Efficient coding explains neural response homeostasis and stimulus-specific adaptation DOI Creative Commons
Edward Young, Yashar Ahmadian

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: Nov. 1, 2023

Abstract In the absence of adaptation, average firing rate neurons would rise or drop when changes in environment make their preferred stimuli more less prevalent. However, by adjusting responsiveness neurons, adaptation can yield homeostasis and stabilise rates at fixed levels, despite stimulus statistics. sensory cortex, is typically also specific, that reduce to over-represented stimuli, but maintain even increase far from ones. Here, we present a normative explanation grounded efficient coding principle, showing this yields an optimal trade-off between fidelity metabolic cost neural firing. Unlike previous theories, formulate problem computation-agnostic manner, enabling our framework apply periphery. We then general Distributed Distributional Codes, specific computational theory representations serving Bayesian inference. demonstrate how homeostatic coding, combined with such representations, provides for stimulus-specific widely observed across brain, scheme be accomplished divisive normalisation adaptive weights. Further, develop model within framework, fitting it previously published experimental data, quantitatively account measures adaption primary visual cortex.

Language: Английский

Excitation creates a distributed pattern of cortical suppression due to varied recurrent input DOI Creative Commons
Jonathan F. O’Rawe, Zhishang Zhou, Anna J. Li

et al.

Neuron, Journal Year: 2023, Volume and Issue: 111(24), P. 4086 - 4101.e5

Published: Oct. 20, 2023

Language: Английский

Citations

13
Cellular-resolution optogenetics reveals attenuation-by-suppression in visual cortical neurons DOI Creative Commons
Paul K. LaFosse, Zhishang Zhou, Jonathan F. O’Rawe

et al.

Proceedings of the National Academy of Sciences, Journal Year: 2024, Volume and Issue: 121(45)

Published: Nov. 1, 2024

The relationship between neurons' input and spiking output is central to brain computation. Studies in vitro anesthetized animals suggest that nonlinearities emerge cells' input-output (IO; activation) functions as network activity increases, yet how neurons transform inputs vivo has been unclear. Here, we characterize cortical principal activation awake mice using two-photon optogenetics. We deliver fixed at the soma while varies with sensory stimuli. find responses optogenetic are nearly unchanged excited, reflecting a linear response regime above resting point. In contrast, dramatically attenuated by suppression. This attenuation powerful means filter arriving suppressed cells, privileging other excited neurons. These results have two major implications. First, somatic neural accord used recent machine learning systems. Second, IO can inputs-not only do stimuli change outputs, but these changes also affect input, attenuating some leaving others unchanged.

Language: Английский

Citations

4
Ensemble priming via competitive inhibition: local mechanisms of sensory context storage and deviance detection in the neocortical column DOI Creative Commons
Ryan Thorpe, Christopher I. Moore, Stephanie R. Jones

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 9, 2025

The process by which neocortical neurons and circuits amplify their response to an unexpected change in stimulus, often referred as deviance detection (DD), has long been thought be the product of specialized cell types and/or routing between mesoscopic brain areas. Here, we explore a different theory, whereby DD emerges from local network-level interactions within column. We propose that deviance-driven neural dynamics can emerge through ensembles have fundamental inhibitory motif: competitive inhibition reciprocally connected under modulation feed-forward selective (dis)inhibition. Using this framework, were able simulate variety phenomena pertaining experimentally observed shifts tuning across neurons, time, stimulus history. Anchoring our approach phenomena, used computation modeling two networks vastly levels biophysical detail test hypotheses on emergent robustness underlying connectivity parameters. With number corollary predictions tested future vivo studies, show ensemble priming via (dis)inhibition acts mechanism for sensory context storage does not require input other areas-a novel theoretical paradigm resolves previously confounding aspects encoding predictive processing neocortex.

Language: Английский

Citations

0
Impact of Local Connectivity Patterns on Excitatory-Inhibitory Network Dynamics DOI Creative Commons
Yuxiu Shao, David Dahmen, Stefano Recanatesi

et al.

PRX Life, Journal Year: 2025, Volume and Issue: 3(2)

Published: May 20, 2025

Networks of excitatory and inhibitory (EI) neurons form a canonical circuit in the brain. Seminal theoretical results on dynamics such networks are based assumption that synaptic strengths depend type they connect, but otherwise statistically independent. Recent physiology datasets, however, highlight prominence specific connectivity patterns go well beyond what is expected from independent connections. While decades influential research have demonstrated strong role basic EI cell structure, extent to which additional features influence remains be fully determined. Here we examine effects pairwise motifs linear excitatory-inhibitory using an analytical framework approximates terms low-rank structures. This approximation mathematical derivation dominant eigenvalues matrix, it predicts impact responses external inputs their interactions with cell-type structure. Our reveal particular pattern connectivity, namely chain motifs, much stronger eigenmodes than other motifs. In particular, over-representation induces positive eigenvalue inhibition-dominated networks, generates potential instability requires revisiting classical excitation-inhibition balance criteria. Examining inputs, show can own induce paradoxical responses, where increased input leads decrease activity due recurrent feedback. These findings direct implications for interpretation experiments optogenetic perturbations measured used infer dynamical regime cortical circuits. Published by American Physical Society 2025

Language: Английский

Citations

0
Efficient coding explains neural response homeostasis and stimulus-specific adaptation DOI Open Access
Edward Young, Yashar Ahmadian

Published: March 4, 2025

In the absence of adaptation, average firing rate neurons would rise or drop when changes in environment make their preferred stimuli more less prevalent. However, by adjusting responsiveness neurons, adaptation can yield homeostasis and stabilise rates at fixed levels, despite stimulus statistics. sensory cortex, is typically also specific, that reduce to over-represented stimuli, but maintain even increase far from ones. Here, we present a normative explanation grounded efficient coding principle, showing this yields an optimal trade-off between fidelity metabolic cost neural firing. Unlike previous theories, formulate problem computation-agnostic manner, enabling our framework apply periphery. We then general Distributed Distributional Codes, specific computational theory representations serving Bayesian inference. demonstrate how homeostatic coding, combined with such representations, provides for stimulus-specific widely observed across brain, scheme be accomplished divisive normalisation adaptive weights. Further, develop model within framework, fitting it previously published experimental data, quantitatively account measures adaption primary visual cortex.

Language: Английский

Citations

0
Efficient coding explains neural response homeostasis and stimulus-specific adaptation DOI Open Access
Edward Young, Yashar Ahmadian

Published: March 4, 2025

In the absence of adaptation, average firing rate neurons would rise or drop when changes in environment make their preferred stimuli more less prevalent. However, by adjusting responsiveness neurons, adaptation can yield homeostasis and stabilise rates at fixed levels, despite stimulus statistics. sensory cortex, is typically also specific, that reduce to over-represented stimuli, but maintain even increase far from ones. Here, we present a normative explanation grounded efficient coding principle, showing this yields an optimal trade-off between fidelity metabolic cost neural firing. Unlike previous theories, formulate problem computation-agnostic manner, enabling our framework apply periphery. We then general Distributed Distributional Codes, specific computational theory representations serving Bayesian inference. demonstrate how homeostatic coding, combined with such representations, provides for stimulus-specific widely observed across brain, scheme be accomplished divisive normalisation adaptive weights. Further, develop model within framework, fitting it previously published experimental data, quantitatively account measures adaption primary visual cortex.

Language: Английский

Citations

0
Behavioral optogenetics in nonhuman primates; a psychological perspective DOI Creative Commons
Arash Afraz

Current Research in Neurobiology, Journal Year: 2023, Volume and Issue: 5, P. 100101 - 100101

Published: Jan. 1, 2023

Optogenetics has been a promising and developing technology in systems neuroscience throughout the past decade. It difficult though to reliably establish potential behavioral effects of optogenetic perturbation neural activity nonhuman primates. This poses challenge on future optogenetics humans as concepts need be developed primates first. Here, I briefly summarize viable approaches taken improve primate optogenetics, then focus one approach: improvements measurement behavior. bring examples from visual behavior show how choice method might conceal large effects. will discuss "cortical detection" task detail an example sensitive that can record cortical stimulation with high fidelity. Finally, encouraged by rich scientific landscape ahead invite developers chronically implantable devices designed for simultaneous recording intervention

Language: Английский

Citations

7
Single cell optogenetics reveals attenuation-by-suppression in visual cortical neurons DOI Creative Commons
Paul K. LaFosse, Zhishang Zhou, Jonathan F. O’Rawe

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: Sept. 14, 2023

Abstract The relationship between neurons’ input and spiking output is central to brain computation. Studies in vitro anesthetized animals suggest nonlinearities emerge cells’ input-output (activation) functions as network activity increases, yet how neurons transform inputs vivo has been unclear. Here, we characterize cortical principal activation awake mice using two-photon optogenetics. We deliver fixed at the soma while varies with sensory stimuli. find responses optogenetic are nearly unchanged excited, reflecting a linear response regime above resting point. In contrast, dramatically attenuated by suppression. This attenuation powerful means filter arriving suppressed cells, privileging other excited neurons. These results have two major implications. First, somatic neural accord used recent machine learning systems. Second, IO can — not only do stimuli change outputs, but these changes also affect input, attenuating some leaving others unchanged. Significance statement How their into outputs fundamental building block of Past studies measured (IO) or states. measure intact brain, where ongoing influence input. Using state-of-the-art methods precise near cell body, soma, discover supralinear-to-linear function, contrary previous findings threshold-linear, strongly saturating, power law functions. function shape allows decrease to, filter, they below firing rates, computation term attenuation-by-suppression.

Language: Английский

Citations

6
Brain state transitions primarily impact the spontaneous rate of slow-firing neurons DOI Creative Commons

Bradley Dearnley,

Melissa M. Jones, Martynas Dervinis

et al.

Cell Reports, Journal Year: 2023, Volume and Issue: 42(10), P. 113185 - 113185

Published: Sept. 28, 2023

The spontaneous firing of neurons is modulated by brain state. Here, we examine how such modulation impacts the overall distribution rates in neuronal populations neocortical, hippocampal, and thalamic areas across natural pharmacologically driven state transitions. We report that all examined combinations area transition category, structure rate similar, with almost fast-firing experiencing proportionally weak modulation, while slow-firing exhibit high inter-neuron variability magnitude, leading to a stronger on average. further demonstrate this linked left-skewed logarithmic scale recapitulated bivariate log-gamma, but not Gaussian, distributions. Our findings indicate preconfigured log-rate rigid long left tail malleable generic property forebrain circuits.

Language: Английский

Citations

4
Exact linear theory of perturbation response in a space- and feature-dependent cortical circuit model DOI Creative Commons

H. Chau,

Kenneth D. Miller, Agostina Palmigiano

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 28, 2024

What are the principles that govern responses of cortical networks to their inputs and emergence these from recurrent connectivity? Recent experiments have probed questions by measuring two-photon optogenetic perturbations single cells in mouse primary visual cortex. A robust theoretical framework is needed determine implications for recurrence. Here we propose a novel analytical approach: formulation dependence cell-type-specific connectivity on spatial distance yields an exact solution linear perturbation response model with multiple cell types space- feature-dependent connectivity. Importantly unlike previous approaches, valid regimes strong as well weak intra-cortical coupling. Analysis reveals structure implied various features single-cell responses, such surprisingly narrow radius nearby excitation beyond which inhibition dominates, number transitions between mean thereafter, feature preferences. Comparison results existing data constraints connection strengths tuning dependence. Finally, provide experimental predictions regarding inhibitory neurons modulation neuronal gain; latter can explain observed differences feature-tuning presence vs. absence stimuli.

Language: Английский

Citations

1