Learning Aquatic Locomotion with Animats

One of the challenges of researching spiking neural networks (SNN) is translation from temporal spiking behavior to classic controller output. While many encoding schemes exist to facilitate this translation, there are few benchmarks for neural networks that inherently utilize a temporal controller. In this work, we consider the common reinforcement problem of animat locomotion in an environment suited for evaluating SNNs. Using this problem, we explore novel methods of reward distribution as they impacts learning. Hebbian learning, in the form of spike time dependent plasticity (STDP), is modulated by a dopamine signal and affected by reward-induced neural activity. Different reward strategies are parameterized and the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is used to find the best strategies for fixed animat morphologies. The contribution of this work is two-fold: to cast the problem of animat locomotion in a form directly applicable to simple temporal controllers, and to demonstrate novel methods for reward modulated Hebbian learning

WONOEP appraisal: New genetic approaches to study epilepsy

New genetic investigation techniques, including next-generation sequencing, epigenetic profiling, cell lineage mapping, targeted genetic manipulation of specific neuronal cell types, stem cell reprogramming, and optogenetic manipulations within epileptic networks are progressively unraveling the mysteries of epileptogenesis and ictogenesis. These techniques have opened new avenues to discover the molecular basis of epileptogenesis and to study the physiologic effects of mutations in epilepsy associated genes on a multilayer level, from cells to circuits. This manuscript reviews recently published applications of these new genetic technologies in the study of epilepsy, as well as work presented by the authors at the genetic session of the XII Workshop on the Neurobiology of Epilepsy (WONOEP 2013) in Quebec, Canada. Next-generation sequencing is providing investigators with an unbiased means to assess the molecular causes of sporadic forms of epilepsy and has revealed the complexity and genetic heterogeneity of sporadic epilepsy disorders. To assess the functional impact of mutations in these newly identified genes on specific neuronal cell types during brain development, new modeling strategies in animals, including conditional genetics in mice and in utero knock-down approaches, are enabling functional validation with exquisite cell-type and temporal specificity. In addition, optogenetics, using cell-type–specific Cre recombinase driver lines, is enabling investigators to dissect networks involved in epilepsy. In addition, genetically encoded cell-type labeling is providing new means to assess the role of the nonneuronal components of epileptic networks such as glial cells. Furthermore, beyond its role in revealing coding variants involved in epileptogenesis, next-generation sequencing can be used to assess the epigenetic modifications that lead to sustained network hyperexcitability in epilepsy, including methylation changes in gene promoters and noncoding ribonucleic acid (RNA) involved in modifying gene expression following seizures. In addition, genetically based bioluminescent reporters are providing new opportunities to assess neuronal activity and neurotransmitter levels both in vitro and in vivo in the context of epilepsy. Finally, genetically rederived neurons generated from patient induced pluripotent stem cells and genetically modified zebrafish have become high-throughput means to investigate disease mechanisms and potential new therapies. Genetics has changed the field of epilepsy research considerably, and is paving the way for better diagnosis and therapies for patients with epilepsy

An open unified deep graph learning framework for discovering drug leads

Computational discovery of ideal lead compounds is a critical process for modern drug discovery. It comprises multiple stages: hit screening, molecular property prediction, and molecule optimization. Current efforts are disparate, involving the establishment of models for each stage, followed by multi-stage multi-model integration. However, this is non-ideal, as clumsy integration of incompatible models increases research overheads, and may even reduce success rates in drug discovery. Facilitating compatibilities requires establishing inherent model consistencies across lead discovery stages. Towards that effect, we propose an open deep graph learning (DGL) based pipeline: generative adversarial feature subspace enhancement (GAFSE), which first unifies the modeling of these stages into one learning framework. GAFSE also offers standardized modular design and streamlined interfaces for future expansions and community support. GAFSE combines adversarial/generative learning, graph attention network, graph reconstruction network, and optimizes the classification/regression loss, adversarial/generative loss, and reconstruction loss simultaneously. Convergence analysis theoretically guarantees model generalization performance. Exhaustive benchmarking demonstrates that the GAFSE pipeline achieves excellent performance across almost all lead discovery stages, while also providing valuable model interpretability. Hence, we believe this tool will enhance the efficiency and productivity of drug discovery researchers.Comment: This article is used as the preliminary studies for the application of Lee Kuan Yew Postdoctoral Fellowship (LKYPDF) 2023 in Singapore. All rights reserve

Design and Control of Compliant Tensegrity Robots Through Simulation and Hardware Validation

To better understand the role of tensegrity structures in biological systems and their application to robotics, the Dynamic Tensegrity Robotics Lab at NASA Ames Research Center has developed and validated two different software environments for the analysis, simulation, and design of tensegrity robots. These tools, along with new control methodologies and the modular hardware components developed to validate them, are presented as a system for the design of actuated tensegrity structures. As evidenced from their appearance in many biological systems, tensegrity ("tensile-integrity") structures have unique physical properties which make them ideal for interaction with uncertain environments. Yet these characteristics, such as variable structural compliance, and global multi-path load distribution through the tension network, make design and control of bio-inspired tensegrity robots extremely challenging. This work presents the progress in using these two tools in tackling the design and control challenges. The results of this analysis includes multiple novel control approaches for mobility and terrain interaction of spherical tensegrity structures. The current hardware prototype of a six-bar tensegrity, code-named ReCTeR, is presented in the context of this validation

WONOEP appraisal: New genetic approaches to study epilepsy

New genetic investigation techniques, including next-generation sequencing, epigenetic profiling, cell lineage mapping, targeted genetic manipulation of specific neuronal cell types, stem cell reprogramming, and optogenetic manipulations within epileptic networks are progressively unraveling the mysteries of epileptogenesis and ictogenesis. These techniques have opened new avenues to discover the molecular basis of epileptogenesis and to study the physiologic effects of mutations in epilepsy associated genes on a multilayer level, from cells to circuits. This manuscript reviews recently published applications of these new genetic technologies in the study of epilepsy, as well as work presented by the authors at the genetic session of the XII Workshop on the Neurobiology of Epilepsy (WONOEP 2013) in Quebec, Canada. Next-generation sequencing is providing investigators with an unbiased means to assess the molecular causes of sporadic forms of epilepsy and has revealed the complexity and genetic heterogeneity of sporadic epilepsy disorders. To assess the functional impact of mutations in these newly identified genes on specific neuronal cell types during brain development, new modeling strategies in animals, including conditional genetics in mice and in utero knock-down approaches, are enabling functional validation with exquisite cell-type and temporal specificity. In addition, optogenetics, using cell-type–specific Cre recombinase driver lines, is enabling investigators to dissect networks involved in epilepsy. In addition, genetically encoded cell-type labeling is providing new means to assess the role of the nonneuronal components of epileptic networks such as glial cells. Furthermore, beyond its role in revealing coding variants involved in epileptogenesis, next-generation sequencing can be used to assess the epigenetic modifications that lead to sustained network hyperexcitability in epilepsy, including methylation changes in gene promoters and noncoding ribonucleic acid (RNA) involved in modifying gene expression following seizures. In addition, genetically based bioluminescent reporters are providing new opportunities to assess neuronal activity and neurotransmitter levels both in vitro and in vivo in the context of epilepsy. Finally, genetically rederived neurons generated from patient induced pluripotent stem cells and genetically modified zebrafish have become high-throughput means to investigate disease mechanisms and potential new therapies. Genetics has changed the field of epilepsy research considerably, and is paving the way for better diagnosis and therapies for patients with epilepsy