Evolving spiking neural networks for temporal pattern recognition in the presence of noise

Creative Commons - Attribution-NonCommercial-NoDerivs 3.0 United StatesNervous systems of biological organisms use temporal patterns of spikes to encode sensory input, but the mechanisms that underlie the recognition of such patterns are unclear. In the present work, we explore how networks of spiking neurons can be evolved to recognize temporal input patterns without being able to adjust signal conduction delays. We evolve the networks with GReaNs, an artificial life platform that encodes the topology of the network (and the weights of connections) in a fashion inspired by the encoding of gene regulatory networks in biological genomes. The number of computational nodes or connections is not limited in GReaNs, but here we limit the size of the networks to analyze the functioning of the networks and the effect of network size on the evolvability of robustness to noise. Our results show that even very small networks of spiking neurons can perform temporal pattern recognition in the presence of input noiseFinal Published versio

Inhibition of spontaneous induction of lambdoid prophages in Escherichia coli cultures: simple procedures with possible biotechnological applications

BACKGROUND: Infections of bacterial cultures by bacteriophages are serious problems in biotechnological laboratories. Apart from such infections, prophage induction in the host cells may also be dangerous. Escherichia coli is a commonly used host in biotechnological production, and many laboratory strains of this bacterium harbour lambdoid prophages. These prophages may be induced under certain conditions leading to phage lytic development. This is fatal for further cultivations as relatively low, though still significant, numbers of phages may be overlooked. Thus, subsequent cultures of non-lysogenic strains may be infected and destroyed by such phage. RESULTS: Here we report that slow growth of bacteria decreases deleterious effects of spontaneous lambdoid prophage induction. Moreover, replacement of glucose with glycerol in a medium stimulates lysogenic development of the phage after infection of E. coli cells. A plasmid was constructed overexpressing the phage 434 cI gene, coding for the repressor of phage promoters which are necessary for lytic development. Overproduction of the cI repressor abolished spontaneous induction of the λimm434 prophage. CONCLUSIONS: Simple procedures that alleviate problems with spontaneous induction of lambdoid prophage and subsequent infection of E. coli strains by these phages are described. Low bacterial growth rate, replacement of glucose with glycerol in a medium and overproduction of the cI repressor minimise the risk of prophage induction during cultivation of lysogenic bacteria and subsequent infection of other bacterial strains

POET: an evo-devo method to optimize the weights of a large artificial neural networks

Large search spaces as those of artificial neural networks are difficult to search with machine learning techniques. The large amount of parameters is the main challenge for search techniques that do not exploit correlations expressed as patterns in the parameter space. Evolutionary computation with indirect genotype-phenotype mapping was proposed as a possible solution, but current methods often fail when the space is fractured and presents irregularities. This study employs an evolutionary indirect encoding inspired by developmental biology. Cellular proliferations and deletions of variable size allow for the definition of both regular large areas and small detailed areas in the parameter space. The method is tested on the search of the weights of a neural network for the classification of the MNIST dataset. The results demonstrate that even large networks such as those required for image classification can be effectively automatically designed by the proposed evolutionary developmental method. The combination of real-world problems like vision and classification, evolution and development, endows the proposed method with aspects of particular relevance to artificial life

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)

POET: An Evo-Devo Method to Optimize the Weights of Large Artificial Neural Networks

This is a conference paper.Large search spaces as those of artificial neural networks are difficult to search with machine learning techniques. The large amount of parameters is the main challenge for search techniques that do not exploit correlations expressed as patterns in the parameter space. Evolutionary computation with indirect genotype-phenotype mapping was proposed as a possible solution, but current methods often fail when the space is fractured and presents irregularities. This study employs an evolutionary indirect encoding inspired by developmental biology. Cellular proliferations and deletions of variable size allow for the definition of both regular large areas and small detailed areas in the parameter space. The method is tested on the search of the weights of a neural network for the classification of the MNIST dataset. The results demonstrate that even large networks such as those required for image classification can be effectively automatically designed by the proposed evolutionary developmental method. The combination of real-world problems like vision and classification, evolution and development, endows the proposed method with aspects of particular relevance to artificial life