inst log frequency, revealed 1/f spectra in both pre- and post-stimulus segments for CS- and CS+ stimuli. The y-intercepts and slopes for average PSDs were significantly different between pre- and post-stimulus segments, owing to the evoked potentials, but not between CS- and CS+ stimulus segments. 6.##### Spatiotemporal patterns were invariant over all frequency bins from 20-100 Hz in the 1/ f domain. Spatiotemporal patterns in the 2-20 Hz domain progressively differed from the invariant patterns with decreasing frequency. 7.##### In the spatial frequency domain, the logarithm of the average spatial FFT power spectra from pre- and post-stimulus neocortical EEG segments, when plotted against the log spatial frequency, fell monotonically from the maximum at the lowest spatial frequency, concavely curving to a linear 1/f spectral domain. This curve in the 1/f spectral domain extended from 0.133 - 0.880 cycles/mm in the PPC and from 0.095 - 0.624 cycles/mm in the neocortices. 8.#####

this paper is to present such a reformulation. The cell assembly provides the cognitive system with flexibility far beyond the simple activation of concepts. Instead of viewing the assembly as simply active or latent we see the activation of the assembly as coming in a series of phases. Each phase of activity serves a different purpose, giving the theory the power and flexibility to handle a wide range of psychological data.

Abstract. This paper explores the capabilities of continuous time recurrent neural networks (CTRNNs) to display reinforcement learning-like abilities on a set of T-Maze and double T-Maze navigation tasks, where the robot has to locate and “remember ” the position of a reward-zone. The “learning ” comes about without modifications of synapse strengths, but simply from internal network dynamics, as proposed by [12]. Neural controllers are evolved in simulation and in the simple case evaluated on a real robot. The evolved controllers are analyzed and the results obtained are discussed. 1

This paper analyzes the effect of this technological progress on the design of nanoelectronic circuits and describes computational paradigms revealing novel features such as distributed storage, fault tolerance, self-organization, and local processing. In particular, linear threshold networks, the associative matrix, self-organizing feature maps, and cellular arrays are investigated from the viewpoint of their potential significance for nanoelectronics. Although these concepts have already been implemented using present technologies, the intention of this paper is to give an impression of their usefulness to system implementations with quantum-effect devices.

Mesoscopic level neurodynamics study the collective dynamical behavior of neural populations. Such models are becoming increasingly important in understanding large-scale brain processes. Brains exhibit aperiodic oscillations with a much more rich dynamical behavior than fixed-point and limitcycle approximation allow. Here we present a discretized model inspired by Freeman’s K-set mesoscopic level population model. We show that this version is capable of replicating the important principles of aperiodic/chaotic neurodynamics while being fast enough for use in real-time autonomous agent applications. This simplification of the K model provides many advantages not only in terms of efficiency but in simplicity and its ability to be analyzed in terms of its dynamical properties. We study the discrete version using a multi-layer, highly recurrent model of the neural architecture of perceptual brain areas. We use this architecture to develop example action selection mechanisms in an autonomous agent.

At present, in communications and virtual technologies, smell is either forgotten or improperly stimulated, because non controlled odorants present in the physical space surrounding the user. Nonetheless a controlled presentation of olfactory information can give advantages in various application fields.

We review a neural network model based on chaotic dynamics [Babloyantz & Lourenco, 1994, 1996] and provide a detailed discussion of its biological and computational relevance. Chaos can be viewed as a "reservoir" containing an infinite number of unstable periodic orbits. In our approach, the periodic orbits are used as coding devices. By considering a large enough number of them, one can in principle expand the information processing capacity of small or moderate-size networks. The system is most of the time in an undetermined state characterized by a chaotic attractor. Depending on the type of an external stimulus, the dynamics is stabilized into one of the available periodic orbits, and the system is then ready to process information. This corresponds to the system being driven into an "attentive" state. We show that, apart from static pattern processing, the model is capable of dealing with moving stimuli. We especially consider in this paper the case of transient visual stimuli, which has a clear biological relevance. The advantages of chaos over more regular regimes are discussed.

This is a practical and theoretical thesis in visual robotics. It describes the development and actual implementation (in a real world, real-time visual robot) of new approaches to three non-linear problems: how to ensure robustness under the harshest conditions of unforeseen reconfiguration, how to provide specialised space-variant sampling regimes according to which task is currently at hand, and how to automatically direct attention using any number of adaptive response layers in concert. Additionally, the descriptions of this practical work are preceded by the exposition of a new theoretical framework for intelligent system evaluation, which offers performance silhouettes as a schematic method. The three practical methods stem from, and are embedded in, the theoretical framework, and all have been suggested, to varying degrees, by knowledge of biological processes or capabilities- self-organisation, visual periphery sensitivity, and adaptive reduction in sensitivity, for example. The overall goal of the research is to develop extremely simple algorithms capable of operating in real-time and endowing a robot with robust essential perceptual capabilities that can operate in all environments. The outcome of the work is a visual robotic system that exhibits seemingly intelligent behaviour in complex, changing, and noisy natural environments. That it does so with minimal help from other sophisticated agents (such as computer science researchers) is a credit to its autonomy and the adaptation of its design to arbitrary environments.

Biological brains are saturated with complex dynamics. Artificial neural network models abstract much of this complexity away and represent the computational process of neuronal groups in terms of simple point, and sometimes periodic attractors.

We discuss the problem of segmentation in pattern recognition. We adopt the model and the general approach in the landmark paper by Wang, Buhmann and von der Malsburg (Neural Computation, (1990), 2, 94--106), and expand their model in a number of ways. We review their solution to the segmentation problem in associative memory, which consists in feature binding being expressed by synchrony relations between oscillators or populations of neurons. We extend the model by introducing a law of synaptic change, which allows the network to learn by structuring itself in response to stimuli with relevant features. We discuss the problem of interference between pattern completion and the learning of new memories. We also propose a form of multiplexing of input information taking advantage of the time-structure of the neurons' response. It is based on the assessment of analog as well as of binary properties of the stimuli and provides for an enhancement of the network's processing capacity. The relevance of the results for biological systems is pointed out. # 2000 Elsevier Science Ltd. All rights reserved.