
Dr Conor Houghton
BSc(Galway), PhD(Cantab.)
Current positions
Associate Professor in Computer Science
School of Engineering Mathematics and Technology
Contact
Press and media
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Research interests
Conor Houghton is a reader in mathematical neuroscience. His research interest is in understanding information processing and coding in the brain and, generally, in mathematical and computational approaches to neuroscience.
Projects and supervisions
Research projects
A Bayesian approach to multilingual neuroimaging data
Principal Investigator
Managing organisational unit
School of Engineering Mathematics and TechnologyDates
01/01/2025 to 01/01/2026
Development of objective methods to quantify reward learning impairments in different species: a ‘cognitive biomarker’ of affective state
Principal Investigator
Managing organisational unit
School of Engineering Mathematics and TechnologyDates
01/12/2024 to 30/11/2027
Data sharing platform for international collaboration
Principal Investigator
Managing organisational unit
Dates
01/12/2022 to 31/03/2023
Data sharing platform for international collaboration
Principal Investigator
Managing organisational unit
School of Computer ScienceDates
01/12/2022 to 31/03/2023
Thesis supervisions
Excitatory and Inhibitory Transmission in Prefrontal Cortex
Supervisors
Contribution of cerebellar sensorimotor representations to behaviour
Supervisors
Bayesian Analysis for Neural Data
Supervisors
Gendered Neural Networks
Supervisors
Neural entrainment to acoustic edges in speech
Supervisors
Reward Processing and Anhedonia
Supervisors
Inferring affective state through biased actions in rats
Supervisors
Publications
Recent publications
28/04/2025Residual Stream Analysis with Multi-Layer SAEs
LEARNING REPRESENTATIONS. INTERNATIONAL CONFERENCE. 13TH 2025. (ICLR 2025)
EnvCraf
An iterated learning model of language change that mixes supervised and unsupervised learning
PLOS Complex Systems
Modeling Nonlinear Oscillator Networks Using Physics-Informed Hybrid Reservoir Computing
Scientific Reports
Jacobian Sparse Autoencoders
Jacobian Sparse Autoencoders