ICMS

Apr 19, 2010 - Apr 21, 2010

15 South College Street, Edinburgh EH8 9AA

Organisers

Name	Institution
Coombes, Stephen	University of Nottingham
Timofeeva, Yulia	University of Warwick

Training Workshop Organiser
Dr Mark van Rossum, University of Edinburgh

 

This is the third annual meeting of the UK Mathematical Neuroscience Network. The network is focussed on using mathematics as the primary tool for elucidating the fundamental mechanisms responsible for experimentally observed behaviour in the applied neurosciences. This three-day conference will provide an overview of the current state of research in mathematical approaches to neuroscience, bringing together both physical and life scientists. Drawing together the field in this way will allow for a critical discussion of the relevant experimental facts and of various mathematical methods and techniques that have been successfully applied to date. Importantly, it will draw attention to, and help develop, those pieces of mathematical theory which are likely to be relevant to future studies of the brain.

The meeting will consist of invited speakers, core members and registered participants. In addition, the schedule will allow for a number of poster presentations.

This event is sponsored by the EPSRC via the UK Mathematical Neuroscience Network

Invited Speakers

Paul Bressloff (University of Oxford)
Romain Brette (Ecole Normale Supérieure, Paris)
Peter Dayan (University College London)
Bard Ermentrout (University of Pittsburgh)
Yixin Guo (Drexel University)
Kevin Gurney (University of Sheffield)
Alex Roxin (Columbia University)
Steven Schiff (Penn State University)
Eric Shea-Brown (University of Washington)
Louis Tao (Peking University)
Paul Tiesinga (Radboud University Nijmegen)
Arjen van Ooyen (VU University Amsterdam)
Xiao-Jing Wang (Yale University )
John White (University of Utah)
Si Wu (Shanghai Institute for Biological Sciences)

Arrangements

Participation
The workshop is now full.
Invited speakers will receive an e-mail invitation.

Publication
Abstracts will also be published on the Frontiers in Computational Neuroscience website.

Venue
The workshop will be held at 15 South College Street, Edinburgh. All lectures will be held in the Newhaven Lecture Theatre. To view this room and a list of the visual equipment available click here. In addition, two blackboards have recently been installed. Follow this link for a map showing the location of 15 South College Street, or this map may also prove useful.

Training Workshop
A training workshop will take place on Sunday 18 April 2010. Further details will follow.

Wireless Access
The workshop venue, 15 South College Street, has wireless access throughout. On arrival you will be given instructions and a code for accessing the wireless network.

Registration
Registration will take place from 08.45 - 09.30, on Monday 19 April.

Registration fee
There is a registration fee of 80.00 GBP for this meeting.

Meals and Refreshments
Morning and afternoon refreshments will be provided at the venue.

A Conference Dinner will take place on Tuesday 20 April at 19.00.

Accommodation
Participants are asked to make their own accommodation arrangements. A list of Edinburgh accommodation of various sorts and prices is available here .

Financial Assistance
Some financial assistance is available to assist graduate students who attend BOTH the training workshop and the conference. Applicants should provide a brief résumé including degrees and at least one recommendation letter explaining their suitability for the activity. The résumé and letter should be e-mailed to Irene Moore. Students requesting financial support will be notified by 24 February 2010.

Programme

Monday 19 April 2010

09.00 - 10.30	Registration Ground Floor, 15 South College Street
10.30 - 10.45	Welcome and introductions
10.45 - 11.30	Arjen van Ooyen (VU University Amsterdam) Impact of dendritic size and dendritic topology on burst firing in pyramidal cells
11.30 - 12.15	John White (University of Utah) Mechanisms of Coherent Activity in the Hippocampal Formation
12.15 - 14.00	Lunch, Ground Floor, 15 South College Street
14.00 - 14.45	Kevin Gurney (University of Sheffield) Computational modelling of the basal ganglia at different levels of abstraction
14.45 - 15.30	Romain Brette (Ecole Normale Supérieure ) Neural computation with synchrony
15.30 - 16.15	Coffee
16.15 - 17.00	Paul Tiesinga (Radboud University Nijmegen) Phase and synchrony dynamics of gamma oscillations in cortical networks
18.00 - 19.00	Poster session and wine reception, Ground Floor, 15 South College Street

Tuesday 20 April 2010

09.00 - 09.45	Bard Ermentrout (University of Pittsburgh) tba
09.45 - 10.30	Xin Guo (UC Berkeley) tba
10.30 - 11.15	Coffee
11.15 - 12.00	tba tba
12.00 - 12.45	Eric Shea-Brown (University of Washington Dynamics and impact of spike-time correlations
12.45 - 14.30	Lunch
14.30 - 16.30	MMN network discussion - all welcome (or sightseeing afternoon for international attendees)
19.00	Conference Dinner - Howies, Waterloo Place

Wednesday 21 April 2010

09.00 - 09.45	Peter Dayan(UCL) Statistical models of visual contextual interactions
09.45 - 10.30	Louis Tao (New Jersey Institute of Technology) Mechanisms of Contrast Adaptation in the Early Visual Pathway
10.30 - 11.15	Coffee
11.15 - 12.00	Si Wu (Penn State University) Dynamics of Continuous Attractor Neural Networks
12.00 - 12.45	Xiao-Jing Wang (University of Washington Attractor models for cognitive functions: theory meets experiments
12.45 - 14.30	Lunch
14.15 - 15.00	Alex Roxin (Universitat Pompeu Fabra) The effect of degree distribution on the dynamics of sparsely connected neuronal networks
15.00 - 15.45	Paul Bressloff (University of Oxford) tba

Presentations:

Presentation Details
Brette, Romain
Neural computation with synchrony
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Neuronal synchronization is ubiquitous in the nervous system, but it is not clear what computational role it might play, if any. I show that structure in stimuli, for example sound periodicity or correlations in binaural signals, results in specific patterns of synchrony in spiking models, which produce increased firing in specific assemblies of postsynaptic neurons. Such elementary neural circuits extract structural properties of stimuli with natural invariances.
Dayan, Peter
Statistical models of visual contextual interactions
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A central hypothesis about early visual processing is that it represents inputs in a coordinate system matched to the statistics of natural scenes. Simple versions of this lead to Gabor-like receptive fields and divisive gain modulation from local surrounds; these have led to influential neural and psychological models of visual processing. However, these accounts are based on an incomplete view of the visual context surrounding each point. We consider exact and approximate versions of a richer statistical model, and show how it captures psychophysical data on the tilt illusion and salience processing (as in Li, 2002), and also physiological data on the effects of such surround stimuli.
Gurney, Kevin
Computational modelling of the basal ganglia at different levels of abstraction
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The basal ganglia are a set of sub-cortical nuclei that are believed to play a critical role in solving the problem of action selection – the competition between brain systems requesting behavioural expression through limited motor resources. More specifically, the basal ganglia are hypothesised to play the role of a central ‘switch’ which examines the salience, or urgency, of action-requests, and allows some actions to proceed while others are prohibited. Over the last decade we have tested the action selection hypothesis of basal ganglia function using a series of computational models at several levels of description. The models range from the ‘systems level’, which use rate-coded neural populations and allow close contact with behavioural expression, through large scale spiking networks, to more detailed models of microcircuits of individual nuclei. Many of the models are grounded directly in the neural substrate by ‘mining’ the known biology for mechanisms supporting the putative computation (selection). However, the basal ganglia also appear to support a well-know decision making algorithm developed initially in the machine learning community. This talk will explore the relation between models at different structural levels of description and the relation between mechanistic and algorithmic accounts.
Roxin, Alex
The effect of degree distribution on the dynamics of sparsely connected neuronal networks
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Most work on sparsely connected networks of spiking neurons has made use of the assumption that there is a fixed probability of connection between any two neurons. This results in a Binomial degree distribution which is sharply peaked about the most likely value for large enough numbers of inputs. This, in turn, ensures that all neurons receive, on average, the same amount of input, allowing for powerful meanfield techniques to be applied. However, much recent electrophysiological work indicates that local cortical circuits are not consistent with the assumption described above. I will discuss how one might relax this assumption and allow for much broader degree distributions. For the case of the incoming connections, the effect of degree distribution can be accounted for in a modified rate equation in which the degree is treated as a continuous, pseudo-spatial variable. This allows one to calculate fixed point solutions and their stability using standard techniques. I will show how varying the degree distribution shifts stability boundaries of various cortical states relative to the Binomial case, and compare analytical results to full network simulations. I will also discuss how the degree distribution for outgoing connections affects correlations in the output of the neurons.
Shea-Brown, Eric
Dynamics and impact of spike-time correlations
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Correlations among neural spike times are ubiquitous, and open the possibility for cooperative coding of sensory inputs across neural populations. The structure of the correlations that emerge across cells, however, can depend strongly on spike generation mechanisms in the individual neurons. In joint work, we investigate the interplay among these mechanisms, network architecture, and the coding of simple classes of inputs.
Tao, Louis
Mechanisms of Contrast Adaptation in the Early Visual Pathway
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The response firing rate of visual cortical neurons is usually reduced after exposure to high-contrast stimuli. This 'contrast adaption' has been attributed to adaptation in the thalamocortical input, in the intrinsic property of visual cortical neurons, or to network mechanisms. Here we show that many of the features of extant data can be explained by a combination of stimulus adaptation in the thalamus and nonlinearity in the recurrent V1 network. Furthermore, spatio-temporal dynamics of experimental data can be rationalized and interpreted within a physiologically realistic model of the visual cortex.
Tiesinga, Paul
Phase and synchrony dynamics of gamma oscillations in cortical networks
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Recent experiments demonstrate that in prefrontal cortex spikes at a specific gamma (30-80 Hz) phase relative to the local field potential are most informative about stimulus identity, where the specific value of the phase depends on the order of the stimulus within the stimulus sequence (Siegel et al, PNAS, in press). Within the context of a model cortical circuit this corresponds to changes in the relative phase between excitatory (E) and inhibitory (I) cells. We study using mathematical analysis and simulations how phase and synchrony can be modulated in the cortical circuit for gamma oscillations generated by the pyramidal interneuron gamma (PING) mechanism. We compare the effect of neuromodulators such as acetylcholine, which can be modeled as a depolarizing current, to that of short current pulses representing optical activation of channelrhodopsin-2 (ChR2) channels. From a functional point of view, our model results predict that targeting I cells by neuromodulators will change the gain local to the circuit, that is, of those neurons receiving local E and I inputs (Buia & Tiesinga, J. Computational Neuroscience 2006), whereas light pulses will modulate the effective strength of connectivity between circuits in different cortical areas according to the Communication through Coherence principle (Fries, Trends in Cognitive Sciences 2005).
van Ooyen, Arjen
Impact of dendritic size and dendritic topology on burst firing in pyramidal cells
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Neurons possess highly branched extensions, called dendrites, which form characteristic tree-like structures. The morphology of these dendritic arborizations can undergo significant changes in many pathological conditions. It is still poorly known, however, how alterations in dendritic morphology affect neuronal activity. Using computational models of pyramidal cells, we study the influence of dendritic tree size and branching structure on burst firing. Burst firing is the generation of two or more action potentials in close succession, a form of neuronal activity that is critically involved in neuronal signaling and synaptic plasticity. We found that there is only a range of dendritic tree sizes that supports burst firing, and that this range is modulated by the branching structure of the tree. We show that shortening as well as lengthening the dendritic tree, and even modifying the pattern in which the branches in the tree are connected, can shift the cell’s firing pattern from bursting to tonic firing, as a consequence of changes in the spatiotemporal dynamics of the dendritic membrane potential. Our results suggest that alterations in pyramidal cell morphology could, via changes in burst firing, ultimately affect cognition.
Wang, Xiao-Jing
Attractor models for cognitive functions: theory meets experiments
White, John
Mechanisms of Coherent Activity in the Hippocampal Formation
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The hippocampal formation is crucial for remembering episodes in one’s life, and evidence suggests that synchronous activity throughout the hippocampus is essential for the mnemonic functions of this brain structure. We have studied the mechanisms of synchronization using electrophysiological and computational methods. More recently, we have exploited methods for introducing real-time control in cellular electrophysiology. These techniques allow us to “knock in” virtual ion channels that can be controlled with great mathematical precision, and to immerse biological neurons in real-time, virtual neuronal networks. These manipulations allow us to test computationally-based hypotheses in living cells. From this work, I will discuss which properties of single cells and neural networks seem crucial for coherent activity in the hippocampal formation. If time permits, I will also discuss work on the consequences of precise spike timing in neuronal network function.
Wu, Si
Dynamics of Continuous Attractor Neural Networks
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Understanding how the dynamics of a neural network is shaped by the network structure, and consequently how the network structure facilitates the functions implemented by the neural system, is at the core of using mathematical models to elucidate brain functions. In this talk, I will introduce our recent results on studying the tracking dynamics of continuous attractor neural networks (CANNs). Due to the translational invariance of neuronal recurrent interactions, CANNs can hold a continuous family of stationary states. They form a continuous manifold in which the neural system is neutrally stable. We systematically explore how this property facilitates the tracking performance of a CANN, which is believed to have clear correspondence with brain functions. By using the wave functions of the quantum harmonic oscillator as the basis, we demonstrate how the dynamics of a CANN is decomposed into different motion modes, corresponding to distortions in the amplitude, position, width or skewness of the network state. We then develop a perturbation approach that utilizes the dominating movement of the network's stationary states in the state space. This method allows us to approximate the network dynamics up to an arbitrary accuracy depending on the order of perturbation used. We quantify the distortions of a Gaussian bump during tracking, and study their effects on the tracking performance. Results are obtained on the maximum speed for a moving stimulus to be trackable and the reaction time for the network to catch up with an abrupt change in the stimulus.

Participants

Name	Institution
Acerbi, Luigi	University of Edinburgh
Ahmadi, Mandana	University College London
Akam, Thomas	UCL
Albantakis, Larissa	UPF Barcelona
Azad, Abul Kalam al	University of Plymouth
Barreto, Ernest	George Mason University
Barrett, David	University College London
Beck, Jeff	University College London
Berthouze, Luc	University of Sussex
Bodova, Katarina	Comenius University
Borisyuk, Roman	University of Plymouth
Bouchain, David	Ulm University
Bressloff, Paul	University of Oxford
Brette, Romain	Ecole Normale Supérieure
Buckwar, Evelyn	Heriot-Watt University
Castejon, Oriol	Universitat Politècnica de Catalunya
Caudron, Quentin	University of Warwick
Chalk, Matthew	University of Edinburgh
Coombes, Stephen	University of Nottingham
da Silva Gomes, Joao Paulo	University of Plymouth
Dayan, Peter	Gatsby Computational Neuroscience Unit
Elijah, Daniel	University of Manchester
Ermentrout, Bard	University of Pittsburgh
Erwin, Harry	University of Sunderland
Fasoli, Diego	INRIA
Faye, Gregory	INRIA
Freestone, Dean	University of Edinburgh
Galtier, Mathieu	INRIA
Gheorghe, Ana Maria	University of Nottingham
Goodman, Dan	Ecole Normale Superieure
Graham, Bruce	University of Stirling
Guillamon, Toni	Universitat Politècnica de Catalunya
Gurney, Kevin	University of Sheffield
Hauser, Florian	Ulm University
Heinke, Dietmar	University of Birmingham
Hennig, Matthias	University of Edinburgh
Hermann, Geoffroy	INRIA
Humble, James	University of Plymouth
Insabato, Andrea	University Pompeu Fabra
Jerzejewska-Szmek, Joanna	University of Warsaw
Khachidze, Irma	Beritashvili Institute of Physiology and Tatishvili Medical Center
Khoeiniha, Soroush	Umeå University
Kollar, Richard	Comenius University
La Camera, Giancarlo	University of Bern
Langdon, Angela	University of New South Wales
Lefebvre, Jérémie	University of Ottawa
Lerchner, Alexander	University College London
Liu, Jindong	University of Sunderland
Lloyd, Kevin	University of East Anglia
Lord, Gabriel	Heriot-Watt University
Lyamzin, Dmitry	LMU Munich
Mamun, Khondaker Abdullah-Al-	University of Southampton
Marten, Frank	University of Bristol
Masud, Mohammad	University of Plymouth
Matthey, Loic	University College London
McGuinness, Darren	UCL
Montemurro, Marcelo	University of Manchester
Mugruza Vassallo, Carlos	University of Dundee
Ngonghala, Calistus	West Virginia University
Niyogi, Ritwik	University College London
Nowacki, Jakub	University of Bristol
Oleynik, Anna	Norwegian University of Life Sciences
Olmi, Simona	Istituto dei Sistemi Complessi
Osinga, Hinke	University of Bristol
Petersen, Rasmus	University of Manchester
Pirmoradian, Sahar	SISSA
Potadar, Nitinkumar	Ramnarain Ruia College
Rabinowitz, Neil	University of Oxford
Richards, Blake	University of Oxford
Riedler, Martin	Heriot-Watt University
Roxin, Alex	Columbia University
Rumbell, Tim	University of Plymouth
Sandford, Caroline	UCL
Schaette, Roland	UCL
Schmidt, Helmut	University of Nottingham
Shea-Brown, Eric	University of Washington
Sitt, Jacobo	University of Buenos Aires
Subramaniam, Saravanan	Deemed University
Tao, Louis	Peking University
Tartaglia, Elisa	EPFL
Taxidis, Ioannis	University of Nottingham
Terry, John	University of Bristol
Thul, Ruediger	University of Nottingham
Tiesinga, Paul	Radboud University Nijmegen
Timofeeva, Yulia	University of Warwick
Tristan, Webb	University Of Warwick
van Ooyen, Arjen	VU University Amsterdam
van Rossum, Mark	University of Edinburgh
Vega Gonzalez, Jose Luis	Newcastle University
Veltz, Romain	INRIA
Wang, Xiao-Jing	Yale University School of Medicine
Wedgwood, Kyle	University of Nottingham
White, John	University of Utah
Williamson, Ross	University College London
Willshaw, David	University of Edinburgh
Wu, Si	Chinese Academy of Sciences
Zeiner, Katharina	University of St Andrews