Organizing the neurons on a firing pattern plot

[celiasmith]

It'd be nice to have an option to organize the neurons on a firing pattern plot (like in the old GUI). Where neurons with similar tuning are near each other. It makes the output look more like calcium imaging and fMRI data.

[tcstewar]

Agreed. Right now, you can sort of do that manually by defining your own encoders, but we should either have a helper function to do that, or build it into the GUI the same way we did in Nengo 1.4....

[tcstewar]

Here's a quick helper function that does this!

import nengo

import numpy as np

def compute_score(encoders, order, width, index):
    N = len(encoders)
    score = 0
    
    e = encoders[order[index]]
    deltas = [1, -1, width, -width]
    for delta in deltas:
        e2 = encoders[order[(index+delta)%N]]        
        score += np.dot(e, e2)
    return score
    

def reorganize(encoders, width, iterations=10, seed=None):
    N = len(encoders)
    order = np.arange(N)
    
    rng = np.random.RandomState(seed=seed)
    items = np.random.randint(N, size=(iterations*N, 2))
    
    for i,j in items:
        if i != j:
            score = compute_score(encoders, order, width, i) + compute_score(encoders, order, width, j)
            order[[i,j]] = order[[j,i]]
            score_swap = compute_score(encoders, order, width, i) + compute_score(encoders, order, width, j)
            if score_swap < score:
                order[[i,j]] = order[[j,i]]
                
    return encoders[order]


model = nengo.Network()
with model:
    
    stim = nengo.Node(lambda t: [np.sin(2*np.pi*t), np.cos(2*np.pi*t)])
    
    ens = nengo.Ensemble(n_neurons=400, dimensions=2)
    enc = nengo.dists.UniformHypersphere(surface=True).sample(ens.n_neurons, d=2)
    enc2 = reorganize(enc, int(np.ceil(np.sqrt(ens.n_neurons))), seed=1, iterations=10) 
    ens.encoders = enc2
    
    nengo.Connection(stim, ens)