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Neural activity of behaving animals, especially in the prefrontal cortex, is highly heterogeneous, with selective responses to diverse aspects of the executed task. We propose a general model of recurrent neural networks that perform complex rule-based tasks, and we show that the diversity of neuronal responses plays a fundamental role when the behavioral(More)
The grand challenge of neuromorphic computation is to develop a flexible brain-inspired architecture capable of a wide array of real-time applications, while striving towards the ultra-low power consumption and compact size of biological neural systems. Toward this end, we fabricated a building block of a modular neuromorphic architecture, a neurosynaptic(More)
Marching along the DARPA SyNAPSE roadmap, IBM unveils a trilogy of innovations towards the TrueNorth cognitive computing system inspired by the brain's function and efficiency. Judiciously balancing the dual objectives of functional capability and implementation/operational cost, we develop a simple, digital, reconfigurable, versatile spiking neuron model(More)
Theoretical studies have shown that memories last longer if the neural representations are sparse, that is, when each neuron is selective for a small fraction of the events creating the memories. Sparseness reduces both the interference between stored memories and the number of synaptic modifications which are necessary for memory storage. Paradoxically, in(More)
Complex tasks often require the memory of recent events, the knowledge about the context in which they occur, and the goals we intend to reach. All this information is stored in our mental states. Given a set of mental states, reinforcement learning (RL) algorithms predict the optimal policy that maximizes future reward. RL algorithms assign a value to each(More)
We describe the response properties of a compact, low power, analog circuit that implements a model of a leaky–Integrate & Fire (I&F) neuron, with spike-frequency adaptation, refractory period and voltage threshold modulation properties. We investigate the statistics of the circuit's output response by modulating its operating parameters, like refractory(More)
We present an analysis of the range of values of synaptic connections to enhance the storage properties of neural networks. Random patterns are shown to a random connected system, imposing speciÿc activities over neuron pairs that might be connected evoking long-term synapse modiÿcations. Two approaches are given to quantify quality retrieval. The ÿrst(More)
The problem of vehicle yaw control is addressed in this paper using a rear active differential. The controller objective is to minimize the yaw-rate error and body slip-angle error of target values. A new Sliding mode controller is designed using a nonlinear 2DOF vehicle model and a sliding surface that integrates the vehicle yaw-rate error and slipangle(More)
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