1,721,034 research outputs found
Gradient-Free Optimization of Artificial and Biological Networks using Learning to Learn
No full textUnderstanding intelligence and how it allows humans to learn, to make decision and form memories, is a long-lasting quest in neuroscience. Our brain is formed by networks of neurons and other cells, however, it is not clear how those networks are trained to learn to solve specific tasks. In machine learning and artificial intelligence it is common to train and optimize neural networks with gradient descent and backpropagation. How to transfer this optimization strategy to biological, spiking networks (SNNs) is still a matter of research. Due to the binary communication scheme between neurons of an SNN via spikes, a direct application of gradient descent and backpropagation is not possible without further approximations. In my work, I present gradient-free optimization techniques that are directly applicable to artificial and biological neural networks. I utilize metaheuristics, such as genetic algorithms and the ensemble Kalman Filter, to optimize network parameters and train networks to learnto solve specific tasks. The optimization is embedded into the concept of meta-learning and learning to learn respectively. The learning to learn concept consists of a two loop optimization procedure. In the first, inner loop the algorithm or network is trained on a family of tasks, and in the second, outer loop the hyper-parameters and parameters of the network are optimized. First, I apply the EnKF on a convolution neural network, resulting in high accuracy when classifying digits. Then, I employ the same optimization procedure on a spiking reservoir network within the L2L framework. The L2L framework, an implementation of the learning to learn concept, allows me to easily deploy and execute multiple instances of the network in parallel on high performance computing systems. In order to understand how the network learning evolves, I analyze the connection weights over multiple generations and investigate a covariance matrix of the EnKF in the principle component space. The analysis not only shows the convergence behaviour of the optimization process, but also how sampling techniques influence the optimization procedure. Next, I embed the EnKF into the L2L inner loop and adapt the hyper-parameters of the optimizer using a genetic algorithm (GA). In contrast to the manual parameter setting, the GA suggests an alternative configuration. Finally, I present an ant colony simulation foraging for food while being steered by SNNs. While training the network, self-coordination and self-organization in the colony emerges. I employ various analysis methods to better understand the ants’ behaviour. With my work I leverage optimization for different scientific domains utilizing meta-learning and illustrate how gradient-free optimization can be applied on biological and artificial networks
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Gradient-free optimization of artificial and biological networks using learning to learn
No full textUnderstanding intelligence and how it allows humans to learn, to make decision and form memories, is a long-lasting quest in neuroscience. Our brain is formed by networks of neurons and other cells, however, it is not clear how those networks are trained to learn to solve specific tasks. In machine learning and artificial intelligence it is common to train and optimize neural networks with gradient descent and backpropagation. How to transfer this optimization strategy to biological, spiking networks (SNNs) is still a matter of research. Due to the binary communication scheme between neurons of an SNN via spikes, a direct application of gradient descent and backpropagation is not possible without further approximations. In my work, I present gradient-free optimization techniques that are directly applicable to artificial and biological neural networks. I utilize metaheuristics, such as genetic algorithms and the ensemble Kalman Filter, to optimize network parameters and train networks to learn to solve specific tasks. The optimization is embedded into the concept of meta-learning and learning to learn respectively. The learning to learn concept consists of a two loop optimization procedure. In the first, inner loop the algorithm or network is trained on a family of tasks, and in the second, outer loop the hyper-parameters and parameters of the network are optimized. First, I apply the EnKF on a convolution neural network, resulting in high accuracy when classifying digits. Then, I employ the same optimization procedure on a spiking reservoir network within the L2L framework. The L2L framework, an implementation of the learning to learn concept, allows me to easily deploy and execute multiple instances of the network in parallel on high performance computing systems. In order to understand how the network learning evolves, I analyze the connection weights over multiple generations and investigate a covariance matrix of the EnKF in the principle component space. The analysis not only shows the convergence behaviour of the optimization process, but also how sampling techniques influence the optimization procedure. Next, I embed the EnKF into the L2L inner loop and adapt the hyper-parameters of the optimizer using a genetic algorithm (GA). In contrast to the manual parameter setting, the GA suggests an alternative configuration. Finally, I present an ant colony simulation foraging for food while being steered by SNNs. While training the network, self-coordination and self-organization in the colony emerges. I employ various analysis methods to better understand the ants’ behaviour. With my work I leverage optimization for different scientific domains utilizing meta-learning and illustrate how gradient-free optimization can be applied on biological and artificial networks
Learning to Learn and Learning to Optimize for High-Throughput Hyperparameter Search using HPC
No full text
Optimizing Spiking Neural Networks with L2L on HPC systems
No full textIn my talk I present the optimization of spiking neural networks (SNN) on HPC system using the L2L framework. I explain the problems when training SNNs to learn to solve tasks, then I introduce the concept of learning to learn and the framework L2L which implements the concept. Furthermore, I describe how optimization can be applied with L2L on SNNs and showcase two examples, namely optimizing a spiking reservoir network to classify digits and a swarm with a foraging behaviour
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