ArticlePLoS computational biology2020
Randomly connected networks generate emergent selectivity and predict decoding properties of large populations of neurons.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Integrated Ising model with global inhibition for decision-making.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Article
- Out of the single-neuron straitjacket: Neurons within assemblies change selectivity and their reconfiguration underlies dynamic coding.The Journal of physiology · 2025Article
- Integrated Ising Model with global inhibition for decision making.bioRxiv : the preprint server for biology · 2024Article
- Emergence of complex oscillatory dynamics in the neuronal networks with long activity time of inhibitory synapses.iScience · 2024Article
- Article
- Latent dynamics of primary sensory cortical population activity structured by fluctuations in the local field potential.Frontiers in computational neuroscience · 2024Article
- Article
- Multitasking via baseline control in recurrent neural networks.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Choice selective inhibition drives stability and competition in decision circuits.Nature communications · 2023Article
- Inhibitory control in neuronal networks relies on the extracellular matrix integrity.Cellular and molecular life sciences : CMLS · 2021Article
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Authors and funding
2 authors.
Funding
Abstract
Modern recording methods enable sampling of thousands of neurons during the performance of behavioral tasks, raising the question of how recorded activity relates to theoretical models. In the context of decision making, functional connectivity between choice-selective cortical neurons was recently reported. The straightforward interpretation of these data suggests the existence of selective pools of inhibitory and excitatory neurons. Computationally investigating an alternative mechanism for these experimental observations, we find that a randomly connected network of excitatory and inhibitory neurons generates single-cell selectivity, patterns of pairwise correlations, and the same ability of excitatory and inhibitory populations to predict choice, as in experimental observations. Further, we predict that, for this task, there are no anatomically defined subpopulations of neurons representing choice, and that choice preference of a particular neuron changes with the details of the task. We suggest that distributed stimulus selectivity and functional organization in population codes could be emergent properties of randomly connected networks.
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