Papers and chapters

Modeling nonlinear oscillator networks using physics-informed hybrid reservoir computing.
Andrew Shannon, Conor Houghton, David Barton, Martin Homer
(Under review, 2024)
arXiv: 2411.05867

Cooperation as well as learning: A commentary on 'How learning can guide evolution' by Hinton and Nowlan.
Conor Houghton
(To appear Complex Systems, 2024)
arXiv: 2409.15609, just-so explanation

Residual stream analysis with multi-layer SAEs.
Tim Lawson, Lucy Farnik, Conor Houghton and Laurence Aitchison
(Under review, 2024)
arXiv: 2409.04185

Hierarchical Bayesian modeling of multi-region brain cell count data.
Sydney Dimmock, Benjamin M.S. Exley, Gerald Moore, Lucy Menage, Alessio Delogu, Simon R Schultz, E Clea Warburton, Conor J Houghton and Cian O'Donnell
(Under review, 2024)
bioRxiv, bioRxiv doi: 10.1101/2024.07.20.603979

Topological and simplicial features in reservoir computing networks.
J McAllister, J Wade, CJ Houghton and C O'Donnell
23rd Annual UK Workshop on Computational Intelligence 2024
pdf: pure.ulster.ac.uk

Golgi cells regulate timing and variability of information transfer in a cerebellar-behavioural loop.
Ensor Rafael Palacios, Conor J Houghton and Paul Chadderton
(Under review, 2024)
bioRxiv, bioRxiv doi: 10.1101/2024.07.10.602852

Investigating the timescales of language processing with EEG and language models.
Davide Turco and Conor Houghton
Conference on Cognitive Computational Neuroscience (CCN 2024)
arxiv: 2406.19884

An Iterated Learning Model of language change that mixes supervised and unsupervised learning.
Jack Bunyan, Seth Bullock and Conor Houghton
(Under review 2024)
arxiv: 2405.20818

Modeling language contact with the Iterated Learning Model.
Seth Bullock and Conor Houghton,
(To appear: ALIFE 2024)
arxiv: 2406.06878

Fostering multi-agent cooperation through implicit responsibility.
Daniel Collins, Conor Houghton and Nirav Ajmeri,
The 2nd International Workshop on Citizen-Centric Multiagent Systems (C-MAS 2024)
doi.org/10.6084/m9.figshare.25719282.v1

A latent variable modeling approach for cognitive EEG data: An example from neurolinguistics.
Davide Turco and Conor Houghton
ICLR 2024 Workshop on Learning from Time Series For Health
openreview forum, openreview pdf

Investigating grammatical abstraction in language models using few-shot learning of novel noun gender.
Priyanka Sukumaran, Conor Houghton and Nina Kazanina
In Findings of the Association for Computational Linguistics: EACL 2024, Eds. Yvette Graham and Matthew Purver (2024) pp 747-765.
aclanthology.org, arXiv: 2403.10338

Nearest-neighbours estimators for conditional mutual information.
Jake Witter and Conor Houghton
(Under review, 2024)
arXiv: 2403.00556

Reward conditioning may not have an effect on category-specific memory.
Priyanka Sukumaran, Nina Kazanina and Conor Houghton
Scientific Reports (2023) 13 22297. nature.com
doi: doi.org/10.1038/s41598-023-48874-z, psyarxiv/3ca8j, psyarxiv doi: doi.org/10.31234/osf.io/3ca8j

Signatures of Bayesian inference emerge from energy efficient synapses.
James Malkin, Cian O'Donnell, Conor Houghton and Laurence Aitchison
eLife (2024) 12:RP92595. elifesciences.org
arxiv.org/abs/2309.03194, doi: doi.org/10.7554/eLife.92595.3

Estimating mutual information for spike trains: a bird song example.
Jake Witter and Conor Houghton
Entropy (2023) 25(10), 1413; doi:10.3390/e25101413
journal, arxiv.org/abs/2309.07618

An Ising-like model for language evolution
Conor Houghton,
ALIFE 2023
doi.org/10.1162/isal_a_00682, arxiv.org/abs/2305.13916, code on github, video abstract, poster.

Spatial community structure impedes language amalgamation in a population-based iterated learning model
George Sains, Conor Houghton and Seth Bullock,
ALIFE 2023
doi.org/10.1162/isal_a_00657, arxiv.org/abs/2305.11962.

Cerebellar state estimation enables resilient coupling across behavioural domains
Ensor Rafael Palacios, Paul Chadderton, Karl Friston and Conor J Houghton,
Scientific Reports (2024) 14:6641
journal, doi: 10.1038/s41598-024-56811-x, bioRxiv, bioRxiv doi: 10.1101/2023.04.28.538674

Social value orientation and integral emotions in multi-agent systems
Daniel Collins, Conor Houghton and Nirav Ajmeri,
International Workshop on Coordination, Organizations, Institutions, Norms and Ethics for Governance of Multi-Agent Systems (COINE 2023).
arxiv.org/abs/2305.05549, proceedings.

Commentary: Beyond the limitations of any imaginable mechanism: large language models and psycholinguistics
Conor Houghton, Nina Kazanina, Priyanka Sukumaran,
Behavioral and Brain Sciences (2023) 46:e395.
doi: doi.org/10.1017/s0140525x23001693, arXiv 2303.00077, twitter thread.

Do LSTMs See gender? Probing the ability of LSTMs to learn abstract syntactic rules
Priyanka Sukumaran, Conor Houghton, Nina Kazanina,
EMNLP 2022 Workshop at BlackBoxNL
arXiv 2211.00153

A robust, compact and diverse population code for competing sounds in auditory cortex
Jian Carlo Nocon, Jake Witter, Conor J Houghton, Howard J Gritton, Xue Han and Kamal Sen,
(in press with Journal of Neurophysiology, 2023)
bioRxiv

Bayesian analysis of phase data in EEG and MEG
Sydney Dimmock, Cian O'Donnell and Conor Houghton,
eLife (2023) 12:e84602. elifesciences.org
PsyArXiv, doi: doi.org/10.7554/eLife.84602

Action-based organization and function of cerebellar cortical microcircuits
Nadia L Cerminara, Martin Garwicz, Henry Darch, Conor Houghton, Dilwyn E Marple-Horvat and Richard Apps,
Journal of Physiology (2022) 10.1113/JP282928
journal, doi: doi.org/10.1113/JP282928, bioRxiv, data: data.bris

Bayesian modeling of language-evoked event-related potentials
Davide Turco and Conor Houghton
Conference on Cognitive Computational Neuroscience (CCN 2022)
arXiv 2207.03392, code (github) doi: 10.48550/arXiv.2207.03392

Syllable-initial phonemes affect neural entrainment to consonant-vowel syllables
M Oana Cucu, Nina Kazanina and Conor Houghton
Frontiers in Neuroscience (2022)
journal, data (osf), code (github) doi: 10.3389/fnins.2022.826105

Objective measures of reward sensitivity and motivation in people with high vs low anhedonia.
Chloe Slaney, Adam M. Perkins, Robert Davis, Ian Penton-Voak, Marcus R. Munafà, Conor J. Houghton, Emma S. J. Robinson,
Psychological Medicine (2023) 53:4324-4332 doi: 10.1017/S0033291722001052
journal first view, medRxiv, data (data.bris)

Editorial: Information Processing in the Cerebellum
Conor Houghton, Philippe Isope, Richard Apps and Nadia L. Cerminara,
Frontiers Systems Neuroscience (2021) 15:752719
journal doi: 10.3389/fnsys.2021.752719

Accounting for uncertainty: inhibition for neural inference in the cerebellum.
Ensor Rafael Palacios, Conor Houghton and Paul Chadderton,
Proceeding of the Royal Society B (2021) 288: 20210276 doi: 10.1098/rspb.2021.0276
journal

Grammatical category and the neural processing of phrases.
Amelia Burroughs, Nina Kazanina and Conor Houghton,
Scientific Reports (2021) 11, 2446
journal doi: 10.1038/s41598-021-81901-5
analysis code (github), data (zenodo), twitter thread.

Do LSTMs know about Principle C?
Jeff Mitchell, Nina Kazanina, Conor Houghton and Jeff Bowers,
CCN 2019 2019
pdf, conference website, doi: 10.32470/ccn.2019.1241-0

Adaptive estimators show information compression in deep neural networks
Ivan Chelombiev, Conor Houghton and Cian O'Donnell,
ICLR 2019
arxiv.org/abs/1902.09037, open review, code

Calculating the mutual information between two spike trains
Conor Houghton,
Neural Computation (2019) 31:330-343
journal doi.org/10.1162/neco_a_01155
bioRxiv, code, paper source, abstract.

Spike train distance.
Thomas Kreuz, Conor Houghton and Jonathan D. Victor,
Encyclopedia of Computational Neuroscience. Eds. Dieter Jaeger and Ranu Jung. (Springer, 2020)
pdf.

Metric Space Analysis of Neural Information Flow.
Conor Houghton,
Encyclopedia of Computational Neuroscience. Eds. Dieter Jaeger and Ranu Jung. (Springer, 2020)
pdf.

Behavioural and computational methods reveal differential effects for how delayed and rapid onset antidepressants effect decision making in rats.
Claire A. Hales, Conor J. Houghton and Emma S. J. Robinson,
European Neuropsychopharmacology (2017) 27:1268-1280.
journal doi.org/10.1016/j.euroneuro.2017.09.008

The dynamic relationship between cerebellar Purkinje cell simple spikes and complex spike spikelet number
Amelia Burroughs, Andrew K. Wise, Jianqiang Xiao, Conor Houghton, Tianyu Tang,
Colleen Y. Suh, Eric J. Lang, Richard Apps and Nadia L. Cerminara,
The Journal of Physiology (2017) 10.1113/JP272259.
PMC5199739 researchgate journal preprint.

Diffusion modelling reveals the decision making processes underlying negative judgement bias in rats.
Claire A. Hales, Emma S. J. Robinson and Conor J. Houghton,
PLoS ONE (2016) 11:e0152592.
journal. abstract, doi: 10.1371/journal.pone.0152592

Commentary: The dentate gyrus and the hilar region revisited.
Conor Houghton,
Behavioral and Brain Sciences (2016) 39 e210.
doi / journal, pdf, researchgate, doi: 10.1017/S0140525X15001818

Calculating mutual information for spike trains and other data with distances but no coordinates.
Conor Houghton,
Royal Society Open Science, 2 (2015) 140391.
journal, doi: 10.1098/rsos.140391

Parameter estimation of neuron models using in-vitro and in-vivo electrophysiological data.
Eoin P. Lynch and Conor J. Houghton,
Frontiers in Neuroinformatics, 9 (2015) 10.
journal, doi: 10.3389/fninf.2015.00010

Metric space analysis of neural information flow.
Conor Houghton,
In Encyclopedia of Computational Neuroscience. Eds. Dieter Jaeger and Ranu Jung. (springer, 2014).
doi: 10.1007/978-1-4614-7320-6_744-1

Supervised learning with complex spikes and spike-timing-dependent plasticity.
Conor Houghton,
PLoS ONE 9 (2014) e99635.
journal, doi: 10.1371/journal.pone.0099635

Dreams, mnemonics, and tuning for criticality.
Barak A. Pearlmutter and Conor J. Houghton,
Behavioral and Brain Sciences, 36 (2013) 625-626.
journal text bibtex.

Population measures of spike train synchrony.
Conor Houghton,
Scholarpedia 8 (2013) 30635.
scholarpedia.

A kernel-based calculation of information on a metric space.
R. Joshua Tobin and Conor J. Houghton,
Entropy 15 (2013) 4540-4552.
pdf journal arXiv bibtex, doi: 10.3390/e15104540

Monitoring spike train synchrony.
Thomas Kreuz, Daniel Chicharro, Conor Houghton, Ralph G. Andrzejak and Florian Mormann,
The Journal Neurophysiology 109 (2013) 1457-1472.
journal arxiv pdf bibtex abstract, doi: 10.1152/jn.00873.2012

Measures of spike train synchrony: From single neurons to populations.
Conor Houghton and Thomas Kreuz,
In Multiscale analysis and nonlinear dynamics: from genes to the brain. Eds: Misha Meyer Pesenson and Heinz Georg Schuster. (Wiley, 2013).
book pdf bibtex.

A simple algorithm for averaging spike trains.
Hannah Julienne and Conor Houghton,
The Journal of Mathematical Neuroscience, 3 (2013) 1-14.
journal bibtex, doi: 10.1186/2190-8567-3-3

On the efficient calculation of van Rossum distances.
Conor Houghton and Thomas Kreuz,
Network, 23 (2012) 48-58.
journal pdf c++ code matlab code bibtex, doi: 10.3109/0954898X.2012.673048

A metric space approach to the information capacity of spike trains.
James Gillespie and Conor Houghton,
The Journal of Computational Neuroscience, 30 (2011) 201-209.
pdf source journal bibtex abstract, doi: 10.1007/s10827-010-0286-8

Measuring representational distances -- the spike-train metrics approach.
Conor Houghton and Jonathan D. Victor,
In Understanding Visual Population Codes - Towards a Common Multivariate Framework for Cell Recording and Functional Imaging. Eds: Nikolaus Kriegeskorte and Gabriel Kreiman. (MIT Press, 2012).
pdf from doc pdf from tex source bibtex.

Sparse coding of birdsong and receptive field structure in songbirds
Garrett Greene, David Barrett, Kamal Sen and Conor Houghton,
Network: Computation in Neural Systems 20 (2009) 162-177.
pdf journal bibtex, doi: 10.1080/09548980903108267

Tuning for criticality: a new hypothesis for sleep.
Barak A. Pearlmutter and Conor J. Houghton,
Neural Computation 21 (2009) 1622-1641.
pdf journal bibtex abstract, doi: 10.1162/neco.2009.05-08-787

Studying spike trains using a van Rossum metric with a synapse-like filter.
Conor Houghton,
Journal of Computational Neuroscience 26 (2009) 149-155.
pdf journal bibtex abstract, doi: 10.1007/s10827-008-0106-6

A new multi-neuron spike-train metric.
Conor Houghton and Kamal Sen,
Neural Computation 20 (2008) 1495-1511.
pdf journal bibtex abstract, doi: 10.1162/neco.2007.10-06-350

Refereed papers in physics

The effect of pion mass on Skyrme configurations.
Conor Houghton and Shane Magee,
hep-th/0602227. Europhys. Lett. 77 (2007) 11001.

A zero-mode quantization of the Skyrmion
Conor Houghton and Shane Magee,
hep-th/0509098. Phys. Lett. B632 (2006) 593-596.

Pulsation and precession of the resonant swinging spring
Peter Lynch and Conor Houghton,
nlin.SI/0303038, Physica D 190 (2004) 38.
abstract.

Icosahedral Skyrmions
Richard Battye, Conor Houghton and Paul Sutcliffe,
hep-th/0210147, J. Math. Phys. 44 (2003) 3543.

Multicloud solutions with massless and massive monopoles.
Conor J. Houghton and Erick J. Weinberg,
hep-th/0207141, Phys. Rev. D66 (2002) 125002.

Folding in the Skyrme model.
Conor Houghton and Steffen Krusch,
hep-th/0104222, J. Math. Phys. 42 (2001) 4079.

On the generalized Legendre transform and monopole metrics.
C.J. Houghton,
hep-th/9910212, JHEP 02 (2000) 042.

On the constraints defining BPS monopoles.
C.J. Houghton, N.S. Manton and N.M. Romao,
hep-th/9909168, Commun. Math. Phys. 212 (2000) 219.

Nahm data and the mass of 1/4-BPS states.
Conor J. Houghton and Kimyeong Lee,
hep-th/9909218, Phys. Rev. D61 (2000) 106001.

Instanton vibations of the 3-Skyrmion.
Conor J. Houghton,
hep-th/9905009, Phys. Rev. D60 (1999) 105003.

Two monopoles of one type and one of another.
Conor J. Houghton, Patrick Irwin and Arthur J. Mountain,
hep-th/9902111, JHEP 04 (1999) 029.

SU(N) monopoles and Platonic symmetry.
Conor J. Houghton and Paul M. Sutcliffe,
hep-th/9708006, J. Math. Phys. 38, 5576-5589 (1997).

Rational maps, monopoles and Skyrmions.
Conor J. Houghton, Nicholas S. Manton and Paul M. Sutcliffe,
hep-th/9705151, Nucl. Phys. B510, 507-537 (1998).
abstract.

New hyper-Kaehler manifolds by fixing monopoles.
Conor J. Houghton,
hep-th/9702161, Phys. Rev. D56, 1220-1227 (1997).

Inversion symmetric 3-monopoles and the Atiyah-Hitchin manifold.
Conor J. Houghton and Paul M. Sutcliffe,
hep-th/9609136, Nonlinearity 9, 1609-1622 (1996).

Monopole scattering with a twist.
Conor J. Houghton and Paul M. Sutcliffe,
hep-th/9601148, Nucl. Phys. B464, 59-84 (1996).
abstract.

Octahedral and dodecahedral monopoles.
Conor J. Houghton and Paul M. Sutcliffe,
hep-th/9601147, Nonlinearity 9, 385-401 (1996).

Tetrahedral and cubic monopoles.
Conor J. Houghton and Paul M. Sutcliffe,
hep-th/9601146, Commun. Math. Phys. 180, 342-362 (1996).

Some miscellaneous reports, notes, slides and posters

The distribution of language populations in an Ising model for language evolution.
Ludo Oliver and Conor Houghton,
Poster at CCS24
doi: doi.org/10.5281/zenodo.13709968

A new iterated learning model of language evolution.
Conor Houghton,
Talk at COMPILA2024
slides: doi.org/10.5281/zenodo.13692349, recording: youtube

Investigating the timescales of language processing with EEG and language models.
Davide Turco and Conor Houghton,
Poster at CCN24
doi: doi.org/10.5281/zenodo.13134148

Modeling language contact with the iterated learning model
Seth Bullock and Conor Houghton
Talk at ALIFE24
doi: doi.org/10.5281/zenodo.12800288

Navigating EEG data analysis for cognitive tasks
Conor Houghton
Talk at the Third London Computational Medicine Meetup hosted by Machine Medicine
youtube

Simple models of language evolution
Conor Houghton
Seminar at the Santa Fe Institute 2023-10-19
doi: doi.org/10.5281/zenodo.10029083

EEG and neurolinguistics
Conor Houghton
Seminar at Nottingham University 2023-10-10
doi: doi.org/10.5281/zenodo.10029076