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• The Basic Functions

  JangoJungle • 06/09/2015 at 01:21 • 0 comments

  During my free time in the past few weeks I've worked on my code, so here's a semi-fleshed out skeleton of the code so far. It will also be on Github in the coming weeks. There's a couple of modifications to do, specifically, a better and more thorough plot of not just the neuron pulse times, but also the synaptic event times, and also the projected pulse time calculations need a little tweaking.

  '''
  # Neural Network Model
  # This program will simulate an event driven model of a pulsed neural network.
  '''
  __author__ = 'JangoJungle'
  
  from numpy import *
  import random as rand
  import matplotlib.pyplot as plt
  
  # A quick function to get the synapse state form for plotting  -- Not Used Yet
  def get_state(w, t, a):
      stateraw = Decimal(w - w * abs(t - a))
      index = 0
      for x in stateraw:
          if x <= 0:
              stateraw[index] = 0
          index += 1
      return stateraw
  
  # declare/initialize variables
  
  # other variables
  next_event_time = 0
  current_time = 0
  fault = 9999999
  count = 0
  interval = linspace(0,49,50)
  
  # synaptic variables
  synaptic_weights = r_[[1, 1, 1]]
  state_of_synapses = r_[[0, 0, 0]] # 1 for up, -1 for down, 0 for idle
  synaptic_delays = r_[[rand.randint(0, 3), rand.randint(0, 3), rand.randint(0, 3)]]
  synaptic_event_times = r_[[1, fault, 1]] # start the trigger and pulse train [pulse train, neuron output, trigger]
  synapse_connection_matrix = r_[1, 1, 1]
  
  # neuron variables
  previous_activity_slope = r_[0]
  current_activity_slope = r_[0]
  threshold = 1.5
  neuron_pulse_times = r_[fault]
  neuron_connection_matrix = c_[[0, 1, 0]] # only one column: only one neuron, connected to synapse 2
  pulse_points = linspace(0,0,50)
   
  # Start loop:
  
  while (count < 50):
  	
  	# Go to next event time
  
  	current_time = next_event_time
  
  	# Calculate the Next Neuron firing time -- how to adjust this for multiple neurons?
  	current_activity_slope[0] = sum(state_of_synapses*synaptic_weights) + previous_activity_slope[0]
  	if current_activity_slope[0] > 0:
  		neuron_pulse_times[0] = threshold/current_activity_slope[0] + current_time
  	else:
  		neuron_pulse_times[0] = fault
  	previous_activity_slope[0] = current_activity_slope[0]
  
  	# Find the next event time and type
  
  	next_synapse_event_time = min(synaptic_event_times)
  	next_synapse = argmin(synaptic_event_times) 	# note: argmin returns a 1d index
  
  	next_neuron_firing_time = min(neuron_pulse_times)
  	next_neuron = argmin(neuron_pulse_times)
  
  	next_event_times = r_[[next_synapse_event_time, next_neuron_firing_time]]
  	next_event_time = min(next_event_times)
  
  	event_type = argmin(next_event_times) 	# 0 for synapse, 1 for neuron
  
  	# Do required operations for each event type
  
  	if next_event_time != fault:
  		'''
  		# If it's a synapse event time, update the states
  		'''
  		if event_type == 0: 	# synaptic event == 0
  			# Update states
  			if state_of_synapses[next_synapse] == 0:
  				state_of_synapses[next_synapse] = synaptic_weights[next_synapse]
  				synaptic_event_times[next_synapse] += 1
  				print ('Synapse {} is firing.'.format(next_synapse))
  			
  			elif state_of_synapses[next_synapse] == synaptic_weights[next_synapse]:
  				state_of_synapses[next_synapse] = -synaptic_weights[next_synapse]
  				synaptic_event_times[next_synapse] += 1
  			
  			elif state_of_synapses[next_synapse] == -synaptic_weights[next_synapse]:
  				state_of_synapses[next_synapse] = 0
  				synaptic_event_times[next_synapse] = fault
  				
  				# reset the pulse train if it's the pulse train
  				if next_synapse == 0:
  					synaptic_event_times[next_synapse] = (2 + current_time + synaptic_delays[next_synapse])
  
  		'''
  		# If it's a neuron firing time, reset the synapses, find all the synapses this neuron is connected to 
  		# and then update those synapses
  		'''
  		if event_type == 1: 	# neuron firing = 1
  			print ('Neuron is firing.')
  			# reset synapses
  			state_of_synapses[0] = state_of_synapses[1] = state_of_synapses[2] = 0
  			synaptic_event_times[0] = synaptic_event_times[1] = synaptic_event_times[2] = fault
  			
  			# find all the synapses this neuron is connected to and update them
  			connected_synapses...

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