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The behavior and skills of living systems depend on the distributed control provided by specialized and highly recurrent neural networks. Learning and memory in these systems is mediated by a set of adaptation mechanisms, known collectively as neuronal plasticity. Translating principles of recurrent neural control and plasticity to artificial agents has(More)
—Reinforcement Learning, or Reward-Dependent Learning, has been very successful at describing how animals and humans adjust their actions so as to increase their gains and reduce their losses in a wide variety of tasks. Empirical studies have furthermore identified numerous neuronal correlates of quantities necessary for such computations. But, in general(More)
It is a long-established fact that neuronal plasticity occupies the central role in generating neural function and computation. Nevertheless, no unifying account exists of how neurons in a recurrent cortical network learn to compute on temporally and spatially extended stimuli. However, these stimuli constitute the norm, rather than the exception, of the(More)
— Using neuronal plasticity in the sensorimotor loop of embodied controllers to autonomously learn behaviors remains a great challenge. The difficulty lies not only in the development of sophisticated plasticity mechanisms, but also in controlling when, where and how to learn, in order to achieve the correct behavior. Borrowing from biology, we develop a(More)
Learning recurrent neural networks as behavior controllers for robots requires measures to guide the learning towards a desired behavior. Organisms in nature solve this problem with feedback signals to assess their behavior and to refine their actions. In line with this, a neural framework is developed where the synaptic learning is controlled by artificial(More)
Supplementing a differential equation with delays results in an infinite-dimensional dynamical system. This property provides the basis for a reservoir computing architecture, where the recurrent neural network is replaced by a single nonlinear node, delay-coupled to itself. Instead of the spatial topology of a network, subunits in the delay-coupled(More)
Delays are ubiquitous in biological systems, ranging from genetic regulatory networks and synaptic conductances, to predator/pray population interactions. The evidence is mounting, not only to the presence of delays as physical constraints in signal propagation speed, but also to their functional role in providing dynamical diversity to the systems that(More)
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