Probing variability in a cognitive map using manifold inference from neural dynamics DOI Creative Commons
Ryan Low, Sam Lewallen, Dmitriy Aronov

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2018, Volume and Issue: unknown

Published: Sept. 16, 2018

Hippocampal neurons fire selectively in local behavioral contexts such as the position an environment or phase of a task, 1-3 and are thought to form cognitive map task-relevant variables. 1,4,5 However, their activity varies over repeated conditions, 6 different runs through same trials. Although widely observed across brain, 7-10 variability is not well understood, could reflect noise structure, encoding additional information. 6,11-13 Here, we introduce conceptual model explain terms underlying, population-level structure single-trial neural activity. To test this model, developed novel unsupervised learning algorithm incorporating temporal dynamics, order characterize population trajectory on nonlinear manifold—a space possible network states. The manifold’s captures correlations between relationships states, constraints arising from underlying architecture inputs. Using measurements time but no information about exogenous variables, recovered hippocampal manifolds during spatial non-spatial tasks rats. Manifolds were low-dimensional smoothly encoded task-related contained extra dimension reflecting beyond measured Consistent with our fired function overall state, fluctuations trials corresponded variation manifold. In particular, allowed system take trajectories despite conditions. Furthermore, temporarily decouple current conditions traverse neighboring manifold points corresponding past, future, nearby Our results suggest that trial-to-trial hippocampus structured, may operation internal processes. well-suited for organizing support memory, 1,5,14 planning, 12,15,16 reinforcement learning. 17,18 general, approach find broader use probing organization computational role circuit dynamics other brain regions.

Language: Английский

Context-dependent relationships between locus coeruleus firing patterns and coordinated neural activity in the anterior cingulate cortex DOI Creative Commons
Siddhartha Joshi, Joshua I. Gold

eLife, Journal Year: 2022, Volume and Issue: 11

Published: Jan. 7, 2022

Ascending neuromodulatory projections from the locus coeruleus (LC) affect cortical neural networks via release of norepinephrine (NE). However, exact nature these effects on activity patterns in vivo is not well understood. Here, we show that awake monkeys, LC activation associated with changes coordinated anterior cingulate cortex (ACC). These relationships, which are largely independent firing rates individual ACC neurons, depend type activation: pairwise correlations tend to be reduced when ongoing (baseline) increases but enhanced external events evoke transient responses. Both relationships covary pupil reflect and arousal. results suggest modulations information processing can result partly ongoing, context-dependent, arousal-related LC-NE system.

Language: Английский

Citations

50
Priority coding in the visual system DOI
Nicole C. Rust, Marlene R. Cohen

Nature reviews. Neuroscience, Journal Year: 2022, Volume and Issue: 23(6), P. 376 - 388

Published: April 11, 2022

Language: Английский

Citations

47
The neuron mixer and its impact on human brain dynamics DOI Creative Commons
Charlotte Luff, Robert L. Peach, Emma‐Jane Mallas

et al.

Cell Reports, Journal Year: 2024, Volume and Issue: 43(6), P. 114274 - 114274

Published: May 25, 2024

A signal mixer facilitates rich computation, which has been the building block of modern telecommunication. This frequency mixing produces new signals at sum and difference frequencies input signals, enabling powerful operations such as heterodyning multiplexing. Here, we report that a neuron is mixer. We found through ex vivo in whole-cell measurements neurons mix exogenous (controlled) endogenous (spontaneous) subthreshold membrane potential oscillations, producing oscillation frequencies, neural originates voltage-gated ion channels. Furthermore, demonstrate evident human brain activity associated with cognitive functions. electroencephalogram displays distinct clusters local inter-region conversion salient posterior alpha-beta oscillations into gamma-band regulates visual attention. Signal may enable individual to sculpt spectrum circuit utilize them for computational operations.

Language: Английский

Citations

9
Identification of excitatory-inhibitory links and network topology in large-scale neuronal assemblies from multi-electrode recordings DOI Creative Commons
Vito Paolo Pastore, Paolo Massobrio,

Aleksandar Godjoski

et al.

PLoS Computational Biology, Journal Year: 2018, Volume and Issue: 14(8), P. e1006381 - e1006381

Published: Aug. 27, 2018

Functional-effective connectivity and network topology are nowadays key issues for studying brain physiological functions pathologies. Inferring neuronal from electrophysiological recordings presents open challenges unsolved problems. In this work, we present a cross-correlation based method reliably estimating not only excitatory but also inhibitory links, by analyzing multi-unit spike activity large-scale networks. The is validated means of realistic simulations populations. New results related to functional estimation identification obtained experimental high-density (i.e., 4096 electrodes) microtransducer arrays coupled in vitro neural populations presented. Specifically, show that: (i) connections accurately identified cortical networks, providing that reasonable firing rate recording length achieved; (ii) small-world topology, with scale-free rich-club features obtained, on condition minimum number active sites available. procedure can be directly extended applied vivo multi-units recordings.

Language: Английский

Citations

75
Probing variability in a cognitive map using manifold inference from neural dynamics DOI Creative Commons
Ryan Low, Sam Lewallen, Dmitriy Aronov

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2018, Volume and Issue: unknown

Published: Sept. 16, 2018

Hippocampal neurons fire selectively in local behavioral contexts such as the position an environment or phase of a task, 1-3 and are thought to form cognitive map task-relevant variables. 1,4,5 However, their activity varies over repeated conditions, 6 different runs through same trials. Although widely observed across brain, 7-10 variability is not well understood, could reflect noise structure, encoding additional information. 6,11-13 Here, we introduce conceptual model explain terms underlying, population-level structure single-trial neural activity. To test this model, developed novel unsupervised learning algorithm incorporating temporal dynamics, order characterize population trajectory on nonlinear manifold—a space possible network states. The manifold’s captures correlations between relationships states, constraints arising from underlying architecture inputs. Using measurements time but no information about exogenous variables, recovered hippocampal manifolds during spatial non-spatial tasks rats. Manifolds were low-dimensional smoothly encoded task-related contained extra dimension reflecting beyond measured Consistent with our fired function overall state, fluctuations trials corresponded variation manifold. In particular, allowed system take trajectories despite conditions. Furthermore, temporarily decouple current conditions traverse neighboring manifold points corresponding past, future, nearby Our results suggest that trial-to-trial hippocampus structured, may operation internal processes. well-suited for organizing support memory, 1,5,14 planning, 12,15,16 reinforcement learning. 17,18 general, approach find broader use probing organization computational role circuit dynamics other brain regions.

Language: Английский

Citations

74