Quentin Huys | University College London (original) (raw)

Papers by Quentin Huys

Abstract We propose a reinforcement based framework for learning in recurrently connected populat... more Abstract We propose a reinforcement based framework for learning in recurrently connected populations of spiking neurons. Learning makes use of a reward signal, which conveys information about the quality of probabilistic inference based on the population spikes, and yet requires predominantly local information to specify synaptic plasticity.

When planning a series of actions, it is usually infeasible to consider all potential future sequ... more When planning a series of actions, it is usually infeasible to consider all potential future sequences; instead, one must prune the decision tree. Provably optimal pruning is, however, still computationally ruinous and the specific approximations humans employ remain unknown.

Decision-making invokes two fundamental axes of control: affect or valence, spanning reward and p... more Decision-making invokes two fundamental axes of control: affect or valence, spanning reward and punishment, and effect or action, spanning invigoration and inhibition. We studied the acquisition of instrumental responding in healthy human volunteers in a task in which we orthogonalized action requirements and outcome valence. Subjects were much more successful in learning active choices in rewarded conditions, and passive choices in punished conditions.

Using fMRI and dynamic causal modelling [1], we previously showed that the brain learns fixed pro... more Using fMRI and dynamic causal modelling [1], we previously showed that the brain learns fixed probabilistic associations between simple auditory and visual stimuli, even when these stimuli are behaviourally irrelevant [2]. During learning, visual cortex activity and auditory-visual connectivity changed in accordance with the learning curve predicted by a Rescorla-Wagner (RW) model of associative learning.

Abstract Our understanding of the input-output function of single cells has been substantially ad... more Abstract Our understanding of the input-output function of single cells has been substantially advanced by biophysically accurate multi-compartmental models. The large number of parameters needing hand tuning in these models has, however, somewhat hampered their applicability and interpretability.

Objectives: Selecting patients with asymmetrical sensorineural hearing loss for further investiga... more Objectives: Selecting patients with asymmetrical sensorineural hearing loss for further investigation continues to pose clinical and medicolegal challenges, given the disparity between the number of symptomatic patients, and the low incidence of vestibular schwannoma as the underlying cause.

Abstract When planning a series of actions, it is usually infeasible to consider all potential fu... more Abstract When planning a series of actions, it is usually infeasible to consider all potential future sequences; instead, one must prune the decision tree. Provably optimal pruning is, however, still computationally ruinous and the specific approximations humans employ remain unknown.

It is a commonplace that psychiatric disorders such as depression and schizophrenia involve subst... more It is a commonplace that psychiatric disorders such as depression and schizophrenia involve substantial disturbances to affectively-charged decision-making. Although our understanding of the latter has substantially advanced, with theoretical concepts drawn from reinforcement learning (RL) providing a normatively-sound framework within which to link psychological, neurobiological and pharmacological results, there have hitherto been only a few applications to psychiatry.

ABSTRACT Depression, like many psychiatric disorders, is a disorder of affect. Over the past deca... more ABSTRACT Depression, like many psychiatric disorders, is a disorder of affect. Over the past decades, a large number of affective issues in depression have been characterised, both in human experiments and animal models of the disorder. Over the same period, experimental neuroscience, helped by computational theories such as reinforcement learning, has provided detailed descriptions of the psychology and neurobiology of affective decisions making.

Page 1. Optimal helplesness Normative models of depression Quentin Huys and Peter Dayan July 2006... more Page 1. Optimal helplesness Normative models of depression Quentin Huys and Peter Dayan July 2006, CNS, Edinburgh Optimal helplesness :: Normative models of depression Quentin Huys, Gatsby Unit – p. 1 Page 2.

Naturally occurring sensory stimuli are dynamic. In this article, we consider how spiking neural ... more Naturally occurring sensory stimuli are dynamic. In this article, we consider how spiking neural populations might transmit information about continuous dynamic stimulus variables. The combination of simple encoders and temporal stimulus correlations leads to a code in which information is not readily available to downstream neurons. Here, we explore a complex encoder that is paired with a simple decoder that allows representation and manipulation of the dynamic information in neural systems.

The Journal of …, Jan 1, 2011

PLoS Computational …, Jan 1, 2011

Neural Networks, Jan 1, 2011

Mathematically rigorous descriptions of key hypotheses and theories are becoming more common in n... more Mathematically rigorous descriptions of key hypotheses and theories are becoming more common in neuroscience and are beginning to be applied to psychiatry. In this article two fictional characters, Dr. Strong and Mr. Micawber, debate the use of such computational models (CMs) in psychiatry. We present four fundamental challenges to the use of CMs in psychiatry: (a) the applicability of mathematical approaches to core concepts in psychiatry such as subjective experiences, conflict and suffering; (b) whether psychiatry is mature enough to allow informative modelling; (c) whether theoretical techniques are powerful enough to approach psychiatric problems; and (d) the issue of communicating clinical concepts to theoreticians and vice versa. We argue that CMs have yet to influence psychiatric practice, but that they help psychiatric research in two fundamental ways: (a) to build better theories integrating psychiatry with neuroscience; and (b) to enforce explicit, global and efficient testing of hypotheses through more powerful analytical methods. CMs allow the complexity of a hypothesis to be rigorously weighed against the complexity of the data. The paper concludes with a discussion of the path ahead. It points to stumbling blocks, like the poor communication between theoretical and medical communities. But it also identifies areas in which the contributions of CMs will likely be pivotal, like an understanding of social influences in psychiatry, and of the co-morbidity structure of psychiatric diseases.

Advances in neural information …, Jan 1, 2009