Jun
9
4:30 PM16:30

A minimal network mechanism accounting for the statistics of babbling vocalizations

By David Hansel, CNRS and Paris Descartes University

Explorative behavior is a key concept in learning theory and is widespread during early stages of development. However how such behavior is actively generated by neuronal mechanisms is poorly understood. We show that babbling¬‐like vocalizations in human and non-human juvenile vocal learners share some common temporal features, which is in sharp contrast with the individual-specific learned vocalizations of the adults. These findings point toward the existence of a general neuronal mechanism for actively generating explorative behaviors. I will show how neuronal variability can intrinsically emerge as a network property and then transferred to the effectors to generate explorative behaviors. I will demonstrate how spatio-temporal correlations are naturally emerging in a  network operating in the balanced excitation-inhibition regime receiving structured feed‐forward inputs. I will then show how these activity patterns can be utilized by the effectors to generate explorative behavior with similar statistical properties as during babbling-like vocalizations. Finally, I will give several testable predictions regarding the structure of spatiotemporal correlations in cortical-homologues areas responsible for babbling‐like vocalizations in juvenile songbirds.

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Jun
9
4:00 PM16:00

Nonlinear processing of nonstationary input by V1 population

By Frederic Chavane, CNRS and Aix-Marseille University

Visual motion integration is traditionally viewed as a cascade of hierarchical processing steps identified using local stationary inputs. However naturalistic inputs are most often non-stationary, such as an object moving along smooth trajectories. This later will generate a sequence of spatio-temporally coherent feed-forward inputs that shall interact with spreads of activity within and between cortical retinotopic maps. In awake monkeys, we investigated how these nested propagations shape the cortical mapping of a motion trajectory. Recording the population response dynamics of apparent motion stimuli using voltage-sensitive dye imaging, we show that non-linear interactions between feedforward input and lateral interactions are essential to shape the spatio-temporal representation of the stimulus velocity (speed and direction). In response to continuous motion along a trajectory, such interactions are responsible for the emergence of anticipatory spiking activity. Predictive and accurate representation of non-stationary motion signals along trajectories thus results from the convergent non-linear interplay of intra- and inter-cortical inputs propagating information faster than the feed-forward sequence.

 

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Jun
9
3:30 PM15:30

Orientation selectivity and its dynamics in Macaque V1

By Dajun Xing, Beijing Normal University 

One of the functions of the cerebral cortex is to increase the selectivity for stimulus features. Both theoretical work and experimental studies in multiple cortical areas have suggested that suppressive mechanisms are involved in feature selectivity. However, the magnitude of the contribution of suppression to tuning selectivity is not yet determined. We studied orientation selectivity in macaque primary visual cortex, V1, as an archetypal example of feature selectivity in the brain and develop a method to estimate the magnitude of the contribution of suppression to orientation selectivity. Our studies showed that untuned suppression, one form of cortical suppression, decreases the orthogonal-to-preferred response ratio (O/P ratio) of V1 cells and untuned suppression has an especially large effect on orientation selectivity for highly selective cells. We conclude that untuned suppression is crucial for the generation of highly orientation-selective cells in V1 cortex.

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Jun
9
2:30 PM14:30

Translational Neuroscience: How does bifurcation theory help us with epileptic surgery?

By Viktor Jirsa, Aix-Marseille University

Seizures  can occur spontaneously and  in a recurrent  manner,  which  defines  epilepsy;  or they  can be induced  in a normal  brain under  a variety  of conditions in most  neuronal  networks  and  species  from flies to humans. Such universality raises the possibility that invariant properties exist that characterize seizures under different physiological and pathological conditions. Starting from first principles of the theory of slow-fast systems in nonlinear dynamics, we conceptualize seizure dynamics mathematically and establish a taxonomy of seizures based on seizure onset and offset bifurcations. We demonstrate  that  only  five  state   variables  linked  by  integral-differential equations  are sufficient  to describe  the onset,  time course  and offset  of ictal-like  discharges as well as their recurrence.  These state variables define the model system called the Epileptor, where two state variables  are responsible for generating rapid  discharges (fast  time  scale),  two  for spike  and  wave  events  (intermediate time  scale)  and  one permittivity variable (slow  time  scale).  The permittivity variable captures effects evolving on slow timescales, including extracellular ionic concentrations and energy metabolism, with time delays of up to seconds as observed clinically. We propose  that normal  and  ictal  activities  coexist:  a separatrix acts  as  a barrier  (or seizure  threshold) between these  states. Seizure onset is reached upon the collision of normal brain trajectories with the separatrix. We show theoretically and experimentally how a system can be pushed toward seizure under a wide variety of conditions. Within our experimental model, the onset and offset of ictal-like discharges are well-defined mathematical events:  a saddle-node and homoclinic bifurcation, respectively. These bifurcations necessitate a baseline shift at onset and a logarithmic scaling of interspike intervals at offset.  These predictions were not only confirmed in our in vitro experiments, but also for focal seizures recorded in different syndromes, brain regions and species (humans and zebrafish). Extending this generic approach rooted in nonlinear dynamics towards human brain networks, we reconstruct personalized connectivity matrices of human epileptic patients using Diffusion Tensor weighted Imaging (DTI). Subsets of brain regions generating seizures in patients with refractory partial epilepsy are referred to as the epileptogenic zone (EZ). During a seizure, paroxysmal activity is not restricted to the EZ, but may recruit other brain regions and propagate activity through large brain networks, which comprise brain regions that are not necessarily epileptogenic. The identification of the EZ is crucial for candidates for neurosurgery and requires unambiguous criteria that evaluate the degree of epileptogenicity of brain regions. Stability analyses of propagating waves provide a set of indices quantifying the degree of epileptogenicity and predict conditions, under which seizures propagate to nonepileptogenic brain regions, explaining the responses to intracerebral electric stimulation in epileptogenic and nonepileptogenic areas. We demonstrate the predictive value of our seizure propagation model by validating it against empirical patient data.  In conjunction, our results provide guidance in the presurgical evaluation of epileptogenicity based on electrographic signatures in intracerebral electroencephalograms.

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Jun
9
2:00 PM14:00

Dynamics of prefrontal cortical activity during learning of cognitive tasks and development

By Christos Constantinidis, Wake Forest School of Medicine

Cognitive functions such as working memory and response inhibition mature relatively late in life, in adolescence or early adulthood, and may be enhanced even in adulthood through cognitive training. To address the changes in prefrontal cortical activity associated with cognitive development, my laboratory has performed a series of experiments recording neuronal activity in adolescent and adult monkeys, as well as before and after training on working memory tasks. We found changes in the proportion of prefrontal neurons recruited during cognitive function, degree of activation, selectivity for stimuli, nature of task information encoded in neural activity, variability of individual responses and correlation between simultaneously recorded neurons. These results reveal the nature of changes in neural activity dynamics that underlie cognitive enhancement in development and as a result of task training.

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Jun
9
1:30 PM13:30

Hierarchical temporal processing in the primate cerebral cortex

By Xiao-Jing Wang, NYU and NYU Shanghai

Decision-making, working memory and other cognitive functions involve many brain regions that interact through feedback loops. To investigate cortical inter-areal networks, we have developed a large-scale model of macaque cortex base on recently published directed and weighted connectivity data. We found that, by taking into account quantitative heterogeneity across cortical areas, this large network naturally displays a hierarchy of timescales: early sensory areas respond rapidly to an external input and the response decays away immediately after stimulus offset (appropriate for sensory processing), whereas association areas higher in the brain hierarchy are capable of integrating inputs over a long time and exhibit persistent activity (suitable for decision-making and working memory). Slower prefrontal and temporal areas have a disproportionate impact on global brain dynamics measured by functional connectivity. The model offers a new platform for investigating dynamics and functions of the large-scale primate brain.

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Jun
9
11:40 AM11:40

Analysis of spiking neural networks with dynamic synapses

By Carl van Vreeswijk, Paris Descartes University

We develop a framework in which the activity of nonlinear pulse-coupled oscillators is posed within the renewal theory. In this approach, the evolution of inter-event density allows for a self-consistent calculation that determines the asynchronous state and its stability. This framework, can readily be extended to the analysis of systems with more state variables. To exhibit this, we study a nonlinear pulse-coupled system, where couplings are dynamic and activity dependent. We investigate stability of this system and we show it undergoes a super-critical Hopf bifurcation to collective synchronization.

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Jun
9
11:10 AM11:10

Bilinearity in spatiotemporal integration of synaptic inputs

By Douglas Zhou, Shanghai Jiao Tong University

A neuron receives thousands of synaptic inputs from its dendrite and integrates them to process information. Many experimental results demonstrate the dendritic integration could be highly nonlinear, yet few theoretical analyses have been performed to obtain a precise quantitative characterization analytically. Based on asymptotic analysis of a passive cable model, we derive a bilinear spatiotemporal dendritic integration rule for a pair of time-dependent synaptic inputs. Surprisingly, the above rule, which is obtained from idealized models, can be verified both in simulations of a realistic pyramidal neuron model and in electrophysiological experiments of rat hippocampal CA1 neurons. Our results demonstrate that the integration of multiple synaptic inputs can be decomposed into the sum of all possible pairwise integration with each paired integration obeying a bilinear rule.

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Jun
9
10:10 AM10:10

Potential Inference Hazards in Neuroscience

By David Cai, NYU and NYU Shanghai

Erroneous inferences can arise in data processing due to sampling hazards. Their resolutions often require one to go deeper to understand underlying dynamical mechanisms. Two illustrative examples will be presented. The first example is potential inference hazards in the application of Grange causality (GC). An effective strategy of overcoming GC sampling issues will be described. In particular, the detailed underlying mechanism for its successful application in the reconstruction of the network topology of nonlinear neuronal networks will be discussed. The second example will illustrate in detail how a large scale computational modeling of the primary visual cortex (V1) has helped to resolve uncertainties about cortical mechanisms inferred from optical imaging of the spatiotemporal dynamics of V1.

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Jun
9
9:30 AM09:30

A causal analysis of the attentional network

By Robert Desimone, Massachusetts Institute of Technology

The responses of neurons in nearly every brain structure with visual functions are modulated when people or animals attend selectively to an object in the visual field. Yet, despite the ubiquitous nature of attention-modulated neural signals, the timing of neuronal responses and synchronous interactions between different areas can be used to distinguish signals for the control of attention from those of visual neurons that are influenced by attention. Causal models are also beginning to emerge from studies that manipulate neural activity with pharmacological or optogenetic methods. 

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