CTRNN 1.3

A package that implements Continuous Time Recurrent Neural Networks

Copyright (c) 2018 Madhavun Candadai Vasu

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Description: CTRNN
=========================
Python package that implements Continuous Time Recurrent Neural Networks (CTRNNs)

See Beer, R.D. (1995). On the dynamics of small continuous-time recurrent neural networks. Adaptive Behavior 3:469-509. for a study of CTRNNs. This implementation was inspired by the C implementation at http://mypage.iu.edu/~rdbeer/Software/EvolutionaryAgents/CTRNNDoc.pdf

Installation instructions::
-------------------------
$ pip install CTRNN

-------------------------

Usage
-----

The CTRNN class has the following functions::

| __init__(self, size=2, step_size=0.1)
Constructer that initializes a random network
with unit time-constants and biases
args = size:integer = network size

| euler_step(self, external_inputs)
Euler stepping the network by self.step_size with provided inputs
args = external_inputs:array[size,] = one float input per neuron

| inverse_sigmoid(self, o)
Computes the inverse of the sigmoid function
args = o:array of any size
returns = inverse_sigmoid(o):array same size as o

| randomize_outputs(self, lb, ub)
Randomize outputs in range [lb,ub]
args = lb:float = lower bound for random range
ub:float = upper bound for random range

| randomize_states(self, lb, ub)
Randomize states in range [lb,ub]
args = lb:float = lower bound for random range
ub:float = upper bound for random range

| sigmoid(self, s)
Computes the sigmoid function on input array
args = s:array of any Size
output = sigmoid(s):array of same size as input

Example
-------

The following code creates a 2-neuron CTRNN sinusoidal oscillator::

# imports
import numpy as np
import matplotlib.pyplot as plt
# importing the CTRNN class
from CTRNN import *

# params
run_duration = 250
net_size = 2
step_size = 0.01

# set up network
network = CTRNN(size=net_size,step_size=step_size)
network.taus = [1.,1.]
network.biases = [-2.75,-1.75]
network.weights[0,0] = 4.5
network.weights[0,1] = 1
network.weights[1,0] = -1
network.weights[1,1] = 4.5

# initialize network
network.randomize_outputs(0.1,0.2)

# simulate network
outputs = []
for _ in range(int(run_duration/step_size)):
network.euler_step([0]*net_size) # zero external_inputs
outputs.append([network.outputs[0],network.outputs[1]])
outputs = np.asarray(outputs)

# plot oscillator output
plt.plot(np.arange(0,run_duration,step_size),outputs[:,0])
plt.plot(np.arange(0,run_duration,step_size),outputs[:,1])
plt.xlabel('Time')
plt.ylabel('Neuron outputs')
plt.show()

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