Uncertainty-modulated prediction errors in cortical microcircuits DOI Open Access
Katharina A. Wilmes, Mihai A. Petrovici, Shankar Sachidhanandam

и другие.

Опубликована: Сен. 27, 2024

Understanding the variability of environment is essential to function in everyday life. The brain must hence take uncertainty into account when updating its internal model world. basis for are prediction errors that arise from a difference between current and new sensory experiences. Although error neurons have been identified layer 2/3 diverse areas, how modulates these learning is, however, unclear. Here, we use normative approach derive should modulate postulate represent uncertainty-modulated (UPE). We further hypothesise circuit calculates UPE through subtractive divisive inhibition by different inhibitory cell types. By implementing calculation UPEs microcircuit model, show types can compute means variances stimulus distribution. With local activity-dependent plasticity rules, computations be learned context-dependently, allow upcoming stimuli their Finally, mechanism enables an organism optimise strategy via adaptive rates.

Язык: Английский

Uncertainty-modulated prediction errors in cortical microcircuits DOI Open Access
Katharina A. Wilmes, Mihai A. Petrovici, Shankar Sachidhanandam

и другие.

Опубликована: Янв. 22, 2025

Understanding the variability of environment is essential to function in everyday life. The brain must hence take uncertainty into account when updating its internal model world. basis for are prediction errors that arise from a difference between current and new sensory experiences. Although error neurons have been identified layer 2/3 diverse areas, how modulates these learning is, however, unclear. Here, we use normative approach derive should modulate postulate represent uncertainty-modulated (UPE). We further hypothesise circuit calculates UPE through subtractive divisive inhibition by different inhibitory cell types. By implementing calculation UPEs microcircuit model, show types can compute means variances stimulus distribution. With local activity-dependent plasticity rules, computations be learned context-dependently, allow upcoming stimuli their Finally, mechanism enables an organism optimise strategy via adaptive rates.

Язык: Английский

Процитировано

0
Uncertainty-modulated prediction errors in cortical microcircuits DOI Creative Commons
Katharina A. Wilmes, Mihai A. Petrovici, Shankar Sachidhanandam

и другие.

eLife, Год журнала: 2025, Номер 13

Опубликована: Июнь 5, 2025

Understanding the variability of environment is essential to function in everyday life. The brain must hence take uncertainty into account when updating its internal model world. basis for are prediction errors that arise from a difference between current and new sensory experiences. Although error neurons have been identified layer 2/3 diverse areas, how modulates these learning is, however, unclear. Here, we use normative approach derive should modulate postulate represent uncertainty-modulated (UPE). We further hypothesise circuit calculates UPE through subtractive divisive inhibition by different inhibitory cell types. By implementing calculation UPEs microcircuit model, show types can compute means variances stimulus distribution. With local activity-dependent plasticity rules, computations be learned context-dependently, allow upcoming stimuli their Finally, mechanism enables an organism optimise strategy via adaptive rates.

Язык: Английский

Процитировано

0
Uncertainty-modulated prediction errors in cortical microcircuits DOI Open Access
Katharina A. Wilmes, Mihai A. Petrovici, Shankar Sachidhanandam

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2023, Номер unknown

Опубликована: Май 12, 2023

Abstract Understanding the variability of environment is essential to function in everyday life. The brain must hence take uncertainty into account when updating its internal model world. basis for are prediction errors that arise from a difference between current and new sensory experiences. Although error neurons have been identified layer 2/3 diverse areas, how modulates these learning is, however, unclear. Here, we use normative approach derive should modulate postulate represent uncertainty-modulated (UPE). We further hypothesise circuit calculates UPE through subtractive divisive inhibition by different inhibitory cell types. By implementing calculation UPEs microcircuit model, show types can compute means variances stimulus distribution. With local activity-dependent plasticity rules, computations be learned context-dependently, allow upcoming stimuli their Finally, mechanism enables an organism optimise strategy via adaptive rates.

Язык: Английский

Процитировано

7
Uncertainty-modulated prediction errors in cortical microcircuits DOI Open Access
Katharina A. Wilmes, Mihai A. Petrovici, Shankar Sachidhanandam

и другие.

Опубликована: Фев. 27, 2024

Understanding the variability of environment is essential to function in everyday life. The brain must hence take uncertainty into account when updating its internal model world. basis for are prediction errors that arise from a difference between current and new sensory experiences. Although error neurons have been identified layer 2/3 diverse areas, how modulates these learning is, however, unclear. Here, we use normative approach derive should modulate postulate represent uncertainty-modulated (UPE). We further hypothesise circuit calculates UPE through subtractive divisive inhibition by different inhibitory cell types. By implementing calculation UPEs microcircuit model, show types can compute means variances stimulus distribution. With local activity-dependent plasticity rules, computations be learned context-dependently, allow upcoming stimuli their Finally, mechanism enables an organism optimise strategy via adaptive rates.

Язык: Английский

Процитировано

0
Computational models of intrinsic motivation for curiosity and creativity DOI
Sophia Becker, Alireza Modirshanechi, Wulfram Gerstner

и другие.

Behavioral and Brain Sciences, Год журнала: 2024, Номер 47

Опубликована: Янв. 1, 2024

Abstract We link Ivancovsky et al.'s novelty-seeking model (NSM) to computational models of intrinsically motivated behavior and learning. argue that dissociating different forms curiosity, creativity, memory based on the involvement distinct intrinsic motivations (e.g., surprise novelty) is essential empirically test conceptual claims NSM.

Язык: Английский

Процитировано

0
Neurons of Macaque Frontal Eye Field Signal Reward-Related Surprise DOI

Michael R. Shteyn,

Carl R. Olson

Journal of Neuroscience, Год журнала: 2024, Номер unknown, С. e0441242024 - e0441242024

Опубликована: Авг. 6, 2024

The frontal eye field (FEF) plays a well-established role in the control of visual attention. strength an FEF neuron's response to stimulus presented its receptive is enhanced if captures spatial attention by virtue salience. A can be rendered salient cognitive factors as well physical attributes. These include surprise. aim present experiment was determine whether surprise-induced salience would result visual-response FEF. Toward this end, we monitored neuronal activity two male monkeys while presenting first cue predicting with high probability that reward delivered at end trial good or bad (large small) and then announcing size impending certainty. second usually confirmed but occasionally violated expectation set up cue. Neurons responded more strongly when it than expectation. increase firing rate accompanied decrease spike-count correlation expected from capture Although both surprise induced firing, effects appeared arise distinct mechanisms indicated fact bad-surprise signal longer latency good-surprise signals varied independently across neurons.

Язык: Английский

Процитировано

0
Uncertainty-modulated prediction errors in cortical microcircuits DOI Open Access
Katharina A. Wilmes, Mihai A. Petrovici, Shankar Sachidhanandam

и другие.

Опубликована: Сен. 27, 2024

Understanding the variability of environment is essential to function in everyday life. The brain must hence take uncertainty into account when updating its internal model world. basis for are prediction errors that arise from a difference between current and new sensory experiences. Although error neurons have been identified layer 2/3 diverse areas, how modulates these learning is, however, unclear. Here, we use normative approach derive should modulate postulate represent uncertainty-modulated (UPE). We further hypothesise circuit calculates UPE through subtractive divisive inhibition by different inhibitory cell types. By implementing calculation UPEs microcircuit model, show types can compute means variances stimulus distribution. With local activity-dependent plasticity rules, computations be learned context-dependently, allow upcoming stimuli their Finally, mechanism enables an organism optimise strategy via adaptive rates.

Язык: Английский

Процитировано

0