Search Results for author: Christopher L. Buckley

Found 12 papers, 2 papers with code

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 find that this structure, known as a Markov blanket (i. e. a boundary precluding direct coupling between internal and external states) and stringent restrictions on its solenoidal flows, both required by the FEP, make it challenging to find systems that fulfil the required assumptions.

Bayesian Inference 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 Variational Inference

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

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

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

Generative models as parsimonious descriptions of sensorimotor loops

no code implementations29 Apr 2019 Manuel Baltieri, Christopher L. Buckley

The Bayesian brain hypothesis, predictive processing and variational free energy minimisation are typically used to describe perceptual processes based on accurate generative models of the world.

Nonmodular architectures of cognitive systems based on active inference

no code implementations22 Mar 2019 Manuel Baltieri, Christopher L. Buckley

We link this to popular formulations of perception and action in the cognitive sciences, and show its limitations when, for instance, external forces are not modelled by an agent.

A Minimal Active Inference Agent

no code implementations13 Mar 2015 Simon McGregor, Manuel Baltieri, Christopher L. Buckley

Research on the so-called "free-energy principle'' (FEP) in cognitive neuroscience is becoming increasingly high-profile.

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