Resonate and fire dynamics in Complex Oscillation Based Test of analog filters

Recently, proposals have been made for enhancing the Oscillation Based Test (OBT) methodology by using non-plain oscillation regimes, leading to so called Complex Oscillation Based Test (COBT). Here we focus on a recently illustrated strategy for the testing of analog 2nd order filters, showing that the COBT dynamics is quite similar to that expressed by Resonate & Fire (R+F) neuron models. In this interpretation, the testing approach can be related to firing-rate measures. A brief description is given of the mathematical models necessary to achieve a precise characterization of firing times, showing how it can be used for testing purposes. A practical example with simulation data is also provided. © 2011 IEEE

Design for testability of high-order OTA-C filters

Copyright © 2016 John Wiley & Sons, Ltd.A study of oscillation-based test for high-order Operational Transconductance Amplifier-C (OTA-C) filters is presented. The method is based on partition of a high-order filter into second-order filter functions. The opening Q-loop and adding positive feedback techniques are developed to convert the second-order filter section into a quadrature oscillator. These techniques are based on an open-loop configuration and an additional positive feedback configuration. Implementation of the two testability design methods for nth-order cascade, IFLF and leapfrog (LF) filters is presented, and the area overhead of the modified circuits is also discussed. The performances of the presented techniques are investigated. Fourth-order cascade, inverse follow-the-leader feedback (IFLF) and LF OTA-C filters were designed and simulated for analysis of fault coverage using the adding positive feedback method based on an analogue multiplexer. Simulation results show that the oscillation-based test method using positive feedback provides high fault coverage of around 97%, 96% and 95% for the cascade, IFLF and LF OTA-C filters, respectively. Copyright ÂPeer reviewe

Energy Efficient Spintronic Device for Neuromorphic Computation

Future computing will require significant development in new computing device paradigms. This is motivated by CMOS devices reaching their technological limits, the need for non-Von Neumann architectures as well as the energy constraints of wearable technologies and embedded processors. The first device proposal, an energy-efficient voltage-controlled domain wall device for implementing an artificial neuron and synapse is analyzed using micromagnetic modeling. By controlling the domain wall motion utilizing spin transfer or spin orbit torques in association with voltage generated strain control of perpendicular magnetic anisotropy in the presence of Dzyaloshinskii-Moriya interaction (DMI), different positions of the domain wall are realized in the free layer of a magnetic tunnel junction to program different synaptic weights. Additionally, an artificial neuron can be realized by combining this DW device with a CMOS buffer. The second neuromorphic device proposal is inspired by the brain. Membrane potential of many neurons oscillate in a subthreshold damped fashion and fire when excited by an input frequency that nearly equals their Eigen frequency. We investigate theoretical implementation of such “resonate-and-fire” neurons by utilizing the magnetization dynamics of a fixed magnetic skyrmion based free layer of a magnetic tunnel junction (MTJ). Voltage control of magnetic anisotropy or voltage generated strain results in expansion and shrinking of a skyrmion core that mimics the subthreshold oscillation. Finally, we show that such resonate and fire neurons have potential application in coupled nanomagnetic oscillator based associative memory arrays

Precise-Spike-Driven synaptic plasticity: Learning hetero-association of spatiotemporal spike patterns

10.1371/journal.pone.0078318PLoS ONE811-POLN

Computational models of cognition

Existing connectionist computational models of neural networks idealise the biological process in the neuron to a discrete summation, and fail to provide an efficient substrate for computation involving the spectral data that is the input to the biological perceptual process. This work presents a computational model of neural function that introduces a continuous analogue process and explores the computational uses of sub-threshold oscillations of the membrane potential. The goal of tins work is to present an in itial examination of the advantages to the practitioner that are afforded by a new computational model of the neuron that includes sub-threshold oscillations as a component on an equal footing with axonal impulses themselves. The relevant. evidence that these effects are important in a biological neural network is presented. The new resonate-and-fire model is presented and mathematically defined, and shown to be a superset of the ubiquitous integrate-and-fire model. The behaviour patterns of the model are explored initially in single neurons and then networks are examined and shown to be capable of exhibiting useful excitation patterns such as tonic oscillation, selective innervation and resonance. An unsupervised learning algorithm is defined and shown to generate networks that naturally organise to perform Fourier-style transforms central to spectral manipulations. Finally, the model is examined with respect to the current theories of computational neuroscience and cognitive science, and its p otential uses in these domains described

Optimization of Efficient Neuron Models With Realistic Firing Dynamics. The Case of the Cerebellar Granule Cell

Biologically relevant large-scale computational models currently represent one of the main methods in neuroscience for studying information processing primitives of brain areas. However, biologically realistic neuron models tend to be computationally heavy and thus prevent these models from being part of brain-area models including thousands or even millions of neurons. The cerebellar input layer represents a canonical example of large scale networks. In particular, the cerebellar granule cells, the most numerous cells in the whole mammalian brain, have been proposed as playing a pivotal role in the creation of somato-sensorial information representations. Enhanced burst frequency (spiking resonance) in the granule cells has been proposed as facilitating the input signal transmission at the theta-frequency band (4–12 Hz), but the functional role of this cell feature in the operation of the granular layer remains largely unclear. This study aims to develop a methodological pipeline for creating neuron models that maintain biological realism and computational efficiency whilst capturing essential aspects of single-neuron processing. Therefore, we selected a light computational neuron model template (the adaptive-exponential integrate-and-fire model), whose parameters were progressively refined using an automatic parameter tuning with evolutionary algorithms (EAs). The resulting point-neuron models are suitable for reproducing the main firing properties of a realistic granule cell from electrophysiological measurements, including the spiking resonance at the theta-frequency band, repetitive firing according to a specified intensityfrequency (I-F) curve and delayed firing under current-pulse stimulation. Interestingly, the proposed model also reproduced some other emergent properties (namely, silent at rest, rheobase and negligible adaptation under depolarizing currents) even though these properties were not set in the EA as a target in the fitness function (FF), proving that these features are compatible even in computationally simple models. The proposed methodology represents a valuable tool for adjusting AdEx models according to a FF defined in the spiking regime and based on biological data. These models are appropriate for future research of the functional implication of bursting resonance at the theta band in large-scale granular layer network models.FEDER/Junta de Andalucia-Consejeria de Economia y Conocimiento under the EmbBrain project A-TIC-276-UGR18University of Granada under the Young Researchers FellowshipMinisterio de Economia y Competitividad (MINECO)-FEDER TIN2016-81041-REuropean Human Brain Project SGA2 ( H2020-RIA) 785907European Human Brain Project SGA3 (European Commission) ( H2020-RIA) 945539CEREBIO P18-FR-237

Brain Learning, Attention, and Consciousness

The processes whereby our brains continue to learn about a changing world in a stable fashion throughout life are proposed to lead to conscious experiences. These processes include the learning of top-down expectations, the matching of these expectations against bottom-up data, the focusing of attention upon the expected clusters of information, and the development of resonant states between bottom-up and top-down processes as they reach an attentive consensus between what is expected and what is there in the outside world. It is suggested that all conscious states in the brain are resonant states, and that these resonant states trigger learning of sensory and cognitive representations. The model which summarize these concepts are therefore called Adaptive Resonance Theory, or ART, models. Psychophysical and neurobiological data in support of ART are presented from early vision, visual object recognition, auditory streaming, variable-rate speech perception, somatosensory perception, and cognitive-emotional interactions, among others. It is noted that ART mechanisms seem to be operative at all levels of the visual system, and it is proposed how these mechanisms are realized by known laminar circuits of visual cortex. It is predicted that the same circuit realization of ART mechanisms will be found in the laminar circuits of all sensory and cognitive neocortex. Concepts and data are summarized concerning how some visual percepts may be visibly, or modally, perceived, whereas amoral percepts may be consciously recognized even though they are perceptually invisible. It is also suggested that sensory and cognitive processing in the What processing stream of the brain obey top-down matching and learning laws that arc often complementary to those used for spatial and motor processing in the brain's Where processing stream. This enables our sensory and cognitive representations to maintain their stability a.s we learn more about the world, while allowing spatial and motor representations to forget learned maps and gains that are no longer appropriate as our bodies develop and grow from infanthood to adulthood. Procedural memories are proposed to be unconscious because the inhibitory matching process that supports these spatial and motor processes cannot lead to resonance.Defense Advance Research Projects Agency; Office of Naval Research (N00014-95-1-0409, N00014-95-1-0657); National Science Foundation (IRI-97-20333

On temporal scale-free non-periodic stimulation and its mechanisms as an infinite improbability drive of the brain’s functional connectogram

Rationalized development of electrical stimulation (ES) therapy is of paramount importance. Not only it will foster new techniques and technologies with increased levels of safety, efficacy, and efficiency, but it will also facilitate the translation from basic research to clinical practice. For such endeavor, design of new technologies must dialogue with state-of-the-art neuroscientific knowledge. By its turn, neuroscience is transitioning—a movement started a couple of decades earlier—into adopting a new conceptual framework for brain architecture, in which time and thus temporal patterns plays a central role in the neuronal representation of sampled data from the world. This article discusses how neuroscience has evolved to understand the importance of brain rhythms in the overall functional architecture of the nervous system and, consequently, that neuromodulation research should embrace this new conceptual framework. Based on such support, we revisit the literature on standard (fixed-frequency pulsatile stimuli) and mostly non-standard patterns of ES to put forward our own rationale on how temporally complex stimulation schemes may impact neuromodulation strategies. We then proceed to present a low frequency, on average (thus low energy), scale-free temporally randomized ES pattern for the treatment of experimental epilepsy, devised by our group and termed NPS (Non-periodic Stimulation). The approach has been shown to have robust anticonvulsant effects in different animal models of acute and chronic seizures (displaying dysfunctional hyperexcitable tissue), while also preserving neural function. In our understanding, accumulated mechanistic evidence suggests such a beneficial mechanism of action may be due to the natural-like characteristic of a scale-free temporal pattern that may robustly compete with aberrant epileptiform activity for the recruitment of neural circuits. Delivering temporally patterned or random stimuli within specific phases of the underlying oscillations (i.e., those involved in the communication within and across brain regions) could both potentiate and disrupt the formation of neuronal assemblies with random probability. The usage of infinite improbability drive here is obviously a reference to the “The Hitchhiker’s Guide to the Galaxy” comedy science fiction classic, written by Douglas Adams. The parallel is that dynamically driving brain functional connectogram, through neuromodulation, in a manner that would not favor any specific neuronal assembly and/or circuit, could re-stabilize a system that is transitioning to fall under the control of a single attractor. We conclude by discussing future avenues of investigation and their potentially disruptive impact on neurotechnology, with a particular interest in NPS implications in neural plasticity, motor rehabilitation, and its potential for clinical translation