A category-free neural population supports evolving demands during decision-making

Relating neural activity to behavior requires an understanding of how neural computations arise from the coordinated dynamics of distributed, recurrently connected neural populations. However, inferring the nature of recurrent dynamics from partial recordings of a neural circuit presents significant challenges. Here, we show that some of these challenges can be overcome by a fine-grained analysis of the dynamics of neural residuals, i.e. trial-by-trial variability around the mean neural population trajectory for a given task condition. Residual dynamics in macaque pre-frontal cortex (PFC) in a saccade-based perceptual decision-making task reveals recurrent dynamics that is time-dependent, but consistently stable, and implies that pronounced rotational structure in PFC trajectories during saccades are driven by inputs from upstream areas. The properties of residual dynamics restrict the possible contributions of PFC to decision-making and saccade generation, and suggest a path towards fully characterizing distributed neural computations with large-scale neural recordings and targeted causal perturbations.

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Tuning the speed-accuracy trade-off to maximize reward rate in multisensory decision-making

eLife ◽

10.7554/elife.06678 ◽

2015 ◽

Vol 4 ◽

Cited By ~ 29

Author(s):

Jan Drugowitsch ◽

Gregory C DeAngelis ◽

Dora E Angelaki ◽

Alexandre Pouget

Keyword(s):

Decision Making ◽

Time Pressure ◽

Human Subjects ◽

Neural Population ◽

Trade Off ◽

Reward Rate ◽

Trade Offs ◽

Sensory Modalities ◽

Effective Decision Making ◽

Speed Accuracy

For decisions made under time pressure, effective decision making based on uncertain or ambiguous evidence requires efficient accumulation of evidence over time, as well as appropriately balancing speed and accuracy, known as the speed/accuracy trade-off. For simple unimodal stimuli, previous studies have shown that human subjects set their speed/accuracy trade-off to maximize reward rate. We extend this analysis to situations in which information is provided by multiple sensory modalities. Analyzing previously collected data (<xref ref-type="bibr" rid="bib4">Drugowitsch et al., 2014</xref>), we show that human subjects adjust their speed/accuracy trade-off to produce near-optimal reward rates. This trade-off can change rapidly across trials according to the sensory modalities involved, suggesting that it is represented by neural population codes rather than implemented by slow neuronal mechanisms such as gradual changes in synaptic weights. Furthermore, we show that deviations from the optimal speed/accuracy trade-off can be explained by assuming an incomplete gradient-based learning of these trade-offs.

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An orderly single-trial organization of population dynamics in premotor cortex predicts behavioral variability

10.1101/376830 ◽

2018 ◽

Cited By ~ 1

Author(s):

Ziqiang Wei ◽

Hidehiko Inagaki ◽

Nuo Li ◽

Karel Svoboda ◽

Shaul Druckmann

Keyword(s):

Decision Making ◽

Brain Function ◽

Premotor Cortex ◽

Neural Correlates ◽

Neural Population ◽

Behavioral Variability ◽

Perceptual Decision Making ◽

Population Activity ◽

Long Time ◽

Dynamical Nature

AbstractAnimals are not simple input-output machines. Their responses to even very similar stimuli are variable. A key, long-standing question in neuroscience is understanding the neural correlates of such behavioral variability. To reveal these correlates, behavior and neural population must be related to one another on single trials. Such analysis is challenging due to the dynamical nature of brain function (e.g. decision making), neuronal heterogeneity and signal to noise difficulties. By analyzing population recordings from mouse frontal cortex in perceptual decision-making tasks, we show that an analysis approach tailored to the coarse grain features of the dynamics was able to reveal previously unrecognized structure in the organization of population activity. This structure was similar on error and correct trials, suggesting what may be the underlying circuit mechanisms, was able to predict multiple aspects of behavioral variability and revealed long time-scale modulation of population activity.

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Neural population dynamics underlying evidence accumulation in multiple rat brain regions

10.1101/2021.10.28.465122 ◽

2021 ◽

Author(s):

Brian DePasquale ◽

Jonathan W Pillow ◽

Carlos Brody

Keyword(s):

Decision Making ◽

Neural Activity ◽

Posterior Parietal Cortex ◽

Dorsal Striatum ◽

Brain Regions ◽

Neural Representation ◽

Neural Population ◽

Evidence Accumulation ◽

Posterior Parietal ◽

The Moment

Accumulating evidence in service of sensory decision making is a core cognitive function. However, previous work has focused either on the dynamics of neural activity during decision-making or on models of evidence accumulation governing behavior. We unify these two perspectives by introducing an evidence-accumulation framework that simultaneously describes multi-neuron population spiking activity and dynamic stimulus-driven behavior during sensory decision-making. We apply our method to behavioral choices and neural activity recorded from three brain regions - the posterior parietal cortex (PPC), the frontal orienting fields (FOF), and the anterior-dorsal striatum (ADS) - while rats performed a pulse-based accumulation task. The model accurately captures the relationship between stimuli and neural activity, the coordinated activity of neural populations, and the distribution of animal choices in response to the stimulus. Model fits show strikingly distinct accumulation models expressed within each brain region, and that all differ strongly from the accumulation strategy expressed at the level of choices. In particular, the FOF exhibited a suboptimal 'primacy' strategy, where early sensory evidence was favored. Including neural data in the model led to improved prediction of the moment-by-moment value of accumulated evidence and the intended-and ultimately made-choice of the animal. Our approach offers a window into the neural representation of accumulated evidence and provides a principled framework for incorporating neural responses into accumulation models.

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Auditory Decisions in the Supplementary Motor Area

10.1101/2020.10.20.347864 ◽

2020 ◽

Cited By ~ 1

Author(s):

Isaac Morán ◽

Javier Perez-Orive ◽

Jonathan Melchor ◽

Tonatiuh Figueroa ◽

Luis Lemus

Keyword(s):

Decision Making ◽

Rhesus Monkeys ◽

Supplementary Motor Area ◽

Sensory Information ◽

Motor Area ◽

Neural Population ◽

Auditory Stimuli ◽

Motor Programs ◽

The Brain ◽

Selection Of

AbstractIn human speech and communication across various species, recognizing and categorizing sounds is fundamental for the selection of appropriate behaviors. But how does the brain decide which action to perform based on sounds? We explored whether the premotor supplementary motor area (SMA), responsible for linking sensory information to motor programs, also accounts for auditory-driven decision making. To this end, we trained two rhesus monkeys to discriminate between numerous naturalistic sounds and words learned as target (T) or non-target (nT) categories. We demonstrated that the neural population is organized differently during the auditory and the movement periods of the task, implying that it is performing different computations in each period. We found that SMA neurons perform acoustic-decision-related computations that transition from auditory to movement representations in this task. Our results suggest that the SMA integrates sensory information while listening to auditory stimuli in order to form categorical signals that drive behavior.

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Population dynamics of choice representation in dorsal premotor and primary motor cortex

10.1101/283960 ◽

2018 ◽

Cited By ~ 8

Author(s):

Diogo Peixoto ◽

Roozbeh Kiani ◽

Chandramouli Chandrasekaran ◽

Stephen I. Ryu ◽

Krishna V. Shenoy ◽

...

Keyword(s):

Decision Making ◽

Stimulus Duration ◽

Primary Motor Cortex ◽

Population Level ◽

Motor Preparation ◽

Neural Population ◽

Decision Making Process ◽

Decision Variable ◽

Single Trial ◽

Population Activity

SummaryStudies in multiple species have revealed the existence of neural signals that lawfully co-vary with different aspects of the decision-making process, including choice, sensory evidence that supports the choice, and reaction time. These signals, often interpreted as the representation of a decision variable (DV), have been identified in several motor preparation circuits and provide insight about mechanisms underlying the decision-making process. However, single-trial dynamics of this process or its representation at the neural population level remain poorly understood. Here, we examine the representation of the DV in simultaneously recorded neural populations of dorsal premotor (PMd) and primary motor (M1) cortices of monkeys performing a random dots direction discrimination task with arm movements as the behavioral report. We show that single-trial DVs covary with stimulus difficulty in both areas but are stronger and appear earlier in PMd compared to M1 when the stimulus duration is fixed and predictable. When temporal uncertainty is introduced by making the stimulus duration variable, single-trial DV dynamics are accelerated across the board and the two areas become largely indistinguishable throughout the entire trial. These effects are not trivially explained by the faster emergence of motor kinematic signals in PMd and M1. All key aspects of the data were replicated by a computational model that relies on progressive recruitment of units with stable choice-related modulation of neural population activity. In contrast with several recent results in rodents, decision signals in PMd and M1 are not carried by short sequences of activity in non-overlapping groups of neurons but are instead distributed across many neurons, which once recruited, represent the decision stably during individual behavioral epochs of the trial.

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Compulsive drinking is associated with neural activity patterns reflecting diminished behavioral control and enhanced seeking representations in dorsal medial prefrontal cortex

10.1101/2021.03.15.435169 ◽

2021 ◽

Author(s):

Nicholas Timme ◽

Baofeng Ma ◽

David N. Linsenbardt ◽

Ethan Cornwell ◽

Taylor Galbari ◽

...

Keyword(s):

Decision Making ◽

Prefrontal Cortex ◽

Medial Prefrontal Cortex ◽

Neural Activity ◽

Behavioral Control ◽

Critical Phenomenon ◽

Activity Patterns ◽

Principal Component ◽

Neural Population ◽

Negative Consequences

Drinking despite negative consequences (compulsive drinking) is a central contributor to high-risk alcohol intake and is associated with poor treatment outcomes in humans. We used a rodent model of compulsive drinking to examine the role played by dorsal medial prefrontal cortex (dmPFC), a brain region involved in maladaptive decision-making in addiction, in this clinically critical phenomenon. We developed novel advances in principal component and change point analyses to dissect neural population representations of specific decision-making variables. Compulsive subjects showed weakened representations of behavioral control signals that relate to drinking within a trial, but strengthened session-wide seeking state representations that were associated with drinking engagement at the start of each drinking opportunity. Finally, chemogenetic-based excitation of dmPFC prevented escalation of compulsive drinking. Collectively, these data indicate that compulsive drinking is associated with alterations in dmPFC neural activity that underlie diminished behavioral control and enhanced seeking.

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Discarding optimality: Throwing out the baby with the bathwater?

Behavioral and Brain Sciences ◽

10.1017/s0140525x18001401 ◽

2018 ◽

Vol 41 ◽

Cited By ~ 1

Author(s):

Patrick Simen ◽

Fuat Balcı

Keyword(s):

Decision Making ◽

Perceptual Decision Making ◽

Reward Rate ◽

Perceptual Decision ◽

Standard Observer ◽

Rate Maximization ◽

Reward Rate Maximization ◽

Descriptive Standard ◽

Observer Model

AbstractRahnev & Denison (R&D) argue against normative theories and in favor of a more descriptive “standard observer model” of perceptual decision making. We agree with the authors in many respects, but we argue that optimality (specifically, reward-rate maximization) has proved demonstrably useful as a hypothesis, contrary to the authors’ claims.

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LPCD framework: Analytical tool or psychological model?

Behavioral and Brain Sciences ◽

10.1017/s0140525x18001383 ◽

2018 ◽

Vol 41 ◽

Author(s):

David Danks

Keyword(s):

Decision Making ◽

Analytical Tool ◽

Mathematical Framework ◽

Bayesian Decision ◽

Perceptual Decision Making ◽

Psychological Model ◽

Psychological Reality ◽

Psychological Theories ◽

Target Article ◽

Bayesian Decision Making

AbstractThe target article uses a mathematical framework derived from Bayesian decision making to demonstrate suboptimal decision making but then attributes psychological reality to the framework components. Rahnev & Denison's (R&D) positive proposal thus risks ignoring plausible psychological theories that could implement complex perceptual decision making. We must be careful not to slide from success with an analytical tool to the reality of the tool components.

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