2. Once you have implemented neo.m, apply the transform to the filtered neural spike data and generate a plot of the NEO signal by running the code in Section 2. Save this plot as: neo.png.Part 3 - Spike detection and alignment (4 Points) Goal: Implement a spike detection and alignment algorithm using the NEO transformation. Procedure: 1. Looking at the plot generated in Section 2, determine an appropriate threshold to apply to the NEO signal to detect the neural spikes. This threshold may be chosen through guess-and-check, or you may determine the threshold using statistics of the NEO signal. Either way, you must define the following variable: a. eps - the threshold for the NEO signal above which a spike is decided to have occurred 2. Once the threshold has been determined, create a MATLAB function file align_spikes.m in which you will implement your spike detection and alignment algorithm. The desired function template is as follows: function [ waveforms, waveidx ] = align_spikes( ts, fdata, neodata, eps ) % @param ts : the timestamps for the neural data [num_samples * 1] % @param fdata : the bandpass filtered neural data [num_samples x 1] % @param neodata : the NEO transformed signal [num_samples x 1] % @param eps : the NEO signal threshold for registering a neural spike [1 x 1] % % @return waveforms : the aligned neural spike waveforms (num_spikes x window_length] % @return waveidx : the corresponding indices in fdata for each neural spike event end Your spike detection and alignment algorithm must comply with the following processing logic: a. Neural spikes must be detected when the NEO signal is greater than the threshold defined by eps. b. The neural waveform is defined as 300 microseconds before and 600 microseconds after the peak of the absolute value of the detected neural spike. c. The recorded index of the neural spike is defined as the index in fdata where the peak of the absolute value of the neural waveform occurs.\fPart 4 - Spike clustering (2 Point) Use clustering of the neural waveforms to determine the number of recorded neurons. Procedure: Using the code in Section 4 of Assr'gnmenLMoo'ufc-3.m, perform principal component analysis (PCA) and k-mean clustering on the detected neural waveforms to determine how many neurons are being recorded by this single channel. The following variable needs to be dened by you: . k the expected number of neurons to find in the clustering Determining the value of k is largely a subjective process. We suggest plotting the PCA- transformed neural waveforms and using your best judgment to determine how many clusters you believe you observe. Once k is determined, run the code in Section 4 to a plot of the k- means clustered neural waveforms. Save this plot as: spikes_cfusiers.png. Part 5 - Spike train reconstruction (1 Point) Goal: Generate spike train reconstructions of each of the recorded neurons. Procedure: Run the code in Section 5 to generate two plots: 1. Plot the template waveform for each recorded neurons' spiking activity. This is the stereotyped action potential as it is projected onto the recording electrode. Save this plot as: spikes_tempfates.png. 2. Plot the reconstructed spike trains for each of the recorded neurons. This is a binary signal that is 1 when a neural spike is detected and 0 otherwise. Save this plot as: spikeareconswctedpng.