scholarly journals Inhibition and excitation shape activity selection: effect of oscillations in a decision-making circuit

2018 ◽  
Author(s):  
Thomas Bose ◽  
Andreagiovanni Reina ◽  
James A.R. Marshall
Keyword(s):  
Decision Making ◽  
Neural Circuit ◽  
Reproductive Value ◽  
Mechanistic Study ◽  
Perceptual Decision Making ◽  
Substantial Impact ◽  
Oscillatory Phase ◽  
Model Animal ◽  
Foraging Decisions ◽  
Overall Performance

AbstractDecision-making is a complex task and requires adaptive mechanisms that facilitate efficient behaviour. Here, we consider a neural circuit that guides the behaviour of an animal in ongoing binary choice tasks. We adopt an inhibition motif from neural network theory and propose a dynamical system characterized by nonlinear feedback, which links mechanism (the implementation of the neural circuit) and function (increasing reproductive value). A central inhibitory unit influences evidence-integrating excitatory units, which in our terms correspond to motivations competing for selection. We determine the parameter regime where the animal exhibits improved decision-making behaviour, and explain different behavioural outcomes by making the link between bifurcation analysis of the nonlinear neural circuit model and decision-making performance. We find that the animal performs best if it tunes internal parameters of the neural circuit in accordance with the underlying bifurcation structure. In particular, variation of inhibition strength and excitation-over-inhibition ratio have a crucial effect on the decision outcome, by allowing the animal to break decision deadlock and to enter an oscillatory phase that describes its internal motivational state. Our findings indicate that this oscillatory phase may improve the overall performance of the animal in an ongoing foraging task. Our results underpin the importance of an integrated functional and mechanistic study of animal activity selection.Author summaryOrganisms frequently select activities, which relate to economic, social and perceptual decision-making problems. The choices made may have substantial impact on their lives. In foraging decisions, for example, animals aim at reaching a target intake of nutrients; it is generally believed that a balanced diet improves reproductive success, yet little is known about the underlying mechanisms that integrate nutritional needs within the brain. In our study, we address this coupling between physiological states and a decision-making circuit in the context of foraging decisions. We consider a model animal that has the drive to eat or drink. The motivation to select and perform one of these activities (i.e. eating or drinking), is processed in artificial neuronal units that have access to information on how hungry and thirsty the animal is at the point it makes the decision. We show that inhibitory and excitatory mechanisms in the neural circuit shape ongoing binary decisions, and we reveal under which conditions oscillating motivations may improve the overall performance of the animal. Our results indicate that inefficient or pathological decision-making may originate from suboptimal modulation of excitation and inhibition in the neurobiological network.

scholarly journals Inhibition and Excitation Shape Activity Selection: Effect of Oscillations in a Decision-Making Circuit

2019 ◽  
Vol 31 (5) ◽  
pp. 870-896 ◽  
Author(s):  
Thomas Bose ◽  
Andreagiovanni Reina ◽  
James A. R. Marshall
Keyword(s):  
Decision Making ◽  
Neural Circuit ◽  
Selection Effect ◽  
Mechanistic Study ◽  
Nonlinear Feedback ◽  
Oscillatory Phase ◽  
Neural Computations ◽  
Underlying Mechanisms ◽  
Decision Circuit ◽  
And Function

Decision making is a complex task, and its underlying mechanisms that regulate behavior, such as the implementation of the coupling between physiological states and neural networks, are hard to decipher. To gain more insight into neural computations underlying ongoing binary decision-making tasks, we consider a neural circuit that guides the feeding behavior of a hypothetical animal making dietary choices. We adopt an inhibition motif from neural network theory and propose a dynamical system characterized by nonlinear feedback, which links mechanism (the implementation of the neural circuit and its coupling to the animal's nutritional state) and function (improving behavioral performance). A central inhibitory unit influences evidence-integrating excitatory units, which in our terms correspond to motivations competing for selection. We determine the parameter regime where the animal exhibits improved decision-making behavior and explain different behavioral outcomes by making the link between accessible states of the nonlinear neural circuit model and decision-making performance. We find that for given deficits in nutritional items, the variation of inhibition strength and ratio of excitation and inhibition strengths in the decision circuit allows the animal to enter an oscillatory phase that describes its internal motivational state. Our findings indicate that this oscillatory phase may improve the overall performance of the animal in an ongoing foraging task and underpin the importance of an integrated functional and mechanistic study of animal activity selection.

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 Residual dynamics resolves recurrent contributions to neural computation

2021 ◽  
Author(s):  
Aniruddh R Galgali ◽  
Maneesh Sahani ◽  
Valerio Mante
Keyword(s):  
Decision Making ◽  
Large Scale ◽  
Neural Circuit ◽  
Neural Population ◽  
Perceptual Decision Making ◽  
Neural Recordings ◽  
Fine Grained ◽  
Neural Computations ◽  
Population Trajectory ◽  
The Mean

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.

scholarly journals Separable neural signatures of confidence during perceptual decisions

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Tarryn Balsdon ◽  
Pascal Mamassian ◽  
Valentin Wyart
Keyword(s):  
Decision Making ◽  
Neural Circuit ◽  
Spectral Power ◽  
Brain Activity ◽  
Neural Representation ◽  
Perceptual Decision Making ◽  
Perceptual Decision ◽  
Decision Confidence ◽  
Electrical Brain Activity ◽  
Human Participants

Perceptual confidence is an evaluation of the validity of perceptual decisions. While there is behavioural evidence that confidence evaluation differs from perceptual decision-making, disentangling these two processes remains a challenge at the neural level. Here, we examined the electrical brain activity of human participants in a protracted perceptual decision-making task where observers tend to commit to perceptual decisions early whilst continuing to monitor sensory evidence for evaluating confidence. Premature decision commitments were revealed by patterns of spectral power overlying motor cortex, followed by an attenuation of the neural representation of perceptual decision evidence. A distinct neural representation was associated with the computation of confidence, with sources localised in the superior parietal and orbitofrontal cortices. In agreement with a dissociation between perception and confidence, these neural resources were recruited even after observers committed to their perceptual decisions, and thus delineate an integral neural circuit for evaluating perceptual decision confidence.

scholarly journals Separable neural signatures of confidence during perceptual decisions

2021 ◽  
Author(s):  
T. Balsdon ◽  
P. Mamassian ◽  
V. Wyart
Keyword(s):  
Decision Making ◽  
Neural Circuit ◽  
Spectral Power ◽  
Brain Activity ◽  
Neural Representation ◽  
Perceptual Decision Making ◽  
Perceptual Decision ◽  
Electrical Brain Activity ◽  
Sensory Evidence ◽  
Human Participants

AbstractPerceptual confidence is an evaluation of the validity of perceptual decisions. While there is behavioural evidence that confidence evaluation differs from perceptual decision-making, disentangling these two processes remains a challenge at the neural level. Here we examined the electrical brain activity of human participants in a protracted perceptual decision-making task where observers tend to commit to perceptual decisions early whilst continuing to monitor sensory evidence for evaluating confidence. Premature decision commitments were revealed by patterns of spectral power overlying motor cortex, followed by an attenuation of the neural representation of perceptual decision evidence. A distinct neural representation was associated with suboptimalities affecting confidence reports, with sources localised in the superior parietal and orbitofrontal cortices. In agreement with a dissociation between perception and confidence, these neural resources were recruited even after observers committed to their perceptual decisions, and thus delineate an integral neural circuit for the computation of confidence.

scholarly journals Quantifying the Landscape of Decision Making From Spiking Neural Networks

2021 ◽  
Vol 15 ◽  
Author(s):  
Leijun Ye ◽  
Chunhe Li
Keyword(s):  
Decision Making ◽  
Visual Motion ◽  
Neural Circuit ◽  
First Passage Time ◽  
Passage Time ◽  
State Transitions ◽  
Perceptual Decision Making ◽  
Transition Mechanism ◽  
Decision Making System ◽  
The Brain

The decision making function is governed by the complex coupled neural circuit in the brain. The underlying energy landscape provides a global picture for the dynamics of the neural decision making system and has been described extensively in the literature, but often as illustrations. In this work, we explicitly quantified the landscape for perceptual decision making based on biophysically-realistic cortical network with spiking neurons to mimic a two-alternative visual motion discrimination task. Under certain parameter regions, the underlying landscape displays bistable or tristable attractor states, which quantify the transition dynamics between different decision states. We identified two intermediate states: the spontaneous state which increases the plasticity and robustness of changes of minds and the “double-up” state which facilitates the state transitions. The irreversibility of the bistable and tristable switches due to the probabilistic curl flux demonstrates the inherent non-equilibrium characteristics of the neural decision system. The results of global stability of decision-making quantified by barrier height inferred from landscape topography and mean first passage time are in line with experimental observations. These results advance our understanding of the stochastic and dynamical transition mechanism of decision-making function, and the landscape and kinetic path approach can be applied to other cognitive function related problems (such as working memory) in brain networks.

Discarding optimality: Throwing out the baby with the bathwater?

2018 ◽  
Vol 41 ◽  
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.

LPCD framework: Analytical tool or psychological model?

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.

Sign in / Sign up

Export Citation Format

Share Document