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 |
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) |
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.
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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. |
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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