David Lipshutz, Dmitri Chklovskii, Lyndon Duong, Eero Simoncelli
We lifted 3 functions out of this paper's own repositories and ran 3 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| lyndond/multi_timescale_whitening | canonical | 3 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| context_adaptation_experiment | Ran | lyndond/multi_timescale_whitening/timescales/simulation_plasticity.py code served (permissive licence) · get_code("97f9f294660264e5") |
| get_cov_controls | Ran | lyndond/multi_timescale_whitening/timescales/simulation_plasticity.py code served (permissive licence) · get_code("c00cc5e024a07ec9") |
| optimal_alignment | Ran | lyndond/multi_timescale_whitening/timescales/simulation_plasticity.py code served (permissive licence) · get_code("09171cdbbb639731") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Neurons in early sensory areas rapidly adapt to changing sensory statistics, both by normalizing the variance of their individual responses and by reducing correlations between their responses. Together, these transformations may be viewed as an adaptive form of statistical whitening. Existing mechanistic models of adaptive whitening exclusively use either synaptic plasticity or gain modulation as the biological substrate for adaptation; however, on their own, each of these models has significant limitations. In this work, we unify these approaches in a normative multi-timescale mechanistic model that adaptively whitens its responses with complementary computational roles for synaptic plasticity and gain modulation. Gains are modified on a fast timescale to adapt to the current statistical context, whereas synapses are modified on a slow timescale to match structural properties of the input statistics that are invariant across contexts. Our model is derived from a novel multi-timescale whitening objective that factorizes the inverse whitening matrix into basis vectors, which correspond to synaptic weights, and a diagonal matrix, which corresponds to neuronal gains. We test our model on synthetic and natural datasets and find that the synapses learn optimal configurations over long timescales that enable adaptive whitening on short timescales using gain modulation.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2308.13633")
get_code_for_paper("2308.13633")
have("2308.13633")
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