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.

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.

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.

Bayesian Decision-Making Based Recommendation Trust Revision Model in Ad Hoc Networks

2009 ◽  
Vol 20 (9) ◽  
pp. 2574-2586 ◽  
Author(s):  
Yu-Xing SUN ◽  
Song-Hua HUANG ◽  
Li-Jun CHEN ◽  
Li XIE
Keyword(s):  
Decision Making ◽  
Ad Hoc Networks ◽  
Ad Hoc ◽  
Bayesian Decision ◽  
Hoc Networks ◽  
Bayesian Decision Making

Mate-Choice Copying as Bayesian Decision Making

2005 ◽  
Vol 165 (3) ◽  
pp. 403
Author(s):  
Uehara ◽  
Yokomizo ◽  
Iwasa
Keyword(s):  
Decision Making ◽  
Mate Choice ◽  
Bayesian Decision ◽  
Mate Choice Copying ◽  
Bayesian Decision Making

scholarly journals Quantum Bayesian Decision-Making

2021 ◽  
Author(s):  
Michael de Oliveira ◽  
Luis Soares Barbosa
Keyword(s):  
Decision Making ◽  
Bayesian Decision ◽  
Bayesian Decision Making

scholarly journals Flexible categorization in perceptual decision making

2020 ◽  
Author(s):  
Genís Prat-Ortega ◽  
Klaus Wimmer ◽  
Alex Roxin ◽  
Jaime de la Rocha
Keyword(s):  
Decision Making ◽  
Large Body ◽  
Network Models ◽  
Perceptual Decision Making ◽  
Drift Diffusion ◽  
Perceptual Decision ◽  
Attractor Dynamics ◽  
Attractor Model ◽  
Winner Take All ◽  
The Brain

AbstractPerceptual decisions require the brain to make categorical choices based on accumulated sensory evidence. The underlying computations have been studied using either phenomenological drift diffusion models or neurobiological network models exhibiting winner-take-all attractor dynamics. Although both classes of models can account for a large body of experimental data, it remains unclear to what extent their dynamics are qualitatively equivalent. Here we show that, unlike the drift diffusion model, the attractor model can operate in different integration regimes: an increase in the stimulus fluctuations or the stimulus duration promotes transitions between decision-states leading to a crossover between weighting mostly early evidence (primacy regime) to weighting late evidence (recency regime). Between these two limiting cases, we found a novel regime, which we name flexible categorization, in which fluctuations are strong enough to reverse initial categorizations, but only if they are incorrect. This asymmetry in the reversing probability results in a non-monotonic psychometric curve, a novel and distinctive feature of the attractor model. Finally, we show psychophysical evidence for the crossover between integration regimes predicted by the attractor model and for the relevance of this new regime. Our findings point to correcting transitions as an important yet overlooked feature of perceptual decision making.

scholarly journals Robust parallel decision-making in neural circuits with nonlinear inhibition

2020 ◽  
Vol 117 (41) ◽  
pp. 25505-25516
Author(s):  
Birgit Kriener ◽  
Rishidev Chaudhuri ◽  
Ila R. Fiete
Keyword(s):  
Decision Making ◽  
Neural Circuits ◽  
Fine Tuning ◽  
Neural Computing ◽  
Hick’S Law ◽  
Noisy Input ◽  
Finite Memory ◽  
Winner Take All ◽  
The Brain ◽  
Take All

An elemental computation in the brain is to identify the best in a set of options and report its value. It is required for inference, decision-making, optimization, action selection, consensus, and foraging. Neural computing is considered powerful because of its parallelism; however, it is unclear whether neurons can perform this max-finding operation in a way that improves upon the prohibitively slow optimal serial max-finding computation (which takes∼N⁡log(N)time for N noisy candidate options) by a factor of N, the benchmark for parallel computation. Biologically plausible architectures for this task are winner-take-all (WTA) networks, where individual neurons inhibit each other so only those with the largest input remain active. We show that conventional WTA networks fail the parallelism benchmark and, worse, in the presence of noise, altogether fail to produce a winner when N is large. We introduce the nWTA network, in which neurons are equipped with a second nonlinearity that prevents weakly active neurons from contributing inhibition. Without parameter fine-tuning or rescaling as N varies, the nWTA network achieves the parallelism benchmark. The network reproduces experimentally observed phenomena like Hick’s law without needing an additional readout stage or adaptive N-dependent thresholds. Our work bridges scales by linking cellular nonlinearities to circuit-level decision-making, establishes that distributed computation saturating the parallelism benchmark is possible in networks of noisy, finite-memory neurons, and shows that Hick’s law may be a symptom of near-optimal parallel decision-making with noisy input.

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