overlappingspikes

Neural spike sorting algorithms for overlapping spikes. Algorithms are all written in MATLAB. Completed as a senior thesis project, advised by Professor Alexander H. Barnett.

• maindriver.m: sorts spikes with and without penalty, with and without time shifts
• decision_bound.m: plots the decision boundaries in the 2-spike case for all algorithms in notimeshifts

synthesis

Creation of synthetic spike signals.

Functions:

• synth.m: synthesizes spike signals (without noise)
• gen_firing.m: generates firing times using a Poisson distribution

Drivers:

• demo_synth.m: tests synth.m, gen_firing.m

detectspikes

Spike detection via maximum likelihood.

Drivers:

• binarytest.m: detection of one vs. no spike using linear classifiers

notimeshifts

Sorting spikes without time shifts. These functions cannot handle sorting spikes with time shifts.

Functions:

• brute_force.m: brute force, NO time shifts, NO penalty
• greedy_sort.m: simple greedy, NO time shifts, NO penalty
• FoBa.m: forward-backward greedy algorithm, currently is no different than greedy_sort.m (in performance)
• count.m: counts the errors for sorting spikes WITHOUT time shifts

Drivers:

• driver_count.m: tests every possible case for count.m
• driver_brute.m: shows that brute-force gets 100% accuracy at eta = 0
• driver_foba.m: compares sorting by simple greedy and foba, shows that error fractions match exactly

timeshifts

Soring spikes with time shifts. These functions can also handle sorting spikes without time shifts.

Functions:

• brute_force_ts.m: brute-force for time shifts with penalty, tests all possible combinations of time-shifted spikes
• combine.m: used in brute_force_ts.m to find all possible combinations of the elements of n vectors (adapted from http://stackoverflow.com/questions/21895335/generate-a-matrix-containing-all-combinations-of-elements-taken-from-n-vectors)
• greedy_likely.m: simple greedy with penalty, for time-shifted spikes
• greedy_pairs.m: greedy with pairs with penalty, for time-shifted spikes
• count_ts2.m: counts the errors for sorting time-shifted spikes

Drivers:

• driver_count_ts2: tests every possible case for count_ts2.m

realdata

Run algorithms on data from Harris, et al. “Accuracy of tetrode spike separation as determined by simultaneous intracellular and extracellular measurements.”

• harrisdata.mat: clips resulting from getharrisdata.m, ready to be sorted

Drivers:

• getharrisdata.m: written by A. H. Barnett, filters harrisdata, extracts clips, uses clustering algotihm to sort clips
• driver_harris.m: sorts the real clips from harrisdata.mat
• driver_harris2.m: creates and sorts synthetic clips using the spike templates (with time shifts) from harrisdata.mat

other

• paulabars.m: written by A. H. Barnett, creates bar graphs for error fractions with error bars (std, /sqrt(N))
• paulabars.m: version 2 of paulabars.m, changes are only in formatting
• terrorbar.m: written by T. Agus (downloaded from: https://www.mathworks.com/matlabcentral/fileexchange/52367-terrorbar-m--error-bars-with-controlled-widths-post-r2014b), used in paulabars.m to address the issue of non-standard error bar caps in pre-2016b MATLAB versions