The diversity of dynamical states in recurrent neural circuits

Abstract

Experiments suggest a wealth of dynamical states in the brain. These range from asynchronous irregular activity to synchronizations, oscillations, activity waves or avalanches. Understanding the mechanisms that can give rise to such a diversity of network states in the healthy and diseased brain is a challenge both for experimentalists and theoreticians.

Also the implications of these different operating regimes of the network dynamics on the ability to encode, process and transmit information is not well understood. In recurrent network models, the sensitivity of neural dynamics to small perturbations or noise can reveal features that are governing the microscopic phase space organization. Optimal computational performance of neuronal networks was hypothesized to be found close to phase transitions,
where the dynamics exhibits universal behavior that is characterized by strong concerted fluctuations between neurons. The diversity of possible states and state transitions in a high dimensional system such as cortex, however, permits a multitude of hypotheses on the “ground state” of different cortical regions.

In this workshop, we bring together experts working on theories to characterize the different dynamical states of recurrent neural networks and identify synaptic, neuronal, and network properties that shape the collective dynamics. We want to relate dynamical states to features of observed neural activity in different cortical regions, work out possibilities to test theoretical predictions by experiments, and discuss functional implications of the dynamics.

Schedule

Tue, Sept 25, 2018
14:00	Brent Doiron, University of Pittsburgh, USA Interleaving asynchronous and synchronous activity in balanced cortical networks with short term synaptic depression
14:40	Laureline Logiaco, Columbia University, New York, USA Efficient, robust and seamless motor sequencing in a thalamocortical circuit
15:20	Braden Brinkman, Stony Brook University, USA Predicting how and when hidden neurons skew measured synaptic interactions
16:00	Coffee Break
16:30	Francesca Mastrogiuseppe, École Normale Supérieure de Paris, France Linking connectivity and dynamics in low-rank recurrent networks
17:10	Jonathan Kadmon, Stanford University, USA Structure and disorder  in recurrent neural networks
17:50	Ulises Pereira Obilinovic, University of Chicago, USA Attractor dynamics in networks with learning rules inferred from in vivo data
Wed, Sep 26, 2018
08:30	Oren Shriki, Ben-Gurion University, Beer-Sheva, Israel Dynamical implications of optimal temporal information encoding in recurrent networks of spiking neurons
09:10	Valerio Mante, University of Zürich, Switzerland Stable, linear, and dynamic attractors in prefrontal population responses
09:50	Serena di Santo, University of Granada, Spain Building a Landau-Ginzburg theory for cortex dynamics
10:30	Coffee Break
11:00	Lyudmila Kushnir, École Normale Supérieure de Paris, France Efficient encoding of predictable inputs by balanced spiking networks
11:40	Birgit Kriener, University of Oslo, Norway The role of strong synapses in the making and breaking of synchrony