A Swarm intelligence approach for biometrics verification and identification

In this paper we investigate a swarm intelligence classification approach for both biometrics verification and identification problems. We model the problem by representing biometric templates as ants, grouped in colonies representing the clients of a biometrics authentication system. The biometric template classification process is modeled as the aggregation of ants to colonies. When test input data is captured -- a new ant in our representation -- it will be influenced by the deposited phermonones related to the population of the colonies. We experiment with the Aggregation Pheromone density based Classifier (APC), and our results show that APC outperforms ``traditional'' techniques -- like 1-nearest-neighbour and Support Vector Machines -- and we also show that performance of APC are comparable to several state of the art face verification algorithms. The results here presented let us conclude that swarm intelligence approaches represent a very promising direction for further investigations for biometrics verification and identification

Sequential Sparsening by Successive Adaptation in Neural Populations

In the principal cells of the insect mushroom body, the Kenyon cells (KC), olfactory information is represented by a spatially and temporally sparse code. Each odor stimulus will activate only a small portion of neurons and each stimulus leads to only a short phasic response following stimulus onset irrespective of the actual duration of a constant stimulus. The mechanisms responsible for the sparse code in the KCs are yet unresolved. Here, we explore the role of the neuron-intrinsic mechanism of spike-frequency adaptation (SFA) in producing temporally sparse responses to sensory stimulation in higher processing stages. Our single neuron model is defined through a conductance-based integrate-and-fire neuron with spike-frequency adaptation [1]. We study a fully connected feed-forward network architecture in coarse analogy to the insect olfactory pathway. A first layer of ten neurons represents the projection neurons (PNs) of the antenna lobe. All PNs receive a step-like input from the olfactory receptor neurons, which was realized by independent Poisson processes. The second layer represents 100 KCs which converge onto ten neurons in the output layer which represents the population of mushroom body extrinsic neurons (ENs). Our simulation result matches with the experimental observations. In particular, intracellular recordings of PNs show a clear phasic-tonic response that outlasts the stimulus [2] while extracellular recordings from KCs in the locust express sharp transient responses [3]. We conclude that the neuron-intrinsic mechanism is can explain a progressive temporal response sparsening in the insect olfactory system. Further experimental work is needed to test this hypothesis empirically. [1] Muller et. al., Neural Comput, 19(11):2958-3010, 2007. [2] Assisi et. al., Nat Neurosci, 10(9):1176-1184, 2007. [3] Krofczik et. al. Front. Comput. Neurosci., 2(9), 2009.Comment: 5 pages, 2 figures, This manuscript was submitted for review to the Eighteenth Annual Computational Neuroscience Meeting CNS*2009 in Berlin and accepted for oral presentation at the meetin

Molecular Evolution in Time Dependent Environments

The quasispecies theory is studied for dynamic replication landscapes. A meaningful asymptotic quasispecies is defined for periodic time dependencies. The quasispecies' composition is constantly changing over the oscillation period. The error threshold moves towards the position of the time averaged landscape for high oscillation frequencies and follows the landscape closely for low oscillation frequencies.Comment: 5 pages, 3 figures, Latex, uses Springer documentclass llncs.cl