scholarly journals Visual evidence accumulation guides decision-making in unrestrained mice

2017 ◽  
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.

scholarly journals Autocorrelation structure at rest predicts value correlates of single neurons during reward-guided choice

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Sean E Cavanagh ◽  
Joni D Wallis ◽  
Steven W Kennerley ◽  
Laurence T Hunt
Keyword(s):  
Decision Making ◽  
Prefrontal Cortex ◽  
Orbitofrontal Cortex ◽  
Receptive Fields ◽  
Neural Mechanism ◽  
Spike Rate ◽  
Accumulation Process ◽  
Single Neurons ◽  
Evidence Accumulation ◽  
Autocorrelation Structure

Correlates of value are routinely observed in the prefrontal cortex (PFC) during reward-guided decision making. In previous work (Hunt et al., 2015), we argued that PFC correlates of chosen value are a consequence of varying rates of a dynamical evidence accumulation process. Yet within PFC, there is substantial variability in chosen value correlates across individual neurons. Here we show that this variability is explained by neurons having different temporal receptive fields of integration, indexed by examining neuronal spike rate autocorrelation structure whilst at rest. We find that neurons with protracted resting temporal receptive fields exhibit stronger chosen value correlates during choice. Within orbitofrontal cortex, these neurons also sustain coding of chosen value from choice through the delivery of reward, providing a potential neural mechanism for maintaining predictions and updating stored values during learning. These findings reveal that within PFC, variability in temporal specialisation across neurons predicts involvement in specific decision-making computations.

scholarly journals A novel device for real-time measurement and manipulation of licking behavior in head-fixed mice

2018 ◽  
Vol 120 (6) ◽  
pp. 2975-2987 ◽  
Author(s):  
Brice Williams ◽  
Anderson Speed ◽  
Bilal Haider
Keyword(s):  
Real Time ◽  
Neural Activity ◽  
Closed Loop ◽  
Neural Circuits ◽  
Neural Circuit ◽  
Visual Behavior ◽  
Sensory Stimuli ◽  
Neural Recordings ◽  
Control Signals ◽  
Sensory Motor

The mouse has become an influential model system for investigating the mammalian nervous system. Technologies in mice enable recording and manipulation of neural circuits during tasks where they respond to sensory stimuli by licking for liquid rewards. Precise monitoring of licking during these tasks provides an accessible metric of sensory-motor processing, particularly when combined with simultaneous neural recordings. There are several challenges in designing and implementing lick detectors during head-fixed neurophysiological experiments in mice. First, mice are small, and licking behaviors are easily perturbed or biased by large sensors. Second, neural recordings during licking are highly sensitive to electrical contact artifacts. Third, submillisecond lick detection latencies are required to generate control signals that manipulate neural activity at appropriate time scales. Here we designed, characterized, and implemented a contactless dual-port device that precisely measures directional licking in head-fixed mice performing visual behavior. We first determined the optimal characteristics of our detector through design iteration and then quantified device performance under ideal conditions. We then tested performance during head-fixed mouse behavior with simultaneous neural recordings in vivo. We finally demonstrate our device’s ability to detect directional licks and generate appropriate control signals in real time to rapidly suppress licking behavior via closed-loop inhibition of neural activity. Our dual-port detector is cost effective and easily replicable, and it should enable a wide variety of applications probing the neural circuit basis of sensory perception, motor action, and learning in normal and transgenic mouse models. NEW & NOTEWORTHY Mice readily learn tasks in which they respond to sensory cues by licking for liquid rewards; tasks that involve multiple licking responses allow study of neural circuits underlying decision making and sensory-motor integration. Here we design, characterize, and implement a novel dual-port lick detector that precisely measures directional licking in head-fixed mice performing visual behavior, enabling simultaneous neural recording and closed-loop manipulation of licking.

scholarly journals Visual Evidence Accumulation Guides Decision-Making in Unrestrained Mice

2018 ◽  
Vol 38 (47) ◽  
pp. 10143-10155 ◽  
Author(s):  
Onyekachi Odoemene ◽  
Sashank Pisupati ◽  
Hien Nguyen ◽  
Anne K. Churchland
Keyword(s):  
Decision Making ◽  
Evidence Accumulation ◽  
Visual Evidence

Dynamical Foundations of the Neural Circuit for Bayesian Decision Making

2009 ◽  
Vol 102 (1) ◽  
pp. 1-6 ◽  
Author(s):  
Kenji Morita
Keyword(s):  
Decision Making ◽  
Bayesian Inference ◽  
Neural Circuits ◽  
Neural Circuit ◽  
Bayesian Decision ◽  
Biophysical Modeling ◽  
Perceptual Decision Making ◽  
Basic Ideas ◽  
Bayesian Decision Making ◽  
The Brain

On the basis of accumulating behavioral and neural evidences, it has recently been proposed that the brain neural circuits of humans and animals are equipped with several specific properties, which ensure that perceptual decision making implemented by the circuits can be nearly optimal in terms of Bayesian inference. Here, I introduce the basic ideas of such a proposal and discuss its implications from the standpoint of biophysical modeling developed in the framework of dynamical systems.

scholarly journals Causal contribution and dynamical encoding in the striatum during evidence accumulation

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Michael M Yartsev ◽  
Timothy D Hanks ◽  
Alice Misun Yoon ◽  
Carlos D Brody
Keyword(s):  
Decision Making ◽  
Posterior Parietal Cortex ◽  
Dorsal Striatum ◽  
Neural Encoding ◽  
Evidence Accumulation ◽  
Decision Making Processes ◽  
Posterior Parietal ◽  
Gradual Accumulation ◽  
Cortical Regions ◽  
Decision Making Behavior

A broad range of decision-making processes involve gradual accumulation of evidence over time, but the neural circuits responsible for this computation are not yet established. Recent data indicate that cortical regions that are prominently associated with accumulating evidence, such as the posterior parietal cortex and the frontal orienting fields, may not be directly involved in this computation. Which, then, are the regions involved? Regions that are directly involved in evidence accumulation should directly influence the accumulation-based decision-making behavior, have a graded neural encoding of accumulated evidence and contribute throughout the accumulation process. Here, we investigated the role of the anterior dorsal striatum (ADS) in a rodent auditory evidence accumulation task using a combination of behavioral, pharmacological, optogenetic, electrophysiological and computational approaches. We find that the ADS is the first brain region known to satisfy the three criteria. Thus, the ADS may be the first identified node in the network responsible for evidence accumulation.

scholarly journals Neural circuits for evidence accumulation and decision making in larval zebrafish

2019 ◽  
Vol 23 (1) ◽  
pp. 94-102 ◽  
Author(s):  
Armin Bahl ◽  
Florian Engert
Keyword(s):  
Decision Making ◽  
Neural Circuits ◽  
Evidence Accumulation ◽  
Larval Zebrafish

scholarly journals Dataset from: Visual evidence accumulation guides decision-making in unrestrained mice

2020 ◽  
Author(s):  
Onyekachi Odoemene ◽  
Sashank Pisupati ◽  
Hien Nguyen ◽  
Anne Churchland
Keyword(s):  
Decision Making ◽  
Evidence Accumulation ◽  
Visual Evidence

scholarly journals Neural population dynamics underlying evidence accumulation in multiple rat brain regions

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.

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

2017 ◽  
Vol 29 (2) ◽  
pp. 368-393 ◽  
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.

Sign in / Sign up

Export Citation Format

Share Document