Neuronal synchrony: peculiarity and generality

Synchronization in neuronal systems is a new and intriguing application of dynamical systems theory. Why are neuronal systems different as a subject for synchronization? (1) Neurons in themselves are multidimensional nonlinear systems that are able to exhibit a wide variety of different activity patterns. Their “dynamical repertoire” includes regular or chaotic spiking, regular or chaotic bursting, multistability, and complex transient regimes. (2) Usually, neuronal oscillations are the result of the cooperative activity of many synaptically connected neurons (a neuronal circuit). Thus, it is necessary to consider synchronization between different neuronal circuits as well. (3) The synapses that implement the coupling between neurons are also dynamical elements and their intrinsic dynamics influences the process of synchronization or entrainment significantly. In this review we will focus on four new problems: (i) the synchronization in minimal neuronal networks with plastic synapses (synchronization with activity dependent coupling), (ii) synchronization of bursts that are generated by a group of nonsymmetrically coupled inhibitory neurons (heteroclinic synchronization), (iii) the coordination of activities of two coupled neuronal networks (partial synchronization of small composite structures), and (iv) coarse grained synchronization in larger systems (synchronization on a mesoscopic scale

Retaining memory after hibernation: performance varies independently of activity levels in wild grey mouse (advance online)

Abstract Hibernation, a hypometabolic state associated with low body temperature and reduced metabolic and activity rates, represents one adaptation to harsh seasonal environmental conditions. As a consequence of hypometabolism, energetically costly neuronal processes also ought to be reduced. Since active neuronal pathways are prerequisites for learning and memory, and because previous studies revealed variable patterns, it remains unclear whether and how hibernating animals retain memories, however. Here, we investigated the effect of seasonally reduced activity on memory retention in 36 wild grey mouse lemurs (Microcebus murinus). Data from activity loggers confirmed that female grey mouse lemurs entered hibernation during the cool dry season, whereas males exhibited episodic bursts of activity throughout the austral winter. Thus, compared to males, we predicted females to show lower memory retention of visual and spatial stimulus?reward associations learned before hibernation. In contrast to our prediction, all individuals performed worse in the post-hibernation testing session in both types of tests, compared to the pre-hibernation learning session, and males (N =?11) performed even worse than females (N =?14) in the post-hibernation testing session. Although females (N =?9) equipped with activity loggers tended to be less active than males (N =?4), sex-specific activity levels were unrelated to interindividual differences in memory retention. Hence, the post-hibernation decrease in performance of grey mouse lemurs may reflect a more general disability to retain stimulus?reward associations than a lack of memory retention due to seasonal hypometabolism, as suggested for some species of bats or squirrels

Oscillator neural network model with distributed native frequencies

We study associative memory of an oscillator neural network with distributed native frequencies. The model is based on the use of the Hebb learning rule with random patterns ($\xi_i^{\mu}=\pm 1$), and the distribution function of native frequencies is assumed to be symmetric with respect to its average. Although the system with an extensive number of stored patterns is not allowed to get entirely synchronized, long time behaviors of the macroscopic order parameters describing partial synchronization phenomena can be obtained by discarding the contribution from the desynchronized part of the system. The oscillator network is shown to work as associative memory accompanied by synchronized oscillations. A phase diagram representing properties of memory retrieval is presented in terms of the parameters characterizing the native frequency distribution. Our analytical calculations based on the self-consistent signal-to-noise analysis are shown to be in excellent agreement with numerical simulations, confirming the validity of our theoretical treatment.Comment: 9 pages, revtex, 6 postscript figures, to be published in J. Phys.

Predicting the psychophysical similarity of faces and non-face complex shapes by image-based measures

AbstractShape representation is accomplished by a series of cortical stages in which cells in the first stage (V1) have local receptive fields tuned to contrast at a particular scale and orientation, each well modeled as a Gabor filter. In succeeding stages, the representation becomes largely invariant to Gabor coding (Kobatake & Tanaka, 1994). Because of the non-Gabor tuning in these later stages, which must be engaged for a behavioral response (Tong, 2003; Tong et al., 1998), a V1-based measure of shape similarity based on Gabor filtering would not be expected to be highly correlated with human performance when discriminating complex shapes (faces and teeth-like blobs) that differ metrically on a two-choice, match-to-sample task. Here we show that human performance is highly correlated with Gabor-based image measures (Gabor simple and complex cells), with values often in the mid 0.90s, even without discounting the variability in the speed and accuracy of performance not associated with the similarity of the distractors. This high correlation is generally maintained through the stages of HMAX, a model that builds upon the Gabor metric and develops units for complex features and larger receptive fields. This is the first report of the psychophysical similarity of complex shapes being predictable from a biologically motivated, physical measure of similarity. As accurate as these measures were for accounting for metric variation, a simple demonstration showed that all were insensitive to viewpoint invariant (nonaccidental) differences in shape

Binding - a proposed experiment and a model

The binding problem is regarded as one of today's key questions about brain function. Several solutions have been proposed, yet the issue is still controversial. The goal of this article is twofold. Firstly, we propose a new experimental paradigm requiring feature binding, the "delayed binding response task". Secondly, we propose a binding mechanism employing fast reversible synaptic plasticity to express the binding between concepts. We discuss the experimental predictions of our model for the delayed binding response task

An experimental multiprocessor system for distributed parallel computations.

The availability of low-cost microprocessor chips with efficient instruction sets for specific numerical tasks (signal processors) has been exploited for building a versatile multiprocessor system, consisting of a host minicomputer augmented by a number of joint processors. The host provides a multiuser-multitasking environment and manages system resources and task scheduling. User applications can call upon one or more joint processors for parallel execution of adequately partitioned, computationally intensive numeric operations. Each joint processor has sufficient local memory for storing procedures and data and has access to regions in host memory for shared data. Kernel processes in the host and in the joint processors provide the necessary mechanism for initialization and synchronization of the distributed parallel execution of procedures

Transmission of Information in Active Networks

Shannon's Capacity Theorem is the main concept behind the Theory of Communication. It says that if the amount of information contained in a signal is smaller than the channel capacity of a physical media of communication, it can be transmitted with arbitrarily small probability of error. This theorem is usually applicable to ideal channels of communication in which the information to be transmitted does not alter the passive characteristics of the channel that basically tries to reproduce the source of information. For an {\it active channel}, a network formed by elements that are dynamical systems (such as neurons, chaotic or periodic oscillators), it is unclear if such theorem is applicable, once an active channel can adapt to the input of a signal, altering its capacity. To shed light into this matter, we show, among other results, how to calculate the information capacity of an active channel of communication. Then, we show that the {\it channel capacity} depends on whether the active channel is self-excitable or not and that, contrary to a current belief, desynchronization can provide an environment in which large amounts of information can be transmitted in a channel that is self-excitable. An interesting case of a self-excitable active channel is a network of electrically connected Hindmarsh-Rose chaotic neurons.Comment: 15 pages, 5 figures. submitted for publication. to appear in Phys. Rev.

GTM: A principled alternative to the self-organizing map

The Self-Organizing Map (SOM) algorithm has been extensively studied and has been applied with considerable success to a wide variety of problems. However, the algorithm is derived from heuristic ideas and this leads to a number of significant limitations. In this paper, we consider the problem of modelling the probability density of data in a space of several dimensions in terms of a smaller number of latent, or hidden, variables. We introduce a novel form of latent variable model, which we call the GTM algorithm (for Generative Topographic Mapping), which allows general non-linear transformations from latent space to data space, and which is trained using the EM (expectation-maximization) algorithm. Our approach overcomes the limitations of the SOM, while introducing no significant disadvantages. We demonstrate the performance of the GTM algorithm on simulated data from flow diagnostics for a multi-phase oil pipeline

Response of an Excitatory-Inhibitory Neural Network to External Stimulation: An Application to Image Segmentation

Neural network models comprising elements which have exclusively excitatory or inhibitory synapses are capable of a wide range of dynamic behavior, including chaos. In this paper, a simple excitatory-inhibitory neural pair, which forms the building block of larger networks, is subjected to external stimulation. The response shows transition between various types of dynamics, depending upon the magnitude of the stimulus. Coupling such pairs over a local neighborhood in a two-dimensional plane, the resultant network can achieve a satisfactory segmentation of an image into ``object'' and ``background''. Results for synthetic and and ``real-life'' images are given.Comment: 8 pages, latex, 5 figure