Presentation Information

1:40 PM - 2:20 PM JST(4:40 AM - 5:20 AM UTC)

[8p-N102-2]Modeling the brain's representation of abstraction and probability

〇Toyoizumi Taro¹ (1.RIKEN CBS)

Keywords:

computational neuroscience,theoretical brain science

Adaptive behavior relies on activity-dependent synaptic plasticity to sculpt internal models of the world. I introduce two complementary frameworks for how the brain encodes abstraction and probability. Regarding abstract representations, we first propose a three-factor plasticity rule for nonlinear dimensionality reduction in a three-layer network inspired by the Drosophila olfactory circuit. This rule approximates the t-SNE algorithm and reproduces experimental findings from fly studies. Next, we describe a dual-pathway hippocampal model—featuring a dense, direct input path and a sparse, indirect input path—where modulation of inhibitory tone toggles recall between abstract categories and concrete exemplars. Regarding probabilistic representations, we exploit chaotic fluctuations in a recurrent network to perform Bayesian posterior sampling. Trained with biologically plausible learning on a cue-integration task, the network reliably approximates target distributions despite chaos-induced sensitivity to initial conditions. Together, these models illustrate how synaptic plasticity and neural dynamics could underlie abstract and probabilistic internal representations.
* K. Yoshida and T. Toyoizumi, Sci. Adv. 11, eadp9048 (2025). DOI:10.1126/sciadv.adp9048
A biological model of nonlinear dimensionality reduction
* L. Kang and T. Toyoizumi, Nat. Commun. 15, 647 (2024). DOI:10.1038/s41467-024-44877-0
Distinguishing examples while building concepts in hippocampal and artificial networks.
* Y. Terada and T. Toyoizumi, PNAS 121:18, e2312992121 (2024). DOI:10.1073/pnas.2312992121
Chaotic neural dynamics facilitate probabilistic computations through sampling.

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