Electronic Neuron Model Research Articles

Controllability of the unijunction transistor based integrate-and-fire electronic spiking neuron

An integrate-and-fire (I&F) electronic neuron model is investigated theoretically and experimentally. The circuit comprises a unijunction transistor, a capacitor, and two resistors. It exhibits the following properties: (a) generates short spikes, (b) the steady state can be stabilized by a proportional or adaptive feedback, (c) high frequency action inhibits the spikes, (d) coupled units can synchronize with each other.

Communication in nano devices: Electronic based biophysical model of a neuron

Investigating new strategies and signaling techniques for nano-devices and systems is quite challenging. The communication systems considered to be feasible in nano-devices are inspired from biophysical systems which communicate with electro-chemical signals organized with respect to excitation. While the electrical pulses transmitted along with the cell membrane the chemical signal transmitted in the synaptic cleft. Developing new chemical signal based communication which termed as the molecular communication with minimum error is now the central deal for the researchers. Strategic approaches to the issue in variety of perspective such as systematic, experimental and electronic circuitry viable for chip based robotic and nano-device design are now available in the literature. Biological signaling pathways, in accordance with the action potentials generated in pre-synaptic neuron some chemical substances called neurotransmitters released into the synaptic cleft and hence the post-synaptic neuron is accordingly triggered. In this way the information transmitted from one cell to another by electro chemical signal carriers. About this process some electronic neuron models have also been introduced to simulate dynamic behavior of neuronal cells. In this study, a novel simple electronic integrate and fire model which has been designed previously was further developed and used to simulate and analyze the communication of neurons. The proposed electronic model not only simulates the neuronal cell’s behavior and also can transmit the information to the following neuron. The rate of correct transmission depends on the synaptic channel model. The characteristics of the used semiconductor components with overall structure of the proposed electronic model are very close to the biophysical nature of neuron and can be designed on semiconductor chips which is the advantage of the model.

A Learning-Enabled Neuron Array IC Based Upon Transistor Channel Models of Biological Phenomena

We present a single-chip array of 100 biologically-based electronic neuron models interconnected to each other and the outside environment through 30,000 synapses. The chip was fabricated in a standard 350 nm CMOS IC process. Our approach used dense circuit models of synaptic behavior, including biological computation and learning, as well as transistor channel models. We use Address-Event Representation (AER) spike communication for inputs and outputs to this IC. We present the IC architecture and infrastructure, including IC chip, configuration tools, and testing platform. We present measurement of small network of neurons, measurement of STDP neuron dynamics, and measurement from a compiled spiking neuron WTA topology, all compiled into this IC.

Modelling of the active reaction of stick insects by a network of neuromimes

Depending on the activity status of the animal, the control system of the femur-tibia joint in stick insects exhibits either a resistance reflex, or the "active reaction", a totally different action pattern (Bassler 1988a). Using analog electronic neuron models, several different neuronal circuits are explored that model the active reaction with all its features. The models differ in complexity, redundance and the robustness against small variations of network parameters (e.g. coupling strengths). The circuit with the highest robustness and redundancy requires interneurons with special features, such as found in real animals. When inserted into a closed loop modeling movement and sensory feedback from the periphery, this circuit produces oscillations similar to searching movements found in the real animal. In addition to intracellular recording methods, the authors propose modeling with realistic neuromimes as a complementary method in the investigation of neuronal networks which have well documented input-output relationships.

Pulse sequences generated by a degenerate analog neuron model.

The response characteristics of an electronic neuron model proposed by the authors are investigated. Periodic stimulating pulse sequences with a fixed frequency are applied to the analog neuron model and the response pulse sequences are studied. In the degenerate case, the state transition of the neuron model during one period of the stimulating pulse sequense is described by a first order piecewise linear difference equation with a jump. It is shown that the periodic response pulse sequences of the neuron model belong to a special class of pulse sequences generated by a simple algorithm, and that the relation between the pulse width (or amplitude) of the stimulating pulse and the firing rate of the neuron model takes the form of an extended Cantor function.

A simple electronic neuron model incorporating both active and passive responses.

An electronic model representing a small patch of neural membrane is described. The patch circuit is incorporated into a discrete element ladder network axon model. Electrotonic spread of subthreshold potentials, active action potential propagation, and synaptic events are demonstrated.

A coincidence model of the processing of quantum signals by the human retina.

Well-known theoretical concepts for visual threshold behavior are: 1. the quantum coincidence model which describes particularly threshold-dependence on presentation time and teststimulus geometry; 2. the quantum fluctuation model which includes description of thresholddependence on luminance and color of a masking background. In this paper it is demonstrated that analog models of static as well as dynamic inhibition in the retina can be extended to more realistic stochastic models. This can be done by consideration of the coincidence operations as expressed in the quantum coincidence and quantum fluctuation models for visual threshold behavior. In this model coincidence detectors indicated as C-gates are the essential components. It is demonstrated that threshold-gates as are frequently used in models in neurophysiology do not permit development of the more realistic asynchronous circuits. Further it is pointed out that electronic neuron models and stochastic and nonstochastic models of nerve-functions of other authors in fact are either special simplifications of the proposed C-gates or lead naturally to C-gates for their further development.

An electronic model of the nerve membrane for demonstration purposes.

ArticleAn electronic model of the nerve membrane for demonstration purposes.R FitzHughR FitzHughPublished Online:01 Jan 1966https://doi.org/10.1152/jappl.1966.21.1.305MoreSectionsPDF (781 KB)Download PDF ToolsExport citationAdd to favoritesGet permissionsTrack citations ShareShare onFacebookTwitterLinkedInWeChat Previous Back to Top Next Download PDF FiguresReferencesRelatedInformation Cited ByReaction Time Improvements by Neural Bistability18 March 2019 | Behavioral Sciences, Vol. 9, No. 3K + Channels: Function‐Structural Overview1 July 2012Persuading Computers to Act More Like Brains28 June 2012Neuromorphic Systems: Past, Present and Future21 November 2009A silicon model of the Hirudo swim oscillatorIEEE Engineering in Medicine and Biology Magazine, Vol. 19, No. 1A parametric examination of VLSI-based neuronal models of cyclic and reciprocal inhibitionIEEE Transactions on Biomedical Engineering, Vol. 43, No. 12A neuromime in VLSIIEEE Transactions on Neural Networks, Vol. 7, No. 2Existence of standing pulse solutions for an excitable activator-inhibitory systemJournal of Dynamics and Differential Equations, Vol. 4, No. 2A Simple Electronic Neuron Model Incorporating Both Active and Passive ResponsesIEEE Transactions on Biomedical Engineering, Vol. BME-26, No. 11Simple simulator of electrical excitation in frog skinMedical & Biological Engineering, Vol. 8, No. 5Implementing Neural Models in Silicon More from this issue > Volume 21Issue 1January 1966Pages 305-8 https://doi.org/10.1152/jappl.1966.21.1.305PubMed5903932History Published online 1 January 1966 Published in print 1 January 1966 Metrics

Model for Spontaneous and Synchronous Activity in Single Neurons

Two response characteristics frequently observed in single neurons of the auditory system are spontaneous activity and quasisynchronous firing to periodic stimuli. These characteristics have been reproduced with a real-time electronic neuron model incorporating a randomly fluctuating threshold and both absolute and relative refractory periods. A wide variety of responses that bracket physiological results can be generated by manipulating noise spectrum and the ratios between either rms noise voltage or stimulus level to resting threshold. “Burst firing” during spontaneous activity is present for low-pass noise when the bandwidth is about 5 cps. Spontaneous interval distributions become more peaked at shorter intervals when either the bandwidth or the noise to threshold ratio is increased. When periodic stimuli are applied, interval histograms are generated that show quasisynchronous firing patterns. Only the details of these histograms depend on stimulus waveshape. The results suggest that the observed quasisynchronous firings are not restricted to auditory neurons, but may occur in any neuron having the above properties.

NEUROMIMES: ACTION OF A RECIPROCALLY INHIBITORY PAIR.

Two electronic neuron models (neuromimes) connected to a common excitatory input and having both self- and mutual-inhibition were used to simulate the pulse patterns which control wing musculature in certain insects. With variation of stimulus frequency, firing patterns changed in discrete steps. Pattern change was found to be hysteretic: the pattern elicited by a given stimulus frequency depended on whether that frequency was approached from above or below. Pattern selection could be controlled by the injection or deletion of a single pulse in the stimulus pulse-train as well as by smooth frequency change.

Electronic neuron models as an aid to neurophysiological research.

As is well known the time courses of many physiological and electrical phenomena are quite similar. Therefore electrical and electrochemical models can be used to simulate physiological processes [Bethe; Bonhoeffer; Burns; Druckrey and Kupfmuller; Eccles (1); Freygang; Hermann; Hodgkin and Huxley; Kupfmuller (1); Lillie; Tasaki].1 In modern automation and computer technology refined apparatus becomes necessary which is able to perform such complex functions as learning and pattern recognition, which seemed hitherto to have been restricted to organisms with highly developed nervous systems (Steinbuch; Wiener). The design of such apparatus being rather difficult, communication and electronic engineers are interested in how nature has solved the problems of information processing in the nervous system. The investigation of nervous systems is one of the tasks of biologists, and engineers hope that their questions can be answered by the biologists. However two difficulties arise: firstly, many biologists are not interested in engineering problems, and secondly, biologists and engineers do not approach problems from the same angle.