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Nonlinear Dynamic Boltzmann Machines for Time-Series Prediction
The dynamic Boltzmann machine (DyBM) has been proposed as a stochastic generative model of multi-dimensional time series, with an exact, learning rule that maximizes the log-likelihood of a givenExpand
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Information dynamics based self-adaptive reservoir for delay temporal memory tasks
Recurrent neural networks of the reservoir computing (RC) type have been found useful in various time-series processing tasks with inherent non-linearity and requirements of variable temporal memory.Expand
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Multiple chaotic central pattern generators with learning for legged locomotion and malfunction compensation
An originally chaotic system can be controlled into various periodic dynamics. When it is implemented into a legged robot's locomotion control as a central pattern generator (CPG), sophisticated gaitExpand
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Distributed recurrent neural forward models with synaptic adaptation and CPG-based control for complex behaviors of walking robots
Walking animals, like stick insects, cockroaches or ants, demonstrate a fascinating range of locomotive abilities and complex behaviors. The locomotive behaviors can consist of a variety of walkingExpand
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A Neurocomputational Model of Goal-Directed Navigation in Insect-Inspired Artificial Agents
Despite their small size, insect brains are able to produce robust and efficient navigation in complex environments. Specifically in social insects, such as ants and bees, these navigationalExpand
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Transfer learning from synthetic to real images using variational autoencoders for robotic applications
Robotic learning in simulation environments provides a faster, more scalable, and safer training methodology than learning directly with physical robots. Also, synthesizing images in a simulationExpand
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Standing on the Shoulders of Giants: Hardware and Neural Architecture Co-Search with Hot Start
Hardware and neural architecture co-search that automatically generates Artificial Intelligence (AI) solutions from a given dataset is promising to promote AI democratization; however, the amount ofExpand
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Conditional generation of multi-modal data using constrained embedding space mapping
We present a conditional generative model that maps low-dimensional embeddings of multiple modalities of data to a common latent space hence extracting semantic relationships between them. TheExpand
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Neuromodulatory adaptive combination of correlation-based learning in cerebellum and reward-based learning in basal ganglia for goal-directed behavior control
Goal-directed decision making in biological systems is broadly based on associations between conditional and unconditional stimuli. This can be further classified as classical conditioningExpand
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The Use of Hebbian Cell Assemblies for Nonlinear Computation
When learning a complex task our nervous system self-organizes large groups of neurons into coherent dynamic activity patterns. During this, a network with multiple, simultaneously active, andExpand
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