Due Date: March 12th
Total marks: 10 (10% of final grade)
Late penalty: 1 mark per day
It is recommended that you use a language with a matrix library and graphing capabilities. Two main suggestions are Python and MATLAB.
You can (and are encouraged to) use code from your previous assignments.
pip install nengo
):import nengo
import numpy as np
def compute_lif_decoder(n_neurons, dimensions, encoders, max_rates, intercepts, tau_ref, tau_rc, radius, x_pts, function):
"""
Parameters:
n_neurons: number of neurons (integer)
dimensions: number of dimensions (integer)
encoders: the encoders for the neurons (array of shape (n_neurons, dimensions))
max_rates: the maximum firing rate for each neuron (array of shape (n_neurons))
intercepts: the x-intercept for each neuron (array of shape (n_neurons))
tau_ref: refractory period for the neurons (float)
tau_rc: membrane time constant for the neurons (float)
radius: the range of values the neurons are optimized over (float)
x_pts: the x-values to use to solve for the decoders (array of shape (S, dimensions))
function: the function to approximate
Returns:
A (the tuning curve matrix)
dec (the decoders)
"""
model = nengo.Network()
with model:
ens = nengo.Ensemble(n_neurons=n_neurons,
dimensions=dimensions,
encoders=encoders,
max_rates=max_rates,
intercepts=[x/radius for x in intercepts],
neuron_type=nengo.LIF(tau_rc=tau_rc, tau_ref=tau_ref),
radius=radius)
sim = nengo.Simulator(model)
x_pts = np.array(x_pts)
if len(x_pts.shape) == 1:
x_pts.shape = x_pts.shape[0], 1
_, A = nengo.utils.ensemble.tuning_curves(ens, sim, inputs=x_pts)
target = [function(xx) for xx in x_pts]
solver = nengo.solvers.LstsqL2()
dec, info = solver(A, target)
return A, dec
def generate_signal(T, max_freq, rms, dt):
"""
Parameters:
T: the period of time to generate a random signal for
max_freq: the highest frequency in the signal
rms: the RMS power of the signal
dt: the time step (usually 0.001)
Returns:
signal (an array of length T/dt containing the random signal)
"""
signal = nengo.processes.WhiteSignal(period=T, high=max_freq, rms=rms)
return signal.run(T, dt=dt)
def generate_spikes(n_neurons, dimensions, encoders, max_rates, intercepts, tau_ref, tau_rc, radius, x, dt):
"""
Parameters:
n_neurons: number of neurons (integer)
dimensions: number of dimensions (integer)
encoders: the encoders for the neurons (array of shape (n_neurons, dimensions))
max_rates: the maximum firing rate for each neuron (array of shape (n_neurons))
intercepts: the x-intercept for each neuron (array of shape (n_neurons))
tau_ref: refractory period for the neurons (float)
tau_rc: membrane time constant for the neurons (float)
radius: the range of values the neurons are optimized over (float)
x: the input signal to feed into the neurons (array of shape (T, dimensions))
dt: the time step of the simulation (usually 0.001)
Returns:
spikes (a (timesteps, n_neurons) array of the spiking outputs)
"""
model = nengo.Network()
with model:
stim = nengo.Node(lambda t: x[int(t/dt)-1])
ens = nengo.Ensemble(n_neurons=n_neurons,
dimensions=dimensions,
encoders=encoders,
max_rates=max_rates,
intercepts=[x/radius for x in intercepts],
neuron_type=nengo.LIF(tau_rc=tau_rc, tau_ref=tau_ref),
radius=radius)
nengo.Connection(stim, ens, synapse=None)
p = nengo.Probe(ens.neurons)
sim = nengo.Simulator(model, dt=dt)
T = len(x)*dt
sim.run(T)
return sim.data[p]
As you did in previous assignments, make a population of 20 LIF neurons representing a 1-dimensional value, and compute a decoder for them. For parameters, $\tau_{ref}$=0.002s, $\tau_{RC}$=0.02s, the maximum firing rates are chosen randomly from a uniform distribution between 100 and 200Hz (at the max radius), and the x-intercepts are chosen randomly from a uniform distribution between -2 and 2. Remember that the $\alpha$ and $J^{bias}$ terms are computed based on these x-intercepts and maximum firing rates.
It is generally easiest to compute decoders using the original method from Assignment 1, where we use the rate-mode approximation for the neurons to generate the $A$ matrix, then find $\Gamma=A^T A + \sigma^2 I$. You can use this approach to find decoders, and these decoders should work even when you simulate the neurons in terms of spikes (in question 2 on). The only difference will be that they will need to be scaled by dt
, your simulation time step.
Use this same method for computing decoders for this whole assignment.
Choose a neuron from part 1 that has a firing rate of somewhere between 20-50Hz for $x$=0. Using that neuron's $\alpha$ and $J^{bias}$ value, construct two neurons: both with the same $\alpha$ and $J^{bias}$, but one with $e$=+1 and the other with $e$=-1. With the function from the last assignment, generate a random input $x(t)$ that is 1 second long, with rms=1, dt=0.001, and an upper limit of 5Hz. Feed that signal into the two neurons and generate spikes. Decode the spikes back into $\hat{x}(t)$ using a post-synaptic current filter $h(t)$ with a time constant of $\tau$=0.005.
Repeat question 2, but with more neurons. Instead of picking particular neurons, randomly generate them with x-intercepts uniformly distributed between -2 and 2 and with maximum firing rates between 100 and 200 Hz. Randomly choose encoder values to be either -1 or +1.
For this question, use two groups of neurons with intercepts between [-1, 1] (i.e. radius = 1) to compute $y = 2x+1$. The first group of neurons will represent $x$ and the second group will represent $y$.
Start by computing decoders. You will need two decoders: one to decode $f(x)=2x+1$ from the first population, and one to decode $f(y)=y$ (the standard representational decoder) from the second population. Remember that $\Upsilon$ can change depending on what function you want to decode.
Use the same neuron parameters as for previous questions (other than the radius), and use 200 randomly generated neurons in each population.
For this question, use three groups of neurons with intercepts from [-1, 1] to compute $z = 2y+0.5x$. Follow the same steps as question 4, but take the decoded outputs from the first two groups of neurons ($f(y)=2y$ and $f(x)=0.5x$), add them together, and feed that into the third group of neurons.
Do the same thing as questions 4 and 5, but with 2-dimensional vectors instead of scalars. Everything else is the same. For your encoders $e$, randomly generate them over the unit circle.
The function to compute is $w = x-3y+2z-2q$. This requires five groups of neurons: $x$, $y$, $z$, $q$, and $w$. Each of them represents a 2-dimensional value. The outputs from $x$, $y$, $z$, and $q$ all feed into $w$.