Dynamical Encoding by Networks of Competing Neuron Groups: Winnerless Competition

Following studies of olfactory processing in insects and fish, we investigate neural networks whose dynamics in phase space is represented by orbits near the heteroclinic connections between saddle regions (fixed points or limit cycles). These networks encode input information as trajectories along the heteroclinic connections. If there are N neurons in the network, the capacity is approximately e(N-1)!, i.e., much larger than that of most traditional network structures. We show that a small winnerless competition network composed of FitzHugh-Nagumo spiking neurons efficiently transforms input information into a spatiotemporal output

Chaotic Free-Space Laser Communication over Turbulent Channel

The dynamics of errors caused by atmospheric turbulence in a self-synchronizing chaos based communication system that stably transmits information over a $\sim$5 km free-space laser link is studied experimentally. Binary information is transmitted using a chaotic sequence of short-term pulses as carrier. The information signal slightly shifts the chaotic time position of each pulse depending on the information bit. We report the results of an experimental analysis of the atmospheric turbulence in the channel and the impact of turbulence on the Bit-Error-Rate (BER) performance of this chaos based communication system.Comment: 4 pages, 5 figure

Odor encoding as an active, dynamical process: experiments, computation, and theory

We examine early olfactory processing in the vertebrate and insect olfactory systems, using a computational perspective. What transformations occur between the first and second olfactory processing stages? What are the causes and consequences of these transformations? To answer these questions, we focus on the functions of olfactory circuit structure and on the role of time in odor-evoked integrative processes. We argue that early olfactory relays are active and dynamical networks, whose actions change the format of odor-related information in very specific ways, so as to refine stimulus identification. Finally, we introduce a new theoretical framework (“winnerless competition”) for the interpretation of these data

Dynamical coding of sensory information with competitive networks

Based on experiments with the locust olfactory system, we demonstrate that model sensory neural networks with lateral inhibition can generate stimulus specific identity-temporal patterns in the form of stimulus-dependent switching among small and dynamically changing neural ensembles (each ensemble being a group of synchronized projection neurons). Networks produce this switching mode of dynamical activity when lateral inhibitory connections are strongly non-symmetric. Such coding uses 'winner-less competitive' (WLC) dynamics. In contrast to the well known winner-take-all competitive (WTA) networks and Hopfield nets, winner-less competition represents sensory information dynamically. Such dynamics are reproducible, robust against intrinsic noise and sensitive to changes in the sensory input. We demonstrate the validity of sensory coding with WLC networks using two different formulations of the dynamics, namely the average and spiking dynamics of projection neurons (PN)

Multi-User Communication Using Chaotic Frequency Modulation

In this paper we consider the use of the chaotic frequency modulation (CFM) in multi-user communications. In this scheme the base station transmits the reference signal with chaotically varying frequency. All users synchronize their chaotic oscillators to this signal and use it to generate their own information-carrying CFM signal. Using numerical simulations and experiments with electronic circuits we evaluate the BER performance of CFM