25 papers · ranked by Valyu relevance
Karl Friston
The slight perversion of the original title of this piece (The Future of the Bayesian Brain) reflects my attempt to write prospectively about ‘Science and Stories’ over the past 20 years. I will meet this challenge by dealing with the future and then turning to its history. The future of the Bayesian brain (in…
Zina-Mary Manjaly, Sandra Iglesias
Mindfulness Based Cognitive Therapy (MBCT) was developed to combine methods from cognitive behavioral therapy and meditative techniques, with the specific goal of preventing relapse in recurrent depression. While supported by empirical evidence from multiple clinical trials, the cognitive mechanisms behind the…
Jacob E. Cheadle, K. J. Davidson-Turner, Bridget J. Goosby
Although research including biological concepts and variables has gained more prominence in sociology, progress assimilating the organ of experience, the brain, has been theoretically and technically challenging. Formal uptake and assimilation have thus been slow. Within psychology and neuroscience, the traditional…
Madhur Mangalam
The Bayesian brain hypothesis-the idea that neural systems implement or approximate Bayesian inference-has become a dominant framework in cognitive neuroscience over the past two decades. While mathematically elegant and conceptually unifying, this paper argues that the hypothesis occupies an ambiguous territory…
Semir Zeki, Oliver Y. Chén
We discuss here what we feel could be an improvement in future discussions of the brain operating as a Bayesian-Laplacian system, by distinguishing between two classes of priors on which the brain’s inferential systems operate. In one category are biological priors (ß priors) and in the other artefactual ones (α…
Hideaki Shimazaki
This article reviews how organisms learn and recognize the world through the dynamics of neural networks from the perspective of Bayesian inference, and introduces a view on how such dynamics is described by the laws for the entropy of neural activity, a paradigm that we call thermodynamics of the Bayesian brain. The…
Charles Findling, Felix Hubert, Luigi Acerbi, Brandon Benson + 52 more
The neural representations of prior information about the state of the world are poorly understood. To investigate this issue, we examined brain-wide Neuropixels recordings and widefield calcium imaging collected by the International Brain Laboratory. Mice were trained to indicate the location of a visual grating…
Sepideh Adamiat, Wouter M. Kouw, Bert de Vries
Bayesian inference offers a principled account of information processing in natural agents. However, it remains an open question how neural mechanisms perform their abstract operations. We investigate a hypothesis where a distributed form of Bayesian inference, namely message passing on factor graphs, is performed by a…
Jacob Raber, Benjamin R. Pittman-Polletta, Reza Rastmanesh
The task of both the brain and the neuroscientist is to reason about large numbers of variables that are both mutually interdependent and uncertain (i.e., probabilistic). This partly explains why statistical models - and Bayesian models in particular - have been increasingly prominent in both theoretical accounts of…
Thomas Parr, Dimitrije Marković, Maxwell James D. Ramstead, Ryan Smith + 2 more
'Ryan Smith' 'Casper Hesp' 'Karl Friston'] While observations in neurobiology provide inspiration for methods in artificial intelligence and machine learning-most famously, in the development of artificial neural networks ([12]; [18]; [19]) -the reciprocal relationship has also proved fruitful. Put simply, many of the…
Ralf M. Haefner, Jeff Beck, Cristina Savin, Mehrdad Salmasi + 1 more
'Xaq Pitkow'] This perspective piece is the result of a Generative Adversarial Collaboration (GAC) tackling the question 'How does neural activity represent probability distributions?'. We have addressed three major obstacles to progress on answering this question: first, we provide a unified language for defining…
Danilo Bzdok, Dorothea L. Floris, André F. Marquand
Functional connectivity fingerprints are among today's best choices to obtain a faithful sampling of an individual's brain and cognition in health and disease. Here we make a case for key advantages of analyzing such connectome profiles using Bayesian analysis strategies. They (i) afford full probability estimates of…
Antonio Kolossa, Bruno Kopp, Tim Fingscheidt
Empirical support for the Bayesian brain hypothesis, although of major theoretical importance for cognitive neuroscience, is surprisingly scarce. The literature still lacks definitive functional neuroimaging evidence that neural activities code and compute Bayesian probabilities. Here, we introduce a new experimental…
Richard D. Lange, Sabyasachi Shivkumar, Ankani Chattoraj, Ralf M. Haefner
The Bayesian Brain hypothesis, according to which the brain implements statistically optimal algorithms, is one of the leading theoretical frameworks in neuroscience. There are two distinct underlying philosophies: one in which the brain recovers experimenter-defined structures in the world from sensory neural activity…
Cláudia D. Vargas, Maria Luíza Rangel, Antonio Galves
Prediction is essentially a statistical inference task. This is in part due to the noise associated to stimuli detection and processing as well as to the intrinsic stochasticity of the brain functioning. This is also due to the fact that predicting means choosing the next state of the upcoming movement given the…
Hansem Sohn, Mehrdad Jazayeri
There are two sharply debated views on how humans make decisions under uncertainty. Bayesian decision theory posits that humans optimize their behavior by establishing and integrating internal models of past sensory experiences (priors) and decision outcomes (cost functions). An alternative model-free hypothesis posits…
Milad Kharratzadeh, Thomas R. Shultz
Bayesian models of cognition hypothesize that human brains make sense of data by representing probability distributions and applying Bayes' rule to find the best explanation for available data. Understanding the neural mechanisms underlying probabilistic models remains important because Bayesian models provide a…
Nikolaus Kriegeskorte, Pamela K. Douglas
To learn how cognition is implemented in the brain, we must build computational models that can perform cognitive tasks, and test such models with brain and behavioral experiments. Cognitive science has developed computational models of human cognition, decomposing task performance into computational components.…
Clare D. Harris, Elise G. Rowe, Roshini Randeniya, Marta I. Garrido
Predictive coding postulates that we make (top-down) predictions about the world and that we continuously compare incoming (bottom-up) sensory information with these predictions, in order to update our models and perception so as to better reflect reality. That is, our so-called “Bayesian brains” continuously create…
Adam Safron
It has been argued that all of cognition can be understood in terms of Bayesian inference. It has also been argued that analogy is the core of cognition. Here I will propose that these perspectives are fully compatible, in that analogical reasoning can be described in terms of Bayesian inference and vice versa, and…
Riley Hickman, Malcolm Sim, Sergio Pablo-García, Ivan Woolhouse + 6 more
Self-driving laboratories (SDLs) are next-generation research and development platforms for closed-loop, autonomous experimentation that combine ideas from artificial intelligence, robotics, and high-performance computing. A critical component of SDLs is the decision-making algorithm used to prioritize experiments to…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
Chenxi Sui, Ziyang Jiang, Genesis Higueros, David Carlson + 1 more
High-performance batteries are poised for electrification of vehicles and therefore mitigate greenhouse gas emissions, which, in turn, promote a sustainable future. However, the design of optimized batteries is challenging due to the nonlinear governing physics and electrochemistry. Recent advancements have…
Koichi Handa, Daichi Fujita, Mariko Hirano, Saki Yoshimura + 2 more
Given the aging populations in advanced countries globally, many pharmaceutical companies have focused on developing central nervous system (CNS) drugs. However, due to the blood-brain barrier, drugs do not easily reach the target area in the brain. Although conventional screening methods for drug discovery involve the…
Yifan Wu, Aron Walsh, Alex Ganose
What is the minimum number of experiments, or calculations, required to find an optimal solution? Relevant chemical problems range from identifying a compound with target functionality within a given phase space to controlling materials synthesis and device fabrication conditions. A common feature in this application…