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Neil Burgess

7 accepted papers

2025

Modelling the control of offline processing with reinforcement learning

NeurIPS 2025poster

Brains reorganise knowledge offline to improve future behaviour, with 'replay' involved in consolidating memories, abstracting patterns from experience, and simulating new scenarios. However, there are few models of how the brain might orchestrate these processes, and of when different types of repl…

Cited by 0SourceScholar
2025

Unfolding the Black Box of Recurrent Neural Networks for Path Integration

NeurIPS 2025poster

Path integration is essential for spatial navigation. Experimental studies have identified neural correlates for path integration, but exactly how the neural system accomplishes this computation remains unresolved. Here, we adopt recurrent neural networks (RNNs) trained to perform a path integration…

Cited by 0SourceScholar
2022

Learning State Representations via Retracing in Reinforcement Learning

ICLR 2022poster

We propose learning via retracing, a novel self-supervised approach for learning the state representation (and the associated dynamics model) for reinforcement learning tasks. In addition to the predictive (reconstruction) supervision in the forward direction, we propose to include "retraced" transi…

2022

Structured Recognition for Generative Models with Explaining Away

NeurIPS 2022accept

A key goal of unsupervised learning is to go beyond density estimation and sample generation to reveal the structure inherent within observed data. Such structure can be expressed in the pattern of interactions between explanatory latent variables captured through a probabilistic graphical model. Al…