examples/

bench runs a benchmark model with 5 layers (3 hidden, Input, Output) all of the same size, for benchmarking different size networks.

boa: This project tests BG, OFC & ACC learning in a CS-driven approach task.

deep_fsa runs a DeepAxon network on the classic Reber grammar finite state automaton problem.

deep_move runs a DeepAxon network predicting the effects of movement on visual inputs.

deep_music runs a DeepAxon network on predicting the next note in a musical sequence of notes.

eqplot plots an equation updating over time in a etable.Table and Plot2D. This is a good starting point for any plotting to explore specific equations.

inhib: This simulation explores how inhibitory interneurons can dynamically control overall activity levels within the network, by providing both feedforward and feedback inhibition to excitatory pyramidal neurons.

neuron: This simulation illustrates the basic properties of neural spiking and rate-code activation, reflecting a balance of excitatory and inhibitory influences (including leak and synaptic inhibition).

pcore: This project simulates the inhibitory dynamics in the STN and GPe leading to integration of Go vs.

ra25 runs a simple random-associator four-layer axon network that uses the standard supervised learning paradigm to learn mappings between 25 random input / output patterns defined over 5x5 input / output layers (i.e., 25 units)

ra25x runs a simple random-associator four-layer axon network that uses the standard supervised learning paradigm to learn mappings between 25 random input / output patterns defined over 5x5 input / output layers (i.e., 25 units)

rl_cond explores the temporal differences (TD) reinforcement learning algorithm under some basic Pavlovian conditioning environments.