Choice selective inhibition drives stability and competition in decision circuits

Roach, James P.; Churchland, Anne K.; Engel, Tatiana A.

doi:10.1038/s41467-023-35822-8

Cited by 19 publications

(25 citation statements)

References 52 publications

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“…Several recent studies have implicated inhibitory processing in decision making, specifically with regard to the maintenance of the decision threshold (e.g., MacDonald et al, 2017;Roach et al, 2023). By this interpretation, in the present study choice learning might have led to reduced inhibitory control over action.…”

Section: Choice Learning Reduced Exponential Variability and The Deci...supporting

confidence: 47%

“…Alternatively, threshold has been associated with caution in performing challenging tasks under time pressure (Forstmann et al, 2008), a process that would depend on inhibitory control. Several recent studies have implicated inhibitory processing in decision making, specifically with regard to the maintenance of the decision threshold (e.g., MacDonald et al, 2017; Roach et al, 2023). By this interpretation, in the present study choice learning might have led to reduced inhibitory control over action.…”

Section: Discussionmentioning

confidence: 99%

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Learning to Choose: Behavioral Dynamics Underlying the Initial Acquisition of Decision Making

White,

Preston,

Swanson

et al. 2024

Preprint

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Current theories of decision making propose that decisions arise through competition between choice options. Computational models of the decision process estimate how quickly information about choice options is integrated and how much information is needed to trigger a choice. Experiments using this approach typically report data from well-trained participants. As such, we do not know how the decision process evolves as a decision-making task is learned for the first time. To address this gap, we used a behavioral design separating learning the value of choice options from learning to make choices. We trained male rats to respond to single visual stimuli with different reward values. Then, we trained them to make choices between pairs of stimuli. Initially, the rats responded more slowly when presented with choices. However, as they gained experience in making choices, this slowing reduced. Response slowing on choice trials persisted throughout the testing period. We found that it was specifically associated with increased exponential variability when the rats chose the higher value stimulus. Additionally, our analysis using drift diffusion modeling revealed that the rats required less information to make choices over time. Surprisingly, we observed reductions in the decision threshold after just a single session of choice learning. These findings provide new insights into the learning process of decision-making tasks. They suggest that the value of choice options and the ability to make choices are learned separately, and that experience plays a crucial role in improving decision-making performance.

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Section: Choice Learning Reduced Exponential Variability and The Deci...supporting

confidence: 47%

Section: Discussionmentioning

confidence: 99%

Learning to Choose: Behavioral Dynamics Underlying the Initial Acquisition of Decision Making

White,

Preston,

Swanson

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…To this end, we examined the connectivity between different subpopulations of excitatory neurons and PV neurons in the PFC module explicitly, by computing the mean connection strength between each pair of subpopulations. This analysis reveals that the excitatory rule neurons and PV rule neurons form a classic winner-take-all network architecture [35] with selective inhibitory populations [36, 37], where excitatory neurons preferring the same rule are more strongly connected, and also more strongly project to PV neurons preferring the same rule (Figure 3a). On the other hand, PV neurons project more strongly to both excitatory neurons and other PV neurons with the opposite rule preference (Figure 3a).…”

Section: Resultsmentioning

confidence: 99%

Flexible gating between subspaces in a neural network model of internally guided task switching

Liu,

Wang

2023

Preprint

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Behavioral flexibility relies on the brain's ability to switch rapidly between multiple tasks, even when the task rule is not explicitly cued but must be inferred through trial and error. The underlying neural circuit mechanism remains poorly understood. We investigated recurrent neural networks (RNNs) trained to perform an analog of the classic Wisconsin Card Sorting Test. The networks consist of two modules responsible for rule representation and sensorimotor mapping, respectively, where each module is comprised of a circuit with excitatory neurons and three major types of inhibitory neurons. We found that rule representation by self-sustained persistent activity across trials, error monitoring and gated sensorimotor mapping emerged from training. Systematic dissection of trained RNNs revealed a detailed circuit mechanism. The networks' dynamical trajectories for different rules reside in separate subspaces of population activity; they become virtually identical and performance was reduced to chance level when dendrite-targeting somatostatin-expressing interneurons were silenced, demonstrating that rule-based gating critically depends on the disinhibitory motif.

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“…Such circuit-based models include mutual inhibition models (Usher & McClelland, 2001), feedforward inhibition models (Ditterich et al, 2003), and pooled inhibition models (Wang, 2002). These basic circuit models have been extended to explain a number of different decision-making experiments with recordings in different regions where neurons show persistent activity encoding accumulated evidence (Engel & Wang, 2011; Lam et al, 2022; Lo & Wang, 2006; Machens et al, 2005; Roach et al, 2023; Wong & Wang, 2006).…”

Section: Introductionmentioning

confidence: 99%

Neural circuit models for evidence accumulation through choice-selective sequences

Brown,

Cho,

Bolkan

et al. 2023

Preprint

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Decision making involves accumulating evidence for or against available options. Traditional circuit models of evidence accumulation generate firing rates that ramp with evidence. However, recent decision-making experiments in rodents have observed neurons across the brain that fire sequentially, rather than persistently, with the subset of neurons in this sequence depending on the animal's choice. We develop two new candidate circuit models that produce such choice-selective sequences and make competing predictions about the nature of evidence encoding. The first model consists of two chains of neurons, where the location along the chain represents the animal's position and the relative amplitude of firing in the two chains represents accumulated evidence. The second model consists of a plane of neurons, with the set of active neurons in this plane encoding evidence and position. The models make distinct experimental predictions for the shapes of neuronal tuning curves and responses to localized optogenetic stimulations. More generally, this work demonstrates how graded-value information may be precisely accumulated within and transferred between neural populations, a set of computations fundamental to many cognitive operations.

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Choice selective inhibition drives stability and competition in decision circuits

Cited by 19 publications

References 52 publications

Learning to Choose: Behavioral Dynamics Underlying the Initial Acquisition of Decision Making

Learning to Choose: Behavioral Dynamics Underlying the Initial Acquisition of Decision Making

Flexible gating between subspaces in a neural network model of internally guided task switching

Neural circuit models for evidence accumulation through choice-selective sequences

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