Learning and discrimination through STDP in a top-down modulated associative memory

This article underlines the learning and discrimination capabilities of a model of associative memory based on artificial networks of spiking neurons. Inspired from neuropsychology and neurobiology, the model implements top-down modulations, as in neocortical layer V pyramidal neurons, with a learning rule based on synaptic plasticity (STDP), for performing a multimodal association learning task. A temporal correlation method of analysis proves the ability of the model to associate specific activity patterns to different samples of stimulation. Even in the absence of initial learning and with continuously varying weights, the activity patterns become stable enough for discrimination

DAMNED: A Distributed and Multithreaded Neural Event-Driven simulation framework

In a Spiking Neural Networks (SNN), spike emissions are sparsely and irregularly distributed both in time and in the network architecture. Since a current feature of SNNs is a low average activity, efficient implementations of SNNs are usually based on an Event-Driven Simulation (EDS). On the other hand, simulations of large scale neural networks can take advantage of distributing the neurons on a set of processors (either workstation cluster or parallel computer). This article presents DAMNED, a large scale SNN simulation framework able to gather the benefits of EDS and parallel computing. Two levels of parallelism are combined: Distributed mapping of the neural topology, at the network level, and local multithreaded allocation of resources for simultaneous processing of events, at the neuron level. Based on the causality of events, a distributed solution is proposed for solving the complex problem of scheduling without synchronization barrier.Comment: 6 page

Supervised Associative Learning in Spiking Neural Network

In this paper, we propose a simple supervised associative learning approach for spiking neural networks. In an excitatory-inhibitory network paradigm with Izhikevich spiking neurons, synaptic plasticity is implemented on excitatory to excitatory synapses dependent on both spike emission rates and spike timings. As results of learning, the network is able to associate not just familiar stimuli but also novel stimuli observed through synchronised activity within the same subpopulation and between two associated subpopulations

Is it possible to discriminate odors with common words ?

Several experiments have been performed in order to study the cognitive processes which are involved in odor recognition. The current report summarizes experimental protocol and analyzes collected data. The goal is to try to recognize odors from descriptors which are selected by subjects from a list. Different groups have to choose in several descriptor lists, some with profound descriptors and some with a few surface descriptors. Profound descriptors are supposed to involved more cognition than surface descriptors. Subjects also have to name the odors. Recorded data are first analyzed, and then learned by an incremental neural classifier. The problem is hard to be learned. It seems very difficult to discriminate the different odors from the sets of descriptors. A variant of the learning algorithm, less sensitive to difficult examples, is proposed. The pertinence of surface descriptors is discussed.Des expériences ont été réalisées pour étudier les processus cognitifs impliqués dans la reconnaissance des odeurs. Ce rapport résume le protocole expérimental et étudie les données collectées. Le but est d'essayer de discriminer des odeurs à partir de descripteurs qui sont choisis par les sujets dans une liste. Plusieurs groupes travaillent avec différentes listes de descripteurs, ces descripteurs pouvant être de surface ou profonds. Les descripteurs profonds sont supposés être imliqués dans des traitememts plus cognitifs que les descripteurs de surface. Les sujets doivent également nommer les odeurs. Les données recueillies sont d'abord analysées, puis apprises par un classifieur neuronal incrémental. Le problème est difficile à apprendre. Il semble très délicat de discriminer les odeurs à partir des jeux de descripteurs. Une variante de l'algorithme d'apprentissage, moins sensible aux exemples difficiles, est proposée. La pertinence des descripteurs de surface est discutée

A supervised learning approach based on STDP and polychronization in spiking neuron networks

We propose a network model of spiking neurons, without preimposed topology and driven by STDP (Spike-Time-Dependent Plasticity), a temporal Hebbian unsupervised learning mode, biologically observed. The model is further driven by a supervised learning algorithm, based on a margin criterion, that has effect on the synaptic delays linking the network to the output neurons, with classification as a goal task. The network processing and the resulting performance are completely explainable by the concept of polychronization, proposed by Izhikevich~\cite{Izh06NComp}. The model emphasizes the computational capabilities of this concept

Risque garanti pour les modèles de discrimination multi-classes

Colloque avec actes et comité de lecture.Nous étudions les performances en généralisation des systèmes de discrimination à catégories multiples. Nous établissons deux bornes sur ces performances, en fonction de deux mesures de capacité de la famille de fonctions calculées : la fonction de croissance et les nombres de couverture. Ces bornes sont évaluées sur un modèle de combinaison de classifieurs estimant les probabilités a posteriori des classes. Ceci permet de comparer l'adéquation des deux mesures de capacité

From Neuronal cost-based metrics towards sparse coded signals classification

International audienceSparse signal decomposition are keys to efficient compression, storage and denoising, but they lack appropriate methods to exploit this sparsity for a classification purpose. Sparse coding methods based on dictionary learning may result in spikegrams, a sparse and temporal representation of signals by a raster of kernel occurrence through time. This paper proposes a method for coupling spike train cost based metrics (from neuroscience) with a spikegram sparse decompositions for clustering multivariate signals. Experiments on character trajectories, recorded by sensors from natural handwriting, prove the validity of the approach, compared with currently available classification performance in literature

Emergence of Temporal and Spatial Synchronous Behaviors in a Foraging Swarm

International audienceBiological populations often exhibit complex and efficient behaviors, where temporal and spatial couplings at the macro-scale population level emerge from interactions at the micro-scale individual level, without any centralized control. This paper specifically investigates the emergence of behavioral synchronization and the division of labor in a foraging swarm of robotic agents. A deterministic model is proposed and used by each agent to decide whether it goes foraging, based on local cues about its fellow ants' behavior. This individual model, based on the competition of two spiking neurons, results in a self-organized division of labor at the population level. Depending on the strength and occurrences of interactions among individuals, the population behavior displays either an asynchronous, or a synchronous aperiodic, or a synchronous periodic division of labor. Further, the benefits of synchronized individual behaviors in terms of overall foraging efficiency are highlighted in a 2D spatial simulation

Cell Microscopic Segmentation with Spiking Neuron Networks

International audienceSpiking Neuron Networks (SNNs) overcome the computational power of neural networks made of thresholds or sigmoidal units. Indeed, SNNs add a new dimension, the temporal axis, to the representation capacity and the processing abilities of neural networks. In this paper, we present how SNN can be applied with efficacy for cell microscopic image segmentation. Results obtained confirm the validity of the approach. The strategy is performed on cytological color images. Quantitative measures are used to evaluate the resulting segmentations