Evolution and Analysis of Embodied Spiking Neural Networks Reveals Task-Specific Clusters of Effective Networks

Elucidating principles that underlie computation in neural networks is currently a major research topic of interest in neuroscience. Transfer Entropy (TE) is increasingly used as a tool to bridge the gap between network structure, function, and behavior in fMRI studies. Computational models allow us to bridge the gap even further by directly associating individual neuron activity with behavior. However, most computational models that have analyzed embodied behaviors have employed non-spiking neurons. On the other hand, computational models that employ spiking neural networks tend to be restricted to disembodied tasks. We show for the first time the artificial evolution and TE-analysis of embodied spiking neural networks to perform a cognitively-interesting behavior. Specifically, we evolved an agent controlled by an Izhikevich neural network to perform a visual categorization task. The smallest networks capable of performing the task were found by repeating evolutionary runs with different network sizes. Informational analysis of the best solution revealed task-specific TE-network clusters, suggesting that within-task homogeneity and across-task heterogeneity were key to behavioral success. Moreover, analysis of the ensemble of solutions revealed that task-specificity of TE-network clusters correlated with fitness. This provides an empirically testable hypothesis that links network structure to behavior.Comment: Camera ready version of accepted for GECCO'1

Model-based prognostics for batteries which estimates useful life and uses a probability density function

This invention develops a mathematical model to describe battery behavior during individual discharge cycles as well as over its cycle life. The basis for the form of the model has been linked to the internal processes of the battery and validated using experimental data. Effects of temperature and load current have also been incorporated into the model. Subsequently, the model has been used in a Particle Filtering framework to make predictions of remaining useful life for individual discharge cycles as well as for cycle life. The prediction performance was found to be satisfactory as measured by performance metrics customized for prognostics for a sample case. The work presented here provides initial steps towards a comprehensive health management solution for energy storage devices

Investigation of BIN1 Isoform in Immortalized Human Diploid Fibroblasts Transformed by SV40 T-Antigen

Bridging intergrator-1, BIN1, functions in membrane recycling, cytoskeleton regulation, DNA repair, and cell proliferation. As a tumor suppressor, BIN1 binds to c-MYC to inhibit S-phase gene transcription aand the N-terminus sequesters PARP, stopping DNA repair, and promoting apoptosis. Decreases in overall levels if BIN1 correlate with poor cancer prognosis. Additionally, BIN1 message can be alternatively spliced, creating isoforms lacking the myc-binding domain. This study was designed to investigate a potential role of BIN1 in viral tumorigenesis by comparing the accumulated levels and isoforms in immortalized human diploid fibroblasts and their simian virus 40 transformed counterparts. RTPCR, revealed several processed transcripts and immunoblotting detected a doublet depending upon the antibody employed. Thus far we have not detected a qualitative difference between the RNA transcripts amplified; however, preliminary data indicate that the lower BIN1 protein band may predominate in the transformed cells. Studies are ongoing to answer these questions