Description of problem
Hello all,
I am new in both of the SNNs and Brian2 simulator and even in the neuroscience knowledge.
Actually, I am working now on an implementation of electrical (artificial) Morris-Lecar model and binary STDP mechanism in order to address a simple CV task which can be hopefully solved with our analog neurons.
My input data are recorded using DVS spiking camera as videos of shape (#frames=1500,h=240,w=180), each cell contains the number of spikes acquired by the camera at a given location and within a given time frame, but I downsampled and reduced it into (100,8,6) for “ram space” reasons and normalized between 0 and 1.
I need my input for the analog neurons to be voltage and this voltage will be translated to current, and when present an excitation I need the input spikes from this excitation to be continuous inputs instead of individual spikes events and need to have some width which means that the duration of this excitation is enough to increase the membrane potential and thus reach the switching threshold.
I have read several discussions here and it was rich and useful to avoid lot of mistakes, I tried to flatten my downsampled array into array of shape (100,6*8) and feed my neurons with my inputs using 2D TimedArray but in fact it is not what I have to do. Maybe I have to use rate coding or temporal coding methods!! but I didn’t know how!! Should I use SpikeGeneratorGroup with the original data recorded by the camera before accumulating the spikes together in the number of them?? Do you have any ideas or suggestions to do?
Minimal code to reproduce problem
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Small parts of the code:
prepare and process the data
downsampling, reshape and reduce
d_data = np.maximum(data[:, :h:30, :ww:30], data[:, :h:30, :ww:30])
print(“original shape:”, data.shape)
print(“Downsampled shape:”, d_data.shape)
original shape: (1500, 180, 240)
Downsampled shape: (1500, 6, 8)
original shape: (1500, 180, 240)
Downsampled shape: (1500, 6, 8)
normalize
d_data = d_data.astype(float)
nd_data = d_data / 255.0
Reshapes the data to a 2D array where each row represents a frame to use in the TimedArray
input_data = nd_data.reshape(nd_data.shape[0], -1)
print(“input data shape is:”, input_data.shape)
input data shape is: (1500, 48)
convert the input data into a TimedArray function which has format that can be used as input current in the neuron model
input_array = TimedArray(input_data * mV, dt = 1 * ms)
num_neurons = input_data.shape[1] # equal to the number of pixels in each frame
print(num_neurons)
PARAMETERS OF THE ELECTRICAL ML MODEL AND BINARY STDP EQUATIONS
…
STDP binary synapses equations
…
Morris-Lecar neuron equation from electrical model
dV/dt = Ig/Cm : volt
Ig = It + INa - IK + Isyn: amp
… equations
It = equation includes Vg : amp
Vg = Vd - input_array(t, i) : volt
Vd = 300 * mV
Threshold condition for spiking
Create pre-synaptic neurons group with num_neurons = 6*8
Neurons post-synaptic group creation with 1 neuron
Synapse group creation
monitors
defaultclock.dt = 0.001ms
run(40ms, report = ‘text’)
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Thank you for helping and sorry if my question seems to be not very clear!