Josephson junction simulation of neurons

Crotty, Patrick; Schult, D. A.; Segall, K.

doi:10.1103/physreve.82.011914

Cited by 146 publications

(125 citation statements)

References 27 publications

Supporting

Mentioning

125

Contrasting

Order By: Relevance

“…In 1952 Alan Lyod Hodgkin and Andrew Huxley studied the behavior of the electrical impulses in squid giant axon. They proposed a system of four nonlinear differential equations and solving these, the solution shows the action potentials behavior, allowing the study of firing threshold and refractory period, which are important electrical characteristics in the neuronal dynamics [3], [4], [5]. The firing threshold is an external stimuli, neccesary for the production of action potential (AP).…”

Section: Introductionmentioning

confidence: 99%

“…The firing threshold is an external stimuli, neccesary for the production of action potential (AP). The refractory period is the temporal lapse after the action potential firing, making hard to fire a second firing [4]. Biophysically, the refractory period is the time that the proteins take from the ionics channels to coming back to their inicial features [3], [4], [6].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Neuronal Synchronization of Electrical Activity, Using the Hodgkin-Huxley Model and RCLSJ Circuit

Téquita

Naranjo

2016

ing.cienc.

View full text Add to dashboard Cite

We simulated the neuronal electrical activity using the Hodgkin-Huxley model (HH) and a superconductor circuit, containing Josephson junctions. These HH model make possible simulate the main neuronal dynamics characteristics such as action potentials, firing threshold and refractory period. The purpose of the manuscript is show a method to syncronize a RCLshunted Josephson junction to a neuronal dynamics represented by the HH model. Thus the RCLSJ circuit is able to mimics the behavior of the HH neuron. We controlated the RCLSJ circuit, using and improved adaptative track scheme, that with the improved Lyapunov functions and the two controllable gain coefficients allowing synchronization of two neuronal models. Results will provide the path to follow forward the understanding neuronal networks synchronization about, generating the intrinsic brain behavior. Sincronización de la actividad eléctrica neuronal, utilizando el modelo de Hodgkin-Huxley y el circuito RCLSJ ResumenSimulamos la actividad eléctrica neuronal mediante el modelo de HodgkinHuxley (HH) y un circuito superconductor, que contiene uniones Josephson. El modelo HH simulan las características principales de la dinámica neuronal tales como potenciales de acción, umbrales de disparo y el perío-dos refractarios. El propósito del manuscrito es mostrar un método para sincronizar un circuito con union Josephson RCLSJ a una dinámica neuronal representado por el modelo HH. Así, el circuito RCLSJ es capaz de imitar el comportamiento de la neurona HH. Controlamos el circuito RCLSJ, utilizando un esquema de control adaptativo, que con funciones de Lyapunov y dos coeficientes de ganancia controlables nos permiten la sincronización de los dos modelos neuronales. Los resultados proporcionan una ruta a seguir adelante en el entendimiento de la sincronización de redes neuronales, generadas por el comportamiento intrinseco del cerebro.Palabras clave: método modelo de Hodgkin-Huxley; uniones Josephson; funciones de Lyapunov; analisis númerico.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Neuronal Synchronization of Electrical Activity, Using the Hodgkin-Huxley Model and RCLSJ Circuit

Téquita

Naranjo

2016

ing.cienc.

View full text Add to dashboard Cite

show abstract

“…These processors can provide an improvement over CMOS in speed by roughly a factor of 100 with extremely high energy efficiency. Our proposed platform will integrate well into such supercomputers, offering neuromorphic capability to von Neumann implementations [128] and additional degrees of freedom to neuromorphic Josephson-junction systems [21,22,129], which are purely electronic.…”

Section: High-performance Application Spacesmentioning

confidence: 99%

“…Firing thresholds and gain are controlled by a dynamic superconducting network, and neurongenerated photonic signals can reconfigure this current-distribution network. By employing superconducting electronics, we can approach zero static power dissipation [20], extraordinary device efficiencies, and utilize Josephson junction circuits including single-flux-quantum devices [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Superconducting Optoelectronic Circuits for Neuromorphic Computing

et al. 2017

View full text Add to dashboard Cite

Neural networks have proven effective for solving many difficult computational problems. Implementing complex neural networks in software is very computationally expensive. To explore the limits of information processing, it will be necessary to implement new hardware platforms with large numbers of neurons, each with a large number of connections to other neurons. Here we propose a hybrid semiconductor-superconductor hardware platform for the implementation of neural networks and large-scale neuromorphic computing. The platform combines semiconducting few-photon light-emitting diodes with superconducting-nanowire single-photon detectors to behave as spiking neurons. These processing units are connected via a network of optical waveguides, and variable weights of connection can be implemented using several approaches. The use of light as a signaling mechanism overcomes fanout and parasitic constraints on electrical signals while simultaneously introducing physical degrees of freedom which can be employed for computation. The use of supercurrents achieves the low power density necessary to scale to systems with enormous entropy. The proposed processing units can operate at speeds of at least 20 MHz with fully asynchronous activity, light-speed-limited latency, and power densities on the order of 1 mW/cm 2 for neurons with 700 connections operating at full speed at 2 K. The processing units achieve an energy efficiency of ≈ 20 aJ per synapse event. By leveraging multilayer photonics with deposited waveguides and superconductors with feature sizes > 100 nm, this approach could scale to systems with massive interconnectivity and complexity for advanced computing as well as explorations of information processing capacity in systems with an enormous number of information-bearing microstates.

show abstract

“…ANN can be realized in many different ways according to the needs of specific applications: for example they can be simulated by computer programs, or implemented by discrete electronic circuits, by microcontrollers, DSPs, ASICs and so on [3,4]. Unconventional electronics can also be used for this task, for example there are different proposals based on the use of superconducting electronics [5][6][7][8]. Superconducting electronics allows computing speeds not possible with conventional semiconductor electronics (with clocks of the order of hundreds of Gigahertz) [9][10][11], but its drawback is the necessity to use cryogenic systems, with relative costs and request of competences.…”

mentioning

confidence: 99%

Artificial neural network based on SQUIDs: demonstration of network training and operation

Chiarello

Carelli

Castellano

et al. 2013

Supercond. Sci. Technol.

View full text Add to dashboard Cite

Abstract. We propose a scheme for the realization of artificial neural networks based on Superconducting Quantum Interference Devices (SQUIDs). In order to demonstrate the operation of this scheme we designed and successfully tested a small network that implements a XOR gate and is trained by means of examples. The proposed scheme can be particularly convenient as support for superconducting applications such as detectors for astrophysics, high energy experiments, medicine imaging and so on. Artificial Neural NetworksArtificial Neural Networks (ANN) are a computing paradigm inspired by biological neural systems [1,2]. They are particularly effective in specific tasks such as pattern recognition, data mining, regression and analysis of series, classification, filtering and so on. An important characteristic of ANN is their capability to learn and to be trained in order to perform some required task. The training can be done once and for all during an initial preparatory phase (which can also be the design phase) if the network is used only for recurrent tasks, or it is possible to have a network that can learn during its use, in order to adapt itself to a changing environment. ANN can be realized in many different ways according to the needs of specific applications: for example they can be simulated by computer programs, or implemented by discrete electronic circuits, by microcontrollers, DSPs, ASICs and so on [3,4]. Unconventional electronics can also be used for this task, for example there are different proposals based on the use of superconducting electronics [5][6][7][8]. Superconducting electronics allows computing speeds not possible with conventional semiconductor electronics (with clocks of the order of hundreds of Gigahertz) [9][10][11], but its drawback is the necessity to use cryogenic systems, with relative costs and request of competences. However there are different examples of important existing applications based on low temperatures and superconductivity (astrophysical detectors, medical imaging, high energy physics, quantum communication and computing, ultra low noise electronics, metrology and so on) where the introduction of a superconducting electronics is not cause of extra cost, but rather it could provide a natural interface between the superconducting system and the room temperature electronics, performing preliminary tasks such as multiplexing, pre-analysis and triggering [12][13][14][15]. In many cases the availability of a fast superconducting ANN close to the system could be a very useful and desirable tool.In the present work we consider a simple and flexible scheme for the realization of ANNs with SQUIDs (Superconducting Quantum Interference Devices), a widely used class of superconducting devices characterized by outstanding performances as ultrasensitive magnetometers and low noise amplifiers [16][17][18]. In order to prove the operation of the proposed scheme we realized and tested a

show abstract

Josephson junction simulation of neurons

Cited by 146 publications

References 27 publications

Neuronal Synchronization of Electrical Activity, Using the Hodgkin-Huxley Model and RCLSJ Circuit

Neuronal Synchronization of Electrical Activity, Using the Hodgkin-Huxley Model and RCLSJ Circuit

Superconducting Optoelectronic Circuits for Neuromorphic Computing

Artificial neural network based on SQUIDs: demonstration of network training and operation

Contact Info

Product

Resources

About