Search Results for author: Alexander Tschantz

Found 16 papers, 5 papers with code

Hybrid Predictive Coding: Inferring, Fast and Slow

no code implementations5 Apr 2022 Alexander Tschantz, Beren Millidge, Anil K Seth, Christopher L Buckley

This is at odds with evidence that several aspects of visual perception - including complex forms of object recognition - arise from an initial "feedforward sweep" that occurs on fast timescales which preclude substantial recurrent activity.

Bayesian Inference Object Recognition

A continuity of Markov blanket interpretations under the Free Energy Principle

no code implementations18 Jan 2022 Anil Seth, Tomasz Korbak, Alexander Tschantz

Bruineberg and colleagues helpfully distinguish between instrumental and ontological interpretations of Markov blankets, exposing the dangers of using the former to make claims about the latter.

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

Active Inference in Robotics and Artificial Agents: Survey and Challenges

no code implementations3 Dec 2021 Pablo Lanillos, Cristian Meo, Corrado Pezzato, Ajith Anil Meera, Mohamed Baioumy, Wataru Ohata, Alexander Tschantz, Beren Millidge, Martijn Wisse, Christopher L. Buckley, Jun Tani

Active inference is a mathematical framework which originated in computational neuroscience as a theory of how the brain implements action, perception and learning.

Bayesian Inference

How particular is the physics of the free energy principle?

no code implementations24 May 2021 Miguel Aguilera, Beren Millidge, Alexander Tschantz, Christopher L. Buckley

We discover that two requirements of the FEP -- the Markov blanket condition (i. e. a statistical boundary precluding direct coupling between internal and external states) and stringent restrictions on its solenoidal flows (i. e. tendencies driving a system out of equilibrium) -- are only valid for a very narrow space of parameters.

Bayesian Inference Variational Inference

Neural Kalman Filtering

1 code implementation19 Feb 2021 Beren Millidge, Alexander Tschantz, Anil Seth, Christopher Buckley

The Kalman filter is a fundamental filtering algorithm that fuses noisy sensory data, a previous state estimate, and a dynamics model to produce a principled estimate of the current state.

Investigating the Scalability and Biological Plausibility of the Activation Relaxation Algorithm

1 code implementation13 Oct 2020 Beren Millidge, Alexander Tschantz, Anil Seth, Christopher L Buckley

The recently proposed Activation Relaxation (AR) algorithm provides a simple and robust approach for approximating the backpropagation of error algorithm using only local learning rules.

Relaxing the Constraints on Predictive Coding Models

no code implementations2 Oct 2020 Beren Millidge, Alexander Tschantz, Anil Seth, Christopher L Buckley

Predictive coding is an influential theory of cortical function which posits that the principal computation the brain performs, which underlies both perception and learning, is the minimization of prediction errors.

Variational Inference

Activation Relaxation: A Local Dynamical Approximation to Backpropagation in the Brain

1 code implementation11 Sep 2020 Beren Millidge, Alexander Tschantz, Anil. K. Seth, Christopher L. Buckley

The backpropagation of error algorithm (backprop) has been instrumental in the recent success of deep learning.

On the Relationship Between Active Inference and Control as Inference

no code implementations23 Jun 2020 Beren Millidge, Alexander Tschantz, Anil. K. Seth, Christopher L. Buckley

Active Inference (AIF) is an emerging framework in the brain sciences which suggests that biological agents act to minimise a variational bound on model evidence.

Decision Making reinforcement-learning +1

Reinforcement Learning as Iterative and Amortised Inference

no code implementations13 Jun 2020 Beren Millidge, Alexander Tschantz, Anil. K. Seth, Christopher L. Buckley

There are several ways to categorise reinforcement learning (RL) algorithms, such as either model-based or model-free, policy-based or planning-based, on-policy or off-policy, and online or offline.

General Classification reinforcement-learning

Predictive Coding Approximates Backprop along Arbitrary Computation Graphs

1 code implementation7 Jun 2020 Beren Millidge, Alexander Tschantz, Christopher L. Buckley

Recently, it has been shown that backprop in multilayer-perceptrons (MLPs) can be approximated using predictive coding, a biologically-plausible process theory of cortical computation which relies only on local and Hebbian updates.

Whence the Expected Free Energy?

no code implementations17 Apr 2020 Beren Millidge, Alexander Tschantz, Christopher L. Buckley

The Expected Free Energy (EFE) is a central quantity in the theory of active inference.

Reinforcement Learning through Active Inference

no code implementations28 Feb 2020 Alexander Tschantz, Beren Millidge, Anil. K. Seth, Christopher L. Buckley

The central tenet of reinforcement learning (RL) is that agents seek to maximize the sum of cumulative rewards.

Decision Making reinforcement-learning

Scaling active inference

no code implementations24 Nov 2019 Alexander Tschantz, Manuel Baltieri, Anil. K. Seth, Christopher L. Buckley

In reinforcement learning (RL), agents often operate in partially observed and uncertain environments.

Efficient Exploration

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