Brain-inspired chips are being designed to efficiently process complex information and accomplish tasks such as pattern recognition, object detection, and noise reduction and filtering. For this research, we are testing complementary metal-oxide-semiconductor (CMOS) devices such as transistors. We are also examining different CMOS circuits including inverters and leaky integrate-and-fire neurons using a microprobe station. The neuron circuits are expected to generate voltage pulses that mimic the action potentials of neurons found in the brain. The frequency of these pulses changes with input stimulus but pulse widths remain constant. This stimulus is directly proportional to the frequency at which spikes are generated and if this increase ...
Background noise in biological cortical microcircuits constitutes a powerful resource to assess thei...
Neuromorphic circuits aim at emulating biological spiking neurons in silicon hardware. Neurons can b...
Hardware implementations of spiking neural networks offer promising solutions for a wide set of task...
Neuro-inspired electronics are being designed to efficiently process complex information and accompl...
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for a...
Abstract. We describe an improved spiking silicon neuron (SN) [6] that approximates the dynamics of ...
International audienceThe emergence of new hardware-oriented algorithms for convolutional neural net...
Ben Dayan Rubin D, Chicca E, Indiveri G. Firing proprieties of an adaptive analog VLSI neuron. Prese...
An increasing number of research groups are developing custom hybrid analog/digital very large scale...
International audienceAs Moore's law reaches its end, traditional computing technology based on the ...
Abstract — This paper presents a novel analogue VLSI circuitry that reproduces spiking and bursting ...
Abstract—Hardware implementations of spiking neural net-works offer promising solutions for a wide s...
Neurological research has revealed that neurons encode information in the timing of spikes. Spiking ...
Nowadays, many software solutions are currently available for simulating neuron models. Less convent...
Ben Dayan Rubin D, Chicca E, Indiveri G. Characterizing the firing properties of an adaptive analog ...
Background noise in biological cortical microcircuits constitutes a powerful resource to assess thei...
Neuromorphic circuits aim at emulating biological spiking neurons in silicon hardware. Neurons can b...
Hardware implementations of spiking neural networks offer promising solutions for a wide set of task...
Neuro-inspired electronics are being designed to efficiently process complex information and accompl...
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for a...
Abstract. We describe an improved spiking silicon neuron (SN) [6] that approximates the dynamics of ...
International audienceThe emergence of new hardware-oriented algorithms for convolutional neural net...
Ben Dayan Rubin D, Chicca E, Indiveri G. Firing proprieties of an adaptive analog VLSI neuron. Prese...
An increasing number of research groups are developing custom hybrid analog/digital very large scale...
International audienceAs Moore's law reaches its end, traditional computing technology based on the ...
Abstract — This paper presents a novel analogue VLSI circuitry that reproduces spiking and bursting ...
Abstract—Hardware implementations of spiking neural net-works offer promising solutions for a wide s...
Neurological research has revealed that neurons encode information in the timing of spikes. Spiking ...
Nowadays, many software solutions are currently available for simulating neuron models. Less convent...
Ben Dayan Rubin D, Chicca E, Indiveri G. Characterizing the firing properties of an adaptive analog ...
Background noise in biological cortical microcircuits constitutes a powerful resource to assess thei...
Neuromorphic circuits aim at emulating biological spiking neurons in silicon hardware. Neurons can b...
Hardware implementations of spiking neural networks offer promising solutions for a wide set of task...