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refractoring

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This commit is contained in:
tonaerospace
2023-05-17 07:36:14 +07:00
parent dee756adac
commit b5c372d570
1 changed files with 48 additions and 26 deletions
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74
src/snn_utils.jl
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@@ -244,31 +244,6 @@ end
firing_rate!(n::compute_neuron) = n.firingRate = (n.firingCounter / n.timeStep) * 1000
firing_diff!(n::compute_neuron) = n.firingDiff = n.firingRate - n.firingRateTarget
function neuroplasticity!(n::compute_neuron, firedNeurons::Vector)
# if there is 0-weight then replace it with new connection
zero_weight_index = findall(iszero.(n.wRec))
if length(zero_weight_index) != 0
""" sampling new connection from list of neurons that fires instead of ramdom choose from
all compute neuron because there is no point to connect to neuron that not fires i.e.
not fire = no information
"""
subscribe_options = filter(x -> x ∉ [n.id], firedNeurons) # exclude this neuron id from the list
filter!(x -> x ∉ n.subscriptionList, subscribe_options) # exclude this neuron's subscriptionList from the list
shuffle!(subscribe_options)
end
new_connection_percent = 10 - ((n.optimiser.eta / 0.0001) / 10) # percent is in range 0.1 to 10
percentage = [new_connection_percent, 100.0 - new_connection_percent] / 100.0
for i in zero_weight_index
if Utils.random_choices([true, false], percentage)
n.subscriptionList[i] = pop!(subscribe_options)
n.wRec[i] = 0.01 # new connection should not send large signal otherwise it would throw
# RSNN off path. Let weight grow by an optimiser
end
end
end
function adjust_internal_learning_rate!(n::compute_neuron)
n.internal_learning_rate = n.error_diff[end] < 0.0 ? n.internal_learning_rate * 0.99 :
n.internal_learning_rate * 1.005
@@ -298,9 +273,11 @@ function synapticConnStrength(currentStrength::AbstractFloat, updown::String, bi
return updatedStrength
end
""" Compute all synaptic connection strength of a neuron. Also mark n.wRec to 0 if wRec goes
below lowerlimit.
"""
function synapticConnStrength!(n::Union{compute_neuron, output_neuron})
for (i, connStrength) in enumerate(n.synapticStrength)
#WORKING
# check whether connStrength increase or decrease based on usage from n.epsilonRec
updown = n.epsilonRec[i] == 0.0 ? "down" : "up"
updatedConnStrength = synapticConnStrength(connStrength, updown)
@@ -312,8 +289,53 @@ function synapticConnStrength!(n::Union{compute_neuron, output_neuron})
end
end
end
function synapticConnStrength!(n::input_neuron) end
function neuroplasticity!(n::compute_neuron, firedNeurons::Vector)
# if there is 0-weight then replace it with new connection
zero_weight_index = findall(iszero.(n.wRec))
if length(zero_weight_index) != 0
""" sampling new connection from list of neurons that fires instead of ramdom choose from
all compute neuron because there is no point to connect to neuron that not fires i.e.
not fire = no information
"""
subscribe_options = filter(x -> x ∉ [n.id], firedNeurons) # exclude this neuron id from the list
filter!(x -> x ∉ n.subscriptionList, subscribe_options) # exclude this neuron's subscriptionList from the list
shuffle!(subscribe_options)
end
new_connection_percent = 10 - ((n.optimiser.eta / 0.0001) / 10) # percent is in range 0.1 to 10
percentage = [new_connection_percent, 100.0 - new_connection_percent] / 100.0
for i in zero_weight_index
if Utils.random_choices([true, false], percentage)
n.subscriptionList[i] = pop!(subscribe_options)
n.wRec[i] = 0.01 # new connection should not send large signal otherwise it would throw
# RSNN off path. Let weight grow by an optimiser
end
end
end