scholarly journals Motor control by precisely timed spike patterns

2017 ◽  
Vol 114 (5) ◽  
pp. 1171-1176 ◽  
Author(s):  
Kyle H. Srivastava ◽  
Caroline M. Holmes ◽  
Michiel Vellema ◽  
Andrea R. Pack ◽  
Coen P. H. Elemans ◽  
...  
Keyword(s):  
Brain Function ◽  
Action Potentials ◽  
Fundamental Problem ◽  
Motor Units ◽  
Spike Timing ◽  
Time Interval ◽  
Additional Information ◽  
Timing Drive ◽  
Spike Patterns ◽  
Individual Motor

A fundamental problem in neuroscience is understanding how sequences of action potentials (“spikes”) encode information about sensory signals and motor outputs. Although traditional theories assume that this information is conveyed by the total number of spikes fired within a specified time interval (spike rate), recent studies have shown that additional information is carried by the millisecond-scale timing patterns of action potentials (spike timing). However, it is unknown whether or how subtle differences in spike timing drive differences in perception or behavior, leaving it unclear whether the information in spike timing actually plays a role in brain function. By examining the activity of individual motor units (the muscle fibers innervated by a single motor neuron) and manipulating patterns of activation of these neurons, we provide both correlative and causal evidence that the nervous system uses millisecond-scale variations in the timing of spikes within multispike patterns to control a vertebrate behavior—namely, respiration in the Bengalese finch, a songbird. These findings suggest that a fundamental assumption of current theories of motor coding requires revision.

scholarly journals Motor control by precisely timed spike patterns

2016 ◽  
Author(s):  
Kyle H. Srivastava ◽  
Caroline M. Holmes ◽  
Michiel Vellema ◽  
Andrea Pack ◽  
Coen P. H. Elemans ◽  
...  
Keyword(s):  
Action Potentials ◽  
Neural Coding ◽  
Fundamental Problem ◽  
Spike Timing ◽  
Prosthetic Devices ◽  
Neural Prosthetic ◽  
Control Behavior ◽  
Timing Drive ◽  
Spike Patterns ◽  
Simple Behavior

SummaryA fundamental problem in neuroscience is to understand how sequences of action potentials (“spikes”) encode information about sensory signals and motor outputs. Although traditional theories of neural coding assume that information is conveyed by the total number of spikes fired (spike rate), recent studies of sensory [1–5] and motor [6] activity have shown that far more information is carried by the millisecond-scale timing patterns of action potentials (spike timing). However, it is unknown whether or how subtle differences in spike timing drive differences in perception or behavior, leaving it unclear whether the information carried by spike timing actually plays a causal role in brain function [1]. Here we demonstrate how a precise spike timing code is read out downstream by the muscles to control behavior. We provide both correlative and causal evidence to show that the nervous system uses millisecond-scale variations in the timing of spikes within multi-spike patterns to regulate a relatively simple behavior – respiration in the Bengalese finch, a songbird. These findings suggest that a fundamental assumption of current theories of motor coding requires revision, and that significant improvements in applications, such as neural prosthetic devices, can be achieved by using precise spike timing information.

scholarly journals Effects of memristive synapse radiation interactions on learning in spiking neural networks

2021 ◽  
Vol 3 (5) ◽  
Author(s):  
Sumedha Gandharava Dahl ◽  
Robert C. Ivans ◽  
Kurtis D. Cantley
Keyword(s):  
Action Potentials ◽  
Radiation Effects ◽  
Synaptic Weight ◽  
Learning Rule ◽  
Spike Timing ◽  
Spiking Neural Network ◽  
Synaptic Action ◽  
Drift Model ◽  
Spatio Temporal ◽  
Post Synaptic Neuron

AbstractThis study uses advanced modeling and simulation to explore the effects of external events such as radiation interactions on the synaptic devices in an electronic spiking neural network. Specifically, the networks are trained using the spike-timing-dependent plasticity (STDP) learning rule to recognize spatio-temporal patterns (STPs) representing 25 and 100-pixel characters. Memristive synapses based on a TiO2 non-linear drift model designed in Verilog-A are utilized, with STDP learning behavior achieved through bi-phasic pre- and post-synaptic action potentials. The models are modified to include experimentally observed state-altering and ionizing radiation effects on the device. It is found that radiation interactions tend to make the connection between afferents stronger by increasing the conductance of synapses overall, subsequently distorting the STDP learning curve. In the absence of consistent STPs, these effects accumulate over time and make the synaptic weight evolutions unstable. With STPs at lower flux intensities, the network can recover and relearn with constant training. However, higher flux can overwhelm the leaky integrate-and-fire post-synaptic neuron circuits and reduce stability of the network.

scholarly journals Investigation of different stimulation patterns with doublet pulses to reduce muscle fatigue

2019 ◽  
Vol 6 ◽  
pp. 205566831982580 ◽  
Author(s):  
Ruslinda Ruslee ◽  
Jennifer Miller ◽  
Henrik Gollee
Keyword(s):  
Electrical Stimulation ◽  
Muscle Fatigue ◽  
Functional Electrical Stimulation ◽  
Muscle Activation ◽  
Motor Units ◽  
Pulse Interval ◽  
Time Interval ◽  
Fatigue Index ◽  
Significant Difference ◽  
Cord Injury

Introduction: Functional electrical stimulation is a common technique used in the rehabilitation of individuals with a spinal cord injury to produce functional movement of paralysed muscles. However, it is often associated with rapid muscle fatigue which limits its applications. Methods: The objective of this study is to investigate the effects on the onset of fatigue of different multi-electrode patterns of stimulation via multiple pairs of electrodes using doublet pulses: Synchronous stimulation is compared to asynchronous stimulation patterns which are activated sequentially (AsynS) or randomly (AsynR), mimicking voluntary muscle activation by targeting different motor units. We investigated these three different approaches by applying stimulation to the gastrocnemius muscle repeatedly for 10 min (300 ms stimulation followed by 700 ms of no-stimulation) with 40 Hz effective frequency for all protocols and doublet pulses with an inter-pulse-interval of 6 ms. Eleven able-bodied volunteers (28 ± 3 years old) participated in this study. Ultrasound videos were recorded during stimulation to allow evaluation of changes in muscle morphology. The main fatigue indicators we focused on were the normalised fatigue index, fatigue time interval and pre-post twitch–tetanus ratio. Results: The results demonstrate that asynchronous stimulation with doublet pulses gives a higher normalised fatigue index (0.80 ± 0.08 and 0.87 ± 0.08) for AsynS and AsynR, respectively, than synchronous stimulation (0.62 ± 0.06). Furthermore, a longer fatigue time interval for AsynS (302.2 ± 230.9 s) and AsynR (384.4 ± 279.0 s) compared to synchronous stimulation (68.0 ± 30.5 s) indicates that fatigue occurs later during asynchronous stimulation; however, this was only found to be statistically significant for one of two methods used to calculate the group mean. Although no significant difference was found in pre-post twitch–tetanus ratio, there was a trend towards these effects. Conclusion: In this study, we proposed an asynchronous stimulation pattern for the application of functional electrical stimulation and investigated its suitability for reducing muscle fatigue compared to previous methods. The results show that asynchronous multi-electrode stimulation patterns with doublet pulses may improve fatigue resistance in functional electrical stimulation applications in some conditions.

Spike-Timing-Dependent Hebbian Plasticity as Temporal Difference Learning

2001 ◽  
Vol 13 (10) ◽  
pp. 2221-2237 ◽  
Author(s):  
Rajesh P. N. Rao ◽  
Terrence J. Sejnowski
Keyword(s):  
Cortical Neuron ◽  
Action Potentials ◽  
Learning Rule ◽  
Spike Timing ◽  
Biophysical Model ◽  
Excitatory Synapses ◽  
Temporal Difference ◽  
Temporal Difference Learning ◽  
Neocortical Neurons ◽  
Hebbian Plasticity

A spike-timing-dependent Hebbian mechanism governs the plasticity of recurrent excitatory synapses in the neocortex: synapses that are activated a few milliseconds before a postsynaptic spike are potentiated, while those that are activated a few milliseconds after are depressed. We show that such a mechanism can implement a form of temporal difference learning for prediction of input sequences. Using a biophysical model of a cortical neuron, we show that a temporal difference rule used in conjunction with dendritic backpropagating action potentials reproduces the temporally asymmetric window of Hebbian plasticity observed physiologically. Furthermore, the size and shape of the window vary with the distance of the synapse from the soma. Using a simple example, we show how a spike-timing-based temporal difference learning rule can allow a network of neocortical neurons to predict an input a few milliseconds before the input's expected arrival.

Cortical Representation of Auditory Space: Information-Bearing Features of Spike Patterns

2002 ◽  
Vol 87 (4) ◽  
pp. 1749-1762 ◽  
Author(s):  
Shigeto Furukawa ◽  
John C. Middlebrooks
Keyword(s):  
Sound Source ◽  
Neuronal Response ◽  
Source Location ◽  
Spike Timing ◽  
Spike Count ◽  
Cortical Representation ◽  
Auditory Space ◽  
Transmitted Information ◽  
Related Information ◽  
Spike Patterns

Previous studies have demonstrated that the spike patterns of cortical neurons vary systematically as a function of sound-source location such that the response of a single neuron can signal the location of a sound source throughout 360° of azimuth. The present study examined specific features of spike patterns that might transmit information related to sound-source location. Analysis was based on responses of well-isolated single units recorded from cortical area A2 in α-chloralose-anesthetized cats. Stimuli were 80-ms noise bursts presented from loudspeakers in the horizontal plane; source azimuths ranged through 360° in 20° steps. Spike patterns were averaged across samples of eight trials. A competitive artificial neural network (ANN) identified sound-source locations by recognizing spike patterns; the ANN was trained using the learning vector quantization learning rule. The information about stimulus location that was transmitted by spike patterns was computed from joint stimulus-response probability matrices. Spike patterns were manipulated in various ways to isolate particular features. Full-spike patterns, which contained all spike-count information and spike timing with 100-μs precision, transmitted the most stimulus-related information. Transmitted information was sensitive to disruption of spike timing on a scale of more than ∼4 ms and was reduced by an average of ∼35% when spike-timing information was obliterated entirely. In a condition in which all but the first spike in each pattern were eliminated, transmitted information decreased by an average of only ∼11%. In many cases, that condition showed essentially no loss of transmitted information. Three unidimensional features were extracted from spike patterns. Of those features, spike latency transmitted ∼60% more information than that transmitted either by spike count or by a measure of latency dispersion. Information transmission by spike patterns recorded on single trials was substantially reduced compared with the information transmitted by averages of eight trials. In a comparison of averaged and nonaveraged responses, however, the information transmitted by latencies was reduced by only ∼29%, whereas information transmitted by spike counts was reduced by 79%. Spike counts clearly are sensitive to sound-source location and could transmit information about sound-source locations. Nevertheless, the present results demonstrate that the timing of the first poststimulus spike carries a substantial amount, probably the majority, of the location-related information present in spike patterns. The results indicate that any complete model of the cortical representation of auditory space must incorporate the temporal characteristics of neuronal response patterns.

Neural Patterns Associated with Ventilatory Movements in Dragonfly Larvae

1970 ◽  
Vol 52 (1) ◽  
pp. 167-175
Author(s):  
P. J. MILL
Keyword(s):  
Control System ◽  
Motor Activity ◽  
Action Potentials ◽  
Motor Units ◽  
The Other ◽  
Ventilatory Control ◽  
Expiratory Phase ◽  
Segmental Nerve ◽  
Ventral Muscle ◽  
Stimulation Of

1. Rhythmic bursts of motor activity associated with the expiratory phase of ventilation have been recorded from the second lateral segmental nerves of posterior abdominal ganglia in Aeshna and Anax larvae. 2. In Aeshna the rhythmic expiratory bursts contain one, or sometimes two, motor units; whereas in Anax there are almost invariably three units. In both animals only one unit is associated with action potentials in the respiratory dorso-ventral muscle. 3. Motor activity synchronized with the expiratory bursts in the second nerves has been recorded from the other lateral nerves and from the last unpaired nerve. In addition the fifth lateral nerves carry inspiratory bursts. 4. It has been confirmed that stimulation of a first segmental nerve can re-set the ventilatory rhythm by initiating an expiratory burst in the second nerves. The original frequency is immediately resumed on cessation of stimulation. 5. The nature of the ventilatory control system in dragonfly larvae is discussed in relation to other rhythmic systems in the arthropods.

Summation of motor unit tensions in the tibialis posterior muscle of the cat under isometric and nonisometric conditions

1991 ◽  
Vol 66 (6) ◽  
pp. 1838-1846 ◽  
Author(s):  
R. K. Powers ◽  
M. D. Binder
Keyword(s):  
Motor Unit ◽  
Muscle Fibers ◽  
Stimulus Frequency ◽  
Muscle Length ◽  
Motor Units ◽  
Force Transducer ◽  
Tibialis Posterior ◽  
Single Motor Units ◽  
Individual Motor ◽  
Stimulation Of

1. The tension produced by the combined stimulation of two to four single motor units of the cat tibialis posterior muscle was compared with the algebraic sum of the tensions produced by each individual motor unit. Comparisons were made under isometric conditions and during imposed changes in muscle length. 2. Under isometric conditions, the tension resulting from combined stimulation of units displayed marked nonlinear summation, as previously reported in other cat hindlimb muscles. On average, the measured tension was approximately 20% greater than the algebraic sum of the individual unit tensions. However, small trapezoidal movements imposed on the muscle during stimulation significantly reduced the degree of nonlinear summation both during and after the movement. This effect was seen with imposed movements as small as 50 microns. 3. The degree of nonlinear summation was not dependent on motor unit size or on stimulus frequency. The effect was also unrelated to tendon compliance because the degree of nonlinear summation of motor unit forces was unaffected by the inclusion of different amounts of the external tendon between the muscle and the force transducer. 4. Our results support previous suggestions that the force measured when individual motor units are stimulated under isometric conditions is reduced by friction between the active muscle fibers and adjacent passive fibers. These frictional effects are likely to originate in the connective tissue matrix connecting adjacent muscle fibers. However, because these effects are virtually eliminated by small movements, linear summation of motor unit tensions should occur at low force levels under nonisometric conditions.(ABSTRACT TRUNCATED AT 250 WORDS)

scholarly journals Value-at-risk modeling and forecasting with range-based volatility models: empirical evidence

2017 ◽  
Vol 28 (75) ◽  
pp. 361-376 ◽  
Author(s):  
Leandro dos Santos Maciel ◽  
Rosangela Ballini
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Garch Models ◽  
Time Interval ◽  
Risk Modeling ◽  
Additional Information ◽  
Volatility Models ◽  
Modeling And Forecasting ◽  
The Difference ◽  
Market Indexes

ABSTRACT This article considers range-based volatility modeling for identifying and forecasting conditional volatility models based on returns. It suggests the inclusion of range measuring, defined as the difference between the maximum and minimum price of an asset within a time interval, as an exogenous variable in generalized autoregressive conditional heteroscedasticity (GARCH) models. The motivation is evaluating whether range provides additional information to the volatility process (intraday variability) and improves forecasting, when compared to GARCH-type approaches and the conditional autoregressive range (CARR) model. The empirical analysis uses data from the main stock market indexes for the U.S. and Brazilian economies, i.e. S&P 500 and IBOVESPA, respectively, within the period from January 2004 to December 2014. Performance is compared in terms of accuracy, by means of value-at-risk (VaR) modeling and forecasting. The out-of-sample results indicate that range-based volatility models provide more accurate VaR forecasts than GARCH models.

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