The structures and functions of correlations in neural population codes

Nature reviews. Neuroscience, Journal Year: 2022, Volume and Issue: 23(9), P. 551 - 567

Published: June 22, 2022

Language: Английский

---

Interpreting neural computations by examining intrinsic and embedding dimensionality of neural activity

Current Opinion in Neurobiology, Journal Year: 2021, Volume and Issue: 70, P. 113 - 120

Published: Sept. 17, 2021

Language: Английский

---

Probing neural codes with two-photon holographic optogenetics

Nature Neuroscience, Journal Year: 2021, Volume and Issue: 24(10), P. 1356 - 1366

Published: Aug. 16, 2021

Language: Английский

---

Natural behavior is the language of the brain

Current Biology, Journal Year: 2022, Volume and Issue: 32(10), P. R482 - R493

Published: May 1, 2022

Language: Английский

---

Ultraflexible electrode arrays for months-long high-density electrophysiological mapping of thousands of neurons in rodents

Nature Biomedical Engineering, Journal Year: 2022, Volume and Issue: 7(4), P. 520 - 532

Published: Oct. 3, 2022

Language: Английский

---

Facemap: a framework for modeling neural activity based on orofacial tracking

Nature Neuroscience, Journal Year: 2023, Volume and Issue: 27(1), P. 187 - 195

Published: Nov. 20, 2023

Recent studies in mice have shown that orofacial behaviors drive a large fraction of neural activity across the brain. To understand nature and function these signals, we need better computational models to characterize relate them activity. Here developed Facemap, framework consisting keypoint tracker deep network encoder for predicting Our algorithm tracking mouse was more accurate than existing pose estimation tools, while processing speed several times faster, making it powerful tool real-time experimental interventions. The Facemap easy adapt data from new labs, requiring as few 10 annotated frames near-optimal performance. We used keypoints inputs which predicts ~50,000 simultaneously-recorded neurons and, visual cortex, doubled amount explained variance compared previous methods. Using this model, found neuronal clusters were well predicted behavior spatially spread out cortex. also behavioral features model had stereotypical, sequential dynamics not reversible time. In summary, provides stepping stone toward understanding brain-wide signals their relation behavior.

Language: Английский

---

Decision-making dynamics are predicted by arousal and uninstructed movements

Cell Reports, Journal Year: 2024, Volume and Issue: 43(2), P. 113709 - 113709

Published: Jan. 26, 2024

During sensory-guided behavior, an animal's decision-making dynamics unfold through sequences of distinct performance states, even while stimulus-reward contingencies remain static. Little is known about the factors that underlie these changes in task performance. We hypothesize can be predicted by externally observable measures, such as uninstructed movements and arousal. Here, using computational modeling visual auditory data from mice, we uncovered lawful relationships between transitions strategic states arousal movements. Using hidden Markov models applied to behavioral choices during sensory discrimination tasks, find animals fluctuate minutes-long optimal, sub-optimal, disengaged states. Optimal state epochs are intermediate levels, reduced variability, pupil diameter movement. Our results demonstrate behaviors predict optimal suggest mice regulate their

Language: Английский

---

Cellpose3: one-click image restoration for improved cellular segmentation

Nature Methods, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 12, 2025

Language: Английский

---

Overwriting an instinct: Visual cortex instructs learning to suppress fear responses

Science, Journal Year: 2025, Volume and Issue: 387(6734), P. 682 - 688

Published: Feb. 6, 2025

Fast instinctive responses to environmental stimuli can be crucial for survival but are not always optimal. Animals adapt their behavior and suppress reactions, the neural pathways mediating such ethologically relevant forms of learning remain unclear. We found that posterolateral higher visual areas (plHVAs) escapes from innate threats through a top-down pathway ventrolateral geniculate nucleus (vLGN). plHVAs no longer necessary after learning; instead, learned relies on plasticity within vLGN populations exert inhibitory control over escape responses. neurons receiving input enhance threat during endocannabinoid-mediated long-term suppression inputs. thus reveal detailed circuit, cellular, synaptic mechanisms underlying experience-dependent fear

Language: Английский

---

Learning produces an orthogonalized state machine in the hippocampus

Nature, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 12, 2025

Abstract Cognitive maps confer animals with flexible intelligence by representing spatial, temporal and abstract relationships that can be used to shape thought, planning behaviour. have been observed in the hippocampus 1 , but their algorithmic form learning mechanisms remain obscure. Here we large-scale, longitudinal two-photon calcium imaging record activity from thousands of neurons CA1 region while mice learned efficiently collect rewards two subtly different linear tracks virtual reality. Throughout learning, both animal behaviour hippocampal neural progressed through multiple stages, gradually revealing improved task representation mirrored behavioural efficiency. The process involved progressive decorrelations initially similar within across tracks, ultimately resulting orthogonalized representations resembling a state machine capturing inherent structure task. This decorrelation was driven individual acquiring task-state-specific responses (that is, ‘state cells’). Although various standard artificial networks did not naturally capture these dynamics, clone-structured causal graph, hidden Markov model variant, uniquely reproduced final states trajectory seen animals. cellular population dynamics constrain underlying cognitive map formation hippocampus, pointing inference as fundamental computational principle, implications for biological intelligence.

Language: Английский

---