activity
20192022
most citedReinforcement Learning through Active Inference

58 citations · 79 across the 5 of their papers we have counts for

collaborators

13 papers

q-bio.NC2022

Capsule Networks as Generative Models

Alex B. Kiefer, Beren Millidge, Alexander Tschantz +1

Capsule networks are a neural network architecture specialized for visual scene recognition. Features and pose information are extracted from a scene and then dynamically routed th…

cs.NE2021

Neural Kalman Filtering

Beren Millidge, Alexander Tschantz, Anil Seth +1

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 curren…

cs.AI2020

Investigating the Scalability and Biological Plausibility of the Activation Relaxation Algorithm

Beren Millidge, Alexander Tschantz, Anil Seth +1

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

q-bio.NC2020

Relaxing the Constraints on Predictive Coding Models

Beren Millidge, Alexander Tschantz, Anil Seth +1

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…

cs.NE2020

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

Beren Millidge, Alexander Tschantz, Anil K Seth +1

The backpropagation of error algorithm (backprop) has been instrumental in the recent success of deep learning. However, a key question remains as to whether backprop can be formul…

q-bio.NC2020

Neural and phenotypic representation under the free-energy principle

Maxwell J. D. Ramstead, Casper Hesp, Alec Tschantz +3

The aim of this paper is to leverage the free-energy principle and its corollary process theory, active inference, to develop a generic, generalizable model of the representational…