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

Correlations and Neuronal Population Information DOI
Adam Kohn, Ruben Coen-Cagli, Ingmar Kanitscheider

et al.

Annual Review of Neuroscience, Journal Year: 2016, Volume and Issue: 39(1), P. 237 - 256

Published: May 5, 2016

Brain function involves the activity of neuronal populations. Much recent effort has been devoted to measuring populations in different parts brain under various experimental conditions. Population patterns contain rich structure, yet many studies have focused on pairwise relationships between members a larger population-termed noise correlations. Here we review progress understanding how these correlations affect population information, information should be quantified, and what mechanisms may give rise As coding theory improved, it made clear that some forms correlation are more important for than others. We argue this is critical lesson those interested responses generally: Descriptions motivated by linked well-specified function. Within context, offer suggestions where current theoretical frameworks fall short.

Language: Английский

Citations

390
Large-scale neural recordings call for new insights to link brain and behavior DOI
Anne E. Urai, Brent Doiron, Andrew M. Leifer

et al.

Nature Neuroscience, Journal Year: 2022, Volume and Issue: 25(1), P. 11 - 19

Published: Jan. 1, 2022

Language: Английский

Citations

259
The spatial structure of correlated neuronal variability DOI
Robert Rosenbaum, Matthew A. Smith, Adam Kohn

et al.

Nature Neuroscience, Journal Year: 2016, Volume and Issue: 20(1), P. 107 - 114

Published: Oct. 31, 2016

Language: Английский

Citations

244
Inhibitory Plasticity: Balance, Control, and Codependence DOI Open Access
Guillaume Hennequin, Everton J. Agnes, Tim P. Vogels

et al.

Annual Review of Neuroscience, Journal Year: 2017, Volume and Issue: 40(1), P. 557 - 579

Published: June 9, 2017

Inhibitory neurons, although relatively few in number, exert powerful control over brain circuits. They stabilize network activity the face of strong feedback excitation and actively engage computations. Recent studies reveal importance a precise balance inhibition neural circuits, which often requires exquisite fine-tuning inhibitory connections. We review synaptic plasticity its roles shaping both feedforward control. discuss necessity complex, codependent mechanisms to build nontrivial, functioning networks, we end by summarizing experimental evidence such interactions.

Language: Английский

Citations

234
Inhibitory Interneurons Regulate Temporal Precision and Correlations in Cortical Circuits DOI Creative Commons
Jessica A. Cardin

Trends in Neurosciences, Journal Year: 2018, Volume and Issue: 41(10), P. 689 - 700

Published: Sept. 25, 2018

Language: Английский

Citations

224
Cell-specific restoration of stimulus preference after monocular deprivation in the visual cortex DOI
Tobias Rose, Juliane Jaepel, Mark Hübener

et al.

Science, Journal Year: 2016, Volume and Issue: 352(6291), P. 1319 - 1322

Published: June 9, 2016

Monocular deprivation evokes a prominent shift of neuronal responses in the visual cortex toward open eye, accompanied by functional and structural synaptic rearrangements. This is reversible, but it unknown whether recovery happens at level individual neurons or reflects population effect. We used ratiometric Ca(2+) imaging to follow activity same excitatory layer 2/3 mouse over months during repeated episodes ocular dominance (OD) plasticity. observed robust shifts eye most neurons. Nevertheless, these cells faithfully returned their pre-deprivation OD binocular recovery. Moreover, initial network correlation structure was largely recovered, suggesting that connectivity may be regained despite experience-dependent

Language: Английский

Citations

201
The Locus Coeruleus Is a Complex and Differentiated Neuromodulatory System DOI Creative Commons
Nelson K. Totah,

Ricardo M. Neves,

Stefano Panzeri

et al.

Neuron, Journal Year: 2018, Volume and Issue: 99(5), P. 1055 - 1068.e6

Published: Aug. 16, 2018

Language: Английский

Citations

169
Dysfunction of cortical GABAergic neurons leads to sensory hyper-reactivity in a Shank3 mouse model of ASD DOI
Qian Chen, Christopher A Deister, Xianhua Gao

et al.

Nature Neuroscience, Journal Year: 2020, Volume and Issue: 23(4), P. 520 - 532

Published: March 2, 2020

Language: Английский

Citations

154
What is the dynamical regime of cerebral cortex? DOI Creative Commons
Yashar Ahmadian, Kenneth D. Miller

Neuron, Journal Year: 2021, Volume and Issue: 109(21), P. 3373 - 3391

Published: Aug. 30, 2021

Language: Английский

Citations

116
The Dynamical Regime of Sensory Cortex: Stable Dynamics around a Single Stimulus-Tuned Attractor Account for Patterns of Noise Variability DOI Creative Commons
Guillaume Hennequin, Yashar Ahmadian, Daniel B. Rubin

et al.

Neuron, Journal Year: 2018, Volume and Issue: 98(4), P. 846 - 860.e5

Published: May 1, 2018

Correlated variability in cortical activity is ubiquitously quenched following stimulus onset, a stimulus-dependent manner. These modulations have been attributed to circuit dynamics involving either multiple stable states ("attractors") or chaotic activity. Here we show that qualitatively different dynamical regime, fluctuations about single, stimulus-driven attractor loosely balanced excitatory-inhibitory network (the stochastic "stabilized supralinear network"), best explains these modulations. Given the input/output functions of neurons, increased drive strengthens effective connectivity. This shifts balance from interactions amplify suppressive inhibitory feedback, quenching correlated around more strongly driven steady states. Comparing previously published and original data analyses, this mechanism, unlike previous proposals, uniquely accounts for spatial patterns fast temporal suppression. Specifying operating regime key understanding computations underlying perception.

Language: Английский

Citations

163