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The power law of sensory adaptation: Simulation by a model of excitability in spider mechanoreceptor neurons

ANNALS OF BIOMEDICAL ENGINEERING 2008年第1期36卷 153-161页

作者： French, Andrew S. Torkkeli, Paivi H. Dalhousie Univ Dept Physiol & Biophys Halifax NS B3H 1X5 Canada

The power law of sensory adaptation was introduced more than 50 years ago. It is characterized by action potential adaptation that follows fractional powers of time or frequency, rather than exponential decays and corresponding frequency responses. Power law adaptation describes the responses of a range of vertebrate and invertebrate sensory receptors to deterministic stimuli, such as steps or sinusoids, and to random (white noise) stimulation. Hypotheses about the physical basis of power law adaptation have existed since its discovery. Its cause remains enigmatic, but the site of power law adaptation has been located in the conversion of receptor potentials into action potentials in some preparations. Here, we used pseudorandom noise stimulation and direct spectral estimation to show that simulations containing only two voltage activated currents can reproduce the power law adaptation in two types of spider mechanoreceptors. Identical simulations were previously used to explain the different responses of these two types of sensory neurons to step inputs. We conclude that power law adaptation results during action potential encoding by nonlinear combination of a small number of activation and inactivation processes with different exponential time constants.

关键词： action potential neural coding information capacity ion channel Hodgkin-Huxley frequency response

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Spike train analysis in a digital neuromorphic system of cutaneous mechanoreceptor

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NEUROCOMPUTING 2020年第0期379卷 343-355页

作者： Yavari, Fatemeh Amiri, Mahmood Rahatabad, Fereydoon Nowshiravan Falotico, Egidio Laschi, Cecilia Islamic Azad Univ Biomed Engn Dept Sci & Res Branch Tehran Iran Kermanshah Univ Med Sci Med Technol Res Ctr Parastar Ave Kermanshah Iran Scuola Super Sant Anna BioRobot Inst Pontedera Italy

In this research, we develop a neuromorphic system to study neural signaling at the level of first order tactile afferents which are slowly adapting type I (SA1) and rapidly adapting type I (RA1) mechanoreceptors. Considering, the linearized Izhikevich model, two digital circuits are developed for both afferents and are executed on the field programmable gate array (FPGA). After implementation of the digital circuits, we investigate how much information is encoded by this hardware-based neuromorphic system. Indeed, the artificial spiking sequences are evoked by applying different force profiles to the sensor connected to the FPGA. Next, the obtained neural responses are classified based on the two fundamental neural coding for brain information processing: spike timing and rate coding. Considering temporal coding, k-nearest neighbors (kNN), support vector machine (SVM) and Decision Tree algorithms are used for forces recognition using acquired artificial spike patterns. The results of classification show that the digital RA1 is susceptible to signal variations, while the digital SA1, on the other hand, is sensitive to the ramp and hold inputs. Furthermore, these responses are better distinguishable to different stimuli when both artificial SA1 and RA1 afferents are regarded. These results, which are functionally compatible with biological observations, yield the promise for fabrication and development of new tactile sensing modules to be employed in bio-robotic and prosthetic applications. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Tactile sensing Spiking model neural coding Hardware implementation

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LIF and Simplified SRM Neurons Encode Signals Into Spikes via a Form of Asynchronous Pulse Sigma-Delta Modulation

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IEEE TRANSACTIONS ON neural NETWORKS AND LEARNING SYSTEMS 2017年第5期28卷 1192-1205页

作者： Yoon, Young C. Qualcomm Corp Res & Dev San Diego CA 92121 USA

We show how two spiking neuron models encode continuous-time signals into spikes (action potentials, time-encoded pulses, or point processes) using a special form of sigma-delta modulation (SDM). In particular, we show that the well-known leaky integrate-and-fire (LIF) neuron and the simplified spike response model (SRM0) neuron encode the continuous-time signals into spikes via a proposed asynchronous pulse SDM (APSDM) scheme. The encoder is clock free using level-crossing sampling with a single-level quantizer, unipolar signaling, differential coding, and pulse-shaping filters. The decoder, in the form of a low-pass filter or bandpass smoothing filter, can be fed with the spikes to reconstruct an estimate of the signal. The density of the spikes reflects the amplitude of the encoded signal. Numerical examples illustrating the concepts and the signaling efficiency of APSDM vis-a-vis SDM for comparable reconstruction accuracies are presented. We anticipate these results will facilitate the design of spiking neurons and spiking neural networks as well as cross fertilizations between the fields of neural coding and the SDM.

关键词： Asynchronous pulse sigma-delta modulation (APSDM) differential coding leaky integrate-and-fire (LIF) neurons level-crossing sampling and Lebesgue sampling neural coding spike response model (SRM) neurons spiking neural networks (SNNs) spiking neurons

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On the functional role of noise correlations in the nervous system

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NEUROCOMPUTING 2002年 44卷 1023-1028页

作者： Wilke, SD Eurich, CW Univ Bremen Inst Theoret Phys D-28334 Bremen Germany

Neurons are known to respond to external stimuli in a noisy manner. This response variability is often correlated across the neural population. However, the functional role of these noise correlations is still unclear. In this paper, Fisher information is used to analyze the effects of noise correlations on the accuracy with which a neural population encodes a stimulus. The existence of limited-range correlations of the type found in cortical tissue yields a saturation of the Fisher information content as a function of the population size only for an additive noise model. We also show that random variability in the correlation coefficient within a neural population can considerably improve the average encoding quality. This means that diversity in the correlations, as found empirically, could be exploited for improving stimulus representation. (C) 2002 Published by Elsevier Science B.V.

关键词： neural coding noise correlations Fisher information

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Visual signals in an optomotor reflex: Systems and information theoretic analysis

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JOURNAL OF COMPUTATIONAL NEUROSCIENCE 2002年第1期13卷 5-21页

作者： Miller, CS Johnson, DH Schroeter, JP Myint, LL Glantz, RM Rice Univ Dept Elect & Comp Engn Dept Biochem & Cell Biol Houston TX 77251 USA

Compensatory optomotor reflexes were examined in crayfish (Procambarus clarkii) with oscillating sine wave gratings and step displacements of a single stripe. A capacitance transducer was used to measure the rotation of the eyestalk about its longitudinal axis. System studies reveal a spatial frequency response independent of velocity and stimulus amplitude and linear contrast sensitivity similar to that of neurons in the visual pathway. The reflex operates at low temporal frequencies (<0.002 Hz to 0.5 Hz) and exhibits a low-pass temporal frequency response with cut-off frequency of 0.1 Hz. Eyestalk rotation increases as a saturable function of the angular stimulus displacement. When compared to the oscillatory response, transient responses are faster, and they exhibit a lower gain for large stimulus displacements. These differences may reflect system nonlinearity and/or the presence of at least two classes of afferents in the visual pathway. Our metric for information transmission is the Kullback-Leibler (K-L) distance, which is inversely proportional to the probability of an error in distinguishing two stimuli. K-L distances are related to differences in responsiveness for variations in spatial frequency, contrast, and angular displacement. The results are interpreted in terms of the neural filters that shape the system response and the constraints that the K-L distances place on information transmission in the afferent visual pathway.

关键词： optomotor reflex information theory visual system linear systems neural coding

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Computation by Time

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neural PROCESSING LETTERS 2016年第1期44卷 103-124页

作者： Walter, Florian Roehrbein, Florian Knoll, Alois Tech Univ Munich Inst Informat 6 D-85748 Garching Germany

Over the last years, the amount of research performed in the field of spiking neural networks has been growing steadily. Spiking neurons are modeled to approximate the complex dynamic behavior of biological neurons. They communicate via discrete impulses called spikes with the actual information being encoded in the timing of these spikes. As already pointed out by Maass in his paper on the third generation of neural network models, this renders time a central factor for neural computation. In this paper, we investigate at different levels of granularity how absolute time and relative timing enable new ways of biologically inspired neural information processing. At the lowest level of single spiking neurons, we give an overview of coding schemes and learning techniques which rely on precisely timed neural spikes. A high-level perspective is provided in the second part of the paper which focuses on the role of time at the network level. The third aspect of time considered in this work is related to the interfacing of neural networks with real-time systems. In this context, we discuss how the concepts of computation by time can be implemented in computer simulations and on specialized neuromorphic hardware. The contributions of this paper are twofold: first, we show how the exact modeling of time in spiking neural networks serves as an important basis for powerful computation based on neurobiological principles. Second, by presenting a range of diverse learning techniques, we prove the biologically plausible applicability of spiking neural networks to real world problems like pattern recognition and path planning.

关键词： Spiking neural network Neurobiological learning Reservoir computing Hierarchical learning neural coding Neuromorphic hardware

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The shape of ears to come: dynamic coding of auditory space

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TRENDS IN COGNITIVE SCIENCES 2001年第6期5卷 261-270页

作者： King, AJ Schnupp, JWH Doubell, TP Univ Oxford Physiol Lab Oxford OX1 3PT England

In order to pinpoint the location of a sound source, we make use of a variety of spatial cues that arise from the direction-dependent manner in which sounds interact with the head, torso and external ears. Accurate sound localization relies on the neural discrimination of tiny differences in the values of these cues and requires that the brain circuits involved be calibrated to the cues experienced by each individual. There is growing evidence that the capacity for recalibrating auditory localization continues well into adult life. Many details of how the brain represents auditory space and of how those representations are shaped by learning and experience remain elusive. However, it is becoming increasingly clear that the task of processing auditory spatial information is distributed over different regions of the brain, some working hierarchically, others independently and in parallel, and each apparently using different strategies for encoding sound source location.

关键词： auditory localization cortex midbrain plasticity sensory experience neural coding multisensory convergence superior colliculus

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Cooperative and temporally structured information in the visual cortex

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SIGNAL PROCESSING 2005年第11期85卷 2124-2136页

作者： Samonds, JM Bonds, AB Vanderbilt Univ Dept Biomed Engn Nashville TN 37235 USA Vanderbilt Univ Dept Elect Engn & Comp Sci Nashville TN 37235 USA

We have examined the information contained in the coordination of groups of cells in the primary visual cortex of the cat recorded with a 5 x 5 microelectrode array. When testing small differences (< 10 degrees) in stimulus orientation with an information-theoretic distance, response discrimination depended on the temporal resolution of response sampling and improved when including response history. Joint activity also enhanced response discrimination and the enhancement was proportionally larger when more cells were sampled. Spike timing and intervals provided information about fine orientation differences. Even a small rotation of the visual stimulus produced clear time shifts in the response structure with little or no change in the spike count. Larger orientation differences (> 10 degrees) are discriminated easily on the basis of the independent spike Counts. We conclude that synchrony provides a reliable mechanism to transmit fine structure of visual information and offers an efficient cooperative neural code that we have only begun to explore. (c) 2005 Elsevier B.V. All rights reserved.

关键词： visual cortex neural coding cooperation synchrony temporal structure information theory Kullback-Leibler (KL) distance metric-space analysis

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What Makes a neural Code Convex?

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SIAM JOURNAL ON APPLIED ALGEBRA AND GEOMETRY 2017年第1期1卷 222-238页

作者： Curto, Carina Gross, Elizabeth Jeffries, Jack Morrison, Katherine Omar, Mohamed Rosen, Zvi Shiu, Anne Youngs, Nora Penn State Univ Dept Math University Pk PA 16802 USA San Jose State Univ Dept Math San Jose CA 95192 USA Univ Utah Dept Math Salt Lake City UT 84112 USA Univ Michigan Dept Math Ann Arbor MI 48109 USA Univ Northern Colorado Sch Math Sci Greeley CO 80639 USA Harvey Mudd Coll Dept Math Claremont CA 91711 USA Univ Calif Berkeley Dept Math Berkeley CA 94720 USA Univ Penn Dept Math Philadelphia PA 19104 USA Texas A&M Univ Dept Math College Stn TX 77843 USA Colby Coll Dept Math Waterville ME 04901 USA

neural codes allow the brain to represent, process, and store information about the world. Combinatorial codes, comprised of binary patterns of neural activity, encode information via the collective behavior of populations of neurons. A code is called convex if its codewords correspond to regions defined by an arrangement of convex open sets in Euclidean space. Convex codes have been observed experimentally in many brain areas, including sensory cortices and the hippocampus, where neurons exhibit convex receptive fields. What makes a neural code convex? That is, how can we tell from the intrinsic structure of a code if there exists a corresponding arrangement of convex open sets? In this work, we provide a complete characterization of local obstructions to convexity. This motivates us to define max intersection-complete codes, a family guaranteed to have no local obstructions. We then show how our characterization enables one to use free resolutions of Stanley Reisner ideals in order to detect violations of convexity. Taken together, these results provide a significant advance in our understanding of the intrinsic combinatorial properties of convex codes.

关键词： neural coding convex codes simplicial complex link Nerve lemma Hochster's formula

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Enhancement of neural stochastic firing in cochlear implant stimulation by the addition of noise: A computational study of the influence of stimulation settings and spontaneous activity

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COMPUTERS IN BIOLOGY AND MEDICINE 2010年第6期40卷 597-606页

作者： Paglialonga, Alessia Fiocchi, Serena Ravazzani, Paolo Tognola, Gabriella Politecn Milan CNR Ist Ingn Biomed Dipartimento BioingnISIB I-20133 Milan Italy

It is known that, in cochlear implants (CIs), the addition of noise to CI stimuli may enhance the stochastic firing in the auditory nerve (AN). The aim of this study was to investigate, by using a model of a fiber of the AN the influence of CI stimulation settings (i.e., stimulation rate and stimulus magnitude) and fiber spontaneous activity on the enhancement of stochastic firing when CI stimuli are combined with noise. Results showed that the stimulation rate had an effect on the enhancement of stochastic firing in the AN, whereas the stimulus magnitude and the spontaneous firing rate had no influence. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Cochlear implants Electrical stimulation Auditory nerve Nerve fiber models Stochastic firing neural coding Stochastic resonance Noise

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