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Published (Version of record)CC BY-NC-ND V4.0, Open Access
Journal article
Learning probabilistic neural representations with randomly connected circuits
Proceedings of the National Academy of Sciences of the United States of America, Vol.117(40), pp.25066-25073
06/Oct/2020
Abstract
The brain represents and reasons probabilistically about complex stimuli and motor actions using a noisy, spike-based neural code. A key building block for such neural computations, as well as the basis for supervised and unsupervised learning, is the ability to estimate the surprise or likelihood of incoming high-dimensional neural activity patterns. Despite progress in statistical modeling of neural responses and deep learning, current approaches either do not scale to large neural populations or cannot be implemented using biologically realistic mechanisms. Inspired by the sparse and random connectivity of real neuronal circuits, we present a new model for neural codes that accurately estimates the likelihood of individual spiking patterns and has a straightforward, scalable, efficiently learnable, and realistic neural implementation. This model’s performance on simultaneously recorded spiking activity of >100 neurons in the monkey visual and prefrontal cortices is comparable or better than that of current models. Importantly, the model can be learned using a small number of samples, and using a local learning rule that utilizes noise intrinsic to neural circuits. Slower, structural changes in random connectivity, consistent with rewiring and pruning processes, further improve the efficiency and sparseness of the resulting neural representations. Our results merge insights from neuroanatomy, machine learning, and theoretical neuroscience to suggest random sparse connectivity as a key design principle for neuronal computation.
Details
- Title
- Learning probabilistic neural representations with randomly connected circuits
- Creators
- Ori Maoz (null) - 972WIS_INST___123Gašper Tkacčik (null) - Institute of Science and Technology AustriaMohamad Saleh Esteki (null) - New York University (United States, New York) - NYURoozbeh Kiani (null) - New York University (United States, New York) - NYUElad Schneidman (null) - 972WIS_INST___123
- Resource Type
- Journal article
- Publication Details
- Proceedings of the National Academy of Sciences of the United States of America, Vol.117(40), pp.25066-25073; 06/Oct/2020
- Number of pages
- 8
- Language
- English
- DOI
- https://doi.org/10.1073/pnas.1912804117
- Grant note
- We thank Udi Karpas, Roy Harpaz, Tal Tamir, Adam Haber, and Amir Bar, for discussions and suggestions, and especially Oren Forkosh and Walter Senn, for invaluable discussions of the learning rule. This work was supported by a European Research Council Grant 311238 (ES), an Israel Science Foundation Grant 1629/12 (ES), research support from Martin Kushner Schnur and Mr. and Mrs. Lawrence Feis (ES), IMH grant R01MH109180 (RS), Simons Collaboration on the Global Brain grant 542997 (RK and ES), Pew Scholarship in Biomedical Sciences (RK), and a CRCNS grant (ES and RK). Author contributions: O.M., G.T., R.K., and E.S. designed research; O.M., G.T., M.S.E., R.K., and E.S. performed research; O.M., G.T., R.K., and E.S. analyzed data; and O.M., G.T., R.K., and E.S. wrote the paper.
- Record Identifier
- 993263880703596
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