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Path	Synopsis
attn_trn attn_trn: test of trn-based attention in basic V1, V2, LIP localist network with gabor inputs.	attn_trn: test of trn-based attention in basic V1, V2, LIP localist network with gabor inputs.
bench bench runs a benchmark model with 5 layers (3 hidden, Input, Output) all of the same size, for benchmarking different size networks.	bench runs a benchmark model with 5 layers (3 hidden, Input, Output) all of the same size, for benchmarking different size networks.
bench_lvis bench runs a benchmark model with 5 layers (3 hidden, Input, Output) all of the same size, for benchmarking different size networks.	bench runs a benchmark model with 5 layers (3 hidden, Input, Output) all of the same size, for benchmarking different size networks.
choose choose: This project tests the Rubicon framework making cost-benefit based choices	choose: This project tests the Rubicon framework making cost-benefit based choices
armaze Package armaze represents an N-armed maze ("bandit") with each Arm having a distinctive CS stimulus at the start (could be one of multiple possibilities) and (some probability of) a US outcome at the end of the maze (could be either positive or negative, with (variable) magnitude and probability.	Package armaze represents an N-armed maze ("bandit") with each Arm having a distinctive CS stimulus at the start (could be one of multiple possibilities) and (some probability of) a US outcome at the end of the maze (could be either positive or negative, with (variable) magnitude and probability.
deep_fsa deep_fsa runs a DeepAxon network on the classic Reber grammar finite state automaton problem.	deep_fsa runs a DeepAxon network on the classic Reber grammar finite state automaton problem.
deep_move deep_move runs a DeepAxon network predicting the effects of movement on visual inputs.	deep_move runs a DeepAxon network predicting the effects of movement on visual inputs.
deep_music deep_music runs a DeepAxon network on predicting the next note in a musical sequence of notes.	deep_music runs a DeepAxon network on predicting the next note in a musical sequence of notes.
dls
armaze Package armaze represents an N-armed maze ("bandit") with each Arm having a distinctive CS stimulus at the start (could be one of multiple possibilities) and (some probability of) a US outcome at the end of the maze (could be either positive or negative, with (variable) magnitude and probability.	Package armaze represents an N-armed maze ("bandit") with each Arm having a distinctive CS stimulus at the start (could be one of multiple possibilities) and (some probability of) a US outcome at the end of the maze (could be either positive or negative, with (variable) magnitude and probability.
hip hip runs a hippocampus model for testing parameters and new learning ideas	hip runs a hippocampus model for testing parameters and new learning ideas
inhib 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.	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.
mpi mpi is a version of ra25 that runs under MPI to learn in parallel across multiple nodes, sharing DWt changes via MPI.	mpi is a version of ra25 that runs under MPI to learn in parallel across multiple nodes, sharing DWt changes via MPI.
neuron 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).	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).
objrec objrec explores how a hierarchy of areas in the ventral stream of visual processing (up to inferotemporal (IT) cortex) can produce robust object recognition that is invariant to changes in position, size, etc of retinal input images.	objrec explores how a hierarchy of areas in the ventral stream of visual processing (up to inferotemporal (IT) cortex) can produce robust object recognition that is invariant to changes in position, size, etc of retinal input images.
pcore_ds
pcore_vs pcore_vs: This project simulates the inhibitory dynamics in the STN and GPe leading to integration of Go vs.	pcore_vs: This project simulates the inhibitory dynamics in the STN and GPe leading to integration of Go vs.
pfcmaint pfcmaint: This project tests prefrontal cortex (PFC) active maintenance mechanisms supported by the pyramidal tract (PT) neurons, in the PTMaint layer type.	pfcmaint: This project tests prefrontal cortex (PFC) active maintenance mechanisms supported by the pyramidal tract (PT) neurons, in the PTMaint layer type.
pvlv effort_plot plots the Rubicon effort cost equations.	effort_plot plots the Rubicon effort cost equations.
cond
ra25 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)
ra25x 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)	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 rl_cond explores the temporal differences (TD) and Rescorla-Wagner reinforcement learning algorithms under some basic Pavlovian conditioning environments.	rl_cond explores the temporal differences (TD) and Rescorla-Wagner reinforcement learning algorithms under some basic Pavlovian conditioning environments.
vspatch vspatch: This project simulates the Ventral Striatum (VS) Patch (striosome) neurons that predict reward to generate an RPE (reward prediction error).	vspatch: This project simulates the Ventral Striatum (VS) Patch (striosome) neurons that predict reward to generate an RPE (reward prediction error).