Properties of proprioceptive neurons in the cuneate nucleus of the cat

1987 ◽  
Vol 57 (4) ◽  
pp. 938-961 ◽  
Author(s):  
D. J. Surmeier ◽  
A. L. Towe
Keyword(s):  
Spectral Analysis ◽  
Muscle Spindle ◽  
Receptive Fields ◽  
Spike Trains ◽  
Cuneate Nucleus ◽  
Periodic Patterns ◽  
Class A ◽  
Periodic Discharge ◽  
Periodic Components ◽  
Spindle Fibers

Fifty-two slowly adapting proprioceptive neurons in the cuneate nucleus of chloralose-anesthetized cats were studied. Recordings were made from 3 mm rostral to the obex to 5 mm caudal. The highest densities of proprioceptive neurons were found above and more than 3 mm caudal to the obex. Analysis of the spike trains produced with the forelimb held fixed revealed three basic periodic patterns. Neurons exhibiting these patterns were partitioned into three groups, referred to as the A, B, and C classes. Class A neurons (42%; 22/52) produced regular spike trains that were qualitatively similar to muscle spindle fibers. Interval distributions for this class were typically unimodal and slightly positively skewed. Adjacent intervals were frequently positively correlated. Spectral analysis suggested that 91% of class A spike trains had one to two periodic components. Class B neurons (21%; 11/52) had additional spikes interposed in their periodic discharge; these "interrupting" spikes did not significantly alter the timing of the dominant periodic discharge. Interval distributions were typically bimodal and adjacent intervals were negatively correlated. Spectral analysis suggested that two or more periodic components were present in their spike trains. Class C neurons (36%; 26/52) had spike trains with a basic rhymicity, but when this specific discharge was interrupted, the subsequent interval was near modal length; thus, they were "reset." Interval distributions were usually multimodal and adjacent intervals were frequently negatively correlated. Spectral analysis suggested that C spike trains usually had four or more periodic components. Estimates of information-carrying capacity of each class using a mean rate code and those of primary muscle spindle fibers suggested that a sizable information loss may occur in synaptic transmission. This potential loss was smaller for A-neurons (40%) than for B- (69%) or C-neurons (64%). Electrical stimulation of cutaneous structures influenced 55% (22/52) of the sample. All were members of the B and C classes. Responses were typically biphasic. The cutaneous receptive fields nearly always included a portion of the forepaw. No relationship was found between movement sensitivity and receptive field topography. Contralateral input was found in half (10/20) the neurons tested.

scholarly journals Sensory computations in the cuneate nucleus of macaques

2021 ◽  
Vol 118 (49) ◽  
pp. e2115772118
Author(s):  
Aneesha K. Suresh ◽  
Charles M. Greenspon ◽  
Qinpu He ◽  
Joshua M. Rosenow ◽  
Lee E. Miller ◽  
...  
Keyword(s):  
Cortical Neurons ◽  
Receptive Fields ◽  
Nerve Fibers ◽  
Cuneate Nucleus ◽  
Local Skin ◽  
Response Properties ◽  
Conventional View ◽  
Random Dot Patterns ◽  
Object Features ◽  
Spatiotemporal Response

Tactile nerve fibers fall into a few classes that can be readily distinguished based on their spatiotemporal response properties. Because nerve fibers reflect local skin deformations, they individually carry ambiguous signals about object features. In contrast, cortical neurons exhibit heterogeneous response properties that reflect computations applied to convergent input from multiple classes of afferents, which confer to them a selectivity for behaviorally relevant features of objects. The conventional view is that these complex response properties arise within the cortex itself, implying that sensory signals are not processed to any significant extent in the two intervening structures—the cuneate nucleus (CN) and the thalamus. To test this hypothesis, we recorded the responses evoked in the CN to a battery of stimuli that have been extensively used to characterize tactile coding in both the periphery and cortex, including skin indentations, vibrations, random dot patterns, and scanned edges. We found that CN responses are more similar to their cortical counterparts than they are to their inputs: CN neurons receive input from multiple classes of nerve fibers, they have spatially complex receptive fields, and they exhibit selectivity for object features. Contrary to consensus, then, the CN plays a key role in processing tactile information.

Intrinsic features contributing to spike train patterning in proprioceptive cuneate neurons

1987 ◽  
Vol 57 (4) ◽  
pp. 962-976 ◽  
Author(s):  
D. J. Surmeier ◽  
A. L. Towe
Keyword(s):  
Computer Simulation ◽  
Spike Train ◽  
Spike Trains ◽  
Antidromic Activation ◽  
Synaptic Potentials ◽  
Class A ◽  
Class B ◽  
Class C ◽  
Discharge Patterns ◽  
Periodic Inputs

The intrinsic processes contributing to the three discharge patterns of proprioceptive cuneate neurons described by Surmeier and Towe were studied experimentally and with computer simulation. Examination of the alterations in excitability produced by antidromic activation suggested that a prolonged inhibition was a concomitant of discharge in proprioceptive cuneate neurons. Computer simulation was performed to test the possible roles of inhibitory hyperpolarizing processes in governing the observed discharge patterns. These simulations used two constant threshold models. The simplest model linearly integrated synaptic potentials until the spike threshold was reached. After the discharge, synaptic potentials that preceded the spike were ignored (i.e., the model was "reset"). The second model was similar to the first except that following a spike two hyperpolarizing processes were activated and preceding events continued to play a role in membrane potential. Simulation of class A spike trains that possessed positive correlations between nearby intervals was successful only with a resetting model. This suggested that class A neurons have fast, no-memory postspike conductance changes, which effectively shunt synaptic charge. Simulation of class B spike trains was possible with the nonresetting model. At least two periodic inputs, which evoked brief, relatively large EPSPs, were required. In addition, a prominent, fast, spike-dependent hyperpolarization and a small-amplitude, slow hyperpolarization were required. Simulation of class C spike trains was also possible with the nonresetting model. Several periodic inputs were required; one input had to evoke a slow suprathreshold EPSP. In contrast to class B simulations, class C spike train simulation required that a large-amplitude, slow hyperpolarization, as well as a brief hyperpolarization, following spike initiation. The results of class B and C simulations suggested that these two groups differed primarily in the amplitude of a slow, hyperpolarizing, postspike conductance. Some role may also be played by the time course of the driving EPSPs.

scholarly journals Mapping Periodic Patterns of Global Vegetation Based on Spectral Analysis of NDVI Time Series

2019 ◽  
Vol 11 (21) ◽  
pp. 2497
Author(s):  
Laura Recuero ◽  
Javier Litago ◽  
Jorge E. Pinzón ◽  
Margarita Huesca ◽  
Maria C. Moyano ◽  
...  
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Land Surface ◽  
Arid Regions ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Periodic Patterns ◽  
Annual Cycles ◽  
Ndvi Time Series ◽  
Global Vegetation

Vegetation seasonality assessment through remote sensing data is crucial to understand ecosystem responses to climatic variations and human activities at large-scales. Whereas the study of the timing of phenological events showed significant advances, their recurrence patterns at different periodicities has not been widely study, especially at global scale. In this work, we describe vegetation oscillations by a novel quantitative approach based on the spectral analysis of Normalized Difference Vegetation Index (NDVI) time series. A new set of global periodicity indicators permitted to identify different seasonal patterns regarding the intra-annual cycles (the number, amplitude, and stability) and to evaluate the existence of pluri-annual cycles, even in those regions with noisy or low NDVI. Most of vegetated land surface (93.18%) showed one intra-annual cycle whereas double and triple cycles were found in 5.58% of the land surface, mainly in tropical and arid regions along with agricultural areas. In only 1.24% of the pixels, the seasonality was not statistically significant. The highest values of amplitude and stability were found at high latitudes in the northern hemisphere whereas lowest values corresponded to tropical and arid regions, with the latter showing more pluri-annual cycles. The indicator maps compiled in this work provide highly relevant and practical information to advance in assessing global vegetation dynamics in the context of global change.

scholarly journals Traditional waveform based spike sorting yields biased rate code estimates

2009 ◽  
Vol 106 (17) ◽  
pp. 6921-6926 ◽  
Author(s):  
Valérie Ventura
Keyword(s):  
Tuning Curve ◽  
Receptive Fields ◽  
Environmental Parameters ◽  
Spike Trains ◽  
Spike Sorting ◽  
Curve Estimation ◽  
Tuning Curves ◽  
Firing Rates ◽  
Neurophysiological Data ◽  
And Behavior

Much of neuroscience has to do with relating neural activity and behavior or environment. One common measure of this relationship is the firing rates of neurons as functions of behavioral or environmental parameters, often called tuning functions and receptive fields. Firing rates are estimated from the spike trains of neurons recorded by electrodes implanted in the brain. Individual neurons' spike trains are not typically readily available, because the signal collected at an electrode is often a mixture of activities from different neurons and noise. Extracting individual neurons' spike trains from voltage signals, which is known as spike sorting, is one of the most important data analysis problems in neuroscience, because it has to be undertaken prior to any analysis of neurophysiological data in which more than one neuron is believed to be recorded on a single electrode. All current spike-sorting methods consist of clustering the characteristic spike waveforms of neurons. The sequence of first spike sorting based on waveforms, then estimating tuning functions, has long been the accepted way to proceed. Here, we argue that the covariates that modulate tuning functions also contain information about spike identities, and that if tuning information is ignored for spike sorting, the resulting tuning function estimates are biased and inconsistent, unless spikes can be classified with perfect accuracy. This means, for example, that the commonly used peristimulus time histogram is a biased estimate of the firing rate of a neuron that is not perfectly isolated. We further argue that the correct conceptual way to view the problem out is to note that spike sorting provides information about rate estimation and vice versa, so that the two relationships should be considered simultaneously rather than sequentially. Indeed we show that when spike sorting and tuning-curve estimation are performed in parallel, unbiased estimates of tuning curves can be recovered even from imperfectly sorted neurons.

scholarly journals Spectral and Cross-Spectral Analysis - a Tutorial for Psychologists and Social Scientists

2020 ◽  
Author(s):  
Matthew J Vowels ◽  
Laura Marika Vowels ◽  
Nathan D Wood
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Effect Size ◽  
Analytical Techniques ◽  
Circular Statistics ◽  
Social Scientists ◽  
Social Phenomena ◽  
Longitudinal Methods ◽  
Engineering Sciences ◽  
Periodic Components

Social scientists have become increasingly interested in using intensive longitudinal methods to study social phenomena that change over time. Many of these phenomena are expected to exhibit cycling fluctuations (e.g., sleep, mood, sexual desire). However, researchers typically employ analytical methods which are unable to model such patterns. We present spectral and cross-spectral analysis as means to address this limitation. Spectral analysis provides a means to interrogate time series from a different, frequency domain perspective, and to understand how the time series may be decomposed into their constituent periodic components. Cross-spectral extends this to dyadic data and allows for synchrony and time offsets to be identified. The techniques are commonly used in the physical and engineering sciences, and we discuss how to apply these popular analytical techniques to the social sciences while also demonstrating how to undertake estimations of significance and effect size. In this tutorial we begin by introducing spectral and cross-spectral analysis, before demonstrating its application to simulated univariate and bivariate individual- and group-level data. We employ cross-power spectral density techniques to understand synchrony between the individual time series in a dyadic time series, and circular statistics and polar plots to understand phase offsets between constituent periodic components. Finally, we present a means to undertake non-parameteric boostrapping in order to estimate the significance, and derive a proxy for effect size. A Jupyter Notebook (Python 3.6) is provided as supplementary material to aid researchers who intend to apply these techniques.

Local Shuffling of Spike Trains Boosts the Accuracy of Spike Train Spectral Analysis

2006 ◽  
Vol 95 (5) ◽  
pp. 3245-3256 ◽  
Author(s):  
Michal Rivlin-Etzion ◽  
Ya'acov Ritov ◽  
Gali Heimer ◽  
Hagai Bergman ◽  
Izhar Bar-Gad
Keyword(s):  
Spectral Analysis ◽  
Spike Train ◽  
Discharge Rate ◽  
Low Frequency ◽  
Spike Trains ◽  
High Frequencies ◽  
Refractory Periods ◽  
Low Frequency Oscillations ◽  
Neuronal Spike Trains ◽  
Frequency Modulations

Spectral analysis of neuronal spike trains is an important tool in understanding the characteristics of neuronal activity by providing insights into normal and pathological periodic oscillatory phenomena. However, the refractory period creates high-frequency modulations in spike-train firing rate because any rise in the discharge rate causes a descent in subsequent time bins, leading to multifaceted modifications in the structure of the spectrum. Thus the power spectrum of the spiking activity (autospectrum) displays elevated energy in high frequencies relative to the lower frequencies. The spectral distortion is more dominant in neurons with high firing rates and long refractory periods and can lead to reduced identification of low-frequency oscillations (such as the 5- to 10-Hz burst oscillations typical of Parkinsonian basal ganglia and thalamus). We propose a compensation process that uses shuffling of interspike intervals (ISIs) for reliable identification of oscillations in the entire frequency range. This compensation is further improved by local shuffling, which preserves the slow changes in the discharge rate that may be lost in global shuffling. Cross-spectra of pairs of neurons are similarly distorted regardless of their correlation level. Consequently, identification of low-frequency synchronous oscillations, even for two neurons recorded by a single electrode, is improved by ISI shuffling. The ISI local shuffling is computed with confidence limits that are based on the first-order statistics of the spike trains, thus providing a reliable estimation of auto- and cross-spectra of spike trains and making it an optimal tool for physiological studies of oscillatory neuronal phenomena.

scholarly journals Sensory computations in the cuneate nucleus of macaques

2021 ◽  
Author(s):  
Aneesha K Suresh ◽  
Charles M. Greenspon ◽  
Qinpu He ◽  
Joshua M Rosenow ◽  
Lee E Miller ◽  
...  
Keyword(s):  
Somatosensory Cortex ◽  
Receptive Fields ◽  
Nerve Fibers ◽  
Geometric Features ◽  
Cuneate Nucleus ◽  
Relevant Stimulus ◽  
Tactile Information ◽  
Conventional View ◽  
Random Dot Patterns ◽  
Stimulus Features

In primates, the responses of individual neurons in primary somatosensory cortex (S1) reflect convergent input from multiple classes of nerve fibers and are selective for behaviorally relevant stimulus features. The conventional view is that these response properties reflect computations that are effected in cortex, implying that sensory signals are not meaningfully processed in the two intervening structures - the Cuneate Nucleus (CN) and the thalamus. To test this hypothesis, we recorded the responses evoked in CN to a battery of stimuli that have been extensively used to characterize tactile coding, including skin indentations, vibrations, random dot patterns, and scanned edges. We found that CN responses are more similar to their S1 counterparts than they are to their inputs: CN neurons receive input from multiple sub-modalities, have spatially complex receptive fields, and exhibit selectivity for geometric features. Thus, CN plays a key role in the processing of tactile information.

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