculate firing rate in time windows

Answer 42

Define bin edges with consistent width Use histcounts(spike_times, bin_edges) Normalize counts by bin width to get rate Store in vector and plot

Question 43

How would you create a swarm plot to compare activity between brain areas

Answer 43

Compute mean firing rate per neuron Create vector of brain area labels (1, 2, 3) Use swarmchart(area_labels, mean_rates, 'k', 'filled') Label axes

Question 44

How would you remove neurons with missing spike data

Answer 44

Loop through neurons Check if isnan(mean_rate(i)) Skip or remove that index from further analysis

Question 45

How would you run PCA on neural firing data

Answer 45

Bin spike data for all neurons into time bins Smooth and standardize (e.g. smoothdata, zscore) Use pca(data') where rows = observations, cols = neurons Use score(:,1:2) for plotting Color by a variable (e.g. head direction)

Question 46

How would you test whether two brain regions have significantly different firing rates

Answer 46

Extract mean rate vectors for each region Use ttest2(motor_rates, parietal_rates) Optionally visualize with boxplot or swarm chart

Question 47

How would you simulate deterministic Hopfield network dynamics

Answer 47

Initialize pattern V Build weight matrix W using Hebbian rule Initialize state S Loop over time steps: compute h = W * S', update S using sign(h) Track overlap over time

Question 48

How would you measure Hopfield network memory capacity

Answer 48

Store N random patterns Perturb a cue for one of them Run dynamics and measure which pattern is recovered Repeat for increasing number of patterns Plot recall % vs. # patterns

Question 49

How would you generate a PSTH for multiple neurons

Answer 49

Define time window around each stimulus Align spike times relative to stimulus time Bin relative spike times across trials Create subplot per neuron, add vertical line at time 0

Question 50

How would you identify the most selective neurons based on their tuning curves

Answer 50

Compute tuning curve for each neuron Measure width of tuning (e.g. full width at half max) Sort/select those with narrowest tuning

Question 51

How would you calculate correlation between spike trains of two neurons

Answer 51

Bin spike trains into vectors (same bin edges) Use corrcoef(vector1, vector2) Optionally apply smoothing first