/
core.py
127 lines (103 loc) · 2.96 KB
/
core.py
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import pyNN.nest as sim
from encoders import ScalarEncoder
import pdb
class MemoryBuilder:
# defaults
syn_local_inh = {
#'model' : 'tsodyks_synapse',
#'tau_rec' : 1000.0, # recovery time
#'tau_psc' : 100.0, # ?
'delay' : 1.0,
'weight': -1000.0
}
syn_prox_in = {
'delay' : 1.0,
'weight' : 100.0
}
syn_basal_in = {
'model' : 'stdp_synapse',
'delay' : 1.0,
'weight' : 10.0
}
column_neuron_params = {
'v_thresh' : -61.0, # mV
'tau_refrac' : 2.0, # ms
'tau_syn_E' : 2.0, # ms
'tau_syn_I' : 2.0
}
I_e_OFF = 0.0
I_e_ON = 1e4
NEURON_MODEL = sim.IF_curr_exp
def __init__(self):
pass
@classmethod
def InitMemoryUnit(self):
sim.ResetKernel()
@classmethod
def CreateColumn(self, numOfCells, localInhibitionRadius=0):
column = sim.create(numOfCells, NEURON_MODEL, self.column_neuron_params, label='blabla')
# connect column with inhibitory connections
inhColumnConnector = sim.FixedProbabilityConnector(0.9, weigths=-10.0)
for neuron in column:
sim.projection(neuron, column, syn_spec = self.syn_local_inh)
return column
@classmethod
def ConnectProximalDendrite(self, column, in_neuron=None):
if in_neuron is None:
in_neuron = nest.Create('iaf_neuron', params = {'I_e' : self.I_e_OFF})
nest.Connect(in_neuron, column, 'all_to_all', syn_spec = self.syn_prox_in)
return in_neuron
@classmethod
def ConnectBasalDendrite(self, neuron, in_neuron=None):
if in_neuron is None:
in_neuron = nest.Create('iaf_neuron', params = {'I_e' : self.I_e_OFF})
#pdb.set_trace()
nest.Connect(in_neuron, [neuron], 'all_to_all', self.syn_basal_in)
return in_neuron
@classmethod
def SetProximalInput(self, neuron, val):
v = self.I_e_OFF
if val is True:
v = self.I_e_ON
nest.SetStatus(neuron, params = {'I_e' : v})
############## RECORDING #################
@classmethod
def RecordColumnVoltages(self, column):
vd = nest.Create('multimeter', len(column), params={'record_from': ['V_m'], 'interval' :0.1})
# pdb.set_trace()
nest.Connect(vd, column, 'one_to_one')
return vd
@classmethod
def RecordColumnSpikes(self, column):
sd = nest.Create('spike_detector', len(column))
nest.Connect(column, sd, 'one_to_one')
return sd
@classmethod
def FetchColumnEvents(self, sd):
spikes = nest.GetStatus(sd, 'events')
return spikes
############## LAYERS (UNITS) #################
@classmethod
def CreateUnit(self,
numOfColumns,
cellsPerColumn,
localInhibitionRadius=0,
localConnectivityRadius=0):
unit = []
# create columns
for i in range(numOfColumns):
c = self.CreateColumn(cellsPerColumn)
unit.append(c)
# connect neighboring columns
for k in range(-localConnectivityRadius, 1, 1):
idx = i + k # index of column to connect to
print(idx)
if idx < 0:
continue
# connect every cell to every other cell
for p in range(cellsPerColumn):
for q in range(cellsPerColumn):
print(p)
print(q)
self.ConnectBasalDendrite(unit[i][p], unit[idx][q])
return unit