We have investigated the role that different connectivity regimes play in the dynamics of a network of Hodgkin-Huxley neurons by computer simulations. The different connectivity topologies exhibit the following features: random topologies give rise to fast system response yet are unable to produce coherent oscillations in the average activity of the network; on the other hand, regular topologies give rise to coherent oscillations, but in a temporal scale that is not in accordance with fast signal processing. Finally, small-world topologies, which fall between random and regular ones, take advantage of the best features of both, giving rise to fast system response with coherent oscillations. PACS numbers: 87.18.Sn, 84.35. + i, 87.18.Bb In a recent Letter by Watts and Strogatz [1] it was shown that small-world (SW) networks enhance signalpropagation speed, computational power, and synchronizability. SW stands for a network whose connectivity topology is placed somewhere between a regular and a completely random connectivity. The main properties of these specific networks are that they can be highly clustered like regular networks and, at the same time, have small path lengths like random ones. Therefore, SW networks may have properties given neither in regular nor in random networks [2]. In this Letter we have extended Watts and Strogatz's general framework by introducing dynamical elements in the network nodes. Our source of inspiration is based on a phenomenon observed in the olfactory antennal lobe (AL) of the locust [3]. The AL is a group of around 800 neurons whose functional role is to relay information from the olfactory receptors to higher areas of the brain for further processing. Two main features have been observed in the dynamics of the AL. First, there is a fast response of the AL when the stimulus is presented. Second, when an odor is presented to the insect, coherent oscillations of 20 Hz in the local field potential (LFP) are measured [3]. Summarizing, fast coherent oscillations are observed. There are also other systems in the brain that present coherent LFP oscillations, hence, hinting to the generality of these phenomena (see [4,5]).The cooperative behavior of large assemblies of dynamical elements has been the subject of many investigations [6][7][8][9][10]. In all of them the connectivity between the elements of the network was either regular (local or global all-to-all) or random. However, none of these studies incorporates a comparative analysis of network dynamics for all the different connectivity topologies.In the present work we want to show that in order to provide fast response and coherent oscillations a SW topology is required. Without the coherent oscillations the AL seems to lose its ability to process the information incoming from the sensors [3]. The model we propose for this study is made of an array of nonidentical Hodgkin-Huxley elements coupled by excitatory synapses. The unit dynamics is described by the following set of coupled ordinary differential equations:ᠨ n a n ͑V ...

O' Keefe and Nadel (1978) distinguish two paradigms for navigation, the "locale system" for map-based navigation and the "taxon (behavioral orientation) system" for route navigation. This article models the taxon system, the map-based system, and their interaction, and argues that the map-based system involves the interaction of hippocampus and other systems.We relate taxes to the notion of an affordance. Just as a rat may have basic taxes for approaching food or avoiding a bright light, so does it have a wider repertoire of affordances for possible actions associated with immediate sensing of its environment. We propose that affordances are extracted by the rat posterior parietal cortex, which guides action selection by the premotor cortex and is influenced also by hypothalamic drive information.The taxon-affordances model (TAM) for taxon-based determination of movement direction is based on models of frog detour behavior, with expectations of future reward implemented using reinforcement learning. The specification of the direction of movement is refined by current affordances and motivational information to yield an appropriate course of action.The world graph (WG) theory expands the idea of a map by developing the hypothesis that cognitive and motivational states interact. This article describes an implementation of this theory, the WG model. The integrated TAM-WG model then allows us to explain data on the behavior of rats with and without fornix lesions, which disconnect the hippocampus from other neural systems.

Abstract-This paper addresses the genetic design of functional link networks (FLNs). FLNs are high-order perceptrons (HOPs) without hidden units. Despite their linear nature, FLNs can capture nonlinear input-output relationships, provided that they are fed with an adequate set of polynomial inputs, which are constructed out of the original input attributes. Given this set, it turns out to be very simple to train the network, as compared with a multilayer perceptron (MLP). However, finding the optimal subset of units is a difficult problem because of its nongradient nature and the large number of available units, especially for high degrees. Some constructive growing methods have been proposed to address this issue. Here, we rely on the global search capabilities of a genetic algorithm to scan the space of subsets of polynomial units, which is plagued by a host of local minima. By contrast, the quadratic error function of each individual FLN has only one minimum, which makes fitness evaluation practically noiseless. We find that surprisingly simple FLNs compare favorably with other more complex architectures derived by means of constructive and evolutionary algorithms on some UCI benchmark data sets. Moreover, our models are especially amenable to interpretation, due to an incremental approach that penalizes complex architectures and starts with a pool of single-attribute FLNs.Index Terms-Evolutionary neural networks, feature subset selection, functional link networks, polynomial regression.

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