Mechanisms of Reward-Modulated STDP and Winner-Take-All in Bayesian Spiking Decision-Making Circuit

2019 ◽  
pp. 162-172
Author(s):  
Hui Yan ◽  
Xinle Liu ◽  
Hong Huo ◽  
Tao Fang
Keyword(s):  
Decision Making ◽  
Winner Take All ◽  
Take All

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.

scholarly journals Covert shift of attention modulates the value encoding in the orbitofrontal cortex

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Yang Xie ◽  
Chechang Nie ◽  
Tianming Yang
Keyword(s):  
Decision Making ◽  
Eye Movement ◽  
Neuronal Activity ◽  
Orbitofrontal Cortex ◽  
Passive Viewing ◽  
Reward Value ◽  
Winner Take All ◽  
Take All ◽  
Attention Shifts

During value-based decision making, we often evaluate the value of each option sequentially by shifting our attention, even when the options are presented simultaneously. The orbitofrontal cortex (OFC) has been suggested to encode value during value-based decision making. Yet it is not known how its activity is modulated by attention shifts. We investigated this question by employing a passive viewing task that allowed us to disentangle effects of attention, value, choice and eye movement. We found that the attention modulated OFC activity through a winner-take-all mechanism. When we attracted the monkeys’ attention covertly, the OFC neuronal activity reflected the reward value of the newly attended cue. The shift of attention could be explained by a normalization model. Our results strongly argue for the hypothesis that the OFC neuronal activity represents the value of the attended item. They provide important insights toward understanding the OFC’s role in value-based decision making.

scholarly journals Efficient reinforcement learning of a reservoir network model of parametric working memory achieved with a cluster population winner-take-all readout mechanism

2015 ◽  
Vol 114 (6) ◽  
pp. 3296-3305 ◽  
Author(s):  
Zhenbo Cheng ◽  
Zhidong Deng ◽  
Xiaolin Hu ◽  
Bo Zhang ◽  
Tianming Yang
Keyword(s):  
Decision Making ◽  
Working Memory ◽  
Prefrontal Cortex ◽  
Reinforcement Learning ◽  
Delay Period ◽  
Information Storage ◽  
Specific Information ◽  
Stable States ◽  
Winner Take All ◽  
Take All

The brain often has to make decisions based on information stored in working memory, but the neural circuitry underlying working memory is not fully understood. Many theoretical efforts have been focused on modeling the persistent delay period activity in the prefrontal areas that is believed to represent working memory. Recent experiments reveal that the delay period activity in the prefrontal cortex is neither static nor homogeneous as previously assumed. Models based on reservoir networks have been proposed to model such a dynamical activity pattern. The connections between neurons within a reservoir are random and do not require explicit tuning. Information storage does not depend on the stable states of the network. However, it is not clear how the encoded information can be retrieved for decision making with a biologically realistic algorithm. We therefore built a reservoir-based neural network to model the neuronal responses of the prefrontal cortex in a somatosensory delayed discrimination task. We first illustrate that the neurons in the reservoir exhibit a heterogeneous and dynamical delay period activity observed in previous experiments. Then we show that a cluster population circuit decodes the information from the reservoir with a winner-take-all mechanism and contributes to the decision making. Finally, we show that the model achieves a good performance rapidly by shaping only the readout with reinforcement learning. Our model reproduces important features of previous behavior and neurophysiology data. We illustrate for the first time how task-specific information stored in a reservoir network can be retrieved with a biologically plausible reinforcement learning training scheme.

Anxiety and Vote Decision Making in Winner-Take-All Elections

2014 ◽  
Vol 47 (3) ◽  
pp. 451-474 ◽  
Author(s):  
Delia Dumitrescu ◽  
André Blais
Keyword(s):  
Decision Making ◽  
Strategic Voting ◽  
Original Study ◽  
Voting Behaviour ◽  
Vote Decision ◽  
The Poor ◽  
Environmental Ngo ◽  
Winner Take All ◽  
Take All

AbstractWe study strategic voting behaviour in winner-take-all elections by means of an original study in which participants vote to collectively decide how much money should be given to an environmental NGO. We find that supporters of the most NGO-friendly party are reluctant to abandon it, despite its poor electoral viability. The poor electoral viability generates significant anxiety among its supporters and the level of anxiety at the time of voting influences their choice. Moderate levels of anxiety increase the probability of defection, but at high levels, anxiety has a paralyzing effect, making voters less likely to abandon their preferred choice.

scholarly journals Covert Shift of Attention Modulates the Value Encoding in the Orbitofrontal Cortex

2017 ◽  
Author(s):  
Yang Xie ◽  
Chechang Nie ◽  
Tianming Yang
Keyword(s):  
Decision Making ◽  
Eye Movement ◽  
Neuronal Activity ◽  
Orbitofrontal Cortex ◽  
Neural Mechanism ◽  
Passive Viewing ◽  
Reward Value ◽  
Winner Take All ◽  
Take All ◽  
Attention Shifts

AbstractDuring value-based decision making, we often evaluate the value of each option sequentially by shifting our attention, even when the options are presented simultaneously. The orbitofrontal cortex (OFC) has been suggested to encode value during value-based decision making. Yet it is not known how its activity is modulated by attention shifts. We investigated this question by employing a passive viewing task that allowed us to disentangle effects of attention, value, choice and eye movement. We found that the attention modulated OFC activity through a winner-take-all mechanism. When we attracted the monkeys’ attention covertly, the OFC neuronal activity reflected the reward value of the newly attended cue. The shift of attention could be explained by a normalization model. Our results strongly argue for the hypothesis that the OFC neuronal activity represents the value of covertly attended item. They provide important insights toward the neural mechanism of value-based decision making.

Interest Groups and Elections

2019 ◽  
pp. 339-357
Author(s):  
Jeffrey M. Berry
Keyword(s):  
Political Parties ◽  
Interest Groups ◽  
Campaign Finance ◽  
Organizational Forms ◽  
Super Pacs ◽  
New Organizational Forms ◽  
Winner Take All ◽  
The Impact ◽  
Take All

The relationships between interest groups, political parties, and elections have always been dynamic, but in recent years change has accelerated in ways that have favored some interests over others. This chapter considers these developments as the result of a variety of factors, the most critical of which are the growth of polarization, a new legal landscape for campaign finance, and new organizational forms. The chapter goes on to suggest, that as bipartisanship has ebbed, elections have become winner-take-all affairs and interest groups are pushed to choose sides. The chapter further suggests that the rise of super PACs is especially notable as wealthy individuals have become increasingly important, single sources of campaign money, supplanting in part traditional interest groups, especially conventional PACs. It concludes that even as sums spent by super PACs and other interest groups have skyrocketed, the impact of their direct spending on persuading voters remains uncertain.

scholarly journals Flexible categorization in perceptual decision making

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Genís Prat-Ortega ◽  
Klaus Wimmer ◽  
Alex Roxin ◽  
Jaime de la Rocha
Keyword(s):  
Decision Making ◽  
Network Models ◽  
Drift Diffusion Model ◽  
Perceptual Decision Making ◽  
Drift Diffusion ◽  
Perceptual Decision ◽  
Attractor Dynamics ◽  
Attractor Model ◽  
Sensory Evidence ◽  
Winner Take All

AbstractPerceptual decisions rely on accumulating sensory evidence. This computation has been studied using either drift diffusion models or neurobiological network models exhibiting winner-take-all attractor dynamics. Although both models can account for a large amount of data, it remains unclear whether their dynamics are qualitatively equivalent. Here we show that in the attractor model, but not in the drift diffusion model, an increase in the stimulus fluctuations or the stimulus duration promotes transitions between decision states. The increase in the number of transitions leads to a crossover between weighting mostly early evidence (primacy) to weighting late evidence (recency), a prediction we validate with psychophysical data. Between these two limiting cases, we found a novel flexible categorization regime, in which fluctuations can reverse initially-incorrect categorizations. This reversal asymmetry results in a non-monotonic psychometric curve, a distinctive feature of the attractor model. Our findings point to correcting decision reversals as an important feature of perceptual decision making.

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