Regular cycling between representations of alternatives in the hippocampus

Cognitive faculties such as imagination, planning, and decision-making require the ability to represent alternative scenarios. In animals, split-second decision-making implies that the brain can represent alternatives at a commensurate speed. Yet despite this insight, it has remained unknown whether there exists neural activity that can consistently represent alternatives in <1 s. Here we report that neural activity in the hippocampus, a brain structure vital to cognition, can regularly cycle between representations of alternative locations (bifurcating paths in a maze) at 8 Hz. This cycling dynamic was paced by the internally generated 8 Hz theta rhythm, often occurred in the absence of overt deliberative behavior, and unexpectedly also governed an additional hippocampal representation defined by alternatives (heading direction). These findings implicate a fast, regular, and generalized neural mechanism underlying the representation of competing possibilities.

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The Emerging Standard Neurobiological Model of Decision Making

10.1093/oxfordhb/9780199844371.013.45 ◽

2018 ◽

Author(s):

Shih-Wei Wu ◽

Paul W. Glimcher

Keyword(s):

Decision Making ◽

Economic Theory ◽

Neural Activity ◽

First Century ◽

The Other ◽

Neoclassical Economic ◽

Internal Representations ◽

Neurobiological Model ◽

Neoclassical Economic Theory ◽

The Brain

The standard neurobiological model of decision making has evolved, since the turn of the twenty-first century, from a confluence of economic, psychological, and neurosci- entific studies of how humans make choices. Two fundamental insights have guided the development of this model during this period, one drawn from economics and the other from neuroscience. The first derives from neoclassical economic theory, which unambiguously demonstrated that logically consistent choosers behave “as if” they had some internal, continuous, and monotonic representation of the values of any choice objects under consideration. The second insight derives from neurobiological studies suggesting that the brain can both represent, in patterns of local neural activity, and compare, by a process of interneuronal competition, internal representations of value associated with different choices.

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Spontaneous Neural Fluctuations Predict Decisions to Attend

Journal of Cognitive Neuroscience ◽

10.1162/jocn_a_00650 ◽

2014 ◽

Vol 26 (11) ◽

pp. 2578-2584 ◽

Cited By ~ 22

Author(s):

Jesse J. Bengson ◽

Todd A. Kelley ◽

Xiaoke Zhang ◽

Jane-Ling Wang ◽

George R. Mangun

Keyword(s):

Decision Making ◽

Neural Activity ◽

Intrinsic Property ◽

Alternative View ◽

Alpha Band ◽

Band Power ◽

Trial Basis ◽

Human Participants ◽

Neural Signaling ◽

The Brain

Ongoing variability in neural signaling is an intrinsic property of the brain. Often this variability is considered to be noise and ignored. However, an alternative view is that this variability is fundamental to perception and cognition and may be particularly important in decision-making. Here, we show that a momentary measure of occipital alpha-band power (8–13 Hz) predicts choices about where human participants will focus spatial attention on a trial-by-trial basis. This finding provides evidence for a mechanistic account of decision-making by demonstrating that ongoing neural activity biases voluntary decisions about where to attend within a given moment.

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Visual evidence accumulation guides decision-making in unrestrained mice

10.1101/195792 ◽

2017 ◽

Cited By ~ 4

Author(s):

Onyekachi Odoemene ◽

Sashank Pisupati ◽

Hien Nguyen ◽

Anne K. Churchland

Keyword(s):

Decision Making ◽

Neural Activity ◽

Neural Circuits ◽

Neural Circuit ◽

Deep Understanding ◽

Neural Mechanism ◽

Neural Structure ◽

Evidence Accumulation ◽

Visual Evidence ◽

Decision Making Behavior

AbstractThe ability to manipulate neural activity with precision is an asset in uncovering neural circuits for decision-making. Diverse tools for manipulating neurons are available for mice, but the feasibility of mice for decision-making studies remains unclear, especially when decisions require accumulating visual evidence. For example, whether mice’ decisions reflect leaky accumulation is not established, and the relevant and irrelevant factors that influence decisions are unknown. Further, causal circuits for visual evidence accumulation have not been established. To address these issues, we measured >500,000 decisions in 27 mice trained to judge the fluctuating rate of a sequence of flashes. Information throughout the 1000ms trial influenced choice, but early information was most influential. This suggests that information persists in neural circuits for ~1000ms with minimal accumulation leak. Further, while animals primarily based decisions on current stimulus rate, they were unable to entirely suppress additional factors: total stimulus brightness and the previous trial’s outcome. Next, we optogenetically inhibited anteromedial (AM) visual area using JAWS. Importantly, light activation biased choices in both injected and uninjected animals, demonstrating that light alone influences behavior. By varying stimulus-response contingency while holding stimulated hemisphere constant, we surmounted this obstacle to demonstrate that AM suppression biases decisions. By leveraging a large dataset to quantitatively characterize decision-making behavior, we establish mice as suitable for neural circuit manipulation studies, including the one here. Further, by demonstrating that mice accumulate visual evidence, we demonstrate that this strategy for reducing uncertainty in decision-making is employed by animals with diverse visual systems.Significance statementTo connect behaviors to their underlying neural mechanism, a deep understanding of the behavioral strategy is needed. This understanding is incomplete in mouse studies, in part because existing datasets have been too small to quantitatively characterize decision-making behavior. To surmount this, we measured the outcome of over 500,000 decisions made by 27 mice trained to judge visual stimuli. Our analyses offer new insights into mice’ decision-making strategies and compares them with those of other species. We then disrupted neural activity in a candidate neural structure and examined the effect on decisions. Our findings establish mice as a suitable organism for visual accumulation of evidence decisions. Further, the results highlight similarities in decision-making strategies across very different species.

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Decision making by urgency gating: theory and experimental support

Journal of Neurophysiology ◽

10.1152/jn.01071.2011 ◽

2012 ◽

Vol 108 (11) ◽

pp. 2912-2930 ◽

Cited By ~ 122

Author(s):

David Thura ◽

Julie Beauregard-Racine ◽

Charles-William Fradet ◽

Paul Cisek

Keyword(s):

Decision Making ◽

Neural Activity ◽

Time Window ◽

Sensory Information ◽

Natural Conditions ◽

Accuracy Criterion ◽

Sensory Evidence ◽

Dot Motion ◽

The Brain ◽

Random Dot Motion

It is often suggested that decisions are made when accumulated sensory information reaches a fixed accuracy criterion. This is supported by many studies showing a gradual build up of neural activity to a threshold. However, the proposal that this build up is caused by sensory accumulation is challenged by findings that decisions are based on information from a time window much shorter than the build-up process. Here, we propose that in natural conditions where the environment can suddenly change, the policy that maximizes reward rate is to estimate evidence by accumulating only novel information and then compare the result to a decreasing accuracy criterion. We suggest that the brain approximates this policy by multiplying an estimate of sensory evidence with a motor-related urgency signal and that the latter is primarily responsible for neural activity build up. We support this hypothesis using human behavioral data from a modified random-dot motion task in which motion coherence changes during each trial.

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A Probabilistic, Distributed, Recursive Mechanism for Decision-making in the Brain

10.1101/036277 ◽

2016 ◽

Author(s):

Javier A. Caballero ◽

Mark D. Humphries ◽

Kevin N. Gurney

Keyword(s):

Decision Making ◽

Basal Ganglia ◽

Neural Activity ◽

Large Scale ◽

Single Equation ◽

Brain Regions ◽

Compulsive Gambling ◽

Choice Behaviour ◽

Usable Information ◽

The Brain

AbstractDecision formation recruits many brain regions, but the procedure they jointly execute is unknown. Here we characterize its essential composition, using as a framework a novel recursive Bayesian algorithm that makes decisions based on spike-trains with the statistics of those in sensory cortex (MT). Using it to simulate the random-dot-motion task, we demonstrate it quantitatively replicates the choice behaviour of monkeys, whilst predicting losses of otherwise usable information from MT. Its architecture maps to the recurrent cortico-basal-ganglia-thalamo-cortical loops, whose components are all implicated in decision-making. We show that the dynamics of its mapped computations match those of neural activity in the sensorimotor cortex and striatum during decisions, and forecast those of basal ganglia output and thalamus. This also predicts which aspects of neural dynamics are and are not part of inference. Our single-equation algorithm is probabilistic, distributed, recursive, and parallel. Its success at capturing anatomy, behaviour, and electrophysiology suggests that the mechanism implemented by the brain has these same characteristics.Author SummaryDecision-making is central to cognition. Abnormally-formed decisions characterize disorders like over-eating, Parkinson’s and Huntington’s diseases, OCD, addiction, and compulsive gambling. Yet, a unified account of decisionmaking has, hitherto, remained elusive. Here we show the essential composition of the brain’s decision mechanism by matching experimental data from monkeys making decisions, to the knowable function of a novel statistical inference algorithm. Our algorithm maps onto the large-scale architecture of decision circuits in the primate brain, replicating the monkeys’ choice behaviour and the dynamics of the neural activity that accompany it. Validated in this way, our algorithm establishes a basic framework for understanding the mechanistic ingredients of decisionmaking in the brain, and thereby, a basic platform for understanding how pathologies arise from abnormal function.

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Neural Circuits Trained with Standard Reinforcement Learning Can Accumulate Probabilistic Information during Decision Making

Neural Computation ◽

10.1162/neco_a_00917 ◽

2017 ◽

Vol 29 (2) ◽

pp. 368-393 ◽

Cited By ~ 1

Author(s):

Nils Kurzawa ◽

Christopher Summerfield ◽

Rafal Bogacz

Keyword(s):

Decision Making ◽

Reinforcement Learning ◽

Neural Activity ◽

Neural Circuits ◽

Likelihood Ratios ◽

Sensory Inputs ◽

Probabilistic Information ◽

Learning Rules ◽

Log Likelihood ◽

The Brain

Much experimental evidence suggests that during decision making, neural circuits accumulate evidence supporting alternative options. A computational model well describing this accumulation for choices between two options assumes that the brain integrates the log ratios of the likelihoods of the sensory inputs given the two options. Several models have been proposed for how neural circuits can learn these log-likelihood ratios from experience, but all of these models introduced novel and specially dedicated synaptic plasticity rules. Here we show that for a certain wide class of tasks, the log-likelihood ratios are approximately linearly proportional to the expected rewards for selecting actions. Therefore, a simple model based on standard reinforcement learning rules is able to estimate the log-likelihood ratios from experience and on each trial accumulate the log-likelihood ratios associated with presented stimuli while selecting an action. The simulations of the model replicate experimental data on both behavior and neural activity in tasks requiring accumulation of probabilistic cues. Our results suggest that there is no need for the brain to support dedicated plasticity rules, as the standard mechanisms proposed to describe reinforcement learning can enable the neural circuits to perform efficient probabilistic inference.

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There Are No Killer Apps but Connecting Neural Activity to Behavior through Computation Is Still a Good Idea

Computational Psychiatry ◽

10.7551/mitpress/9780262035422.003.0013 ◽

2016 ◽

Author(s):

P. Read Montague

Keyword(s):

Decision Making ◽

Cognitive Function ◽

Simple Model ◽

Behavioral Economics ◽

Cognitive Science ◽

Neural Activity ◽

Mental States ◽

And Behavior ◽

The Relationship ◽

The Brain

The quest to understand the relationship between neural activity and behavior has been ongoing for well over a hundred years. Although research based on the stimulus-and-response approach to behavior, advocated by behaviorists, flourished during the last century, this view does not, by design, account for unobservable variables (e.g., mental states). Putting aside this approach, modern cognitive science, cognitive neuroscience, neuroeconomics, and behavioral economics have sought to explain this connection computationally. One major hurdle lies in the fact that we lack even a simple model of cognitive function. This chapter sketches an application that connects neuromodulator function to decision making and the valuation that underlies it. The nature of this hypothesized connection offers a fruitful platform to understand some of the informational aspects of dopamine function in the brain and how it exposes many different ways of understanding motivated choice.

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The Visible Invisible

Neuro ◽

10.23943/princeton/9780691149608.003.0003 ◽

2013 ◽

Author(s):

Nikolas Rose ◽

Joelle M. Abi-Rached

Keyword(s):

Nineteenth Century ◽

Neural Activity ◽

Brain Structure ◽

Clinical Medicine ◽

Mental States ◽

The Body ◽

Structural Imaging ◽

The Gaze ◽

The Mind ◽

The Brain

This chapter explores the diverse attempts to render “mind” thinkable by means of images. Advances in clinical medicine from the nineteenth century onward went hand in hand with the penetration of the gaze of the doctor into depths of the body itself. There are now many examples of analogous advances linked to the structural imaging of the brain—in the detection of tumors, the identification of lesions, and the mapping of the damage caused by injury or stroke. Thanks to such images, the mind of the neuroscientist, the neurologist, and the psychiatrist now seem able to “walk among the tissues themselves.” Yet, however similar the images of brain function are to those of brain structure, they mislead if they seem to allow the mind of the neuroscientist to walk among thoughts, feelings, or desires. Technology alone, even where it appears to measure neural activity, cannot enable the gaze to bridge the gap between molecules and mental states.

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