Directories
¶
| Path | Synopsis |
|---|---|
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attn_trn: test of trn-based attention in basic V1, V2, LIP localist network with gabor inputs.
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attn_trn: test of trn-based attention in basic V1, V2, LIP localist network with gabor inputs. |
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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. |
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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. |
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td
command
td simulates a simple td agent
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td simulates a simple td agent |
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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.
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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. |
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equations provides an interactive exploration of the various equations underlying the axon models, largely from the chans collection of channels.
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equations provides an interactive exploration of the various equations underlying the axon models, largely from the chans collection of channels. |
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