GitHub - rkp8000/nothing_but_reruns: Analysis of the ability of networks to replay patterns

How can a neural network replay recently experienced sequential patterns using changes in ensemble excitability? How does this ability depend on the network architecture? What are the benefits that accrue from a network's spontaneous activity mirroring its activity when driven by a stimulus?

Name		Name	Last commit message	Last commit date
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db		db
figure_bases		figure_bases
figures		figures
images		images
network_models		network_models
tests		tests
.gitignore		.gitignore
README.md		README.md
_00_data_collect.ipynb		_00_data_collect.ipynb
_0_introduction.ipynb		_0_introduction.ipynb
_1_feed_forward_demonstrations.ipynb		_1_feed_forward_demonstrations.ipynb
_2_capacity_analysis_variable_architectures.ipynb		_2_capacity_analysis_variable_architectures.ipynb
_3_current_stimulus_decoding.ipynb		_3_current_stimulus_decoding.ipynb
_4_past_stimulus_decoding.ipynb		_4_past_stimulus_decoding.ipynb
_5_dynamic_implementation.ipynb		_5_dynamic_implementation.ipynb
_6_LIF_implementation.ipynb		_6_LIF_implementation.ipynb
_S1_lif_demo.ipynb		_S1_lif_demo.ipynb
__database_modification_tests.ipynb		__database_modification_tests.ipynb
_paper_draft.ipynb		_paper_draft.ipynb
_poster.ipynb		_poster.ipynb
_supplementary_draft.ipynb		_supplementary_draft.ipynb
connectivity.py		connectivity.py
cosyne.ipynb		cosyne.ipynb
network.py		network.py
plot.py		plot.py
sandbox.ipynb		sandbox.ipynb
shortcuts.py		shortcuts.py
tests.ipynb		tests.ipynb

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