Analogue VLSI ‘integrate-and-fire’ neuronwith frequency adaptation

Schultz, Simon R.; Jabri, Marwan A.

doi:10.1049/el:19950932

Cited by 38 publications

(22 citation statements)

References 2 publications

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“…An alternative design in which an explicit threshold voltage can be set and which implements spike-frequency adaptation is proposed in [25]. This design, however, also has large power consumption for the same reasons as the Axon-Hillock circuit.…”

Section: Vlsi Device's Neuronsmentioning

confidence: 99%

A VLSI Array of Low-Power Spiking Neurons and Bistable Synapses With Spike-Timing Dependent Plasticity

Indiveri

Chicca

Douglas

2006

IEEE Trans. Neural Netw.

872

570

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Abstract-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)configure 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 describe the analog circuits designed to implement the silicon neurons and synapses and present experimental data showing the neuron's response properties and the synapses characteristics, in response to AER input spike trains. Our results indicate that these circuits can be used in massively parallel VLSI networks of I&F neurons to simulate real-time complex spike-based learning algorithms.Index Terms-Address-event representation (AER), analog VLSI, integrate-and-fire (I&F) neurons, neuromorphic circuits, spike-based learning, spike-timing dependent plasticity (STDP).

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Section: Vlsi Device's Neuronsmentioning

confidence: 99%

A VLSI Array of Low-Power Spiking Neurons and Bistable Synapses With Spike-Timing Dependent Plasticity

Indiveri

Chicca

Douglas

2006

IEEE Trans. Neural Netw.

872

570

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show abstract

“…The circuit is simple and compact, making it area and power efficient when implemented on the chip as compared to the previous neuron circuits [3][4][5][6][7]. In addition, the interconnect circuit between neurons through synaptic device has also been implemented.…”

Section: Discussionmentioning

confidence: 99%

“…Once the voltage over the capacitor goes above the threshold voltage, the neuron fires and sends out the output pulse. The attempts to build a neuron circuit based on "Axon-Hillock" model proposed by Mead [2] has resulted in various integrate-and-fire neuron circuits [3][4][5][6][7]. But, in these previous works, there has always been a trade-off between actual size of the circuit and mimicking temporal characteristics of biological neuron: temporal integration, threshold triggering, depolarization, repolarization, hyperpolarization and refractory period [8].…”

Section: Introductionmentioning

confidence: 99%

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Neuron Circuit Using a Thyristor and Inter-neuron Connection with Synaptic Devices

Ranjan¹,

Kwon²,

Park³

et al. 2015

JSTS:Journal of Semiconductor Technology and Science

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Abstract-We propose a simple and compact thyristor-based neuron circuit. The thyristor exhibits bi-stable characteristics that can mimic the action potential of the biological neuron, when it is switched between its OFF-state and ON-state with the help of assist circuit. In addition, a method of inter-neuron connection with synaptic devices is proposed, using double current mirror circuit. The circuit utilizes both short-term and long-term plasticity of the synaptic devices by flowing current through them and transferring it to the post-synaptic neuron. The double current mirror circuit is capable of shielding the pre-synaptic neuron from the post synapticneuron while transferring the signal through it, maintaining the synaptic conductance unaffected by the change in the input voltage of the post-synaptic neuron.

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“…For instance, the Axon-Hillock circuit [3], widely known as an electronic analogue of the integrateand-fire neuron (IFN) model, can only act as a low-pass filter for a sequence of pulses. For increasing their performance, alternative silicon spiking neurons [14]- [21], such as a low-power IFN circuit with frequency adaptation [17] and asynchronous chaotic spiking neuron circuits [19], [20], have been previously developed. These circuits increase selectivity to inputs into a silicon spiking neuron due to synchrony detection and temporal filtering properties.…”

Section: Introductionmentioning

confidence: 99%

Burst Synchronization in Two Pulse-Coupled Resonate-and-Fire Neuron Circuits

Nakada

Asai

Hayashi

Professional Practice in Artificial Intelligence

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The present paper addresses burst synchronization in out of phase observed in two pulse-coupled resonate-and-fire neuron (RFN) circuits. The RFN circuit is a silicon spiking neuron that has second-order membrane dynamics and exhibits fast subthreshold oscillation of membrane potential. Due to such dynamics, the behavior of the RFN circuit is sensitive to the timing of stimuli. We investigated the effects of the sensitivity and the mutual interaction on the dynamic behavior of two pulse-coupled RFN circuits, and will demonstrate out of phase burst synchronization and bifurcation phenomena through circuit simulations.

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Analogue VLSI ‘integrate-and-fire’ neuronwith frequency adaptation

Cited by 38 publications

References 2 publications

A VLSI Array of Low-Power Spiking Neurons and Bistable Synapses With Spike-Timing Dependent Plasticity

A VLSI Array of Low-Power Spiking Neurons and Bistable Synapses With Spike-Timing Dependent Plasticity

Neuron Circuit Using a Thyristor and Inter-neuron Connection with Synaptic Devices

Burst Synchronization in Two Pulse-Coupled Resonate-and-Fire Neuron Circuits

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