examples/

attn_trn: test of trn-based attention in basic V1, V2, LIP localist network with gabor inputs.

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

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

sim is a simple simulation to run the env example

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.

hip runs a hippocampus model on the AB-AC paired associate learning task

hip_bench runs a hippocampus model for testing parameters and new learning ideas

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.

kinaseq plots kinase learning simulation over time

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)

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).

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)

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)

sir illustrates the dynamic gating of information into PFC active maintenance, by the basal ganglia (BG).

urakubo: This simulation replicates the Urakubo et al, 2008 detailed model of spike-driven learning, including intracellular Ca-driven signaling, involving CaMKII, CaN, PKA, PP1.