Is the core function of orbitofrontal cortex to signal values or make predictions? DOI
Jingfeng Zhou, Matthew P.H. Gardner, Geoffrey Schoenbaum

et al.

Current Opinion in Behavioral Sciences, Journal Year: 2021, Volume and Issue: 41, P. 1 - 9

Published: Feb. 25, 2021

Language: Английский

The transition to compulsion in addiction DOI
Christian Lüscher, Trevor W. Robbins, Barry J. Everitt

et al.

Nature reviews. Neuroscience, Journal Year: 2020, Volume and Issue: 21(5), P. 247 - 263

Published: March 30, 2020

Language: Английский

Citations

382
The role of population structure in computations through neural dynamics DOI
Alexis Dubreuil, Adrian Valente, Manuel Beirán

et al.

Nature Neuroscience, Journal Year: 2022, Volume and Issue: 25(6), P. 783 - 794

Published: June 1, 2022

Language: Английский

Citations

151
The geometry of cortical representations of touch in rodents DOI
Ramon Nogueira, Chris C. Rodgers, Randy M. Bruno

et al.

Nature Neuroscience, Journal Year: 2023, Volume and Issue: 26(2), P. 239 - 250

Published: Jan. 9, 2023

Language: Английский

Citations

52
Resolving the prefrontal mechanisms of adaptive cognitive behaviors: A cross-species perspective DOI Creative Commons
Ileana L. Hanganu‐Opatz, Thomas Klausberger, Torfi Sigurdsson

et al.

Neuron, Journal Year: 2023, Volume and Issue: 111(7), P. 1020 - 1036

Published: April 1, 2023

Language: Английский

Citations

51
Mixed selectivity: Cellular computations for complexity DOI Creative Commons
Kay M. Tye, Earl K. Miller, Felix H. Taschbach

et al.

Neuron, Journal Year: 2024, Volume and Issue: 112(14), P. 2289 - 2303

Published: May 9, 2024

The property of mixed selectivity has been discussed at a computational level and offers strategy to maximize power by adding versatility the functional role each neuron. Here, we offer biologically grounded implementational-level mechanistic explanation for in neural circuits. We define pure, linear, nonlinear discuss how these response properties can be obtained simple Neurons that respond multiple, statistically independent variables display selectivity. If their activity expressed as weighted sum, then they exhibit linear selectivity; otherwise, Neural representations based on diverse are high dimensional; hence, confer enormous flexibility downstream readout circuit. However, circuit cannot possibly encode all possible mixtures simultaneously, this would require combinatorially large number neurons. Gating mechanisms like oscillations neuromodulation solve problem dynamically selecting which transmitted readout.

Language: Английский

Citations

33
Evolving schema representations in orbitofrontal ensembles during learning DOI
Jingfeng Zhou, Chunying Jia,

Marlian Montesinos-Cartagena

et al.

Nature, Journal Year: 2020, Volume and Issue: 590(7847), P. 606 - 611

Published: Dec. 23, 2020

Language: Английский

Citations

115
Behavior- and Modality-General Representation of Confidence in Orbitofrontal Cortex DOI Creative Commons
Paul Masset, Torben Ott, Armin Lak

et al.

Cell, Journal Year: 2020, Volume and Issue: 182(1), P. 112 - 126.e18

Published: June 5, 2020

Language: Английский

Citations

103
Reinforcement biases subsequent perceptual decisions when confidence is low, a widespread behavioral phenomenon DOI Creative Commons
Armin Lak, Emily Hueske, Junya Hirokawa

et al.

eLife, Journal Year: 2020, Volume and Issue: 9

Published: April 14, 2020

Learning from successes and failures often improves the quality of subsequent decisions. Past outcomes, however, should not influence purely perceptual decisions after task acquisition is complete since these are designed so that only sensory evidence determines correct choice. Yet, numerous studies report outcomes can bias decisions, causing spurious changes in choice behavior without improving accuracy. Here we show effects reward on principled: past rewards future choices specifically when previous was difficult hence decision confidence low. We identified this phenomenon six datasets four laboratories, across mice, rats, humans, modalities olfaction audition to vision. choice-updating strategy be explained by reinforcement learning models incorporating statistical into their teaching signals. Thus, mechanisms continually engaged produce systematic adjustments even well-learned order optimize an uncertain world.

Language: Английский

Citations

100
Transient and Persistent Representations of Odor Value in Prefrontal Cortex DOI Creative Commons
Peter Y. Wang, Cristian Boboilă, Matthew Chin

et al.

Neuron, Journal Year: 2020, Volume and Issue: 108(1), P. 209 - 224.e6

Published: Aug. 21, 2020

Language: Английский

Citations

89
Distinct temporal difference error signals in dopamine axons in three regions of the striatum in a decision-making task DOI Creative Commons
Iku Tsutsui‐Kimura, Hideyuki Matsumoto, Korleki Akiti

et al.

eLife, Journal Year: 2020, Volume and Issue: 9

Published: Dec. 21, 2020

Different regions of the striatum regulate different types behavior. However, how dopamine signals differ across striatal and regulates behaviors remain unclear. Here, we compared axon activity in ventral, dorsomedial, dorsolateral striatum, while mice performed a perceptual value-based decision task. Surprisingly, was similar all three areas. At glance, multiplexed variables such as stimulus-associated values, confidence, reward feedback at phases Our modeling demonstrates, however, that these modulations can be inclusively explained by moment-by-moment changes expected reward, is temporal difference error. A major between areas overall level responses: responses were positively shifted, lacking inhibitory to negative prediction errors. The differences put specific constraints on properties controlled regions.

Language: Английский

Citations

89