Chaos Characterization of Pulse-Coupled Neural Networks in Balanced State

In the present work the formalism of Ergodic theory, used for the statistical study of complex, nonlinear dynamical systems of N ≫ 1 dimensions in general, is applied to the time evolution of large-scale pulse-coupled neural networks in the so-called balanced state. The aim is to measure the ergodic properties of such systems, consider how they are related to the network parameters, and finally characterize dynamically the participation of individual network nodes (the “neurons”) in the collective dynamics

Analog Memories in a Balanced Rate-Based Network of E-I Neurons

Abstract The persistent and graded activity often observed in cortical circuits is sometimes seen as a signature of autoassociative retrieval of memories stored earlier in synaptic efficacies. However, despite decades of theoretical work on the subject, the mechanisms that support the storage and retrieval of memories remain unclear. Previous proposals concerning the dynamics of memory networks have fallen short of incorporating some key physiological constraints in a unified way. Specifically, some models violate Dale's law (i.e. allow neurons to be both excitatory and inhibitory), while some others restrict the representation of memories to a binary format, or induce recall states in which some neurons fire at rates close to saturation. We propose a novel control-theoretic framework to build functioning attractor networks that satisfy a set of relevant physiological constraints. We directly optimize networks of excitatory and inhibitory neurons to force sets of arbitrary analog patterns to become stable fixed points of the dynamics. The resulting networks operate in the balanced regime, are robust to corruptions of the memory cue as well as to ongoing noise, and incidentally explain the reduction of trial-to-trial variability following stimulus onset that is ubiquitously observed in sensory and motor cortices. Our results constitute a step forward in our understanding of the neural substrate of memory

Adaptation of Drosophila larva foraging in response to changes in food resources.

Peer reviewed: TrueFunder: Max-Planck-Gesellschaft; FundRef: http://dx.doi.org/10.13039/501100004189Funder: Alexander von Humboldt-Stiftung; FundRef: http://dx.doi.org/10.13039/100005156All animals face the challenge of finding nutritious resources in a changing environment. To maximize lifetime fitness, the exploratory behavior has to be flexible, but which behavioral elements adapt and what triggers those changes remain elusive. Using experiments and modeling, we characterized extensively how Drosophila larvae foraging adapts to different food quality and distribution and how the foraging genetic background influences this adaptation. Our work shows that different food properties modulated specific motor programs. Food quality controls the traveled distance by modulating crawling speed and frequency of pauses and turns. Food distribution, and in particular the food-no food interface, controls turning behavior, stimulating turns toward the food when reaching the patch border and increasing the proportion of time spent within patches of food. Finally, the polymorphism in the foraging gene (rover-sitter) of the larvae adjusts the magnitude of the behavioral response to different food conditions. This study defines several levels of control of foraging and provides the basis for the systematic identification of the neuronal circuits and mechanisms controlling each behavioral response