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A Probabilistic, Distributed, Recursive Mechanism for Decision-making in the Brain
[article]
2016
bioRxiv
pre-print
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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). ...
Our single-equation algorithm is probabilistic, distributed, recursive, and parallel. ...
Acknowledgments We thank Anne Churchland, Roozbeh Kiani, Michael Shadlen, Long Ding, and Joshua Gold for sharing their experimental data and the Humphries lab (Abhinav Singh, Mathew Evans, and Silvia Maggi ...
doi:10.1101/036277
fatcat:qncnakln5bhhhizrrzjoxuguhm
A probabilistic, distributed, recursive mechanism for decision-making in the brain
2018
PLoS Computational Biology
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Acknowledgments We thank Anne Churchland, Roozbeh Kiani, Michael Shadlen, Long Ding, and Joshua Gold for sharing their experimental data and the Humphries lab (Abhinav Singh, Mathew Evans, and Silvia Maggi ...
), Rafal Bogacz, and Long Ding for discussions. ...
Why implement a recursive procedure in the brain? ...
doi:10.1371/journal.pcbi.1006033
pmid:29614077
pmcid:PMC5882111
fatcat:ud566igmozb4hexxvfj6mdjdn4
Action video games as exemplary learning tools
2010
Frontiers in Neuroscience
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Inference A Bayes' optimal solution for evidence accumulation -A--• Computing the posterior distribution given all activity patterns from MT up to the current time in a recursive fashion: Posterior at ...
model: -Computing the posterior distribution given all activity patterns from MT up to the current time in a recursive fashion: Computing the posterior distribution given all activity patterns from MT ...
doi:10.3389/conf.fnins.2010.03.00042
fatcat:e4maawsekvhxvlckukdwzrc5ri
Context-Aware Recursive Bayesian Graph Traversal in BCIs
[article]
2017
arXiv
pre-print
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based decision-making mechanism. ...
In this study we proposed two probabilistic graphical models (PGMs), using context information and previously observed EEG evidences to estimate a probability distribution over the decision space in graph ...
to significant delay in decision making. ...
arXiv:1703.02938v1
fatcat:5ickble5rve65ebyetgis6462u
Confidence as Bayesian Probability: From Neural Origins to Behavior
2015
Neuron
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This computational view entails that there are distinct forms in which confidence is represented and used in the brain, including distributional confidence, pertaining to neural representations of probability ...
Here, we explore how a definition of confidence as Bayesian probability can unify these viewpoints. ...
It was also supported in part by a grant from the Simons Foundation (325057) to Z.F.M. ...
doi:10.1016/j.neuron.2015.09.039
pmid:26447574
fatcat:dxr7r2x6uncernsao24mxjomji
Computational rationality: A converging paradigm for intelligence in brains, minds, and machines
2015
Science
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Advances include the development of representations and inferential procedures for large-scale probabilistic inference and machinery for enabling reflection and decisions about tradeoffs in effort, precision ...
After growing up together, and mostly growing apart in the second half of the 20th century, the fields of artificial intelligence (AI), cognitive science, and neuroscience are reconverging on a shared ...
systemsto cut through the complexity of probabilistic inference and decision-making (9) . ...
doi:10.1126/science.aac6076
pmid:26185246
fatcat:vpr6ke7czvhdpa4dnebvize7yq
Recursive Bayesian Coding for BCIs
2017
IEEE transactions on neural systems and rehabilitation engineering
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We offer a recursive Bayesian decision framework which incorporates context prior distributions (e.g. language model priors in spelling applications), accounts for varying brain symbol accuracy and is ...
Decision trees, as exemplified in [10] or [11], also offer the ability to choose among a large dictionary of task symbols with few brain symbols. ...
A probability threshold of α = .85 was used to make decisions in the recursive codes; no threshold was used in decision tree codes. ...
doi:10.1109/tnsre.2016.2590959
pmid:27416602
pmcid:PMC5536189
fatcat:loliworwq5bkbi53waj3kf5lwm
Statistically optimal perception and learning: from behavior to neural representations
2010
Trends in Cognitive Sciences
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sensory stimuli are interpreted in terms of the objects and features that gave rise to them [5] . ...
However, because sensory input in general is noisy and ambiguous, there is usually a range of different possible ...
Questions for future research Exact probabilistic computation in the brain is not feasible. ...
doi:10.1016/j.tics.2010.01.003
pmid:20153683
pmcid:PMC2939867
fatcat:koqqj3n7xbdzrjzw44bvsvroui
Probabilistic models of cognition: exploring representations and inductive biases
2010
Trends in Cognitive Sciences
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that the mind executes to produce this solution; and a 'hardware' level specifying how those processes are instantiated in the brain. ...
Posterior distribution: a probability distribution over hypotheses reflecting the learner's degree of belief in each hypothesis in light of the information provided by the observed data. ...
For instance, Pouget, Beck, Ma and colleagues have studied how to implement Bayesian parameter estimation and decision-making using probabilistic population codes in networks of spiking neurons [54] . ...
doi:10.1016/j.tics.2010.05.004
pmid:20576465
fatcat:rhtsthe6pncu3dldvyf67wdpfu
Letting structure emerge: connectionist and dynamical systems approaches to cognition
2010
Trends in Cognitive Sciences
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that the mind executes to produce this solution; and a 'hardware' level specifying how those processes are instantiated in the brain. ...
Posterior distribution: a probability distribution over hypotheses reflecting the learner's degree of belief in each hypothesis in light of the information provided by the observed data. ...
For instance, Pouget, Beck, Ma and colleagues have studied how to implement Bayesian parameter estimation and decision-making using probabilistic population codes in networks of spiking neurons [54] . ...
doi:10.1016/j.tics.2010.06.002
pmid:20598626
pmcid:PMC3056446
fatcat:jvoodhp3c5euhacn77yt6d2r5e
Nonlinear Probabilistic Predictive Neural Computations
2012
NeuroQuantology
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The modern neuroscience demands a mechanism for the theory of mind. ...
Summarizing that human brain is working due the computational processes of probabilistically calculated predictions of reality. ...
Acknowledgments The author gratefully acknowledge the assistance of Dr. Marta Ballova, Ing. Konrad Balla, Livuska Ballova, and Ing. Jozef Balla. ...
doi:10.14704/nq.2012.10.2.554
fatcat:t7jbhj2cfbbm5ndlwy6pouvqvu
A Revision of Coding Theory for Learning from Language
2004
Electronical Notes in Theoretical Computer Science
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A differentiation 1 [1] has shown that Zipf's law is met at least by strings of independently tossed letters and spaces. [19] reports on change in the law's exponent from −1 to −3 for ranks ≈ 10 4 , which ...
Elements of a quantitative-symbolic theory of human language communication based on power-law entropic sublinearity are induced from independent results in quantitative linguistics, statistical NLP, information ...
[8] proposes the same mechanism also for inconscious brain processing in the real time.
How to forget the past? ...
doi:10.1016/s1571-0661(05)82574-5
fatcat:t4r6hquy6zc43ew5v7hx5eucey
Neural Basis of Strategic Decision Making
2016
Trends in Neurosciences
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Social decision making In theories of decision making, utilities and values play a central role. ...
This might arise partly from the limitations in cognitive abilities necessary for recursive reasoning about the behaviors of others. ...
Hackjin Kim and Sunhae Sul for their helpful discussion. This work was supported by the National Institute of Health (R01 DA029330 and R21 MH104460). ...
doi:10.1016/j.tins.2015.11.002
pmid:26688301
pmcid:PMC4713315
fatcat:5lwem3xpknhqxppywo5vfyw2mi
Learning and executing goal-directed choices by internally generated sequences in spiking neural circuits
2017
PLoS Computational Biology
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In this study, we propose a novel neural mechanism for implementing spatial decision making in cued-choice tasks. ...
Recent neural ensemble recordings have established a link between goal-directed spatial decision making and internally generated neural sequences in the hippocampus of rats. ...
in the spatial decision making circuit. ...
doi:10.1371/journal.pcbi.1005669
pmid:28759562
pmcid:PMC5552356
fatcat:m6tybwpnwbckjdjlyemtnnmbx4
Active recursive Bayesian inference using Rényi information measures
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2004.03139v1 fatcat:zjhbmtrtr5ec7msrinm7wmsiay
The proposed active RBI framework is applied to the trajectory of the posterior changes in the probability simplex that provides a coordinated active querying and decision making with specified confidence ...
Recursive Bayesian inference (RBI) provides optimal Bayesian latent variable estimates in real-time settings with streaming noisy observations. ...
INTRODUCTION Recursive Bayesian inference (RBI) is a general probabilistic framework to estimate the unknown probability distribution of latent states through a recursive querying process over time. ...
arXiv:2004.03139v2
fatcat:gtjwqg2pija25l3qvgkhf45ni4
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