TODO.md

Network Modifications

Activation Function

• modify activation function in neurons.

Astrocyte Energy Regeneration

• accumulation rate //test
  • Option to regenerate as wave function over time.
• if it’s too high, they would never run out
• if it’s too low, the signals would still die out
• modify astrocyte initialized energy (currently set at max).
  • random between min and max provided values

Threshold

• each neuron has to receive a certain # of signals to fire.

• build-up of signals has to equal firing threshold.

• non-negative value that stores # of signals received = acc.

  • multiplies 0.9 with acc (decays at a constant rate)
    • adds 1/6 if a signal is received (enough to outweigh decay)
      • when it hits the firing threshold it will fire

• refractory period = 5 seconds (default), can be modified.

m ← max(m + signals(now) - 0.1, 0) // decay cannot make value less than 0
if m >= 3 && enough time has passed // refractory period
	then
		m ← 0
		signal

• should result in less signals
• calculate average signals received per neuron
  • threshold & refractory period can be modified in real time.

Weights

• Human Like Connectivity

  • neuron randomly chooses their number of connections from a power-law distribution.
    • few neurons have many connections, most neurons have few connections.
    • what happens to max distance allowed for connection?
  • graphical interface allowing to specify the distribution.
  • make connectivity look like human brain connectivity matrices.
  • primary sensory inputs, motor (decision) outputs.
  • one directional connection.

• randomly assign weights to connections.

  • modify above to
    • mi ← max(mi + SUM0,connections.length(wi*signalsi(now)) - 0.1, 0)

• initialization

  • eventually: interaction of weight and distance between neurons
  • eventually: bell curve probability of weights

• 0-5-10 probability increases - higher at 5 then 0 and 10

Inhibitors

• 20% of neurons are inhibitors
• inhibitors can have a larger weight value than excitatory
  • 0-p of positive and 0-n of negative and possibly modify these values
• neuron has boolean: inhibitor true/false -eventually: colorful difference between inhibitory and excitatory signals

STDP

• implement Spike-timing dependent plasticity, and a learning task. -reinforcement-stdp?
• update connection weights, based on firing patterns.