William Dorrell
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View article: A tale of two algorithms: Structured slots explain prefrontal sequence memory and are unified with hippocampal cognitive maps
A tale of two algorithms: Structured slots explain prefrontal sequence memory and are unified with hippocampal cognitive maps Open
Remembering events is crucial to intelligent behavior. Flexible memory retrieval requires a cognitive map and is supported by two key brain systems: hippocampal episodic memory (EM) and prefrontal working memory (WM). Although an understan…
View article: Bilateral Alignment of Receptive Fields in the Olfactory Cortex
Bilateral Alignment of Receptive Fields in the Olfactory Cortex Open
Each olfactory cortical hemisphere receives ipsilateral odor information directly from the olfactory bulb and contralateral information indirectly from the other cortical hemisphere. Since neural projections to the olfactory cortex (OC) ar…
View article: Range, not Independence, Drives Modularity in Biologically Inspired Representations
Range, not Independence, Drives Modularity in Biologically Inspired Representations Open
Why do biological and artificial neurons sometimes modularise, each encoding a single meaningful variable, and sometimes entangle their representation of many variables? In this work, we develop a theory of when biologically inspired netwo…
View article: On prefrontal working memory and hippocampal episodic memory: Unifying memories stored in weights and activity slots
On prefrontal working memory and hippocampal episodic memory: Unifying memories stored in weights and activity slots Open
Remembering events in the past is crucial to intelligent behaviour. Flexible memory retrieval, beyond simple recall, requires a cognitive map, or model of how sensations, actions, and latent environmental or task states are all related to …
View article: A Cellular Basis for Mapping Behavioural Structure
A Cellular Basis for Mapping Behavioural Structure Open
To flexibly adapt to new situations, our brains must understand the regularities in the world, but also in our own patterns of behaviour. A wealth of findings is beginning to reveal the algorithms we use to map the outside world 1–6 . In c…
View article: Disentanglement via Latent Quantization
Disentanglement via Latent Quantization Open
In disentangled representation learning, a model is asked to tease apart a dataset's underlying sources of variation and represent them independently of one another. Since the model is provided with no ground truth information about these …
View article: Meta-Learning the Inductive Biases of Simple Neural Circuits
Meta-Learning the Inductive Biases of Simple Neural Circuits Open
Training data is always finite, making it unclear how to generalise to unseen situations. But, animals do generalise, wielding Occam's razor to select a parsimonious explanation of their observations. How they do this is called their induc…
View article: Disentanglement with Biological Constraints: A Theory of Functional Cell Types
Disentanglement with Biological Constraints: A Theory of Functional Cell Types Open
Neurons in the brain are often finely tuned for specific task variables. Moreover, such disentangled representations are highly sought after in machine learning. Here we mathematically prove that simple biological constraints on neurons, n…
View article: Actionable Neural Representations: Grid Cells from Minimal Constraints
Actionable Neural Representations: Grid Cells from Minimal Constraints Open
To afford flexible behaviour, the brain must build internal representations that mirror the structure of variables in the external world. For example, 2D space obeys rules: the same set of actions combine in the same way everywhere (step n…
View article: Acoustic twisted bilayer graphene
Acoustic twisted bilayer graphene Open
Twisted van der Waals (vdW) heterostructures have recently emerged as an attractive platform to study tunable correlated electron systems. However, the quantum mechanical nature of vdW heterostructures makes their theoretical and experimen…
View article: An approach to synaptic learning for autonomous motor control
An approach to synaptic learning for autonomous motor control Open
In the realm of motor control, artificial agents cannot match the performance of their biological counterparts. We thus explore a neural control architecture that is both biologically plausible, and capable of fully autonomous learning. Th…
View article: A differential Hebbian framework for biologically-plausible motor control
A differential Hebbian framework for biologically-plausible motor control Open
In this paper we explore a neural control architecture that is both biologically plausible, and capable of fully autonomous learning. It consists of feedback controllers that learn to achieve a desired state by selecting the errors that sh…
View article: Bilateral Alignment of Receptive Fields in the Olfactory Cortex
Bilateral Alignment of Receptive Fields in the Olfactory Cortex Open
Each olfactory cortical hemisphere receives ipsilateral odor information directly from the olfactory bulb and contralateral information indirectly from the other cortical hemisphere. Since neural projections to the olfactory cortex are dis…