Metastable Dynamics of Neural Ensembles Workshop CNS 2013

Date: July 14–18 2013

This workshop will have the participation of some of the most recognized European experts in neural modelling in the context of the 23th Conference on Computational Neuroscience CNS 2013.

Organizers

Emili Balaguer–Ballester School of Engineering and Computing, Bournemouth University, UK. Bernstein Center for Computational Neuroscience, University of Heidelberg, Germany.
Gustavo Deco Department of Technology, University Pompeu Fabra, Barcelona, Spain.

Collaborator

Dr. Abdelhamid Bouchachia, BU

Scope of the workshop

Is the traditional view on brain activity dynamics, in which the cognitive flow of information wanders through multiple stable states driven by task–dependent inputs, still a robust model? This picture has been recently challenged both empirically and from the modelling perspective. In this workshop we will address a range of recent complementary views of cortical activity dynamics.

In several contemporary models, intrinsic activity fluctuations drive default transitions between metastable states shaped by anatomical connectivity, even in the absence of external stimuli. Noise enriches the dynamical repertoire of deterministic states; creating flexible "ghost" attractors which permit the effective processing of task–related cognitive entities. Another proposed metaphor of transient brain dynamics consisted of successions of metastable saddle states; dynamical objects which are particularly reliable but form which neural activity eventually switches among them, even without the intervention of noise.

In this workshop we will have modelling and data analysis contributions which focus on metastable activity dynamics, analyses of non–stationary neural recordings and transient dynamics during cognitive processing. The next topics will be discussed:

Attracting and transient dynamics of neural ensembles
Non–stationary neural recordings and data analyses
Cortical activity dynamics at resting state
Transient dynamics during perception
Travelling waves in cortex
Cognitive processing dynamics.

Schedule:

This event will take place on the July 17 2013.

Morning

9:25 Welcome
9:30 Daniel Durstewitz, Bernstein-Center for Computational Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim/ Heidelberg University | Abstract
Highly Chaotic Yet Ordered: Cortical Dynamics as Stochastic Transitions Among Semi-Attracting States

Abstract: ‘Edge–of–chaos’ computation, associated with power–law distributions and long–memory characteristics, has been an appealing and influential concept in computational neuroscience over the last decade. The idea (observation) is that bringing systems close to the transition from ordered (convergent) to disordered (divergent) dynamics is highly beneficial for different types of computation: Network dynamics is sufficiently complex to allow for interesting computations, yet not (too) divergent such that it would quickly loose information about the actual inputs. In my talk I will argue that the cortex is way beyond that transition, i.e. is highly chaotic and noisy, and will make suggestions on how to reconcile this with the idea of ordered dynamics and sensible computations.

10:00 Pablo Varona | Abstract
Models and novel experimental tools to address information processing in transient neural dynamics

Abstract: In this talk I will describe the formalism of stable heteroclinic channels to characterize transient sequential neural dynamics at many stages of the sensory-motor transformation: from sensory encoding, to motor coordination. I will also illustrate how novel closed-loop activity-dependent stimulation techniques can be used to unveil information processing in transient neural activity and achieve control of normal or pathological states
10:30 Coffee break
10:45 Ruben Moreno–Bote, Foundation Sant Joan de Déu, Barcelona | Abstract
Poisson–like spiking and contrast invariant sampling in multi-stable networks with probabilistic synapses

Abstract: Neuronal activity in cortex is variable both spontaneously and during stimulation, and has the remarkable property that it is Poisson-like over broad ranges of firing rates covering from virtually zero to hundreds of spikes per second. The mechanisms that can lead to cortical-like spiking variability over such broad range are currently unknown. We show that neuronal networks endowed with probabilistic synaptic transmission, a well-documented source of variability in cortex, robustly generates Poisson-like variability over several orders of magnitude in their firing rate without fine-tuning of the network parameters. This mechanism predicts that the size of membrane potential fluctuations is approximately constant with firing rate and that the variance of the synaptic conductances is proportional to the mean conductances. We finally show that probabilistic synapses allow networks to sample their states in a contrast-invariant manner, providing cortical circuits with a natural way to perform probabilistic inference in ambiguous conditions.
11:15 Gustavo Deco, University Pompeu Fabra, Barcelona

The link between Structure and Dynamics in Whole Brain Models
11:45 Short discussion

Afternoon

13:30 Marcello Massimini, Department of Clinical Sciences "Luigi Sacco", University of Milan | Abstract
Consciousness and brain complexity: from theory to practice

Abstract: Theoretical considerations suggest that consciousness depends on the ability of neural elements to engage in complex activity patterns that are, at once, distributed within a system of interacting cortical areas (integrated) and differentiated in space and time (information-rich). We thus hypothesized that the level of consciousness could be estimated empirically by perturbing the cortex to engage distributed interactions and by measuring the information content (algorithmic complexity) of the resulting responses. We found that the algorithmic complexity of cortical responses to transcranial magnetic stimulation reliably discriminated the level of consciousness in single individuals across different conditions in which consciousness was altered physiologically, pharmacologically and pathologically. This theoretically motivated quantification of brain complexity allows establishing a reliable, graded measurement scale along the consciousness/unconsciousness spectrum and provides a principled approach for estimating the level of consciousness at the bedside.

14:00 Mavi Sanchez-Vives, IDIBAPS, Barcelona

Organization of emergent patterns of activity in control and altered cortical networks
14:30 Coffee break
14:45 Maurizio Mattia, Instituto Superiore di Sanitá, Rome | Abstract
Multiscale nonlinear dynamics of slow oscillations across cortical surface

Abstract: Slow oscillations (< 1 Hz) in which Up states of sustained neural activity alternate with almost quiescent Down states, are similarly expressed by mammal brains during slow-wave-sleep (SWS) and under anesthesia [1]. Transitions between Up and Down states occur orderly in time and space across cortical surface, appearing like propagating waves in anesthetised rodents [2,3] and sleeping humans [4]. Propagation speed of wave fronts and state durations are so balanced to have ongoing Up and Down states distributed across the whole cerebral cortex. Such large scale phenomenon emerges from the synaptic interaction between fatigue-sensitive bistable densely connected local networks of neurons [5,6,7]. In these 'mesoscopic cortical modules', high firing Up states result from synaptic reverberation and have stability progressively weakened by the consumption of synaptic resources and activity-dependent changes in perisomatic ionic concentrations. These fatigue processes drive the quasi-periodic transitions to the Down state, where modules are attracted until all resources restore. Further inspecting these Up/Down slow oscillations at higher spatial resolution, modules display a microscopic structure across cortical layers: deeper neuronal pools are the first to react during Up state onsets, rapidly followed by Down-to-Up transitions occurring in more superficial layers [8,9]. Here, we discuss about this multiscale nonlinear system comparing in vivo recordings from anesthetized rats and computational models. In particular we will focus on heterogeneities observed in propagating waves and Up state onsets across cortical layers, discussing about the different contributions of cortico-cortical connectivity and local organization of cortical modules to such variability.

1. Timofeev I, Chauvette S: Thalamocortical oscillations: local control of EEG slow waves. Curr Top Med Chem 2011, 11: 2457-2471.
2. Huang X, Xu W, Liang J, Takagaki K, Gao X, Wu JY: Spiral wave dynamics in neocortex. Neuron 2010, 68: 978-990.
3. Ruiz-Mejias M, Ciria-Suarez L, Mattia M, Sanchez-Vives MV. Slow and fast rhythms generated in the cerebral cortex of the anesthetized mouse. J Neurophysiol 2011, 106: 2910-2921.
4. Massimini M, Huber R, Ferrarelli F, Hill S, Tononi G. The sleep slow oscillation as a traveling wave. J Neurosci 2004, 24: 6862-6870.
5. Sanchez-Vives MV, McCormick DA. Cellular and network mechanisms of rhythmic recurrent activity in neocortex. Nat Neurosci 2000, 3: 1027-1034.
6. Compte A, Sanchez-Vives MV, McCormick DA, Wang XJ. Cellular and network mechanisms of slow oscillatory activity (<1 Hz) and wave propagations in a cortical network model. J Neurophysiol 2003, 89: 2707-2725.
7. Mattia M, Sanchez-Vives MV. Exploring the spectrum of dynamical regimes and timescales in spontaneous cortical activity. Cogn Neurodyn 2012, 6: 239-250.
8. Sakata S, Harris KD. Laminar structure of spontaneous and sensory-evoked population activity in auditory cortex. Neuron 2009, 64: 404-418.
9. Chauvette S, Volgushev M, Timofeev I. Origin of active states in local neocortical networks during slow sleep oscillation. Cereb Cortex 2010, 20: 2660-2674.
15:15 Emili Balaguer-Ballester, Bournemouth University and Bernstein Centre for Computational Neuroscience Heidelberg-Mannheim. | Abstract
Can we Identify Latent Non-Stationary Dynamics in Neural Populations?

Abstract: Neurophysiological recordings in animals performing exactly the same task often vary from trial-to-trial; but the origin and dynamics of such observed variability is not easily accessible by current approaches. During the talk, I will propose that deterministic sources of observed variability in biophysical recordings can be empirically identified in some circumstances; particularly when dynamics can be mapped to a metastable framework. First, we will theoretically study trajectory-based statistics to discern whether trial-to-trial variability was originated by non-autonomous processes or by noise fluctuations. Analyses show that trajectory analyses are useful to detect significant changes in a subjacent attracting dynamics. Second, these analyses will be evaluated in frontal cortex ensemble recordings; suggesting that observed trial-to-trial variability in data is consistent with a underlying deterministic trend.
15:40 Short Symposium