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Caswell Barry

7 accepted papers

2026

From movement to cognitive maps: recurrent neural networks reveal how locomotor development shapes hippocampal spatial coding

Marco P Abrate, Laurenz Muessig, Francesca Cacucci, Joshua P Bassett, Hui Min Tan, Thomas Joseph Wills +1
ICLR 2026oral

The hippocampus contains neurons whose firing correlates with an animal's location and orientation in space. Collectively, these neurons are held to support a cognitive map of the environment, enabling the recall of and navigation to specific locations. Although recent studies have characterised the…

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2025

SIMPL: Scalable and hassle-free optimisation of neural representations from behaviour

Tom George, Pierre Glaser, Kim Stachenfeld, Caswell Barry, Claudia Clopath
ICLR 2025poster

Neural activity in the brain is known to encode low-dimensional, time-evolving, behaviour-related variables. A long-standing goal of neural data analysis has been to identify these variables and their mapping to neural activity. A productive and canonical approach has been to simply visualise neural…

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2023

A generative model of the hippocampal formation trained with theta driven local learning rules

Tom George, Kim Stachenfeld, Caswell Barry, Claudia Clopath, Tomoki Fukai
NeurIPS 2023poster

Advances in generative models have recently revolutionised machine learning. Meanwhile, in neuroscience, generative models have long been thought fundamental to animal intelligence. Understanding the biological mechanisms that support these processes promises to shed light on the relationship betwee…

Cited by 11Source ↗PDF ↗Scholar ↗
2023

Probing Neural Representations of Scene Perception in a Hippocampally Dependent Task Using Artificial Neural Networks

Markus Frey, Christian F. Doeller, Caswell Barry
CVPR 2023poster

Deep artificial neural networks (DNNs) trained through backpropagation provide effective models of the mammalian visual system, accurately capturing the hierarchy of neural responses through primary visual cortex to inferior temporal cortex (IT). However, the ability of these networks to explain rep…

Cited by 3Source ↗PDF ↗Scholar ↗
2022

How to Stay Curious while avoiding Noisy TVs using Aleatoric Uncertainty Estimation

Augustine Mavor-Parker, Kimberly Young, Caswell Barry, Lewis Griffin
ICML 2022spotlight

When extrinsic rewards are sparse, artificial agents struggle to explore an environment. Curiosity, implemented as an intrinsic reward for prediction errors, can improve exploration but it is known to fail when faced with action-dependent noise sources (‘noisy TVs’). In an attempt to make exploring…

Cited by 37Source ↗PDF ↗code ↗Scholar ↗
2020

MEMO: A Deep Network for Flexible Combination of Episodic Memories

Andrea Banino, Adrià Puigdomènech Badia, Raphael Köster, Martin J. Chadwick, Vinicius Zambaldi, Demis Hassabis +4
ICLR 2020poster

Recent research developing neural network architectures with external memory have often used the benchmark bAbI question and answering dataset which provides a challenging number of tasks requiring reasoning. Here we employed a classic associative inference task from the human neuroscience literatur…

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2018

Generalisation of structural knowledge in the hippocampal-entorhinal system

James Whittington, Timothy Muller, Shirely Mark, Caswell Barry, Tim Behrens
NeurIPS 2018oral

A central problem to understanding intelligence is the concept of generalisation. This allows previously learnt structure to be exploited to solve tasks in novel situations differing in their particularities. We take inspiration from neuroscience, specifically the hippocampal-entorhinal system known…

Cited by 61Source ↗PDF ↗Scholar ↗