Search Results for author: Karl Friston

Found 31 papers, 3 papers with code

The graphical brain and deep inference

no code implementations AMTA 2022 Karl Friston

This presentation considers deep temporal models in the brain.

Mortal Computation: A Foundation for Biomimetic Intelligence

no code implementations16 Nov 2023 Alexander Ororbia, Karl Friston

This review motivates and synthesizes research efforts in neuroscience-inspired artificial intelligence and biomimetic computing in terms of mortal computation.

Canonical Cortical Field Theories

no code implementations21 Aug 2023 Gerald K. Cooray, Vernon Cooray, Karl Friston

Moreover, the neural field model was invariant, within a set of parameters, to the dynamical system used to model each neuronal mass.

Hierarchical generative modelling for autonomous robots

no code implementations15 Aug 2023 Kai Yuan, Noor Sajid, Karl Friston, Zhibin Li

We approach this problem by hierarchical generative modelling equipped with multi-level planning-for autonomous task completion-that mimics the deep temporal architecture of human motor control.

Brain in the Dark: Design Principles for Neuro-mimetic Learning and Inference

no code implementations14 Jul 2023 Mehran H. Bazargani, Szymon Urbas, Karl Friston

Even though the brain operates in pure darkness, within the skull, it can infer the most likely causes of its sensory input.

Variational Inference

Spectral Dynamic Causal Modelling: A Didactic Introduction and its Relationship with Functional Connectivity

no code implementations23 Jun 2023 Leonardo Novelli, Karl Friston, Adeel Razi

We present a didactic introduction to spectral Dynamic Causal Modelling (DCM), a Bayesian state-space modelling approach used to infer effective connectivity from non-invasive neuroimaging data.

Curvature-Sensitive Predictive Coding with Approximate Laplace Monte Carlo

no code implementations9 Mar 2023 Umais Zahid, Qinghai Guo, Karl Friston, Zafeirios Fountas

In part, this has been due to the poor performance of models trained with PC when evaluated by both sample quality and marginal likelihood.

A variational synthesis of evolutionary and developmental dynamics

no code implementations8 Mar 2023 Karl Friston, Daniel Ari Friedman, Axel Constant, V. Bleu Knight, Thomas Parr, John O. Campbell

This paper introduces a variational formulation of natural selection, paying special attention to the nature of "things" and the way that different "kinds" of "things" are individuated from - and influence - each other.

Model Selection

Bistable perception, precision and neuromodulation

no code implementations19 Dec 2022 Filip Novicky, Thomas Parr, Karl Friston, M. Berk Mirza, Noor Sajid

Bistable perception follows from observing a static, ambiguous, (visual) stimulus with two possible interpretations.

Bayesian Inference

Natural Language Syntax Complies with the Free-Energy Principle

no code implementations27 Oct 2022 Elliot Murphy, Emma Holmes, Karl Friston

Natural language syntax yields an unbounded array of hierarchically structured expressions.

The Free Energy Principle drives neuromorphic development

no code implementations20 Jul 2022 Chris Fields, Karl Friston, James F. Glazebrook, Michael Levin, Antonino Marcianò

We show how any system with morphological degrees of freedom and locally limited free energy will, under the constraints of the free energy principle, evolve toward a neuromorphic morphology that supports hierarchical computations in which each level of the hierarchy enacts a coarse-graining of its inputs, and dually a fine-graining of its outputs.

Global dynamics of neural mass models

no code implementations30 Jun 2022 Gerald Cooray, Richard Rosch, Karl Friston

Simulations evince a complex phase-space structure for these kinds of models; including stationary points and limit cycles and the possibility for bifurcations and transitions among different modes of activity.

EEG Electroencephalogram (EEG)

Geometric Methods for Sampling, Optimisation, Inference and Adaptive Agents

no code implementations20 Mar 2022 Alessandro Barp, Lancelot Da Costa, Guilherme França, Karl Friston, Mark Girolami, Michael I. Jordan, Grigorios A. Pavliotis

In this chapter, we identify fundamental geometric structures that underlie the problems of sampling, optimisation, inference and adaptive decision-making.

counterfactual Decision Making

pymdp: A Python library for active inference in discrete state spaces

1 code implementation11 Jan 2022 Conor Heins, Beren Millidge, Daphne Demekas, Brennan Klein, Karl Friston, Iain Couzin, Alexander Tschantz

Active inference is an account of cognition and behavior in complex systems which brings together action, perception, and learning under the theoretical mantle of Bayesian inference.

Bayesian Inference

Insula Interoception, Active Inference and Feeling Representation

no code implementations23 Dec 2021 Alan S. R. Fermin, Karl Friston, Shigeto Yamawaki

The body sends interoceptive visceral information through deep brain structures to the cerebral cortex.

Active inference, Bayesian optimal design, and expected utility

no code implementations21 Sep 2021 Noor Sajid, Lancelot Da Costa, Thomas Parr, Karl Friston

Conversely, active inference reduces to Bayesian decision theory in the absence of ambiguity and relative risk, i. e., expected utility maximization.

Bayesian brains and the Rényi divergence

no code implementations12 Jul 2021 Noor Sajid, Francesco Faccio, Lancelot Da Costa, Thomas Parr, Jürgen Schmidhuber, Karl Friston

Under the Bayesian brain hypothesis, behavioural variations can be attributed to different priors over generative model parameters.

Bayesian Inference Variational Inference

Exploration and preference satisfaction trade-off in reward-free learning

no code implementations ICML Workshop URL 2021 Noor Sajid, Panagiotis Tigas, Alexey Zakharov, Zafeirios Fountas, Karl Friston

In this paper, we pursue the notion that this learnt behaviour can be a consequence of reward-free preference learning that ensures an appropriate trade-off between exploration and preference satisfaction.

OpenAI Gym

Active Inference Tree Search in Large POMDPs

no code implementations25 Mar 2021 Domenico Maisto, Francesco Gregoretti, Karl Friston, Giovanni Pezzulo

Here, we introduce a novel method to plan in POMDPs--Active Inference Tree Search (AcT)--that combines the normative character and biological realism of a leading planning theory in neuroscience (Active Inference) and the scalability of tree search methods in AI.

Hippocampus

Reward Maximisation through Discrete Active Inference

no code implementations17 Sep 2020 Lancelot Da Costa, Noor Sajid, Thomas Parr, Karl Friston, Ryan Smith

Precisely, we show the conditions under which active inference produces the optimal solution to the Bellman equation--a formulation that underlies several approaches to model-based reinforcement learning and control.

Decision Making Model-based Reinforcement Learning +2

Action and Perception as Divergence Minimization

1 code implementation3 Sep 2020 Danijar Hafner, Pedro A. Ortega, Jimmy Ba, Thomas Parr, Karl Friston, Nicolas Heess

While the narrow objectives correspond to domain-specific rewards as typical in reinforcement learning, the general objectives maximize information with the environment through latent variable models of input sequences.

Decision Making Representation Learning

Deep active inference agents using Monte-Carlo methods

1 code implementation NeurIPS 2020 Zafeirios Fountas, Noor Sajid, Pedro A. M. Mediano, Karl Friston

In a more complex Animal-AI environment, our agents (using the same neural architecture) are able to simulate future state transitions and actions (i. e., plan), to evince reward-directed navigation - despite temporary suspension of visual input.

Sophisticated Inference

no code implementations7 Jun 2020 Karl Friston, Lancelot Da Costa, Danijar Hafner, Casper Hesp, Thomas Parr

In this paper, we consider a sophisticated kind of active inference, using a recursive form of expected free energy.

Active Learning counterfactual

Neural dynamics under active inference: plausibility and efficiency of information processing

no code implementations22 Jan 2020 Lancelot Da Costa, Thomas Parr, Biswa Sengupta, Karl Friston

We then show that these neuronal dynamics approximate natural gradient descent, a well-known optimisation algorithm from information geometry that follows the steepest descent of the objective in information space.

Approximate Bayesian inference as a gauge theory

no code implementations17 May 2017 Biswa Sengupta, Karl Friston

In a published paper [Sengupta, 2016], we have proposed that the brain (and other self-organized biological and artificial systems) can be characterized via the mathematical apparatus of a gauge theory.

Bayesian Inference Variational Inference

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