Learning to integrate parts for whole through correlated neural variability DOI Creative Commons
Zhichao Zhu, Yang Qi,

Wenlian Lu

et al.

PLoS Computational Biology, Journal Year: 2024, Volume and Issue: 20(9), P. e1012401 - e1012401

Published: Sept. 3, 2024

Neural activity in the cortex exhibits a wide range of firing variability and rich correlation structures. Studies on neural coding indicate that correlated can influence quality codes, either beneficially or adversely. However, mechanisms by which is transformed processed across populations to achieve meaningful computation remain largely unclear. Here we propose theory covariance with spiking neurons offers unifying perspective representation noise. We employ recently proposed computational framework known as moment network resolve nonlinear coupling task-driven approach constructing models for performing covariance-based perceptual tasks. In particular, demonstrate how information initially encoded entirely within upstream neurons’ be passed, near-lossless manner, mean rate downstream neurons, turn used inform inference. The addresses an important question brain extracts from noisy sensory stimuli generate stable whole indicates more direct role plays cortical processing.

Language: Английский

Mapping conservative Fokker–Planck entropy in neural systems DOI Creative Commons
Erik D. Fagerholm, Gregory Scott, Robert Leech

et al.

Journal of Physics D Applied Physics, Journal Year: 2025, Volume and Issue: 58(14), P. 145401 - 145401

Published: Feb. 18, 2025

Abstract Mapping the flow of information through networks brain remains one most important challenges in computational neuroscience. In certain cases, this can be approximated by considering just two contributing factors—a predictable drift and a randomized diffusion. We show here that uncertainty associated with such drift-diffusion process calculated terms entropy Fokker–Planck equation. This entropic evolution comprises components: an irreversible spread always increases over time reversible current increase or decrease locally within system. apply dynamic decomposition to two-photon imaging data collected murine visual cortex. Our analysis reveals maps conserved emanating from lateral medial, anterolateral, rostrolateral regions toward primary cortex (V1). These results highlight role V1 as sink, facilitating redistribution throughout findings offer new insights into hierarchical organization cortical processing provide framework for exploring dynamics complex dynamical systems.

Language: Английский

Citations

0
Learning to integrate parts for whole through correlated neural variability DOI Creative Commons
Zhichao Zhu, Yang Qi,

Wenlian Lu

et al.

PLoS Computational Biology, Journal Year: 2024, Volume and Issue: 20(9), P. e1012401 - e1012401

Published: Sept. 3, 2024

Neural activity in the cortex exhibits a wide range of firing variability and rich correlation structures. Studies on neural coding indicate that correlated can influence quality codes, either beneficially or adversely. However, mechanisms by which is transformed processed across populations to achieve meaningful computation remain largely unclear. Here we propose theory covariance with spiking neurons offers unifying perspective representation noise. We employ recently proposed computational framework known as moment network resolve nonlinear coupling task-driven approach constructing models for performing covariance-based perceptual tasks. In particular, demonstrate how information initially encoded entirely within upstream neurons’ be passed, near-lossless manner, mean rate downstream neurons, turn used inform inference. The addresses an important question brain extracts from noisy sensory stimuli generate stable whole indicates more direct role plays cortical processing.

Language: Английский

Citations

1