23 papers · ranked by Valyu relevance
Eelke Spaak
The idea that the brain is a probabilistic (Bayesian) inference machine, continuously trying to figure out the hidden causes of its inputs, has become very influential in cognitive (neuro)science over recent decades. Here I present a relatively straightforward generalization of this idea: the primary computational task…
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…
Kevin McKee, Ian Crandell, Rishidev Chaudhuri, Randall C. O’Reilly
Bayesian interpretations of neural processing require that biological mechanisms represent and operate upon probability distributions in accordance with Bayes' theorem. Many have speculated that synaptic failure constitutes a mechanism of variational, i.e., approximate, Bayesian inference in the brain. Whereas models…
Karl Friston
This review considers computational psychiatry from a particular viewpoint: namely, a commitment to explaining psychopathology in terms of pathophysiology. It rests on the notion of a generative model as underwriting (i) sentient processing in the brain, and (ii) the scientific process in psychiatry. The story starts…
Robert Worden
A model of the evolution of cognition is used to derive a Requirement Equation (RE), which defines what computations the fittest possible brain must make – or must choose actions as if it had made those computations. The terms in the RE depend on factors outside an animal's brain, which can be modelled without making…
Zafeirios Fountas, Alexey Zakharov
Enquiries concerning the underlying mechanisms and the emergent properties of a biological brain have a long history of theoretical postulates and experimental findings. Today, the scientific community tends to converge to a single interpretation of the brain's cognitive underpinnings – that it is a Bayesian inference…
Jyrki Suomala, Janne Kauttonen
Despite the success of artificial intelligence (AI), we are still far away from AI that model the world as humans do. This study focuses for explaining human behavior from intuitive mental models’ perspectives. We describe how behavior arises in biological systems and how the better understanding of this biological…
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…
Nicholas M. Singletary, Jacqueline Gottlieb, Guillermo Horga
Making adaptive decisions often requires inferring unobservable states based on unreliable information. Bayesian logic prescribes that individuals form probabilistic beliefs about a state by integrating the likelihood of new evidence with their prior beliefs, but human neuroimaging studies on probability…
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…
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…
Patrick Q. Zhang, Michael J. Jutras, Adam J.O. Dede, Edgar Y. Walker + 2 more
Adaptive behavior requires maintaining and updating probabilistic beliefs about the world, yet how distributed brain circuits implement such computations remains unknown. We recorded from over 1,400 neurons across six brain regions in monkeys performing a multi-dimensional inference task requiring them to infer hidden…
Chin-Hsuan Sophie Lin, Trang Thuy Do, Lee Unsworth, Marta I. Garrido
Numerous studies have found that the Bayesian framework, which formulates the optimal integration of the knowledge of the world (i.e. prior) and current sensory evidence (i.e. likelihood), captures human behaviours sufficiently well. However, there are debates regarding whether humans use precise but cognitively…
Wiktor Rorot
The goal of the paper is to review existing work on consciousness within the frameworks of Predictive Processing, Active Inference, and Free Energy Principle. The emphasis is put on the role played by the precision and complexity of the internal generative model. In the light of those proposals, these two properties…
Brandon S Coventry, Edward L Bartlett
Typical statistical practices in the biological sciences have been increasingly called into question due to difficulties in replication of an increasing number of studies, many of which are confounded by the relative difficulty of null significance hypothesis testing designs and interpretation of p-values. Bayesian…
Caroline Bévalot, Florent Meyniel
The brain combines sensory information and prior information, taking into account uncertainty, to perceive the world. This inference process approaches optimality in humans but with inter-individual differences associated with psychological traits. Previous results on these differences are in fact highly heterogeneous…
Robert Worden
Neural theories of consciousness face three difficulties: (1) The selection problem: how are those neurons which create consciousness selected, from all the other neurons which do not? (2) the precision problem: how do neurons hold a detailed internal model of 3D space, as the origin of our spatial conscious…
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…
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…
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…
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…