Learning probabilistic neural representations with randomly connected circuits Maoz, Ori Tkačik, Gašper Esteki, Mohamad Saleh Kiani, Roozbeh Schneidman, Elad ddc:570 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 model for neural codes that accurately estimates the likelihood of individual spiking patterns and has a straightforward, scalable, efficient, 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 with or better than that of state-of-the-art 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. National Academy of Sciences 2020 info:eu-repo/semantics/article doc-type:article text http://purl.org/coar/resource_type/c_6501 https://research-explorer.app.ist.ac.at/record/8698 https://research-explorer.app.ist.ac.at/download/8698/8713 Maoz O, Tkačik G, Esteki MS, Kiani R, Schneidman E. Learning probabilistic neural representations with randomly connected circuits. <i>Proceedings of the National Academy of Sciences of the United States of America</i>. 2020;117(40):25066-25073. doi:<a href="https://doi.org/10.1073/pnas.1912804117">10.1073/pnas.1912804117</a> eng info:eu-repo/semantics/altIdentifier/doi/10.1073/pnas.1912804117 info:eu-repo/semantics/altIdentifier/issn/00278424 info:eu-repo/semantics/altIdentifier/issn/10916490 info:eu-repo/semantics/altIdentifier/pmid/32948691 info:eu-repo/semantics/openAccess