Directories ¶
Path | Synopsis |
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bench runs a benchmark model with 5 layers (3 hidden, Input, Output) all of the same size, for benchmarking different size networks.
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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 DeepLeabra network on the classic Reber grammar finite state automaton problem.
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deep_fsa runs a DeepLeabra network on the classic Reber grammar finite state automaton problem. |
sim is a simple simulation to run the env example
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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.
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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
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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
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hip_bench runs a hippocampus model for testing parameters and new learning ideas |
testing_effect
hip_bench runs a hippocampus model for testing parameters and new learning ideas
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hip_bench runs a hippocampus model for testing parameters and new learning ideas |
ra25 runs a simple random-associator four-layer leabra 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)
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ra25 runs a simple random-associator four-layer leabra 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 leabra 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)
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ra25 runs a simple random-associator four-layer leabra 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).
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sir illustrates the dynamic gating of information into PFC active maintenance, by the basal ganglia (BG). |
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