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We consider a simplified Boolean model of the basal ganglia-thalamocortical network, and study the effect of a spike-timing-dependent plasticity (STDP) rule on the stabilization of its attractor dynamics. More precisely, we introduce an adaptive STDP rule which constantly updates its learning rate based on the attractors that the network encounters during a window of past time steps. This so-called network memory is assumed to be dynamic: its duration is step-wise increased every time a trigger input pattern is detected, and is decreased otherwise. In this context, we show that well-adjusted trigger inputs can fine tune the network memory and its associated STDP rule in such a way to drive the network into stable and rich attractor dynamics. We discuss how this feature might be related to reward learning processes in the neurobiological context.

The attractor-based complexity of a Boolean neural network is a measure which refers to the ability of the network to perform more or less complicated classification tasks of its inputs via the manifestation of meaningful or spurious attractor dynamics. Here, we study the attractor-based complexity of a Boolean model of the basal ganglia-thalamocortical network. We show that the regulation of the interactive feedback is significantly involved in the maintenance of an optimal level of complexity. We also show that the complexity of the network depends sensitively on the values of its synaptic connections. These considerations support the general rationale that the synaptic plasticity and the interactive architecture play a crucial role in the computational and dynamical capabilities of biological neural networks.

We consider a model of so-called hybrid recurrent neural networks composed with Boolean input and output cells as well as sigmoid internal cells. When subjected to some infinite binary input stream, the Boolean output cells necessarily exhibit some attractor dynamics, which is assumed to be of two possible kinds, namely either meaningful or spurious, and which underlies the arising of spatiotemporal patterns of output discharges. In this context, we show that rational-weighted neural networks are computationally equivalent to deterministic Muller Turing machines, whereas all other models of real-weighted or evolving neural networks are equivalent to each other, and strictly more powerful than deterministic Muller Turing machines. In this precise sense, the analog and evolving neural networks are super-Turing. We further provide some precise mathematical characterization of the expressive powers of all these neural models. These results constitute a generalization to the current computational context of those obtained in the cases of classical as well as interactive computations. They support the idea that recurrent neural networks represent a natural model of computation beyond the Turing limits.

Base stations have been identified to be the most power consuming part in current mobile networks. Since their power profile only depends to a small fraction on the actual traffic load, putting some base stations in sleep mode has been identified as a solution to scale the network power consumption with the actual load. This paper presents a new system architecture based on the paradigm of “cell on-demand”, which is currently studied in the Beyond Cellular Green Generation (BCG2) project of the GreenTouch consortium. We first outline the key characteristics of the new system architecture, then, we present a framework for position information processing in order to predict the channel quality between a user and a set of switched-off base stations that can potentially be activated by the system to serve the user. Experimental evaluation on a small scale testbed shows the viability of the proposed framework by achieving estimation errors of 20%.

The robust regression analysis works on data affected by deviations from a general assumption of normality. Currently the field of robust linear regression analysis is well developed and there are number of stable and verified by time methods. In contrast the robust structural modeling and high-order model parameter estimation are still under active development.

OpenAdap.net (OAN) is an information system aimed at knowledge dissemination and processing for sustaining communities of users who share the same knowledge representation within their field of interest. Such information system is not only the technology a community uses, but also the way in which the members of the community interact with the technology and the way in which the technology processes knowledge within the community. OpenAdap.net provides multiple business opportunities through the introduction of an innovative distributed working environment and creative user collaborations. In the framework, OAN-aware applications are established software pieces, open or closed sourced, developed to their own independent goals, which are extended to access the shared resources through the use of OAN-specific code.

This paper presents a new unified design flow developed within the Perplexus project that aims to accelerate parallelizable data-intensive applications in the context of ubiquitous computing. This contribution relies on the JubiTool: a set of integrated tools (JubiSplitter, JubiCompiler, UbiAssembler), allowing respectively to extract, compile and assemble parallelizable parts of applications described in Jubi language. Jubi is a modified Java agent based language (JADE) dedicated to the Ubichip (the bio-inspired chip developed within the confines of the Perplexus project). By appending hardware directives to a software agent description, the inherent flexibility of software is combined with the runtime performance of a hardware execution. In the case of typical Perplexus applications such as the Spiking Neural Network Simulator, this contribution takes profit of the intrinsic property of the Ubichip in terms of parallelism resulting in an expected speedup of at least one order of magnitude. Finally, this hybrid (SW/HW) flow could be easily modified and adapted to support other kind of distributed platforms

This paper introduces Perplexus, a European project that aims to develop a scalable hardware platform made of custom reconfigurable devices endowed with bio-inspired capabilities. This platform will enable the simulation of large-scale complex systems and the study of emergent complex behaviors in a virtually unbounded wireless network of computing modules. The final infrastructure will be used as a simulation tool for three applications: neurobiological modeling, culture dissemination modeling, and cooperative collective robotics. The Perplexus platform will provide a novel modeling framework thanks to the pervasive nature of the hardware platform, its bio-inspired capabilities, its strong interaction with the environment, and its dynamic topology.

In a series of behavioral experiments rats were trained to discriminate between synthetic vowels characterized by an increase in fundamental frequency correlated with an upward shift in formant frequencies. The results demonstrate that rats are able to generalize the discrimination to new instances of the same vowels and that the performance depended on the relation between fundamental and formant frequencies that they had previously been exposed to. Simulation results using artificial neural networks could reproduce most of the behavioral results and suggest that equivalence classes for vowels are associated with an experience-driven process based on general properties of peripheral auditory coding mixed with elementary learning mechanisms.

The present study is devoted to the problem of automatic sorting of extracellularly recorded action potentials of neurons. The classification of spike waveform is considered as a pattern recognition problem of segments of signal that corresponds to the appearance of spikes. Nonlinear oscillating model with perturbation is used to describe the waveforms of spikes. It allows characterizing the signal distortions in both amplitude and phase. The spikes generated by one neuron assumed to be described by the same equation and should be recognized as one class. The problem of spike recognition is reduced to the separation of mixture of normal distributions in the transformed feature space. An unsupervised iteration-learning algorithm that estimates the number of classes and their centers is developed. It scans the learning set in order to evaluate spikes trajectories in phase space with maximal probability density in their neighborhood. To estimate the trajectories the integral operators with piece-wise polynomial kernels were used that provides computational efficiency. The new algorithm was tested on simulated and real data sets.