ArticleProceedings of the National Academy of Sciences of the United States of America2021
Nonlinear convergence boosts information coding in circuits with parallel outputs.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Article
- Convergent motifs of early olfactory processing are recapitulated by layer-wise efficient coding.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Nonlinear integration of sensory inputs and behavioral state by a single neuron in C. elegans.Current biology : CB · 2025Article
- Convergent motifs of early olfactory processing are recapitulated by layer-wise efficient coding.bioRxiv : the preprint server for biology · 2025Article
- Temporal resolution of spike coding in feedforward networks with signal convergence and divergence.PLoS computational biology · 2025Article
- Non-negative connectivity causes bow-tie architecture in neural circuits.Frontiers in neural circuits · 2025Article
- Review
- Efficient population coding depends on stimulus convergence and source of noise.PLoS computational biology · 2021Article
- Brains are Probabilistic, Electrophysiologically Intricate and Triune: A Biased- Random Walk Perspective on Computational Neuroscience.International journal of psychological researchArticle
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3 authors.
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Abstract
Neural circuits are structured with layers of converging and diverging connectivity and selectivity-inducing nonlinearities at neurons and synapses. These components have the potential to hamper an accurate encoding of the circuit inputs. Past computational studies have optimized the nonlinearities of single neurons, or connection weights in networks, to maximize encoded information, but have not grappled with the simultaneous impact of convergent circuit structure and nonlinear response functions for efficient coding. Our approach is to compare model circuits with different combinations of convergence, divergence, and nonlinear neurons to discover how interactions between these components affect coding efficiency. We find that a convergent circuit with divergent parallel pathways can encode more information with nonlinear subunits than with linear subunits, despite the compressive loss induced by the convergence and the nonlinearities when considered separately.
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