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Bursting Neurons in the Hippocampal Formation Encode Features of LFP Rhythms

FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2016年第DEC期10卷 133页

作者： Constantinou, Maria Gonzalo Cogno, Soledad Elijah, Daniel H. Kropff, Emilio Gigg, John Samengo, Ines Montemurrol, Marcelo A. Univ Manchester Fac Biol Med & Hlth Manchester Lancs England Ctr Atom Bariloche San Carlos De Bariloche Argentina Inst Balseiro San Carlos De Bariloche Argentina Consejo Nacl Invest Cient & Tecn Leloir Inst IIBBA Buenos Aires DF Argentina

Burst spike patterns are common in regions of the hippocampal formation such as the subiculum and medial entorhinal cortex (MEC). Neurons in these areas are immersed in extracellular electrical potential fluctuations often recorded as the local field potential (LFP). LFP rhythms within different frequency bands are linked to different behavioral states. For example, delta rhythms are often associated with slow-wave sleep, inactivity and anesthesia;whereas theta rhythms are prominent during awake exploratory behavior and REM sleep. Recent evidence suggests that bursting neurons in the hippocampal formation can encode LFP features. We explored this hypothesis using a two-compartment model of a bursting pyramidal neuron driven by time-varying input signals containing spectral peaks at either delta or theta rhythms. The model predicted a neural code in which bursts represented the instantaneous value, phase, slope and amplitude of the driving signal both in their timing and size (spike number). To verify whether this code is employed in vivo, we examined electrophysiological recordings from the subiculum of anesthetized rats and the MEC of a behaving rat containing prevalent delta or theta rhythms, respectively. In both areas, we found bursting cells that encoded information about the instantaneous voltage, phase, slope and/or amplitude of the dominant LFP rhythm with essentially the same neural code as the simulated neurons. A fraction of the cells encoded part of the information in burst size, in agreement with model predictions. These results provide in-vivo evidence that the output of bursting neurons in the mammalian brain is tuned to features of the LFP.

关键词： bursting local field potential subiculum entorhinal cortex information theory neural coding

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FRACTIONAL CALCULUS OPERATORS-BLOCH-TORREY PARTIAL DIFFERENTIAL EQUATION-ARTIFICIAL neural NETWORKS-COMPUTATIONAL COMPLEXITY MODELING OF THE MICRO-MACROSTRUCTURAL BRAIN TISSUES WITH DIFFUSION MRI SIGNAL PROCESSING AND NEURONAL MULTI-COMPONENTS

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FRACTALS-COMPLEX GEOMETRY PATTERNS AND SCALING IN NATURE AND SOCIETY 2023年第10期31卷 2340204-2340204页

作者： Karaca, Yeliz Univ Massachusetts UMass Chan Med Sch 55 Lake Ave North Worcester MA 01655 USA

Fractional calculus and fractional-order calculus are arranged in lineage as regards the mathematical models with complexity-theoretical tenets capable of capturing the subtle molecular dynamics by the integration of power-law convolution kernels into time- and space-related derivatives emerging in equations concerning the Magnetic Resonance Imaging (MRI) phenomena to which the fractional models of diffusion and relaxation are applied. Endowed with an intricate level of complexity and a unique physical and structural scaffolding at molecular and cellular levels with numerous synapses forming elaborate neural networks which entail in-depth probing and computing of patterns and signatures in individual cells and neurons, human brain as a heterogeneous medium is constituted of tissues with cells of different sizes and shapes, distributed across an extra-cellular space. Characterization of the unique brain cells is sought after to unravel the connections between different cells and tissues for accurate, reliable, robust and optimal models and computing. Accordingly, Diffusion Magnetic Resonance Imaging (DMRI), as a noninvasive and experimental imaging technique with clinical and research applications, provides a measure related to the diffusion characteristics of water in biological tissues, particularly in the brain tissues. Compatible with these aspects and beyond the diffusion coefficients' measurement, DMRI technique aims to exceed the spatial resolution of the MRI images and draw inferences from the microstructural properties of the related medium. Thus, novel tools become essential for the description of the biological (organelles, membranes, macromolecules and so on) and neurological (axons, dendrites, neurons and so forth) tissues' complexity. Mathematical model-based computational analyses with multifaceted methods to extract information from the DMRI with SpinDoctor into neuronal dynamics can provide quantitative parametric instruments in order to reflect the

关键词： Mathematical Neuroscience Bloch-Torrey Partial Differential Equation Complexity-Theoretical Tenets Computational (Algorithmic) Complexity Complex Systems Fractional Calculus Complexity Diffusion Magnetic Resonance Imaging (DMRI) SpinDoctor Diffusion Gradients Fractional Differential Equations Mittag-Leffler Functions Caputo Fractional-Order Derivative Artificial neural Networks Complexity-Order Optimization neural coding Differentiable Functions Microstructure Estimation Feedforward Back Propagation (FFBP) Algorithm Optimal Predictive Dimension of Changes Complex-Order Valued neural Network Fractional-Order Derivatives Integer-Order Differential Equations Laplace Eigenfunctions Finite Element Discretization Next-Generation Matrix Formalism Accurate Neuron Geometry Models Neuroplasticity

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Variability and Randomness of the Instantaneous Firing Rate

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FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2021年 15卷 620410页

作者： Tomar, Rimjhim Kostal, Lubomir Czech Acad Sci Inst Physiol Dept Computat Neurosci Prague Czech Republic Charles Univ Prague Med Fac 2 Prague Czech Republic

The apparent stochastic nature of neuronal activity significantly affects the reliability of neuronal coding. To quantify the encountered fluctuations, both in neural data and simulations, the notions of variability and randomness of inter-spike intervals have been proposed and studied. In this article we focus on the concept of the instantaneous firing rate, which is also based on the spike timing. We use several classical statistical models of neuronal activity and we study the corresponding probability distributions of the instantaneous firing rate. To characterize the firing rate variability and randomness under different spiking regimes, we use different indices of statistical dispersion. We find that the relationship between the variability of interspike intervals and the instantaneous firing rate is not straightforward in general. Counter-intuitively, an increase in the randomness (based on entropy) of spike times may either decrease or increase the randomness of instantaneous firing rate, in dependence on the neuronal firing model. Finally, we apply our methods to experimental data, establishing that instantaneous rate analysis can indeed provide additional information about the spiking activity.

关键词： variability randomness firing rate entropy rate coding neural coding temporal coding instantaneous firing rate

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Effect of scanning speed on texture-elicited vibrations

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JOURNAL OF THE ROYAL SOCIETY INTERFACE 2020年第167期17卷 20190892-20190892页

作者： Greenspon, Charles M. McLellan, Kristine R. Lieber, Justin D. Bensmaia, Sliman J. Univ Chicago Dept Organismal Biol & Anat 1025 E 57th St Chicago IL 60637 USA Univ Chicago Comm Computat Neurosci Chicago IL 60637 USA Univ Chicago Grossman Inst Neurosci Quantitat Biol & Human Beh Chicago IL 60637 USA

To sense the texture of a surface, we run our fingers across it, which leads to the elicitation of skin vibrations that depend both on the surface and on exploratory parameters, particularly scanning speed. The transduction and processing of these vibrations mediate the ability to discern fine surface features. The objective of the present study is to characterize the effect of changes in scanning speed on texture-elicited vibrations to better understand how the exploratory movements shape the neuronal representation of texture. To this end, we scanned a variety of textures across the fingertip of human participants at a variety of speeds (10-160 mm s(-1)) while measuring the resulting vibrations using a laser Doppler vibrometer. First, we found that the intensity of the vibrations-as indexed by root-mean-square velocity-increases with speed but that the skin displacement remains constant. Second, we found that the frequency composition of the vibrations shifts systematically to higher frequencies with increases in scanning speed. Finally, we show that the speed-dependent shift in frequency composition accounts for the speed-dependent change in intensity.

关键词： touch neural coding haptics somatosensory coding vibrotaction

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Limb-State Information Encoded by Peripheral and Central Somatosensory Neurons: Implications for an Afferent Interface

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IEEE TRANSACTIONS ON neural SYSTEMS AND REHABILITATION ENGINEERING 2011年第5期19卷 501-513页

作者： Weber, Douglas J. London, Brian M. Hokanson, James A. Ayers, Christopher A. Gaunt, Robert A. Torres, Ricardo R. Zaaimi, Boubker Miller, Lee E. Univ Pittsburgh Dept Phys Med & Rehabil Pittsburgh PA 15213 USA Univ Pittsburgh Dept Bioengn Pittsburgh PA 15213 USA Northwestern Univ Dept Physiol Feinberg Sch Med Chicago IL 60611 USA Univ Pittsburgh Dept Phys Med & Rehabil Pittsburgh PA 15213 USA Northwestern Univ Dept Physiol Chicago IL 60611 USA Northwestern Univ Dept Phys Med & Rehabil Feinberg Sch Med Chicago IL 60611 USA Northwestern Univ Dept Biomed Engn Chicago IL 60611 USA

A major issue to be addressed in the development of neural interfaces for prosthetic control is the need for somatosensory feedback. Here, we investigate two possible strategies: electrical stimulation of either dorsal root ganglia (DRG) or primary somatosensory cortex (S1). In each approach, we must determine a model that reflects the representation of limb state in terms of neural discharge. This model can then be used to design stimuli that artificially activate the nervous system to convey information about limb state to the subject. Electrically activating DRG neurons using naturalistic stimulus patterns, modeled on recordings made during passive limb movement, evoked activity in S1 that was similar to that of the original movement. We also found that S1 neural populations could accurately discriminate different patterns of DRG stimulation across a wide range of stimulus pulserates. In studying the neural coding in S1, we also decoded the kinematics of active limb movement using multi-electrode recordings in the monkey. Neurons having both proprioceptive and cutaneous receptive fields contributed equally to this decoding. Some neurons were most informative of limb state in the recent past, but many others appeared to signal upcoming movements suggesting that they also were modulated by an efference copy signal. Finally, we show that a monkey was able to detect stimulation through a large percentage of electrodes implanted in area 2. We discuss the design of appropriate stimulus paradigms for conveying time-varying limb state information, and the relative merits and limitations of central and peripheral approaches.

关键词： Dorsal root ganglia multielectrode array neural coding neural prostheses sensory cortex sensory feedback somatosensory

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Biophysical parameters control signal transfer in spiking network

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FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2023年 17卷 1011814页

作者： Garnier Artinano, Tomas Andalibi, Vafa Atula, Iiris Maestri, Matteo Vanni, Simo Helsinki Univ Hosp Helsinki Univ Hosp HUS Neuroctr Neurol Helsinki Finland Univ Helsinki Dept Neurosci Clinicum Helsinki Finland Indiana Univ Bloomington Dept Comp Sci Bloomington IN USA Univ Bologna Dept Biomed & Neuromotor Sci Bologna Italy Univ Helsinki Dept Physiol Medicum Helsinki Finland

IntroductionInformation transmission and representation in both natural and artificial networks is dependent on connectivity between units. Biological neurons, in addition, modulate synaptic dynamics and post-synaptic membrane properties, but how these relate to information transmission in a population of neurons is still poorly understood. A recent study investigated local learning rules and showed how a spiking neural network can learn to represent continuous signals. Our study builds on their model to explore how basic membrane properties and synaptic delays affect information transfer. MethodsThe system consisted of three input and output units and a hidden layer of 300 excitatory and 75 inhibitory leaky integrate-and-fire (LIF) or adaptive integrate-and-fire (AdEx) units. After optimizing the connectivity to accurately replicate the input patterns in the output units, we transformed the model to more biologically accurate units and included synaptic delay and concurrent action potential generation in distinct neurons. We examined three different parameter regimes which comprised either identical physiological values for both excitatory and inhibitory units (Comrade), more biologically accurate values (Bacon), or the Comrade regime whose output units were optimized for low reconstruction error (HiFi). We evaluated information transmission and classification accuracy of the network with four distinct metrics: coherence, Granger causality, transfer entropy, and reconstruction error. ResultsBiophysical parameters showed a major impact on information transfer metrics. The classification was surprisingly robust, surviving very low firing and information rates, whereas information transmission overall and particularly low reconstruction error were more dependent on higher firing rates in LIF units. In AdEx units, the firing rates were lower and less information was transferred, but interestingly the highest information transmission rates were no longer overlapping wit

关键词： microcircuit spiking network model neural coding predictive coding classification

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A Low-Rank Method for Characterizing High-Level neural Computations

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FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2017年 11卷 68页

作者： Kaardal, Joel T. Theunissen, Frederic E. SharpeeL, Tatyana O. Salk Inst Biol Studies Computat Neurobiol Lab 10010 N Torrey Pines Rd La Jolla CA 92037 USA Salk Inst Biol Studies Crick Jacobs Ctr Theoret & Computat Biol 10010 N Torrey Pines Rd La Jolla CA 92037 USA Univ Calif San Diego Ctr Theoret Biol Phys La Jolla CA 92093 USA Univ Calif Berkeley Dept Psychol 3210 Tolman Hall Berkeley CA 94720 USA

The signal transformations that take place in high-level sensory regions of the brain remain enigmatic because of the many nonlinear transformations that separate responses of these neurons from the input stimuli. One would like to have dimensionality reduction methods that can describe responses of such neurons in terms of operations on a large but still manageable set of relevant input features. A number of methods have been developed for this purpose, but often these methods rely on the expansion of the input space to capture as many relevant stimulus components as statistically possible. This expansion leads to a lower effective sampling thereby reducing the accuracy of the estimated components. Alternatively, so-called low-rank methods explicitly search for a small number of components in the hope of achieving higher estimation accuracy. Even with these methods, however, noise in the neural responses can force the models to estimate more components than necessary, again reducing the methods' accuracy. Here we describe how a flexible regularization procedure, together with an explicit rank constraint, can strongly improve the estimation accuracy compared to previous methods suitable for characterizing neural responses to natural stimuli. Applying the proposed low-rank method to responses of auditory neurons in the songbird brain, we find multiple relevant components making up the receptive field for each neuron and characterize their computations in terms of logical OR and AND computations. The results highlight potential differences in how invariances are constructed in visual and auditory systems.

关键词： neural coding auditory cortex computational neuroscience receptive fields dimensionality reduction

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Limit cycles, noise, and chaos in hearing

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MICROSCOPY RESEARCH AND TECHNIQUE 2004年第6期63卷 400-412页

作者： Stoop, R Vyver, JJVD Kern, A UNIZH ETHZ Inst Neuroinformat CH-8057 Zurich Switzerland Univ Appl Sci NW Switzerland CH-4600 Olten Switzerland

Based on insight obtained :from a newly developed cochlea model, we argue that noise-driven limit cycles are the basic ingredient in the mammalian cochlea hearing process. For insect audition, we provide evidence in favor of the persistence of this principle. We emphasize the role of bifurcations for the emergence of broad-range sound perception, both in the frequency and amplitude domain, and indicate that this crucially depends on the correct coupling between limit cycles. We review the limit-cycle coupling universality, and outline how it can be used to encode information. Cortical noise is the microscopic basis for this encoding, whereas chaos emerges as the macroscopic expression of computation being done in the network. Large neuron firing variability is one possible consequence of the proposed mechanism that may apply to both vertebrate and insect hearing. (C) 2004 Wiley-Liss, Inc.

关键词： limit cycles bifurcations noise chaos stochastic resonance neural coding variability

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Using intersection information to map stimulus information transfer within neural networks

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BIOSYSTEMS 2019年 185卷 104028-000页

作者： Pica, Giuseppe Soltanipour, Mohammadreza Panzeri, Stefano Ist Italiano Tecnol Ctr Neurosci & Cognit Syst UniTn Neural Computat Lab I-38068 Rovereto TN Italy Univ Trento Ctr Mind Brain Sci I-38068 Rovereto Italy

Analytical tools that estimate the directed information flow between simultaneously recorded neural populations, such as directed information or Granger causality, typically focus on measuring how much information is exchanged between such populations. However, understanding how sensory information is processed through the brain and how it is used to generate behaviors requires estimating specifically the amount of stimulus information that is transmitted. Here we use the concept of intersection information to make progress on how to perform this measure. We develop the concept of transmitted intersection information, which measures how much of the stimulus information present in one population at a certain time is transmitted to a second population at a later time. We show that this measure of stimulus-specific information transfer has several appealing properties, such as being non-negative, and being bounded by the amount of stimulus information present in each of the two populations and by the total amount of information transmitted between the two populations. Applying this measure to simulated neurons or pools of neurons connected by feed-forward synapses, we show that it can discern cases when the information transmitted from one population to another is about specific stimulus features encoded by the sending population from cases in which the information transmitted is not about the stimuli. We also show that this measure has a good statistical sensitivity from trial numbers that can be collected in real data. Our results highlight the promise of using the concept of intersection information to map stimulus-specific information transfer across neural populations.

关键词： Information transmission neural coding

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Distributed sensory coding by cerebellar complex spikes in units of cortical segments

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CELL REPORTS 2021年第6期37卷 109966-109966页

作者： Michikawa, Takayuki Yoshida, Takamasa Kuroki, Satoshi Ishikawa, Takahiro Kakei, Shinji Kimizuka, Ryo Saito, Atsushi Yokota, Hideo Shimizu, Akinobu Itohara, Shigeyoshi Miyawaki, Atsushi RIKEN Ctr Adv Photon Biotechnol Opt Res Team Wako Saitama 3510198 Japan RIKEN Ctr Brain Sci Lab Cell Funct Dynam Wako Saitama 3510198 Japan RIKEN Ctr Brain Sci Lab Behav Genet Wako Saitama 3510198 Japan Tokyo Metropolitan Inst Med Sci Movement Disorders Project Setagaya Ku Tokyo 1568506 Japan Tokyo Univ Agr & Technol Inst Engn Koganei Tokyo 1848588 Japan RIKEN Ctr Adv Photon Image Proc Res Team Wako Saitama 3510198 Japan Toyo Univ Fac Sci & Engn Dept Biomed Engn Kawagoe Saitama 3508585 Japan Korea Inst Sci & Technol Ctr Funct Connect Seoul 02792 South Korea

Sensory processing is essential for motor control. Climbing fibers from the inferior olive transmit sensory signals to Purkinje cells, but how the signals are represented in the cerebellar cortex remains elusive. To examine the olivocerebellar organization of the mouse brain, we perform quantitative Ca2+ imaging to measure complex spikes (CSs) evoked by climbing fiber inputs over the entire dorsal surface of the cerebellum simultaneously. The surface is divided into approximately 200 segments, each composed of similar to 100 Purkinje cells that fire CSs synchronously. Our in vivo imaging reveals that, although stimulation of four limb muscles individually elicits similar global CS responses across nearly all segments, the timing and location of a stimulus are derived by Bayesian inference from coordinated activation and inactivation of multiple segments on a single trial basis. We propose that the cerebellum performs segment-based, distributed-population coding that represents the conditional probability of sensory events.

关键词： neural coding cerebellum climbing fiber Purkinje cell olivocerebellar system somatotopy genetically encoded calcium indicator fluorescence resonance energy transfer transgenic mouse Bayesian inference

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