Indiveri G, Chicca E, Douglas RJ. A VLSI array of low-power spiking neurons and bistable synapses with spike-timing dependent plasticity. IEEE Transactions on Neural Networks. 2006;17(1):211-221.We present a mixed-mode analog/digital VLSI device comprising an array of leaky integrate-and-fire (I&F) neurons, adaptive synapses with spike-timing dependent plasticity, and an asynchronous event based communication infrastructure that allows the user to (re)con figure networks of spiking neurons with arbitrary topologies. The asynchronous communication protocol used by the silicon neurons to transmit spikes (events) off-chip and the silicon synapses to receive spikes from the outside is based on the "address-event representation" (AER). We descri...
We describe an analog VLSI circuit implementing spike-driven synaptic plasticity, embedded in a netw...
We describe an analog VLSI circuit implementing spike-driven synaptic plasticity, embedded in a netw...
Real-time classification of patterns of spike trains is a difficult computational problem that both ...
Indiveri G, Chicca E, Douglas R. A VLSI reconfigurable network of integrate-and-fire neurons with sp...
Chicca E, Indiveri G, Douglas RJ. An event-based VLSI network of integrate-and-fire neurons. Present...
Hardware implementations of spiking neural networks offer promising solutions for computational task...
We describe and demonstrate the key features of a neuromorphic, analog VLSI chip (termed F-LANN) hos...
Hardware implementations of spiking neural networks offer promising solutions for computational task...
We describe and demonstrate the key features of a neuromorphic, analog VLSI chip (termed F-LANN) hos...
Hardware implementations of spiking neural networks offer promising solutions for computational task...
Chicca E, Indiveri G, Douglas R. An adaptive silicon synapse. Presented at the Proceedings of the 20...
The ability to carry out signal processing, classification, recognition, and computation in artifici...
The ability to carry out signal processing, classification, recognition, and computation in artifici...
The ability to carry out signal processing, classification, recognition, and computation in artifici...
The ability to carry out signal processing, classification, recognition, and computation in artifici...
We describe an analog VLSI circuit implementing spike-driven synaptic plasticity, embedded in a netw...
We describe an analog VLSI circuit implementing spike-driven synaptic plasticity, embedded in a netw...
Real-time classification of patterns of spike trains is a difficult computational problem that both ...
Indiveri G, Chicca E, Douglas R. A VLSI reconfigurable network of integrate-and-fire neurons with sp...
Chicca E, Indiveri G, Douglas RJ. An event-based VLSI network of integrate-and-fire neurons. Present...
Hardware implementations of spiking neural networks offer promising solutions for computational task...
We describe and demonstrate the key features of a neuromorphic, analog VLSI chip (termed F-LANN) hos...
Hardware implementations of spiking neural networks offer promising solutions for computational task...
We describe and demonstrate the key features of a neuromorphic, analog VLSI chip (termed F-LANN) hos...
Hardware implementations of spiking neural networks offer promising solutions for computational task...
Chicca E, Indiveri G, Douglas R. An adaptive silicon synapse. Presented at the Proceedings of the 20...
The ability to carry out signal processing, classification, recognition, and computation in artifici...
The ability to carry out signal processing, classification, recognition, and computation in artifici...
The ability to carry out signal processing, classification, recognition, and computation in artifici...
The ability to carry out signal processing, classification, recognition, and computation in artifici...
We describe an analog VLSI circuit implementing spike-driven synaptic plasticity, embedded in a netw...
We describe an analog VLSI circuit implementing spike-driven synaptic plasticity, embedded in a netw...
Real-time classification of patterns of spike trains is a difficult computational problem that both ...