Analog VLSI: Circuits and Principles
Analog VLSI: Circuits and Principles
Modeling short-term synaptic depression in Silicon
Neural Computation
Polychronization: Computation with Spikes
Neural Computation
What Can a Neuron Learn with Spike-Timing-Dependent Plasticity?
Neural Computation
Neural Systems as Nonlinear Filters
Neural Computation
Advances in Design and Application of Spiking Neural Networks
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Fuzzy-neural computation and robotics
Synaptic Dynamics in Analog VLSI
Neural Computation
Competitive stdp-based spike pattern learning
Neural Computation
Forward- and backpropagation in a silicon dendrite
IEEE Transactions on Neural Networks
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
Temporal order detection and coding in nervous systems
Neural Computation
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Spike-based neuromorphic electronic architectures offer an attractive solution for implementing compact efficient sensory-motor neural processing systems for robotic applications. Such systems typically comprise event-based sensors and multi-neuron chips that encode, transmit, and process signals using spikes. For robotic applications, the ability to sustain real-time interactions with the environment is an essential requirement. So these neuromorphic systems need to process sensory signals continuously and instantaneously, as the input data arrives, classify the spatio-temporal information contained in the data, and produce appropriate motor outputs in real-time. In this paper we evaluate the computational approaches that have been proposed for classifying spatio-temporal sequences of spike-trains, derive the main principles and the key components that are required to build a neuromorphic system that works in robotic application scenarios, with the constraints imposed by the biologically realistic hardware implementation, and present possible system-level solutions.