1,103 research outputs found
Connectivity reflects coding: A model of voltage-based spike-timing-dependent-plasticity with homeostasis
Electrophysiological connectivity patterns in cortex often show a few strong connections in a sea of weak connections. In some brain areas a large fraction of strong connections are bidirectional, in others they are mainly unidirectional. In order to explain these connectivity patterns, we use a model of Spike-Timing-Dependent Plasticity where synaptic changes depend on presynaptic spike arrival and the postsynaptic membrane potential. The model describes several nonlinear effects in STDP experiments, as well as the voltage dependence of plasticity under voltage clamp and classical paradigms of LTP/LTD induction. We show that in a simulated recurrent network of spiking neurons our plasticity rule leads not only to receptive field development, but also to connectivity patterns that reflect the neural code: for temporal coding paradigms strong connections are predominantly unidirectional, whereas they are bidirectional under rate coding. Thus variable connectivity patterns in the brain could reflect different coding principles across brain areas
Extracting non-linear integrate-and-fire models from experimental data using dynamic I–V curves
The dynamic I–V curve method was recently introduced for the efficient experimental generation of reduced neuron models. The method extracts the response properties of a neuron while it is subject to a naturalistic stimulus that mimics in vivo-like fluctuating synaptic drive. The resulting history-dependent, transmembrane current is then projected onto a one-dimensional current–voltage relation that provides the basis for a tractable non-linear integrate-and-fire model. An attractive feature of the method is that it can be used in spike-triggered mode to quantify the distinct patterns of post-spike refractoriness seen in different classes of cortical neuron. The method is first illustrated using a conductance-based model and is then applied experimentally to generate reduced models of cortical layer-5 pyramidal cells and interneurons, in injected-current and injected- conductance protocols. The resulting low-dimensional neuron models—of the refractory exponential integrate-and-fire type—provide highly accurate predictions for spike-times. The method therefore provides a useful tool for the construction of tractable models and rapid experimental classification of cortical neurons
Pattern formation in oscillatory complex networks consisting of excitable nodes
Oscillatory dynamics of complex networks has recently attracted great
attention. In this paper we study pattern formation in oscillatory complex
networks consisting of excitable nodes. We find that there exist a few center
nodes and small skeletons for most oscillations. Complicated and seemingly
random oscillatory patterns can be viewed as well-organized target waves
propagating from center nodes along the shortest paths, and the shortest loops
passing through both the center nodes and their driver nodes play the role of
oscillation sources. Analyzing simple skeletons we are able to understand and
predict various essential properties of the oscillations and effectively
modulate the oscillations. These methods and results will give insights into
pattern formation in complex networks, and provide suggestive ideas for
studying and controlling oscillations in neural networks.Comment: 15 pages, 7 figures, to appear in Phys. Rev.
Síndrome del túnel carpiano. Evaluación clínica y ayudas diagnósticas
The Carpal tunnel syndrome is a compression of the median nerve at the level of the wrist. Symptoms include pain and paresthesia, especially at night, and improves with arm elevation and movement of the hand. Treatment begins with rest of the hand, anti inflammatory drugs and nocturnal splint in extension of the wrist. If there is not improvement, a corticoid injection with local anesthesia into the wrist can be used with improvement of symptoms.
Surgery is indicated in failed orthopaedic treatment, when nerve conduction studies show pathologic changes or if a sensitive o motor deficit is demonstrated. Surgery relieves pain but does not improve previous sensitive and motor deficits.
Keywords: Carpal tunnel syndrome. Median nerve. Compression neuropaty. El síndrome del túnel carpiano deriva de la compresión del nervio mediano a nivel de la muñeca. Los síntomas más frecuentes son dolor y parestesias de predominio nocturno con compromiso del sueño, que pueden ceder con elevación del brazo y agitación de la mano. El objetivo de este artículo es hacer una revisión actualizada del tema aunando la experiencia del autor. Puede iniciarse el tratamiento recomendando reposo de la mano, antinflamatorios no esteroideos y/o férula palmar nocturna en extensión que abarque mano y antebrazo. Si persisten los síntomas, puede realizarse una infiltración de corticoides, ya que existen evidencias que la inyección local de metil-prednisolona mejora los síntomas.
Está indicada la cirugía si persisten los síntomas a pesar del tratamiento médico, si el estudio electrofisiológico es muy patológico o si existe déficit sensitivo o motor establecido. La cirugía es eficaz en la mejoría del dolor con lenta o nula recuperación de déficit sensitivo y motor previos.
Palabras clave: Síndrome túnel carpiano. Nervio mediano. Neuropatía compresiva.
 
A History of Spike-Timing-Dependent Plasticity
How learning and memory is achieved in the brain is a central question in neuroscience. Key to today’s research into information storage in the brain is the concept of synaptic plasticity, a notion that has been heavily influenced by Hebb's (1949) postulate. Hebb conjectured that repeatedly and persistently co-active cells should increase connective strength among populations of interconnected neurons as a means of storing a memory trace, also known as an engram. Hebb certainly was not the first to make such a conjecture, as we show in this history. Nevertheless, literally thousands of studies into the classical frequency-dependent paradigm of cellular learning rules were directly inspired by the Hebbian postulate. But in more recent years, a novel concept in cellular learning has emerged, where temporal order instead of frequency is emphasized. This new learning paradigm – known as spike-timing-dependent plasticity (STDP) – has rapidly gained tremendous interest, perhaps because of its combination of elegant simplicity, biological plausibility, and computational power. But what are the roots of today’s STDP concept? Here, we discuss several centuries of diverse thinking, beginning with philosophers such as Aristotle, Locke, and Ribot, traversing, e.g., Lugaro’s plasticità and Rosenblatt’s perceptron, and culminating with the discovery of STDP. We highlight interactions between theoretical and experimental fields, showing how discoveries sometimes occurred in parallel, seemingly without much knowledge of the other field, and sometimes via concrete back-and-forth communication. We point out where the future directions may lie, which includes interneuron STDP, the functional impact of STDP, its mechanisms and its neuromodulatory regulation, and the linking of STDP to the developmental formation and continuous plasticity of neuronal networks
The Impact of Muscular Strength on Cardiovascular Disease Risk Factors
The purpose of this study was to determine the associations between isokinetic leg muscular strength and cardiovascular disease (CVD) risk factor characterizations in Americans aged 50 and older. Using a publicly available dataset from the National Health and Nutrition Examination Survey (NHANES), a secondary analysis was conducted on participants (males ≥50 yrs; females ≥55 yrs; N=10,858) pooled from 1999 to 2002. CVD risk factors were determined using the American College of Sports Medicine (ACSM) cutoff values. CVD risk factor characterization was determined by creating CVD risk factor profiles (i.e., the total number of CVD risk factors an individual possesses), then separating participants into low (0-2 CVD risk factors), moderate (3-5), and high (6-8) risk groups. Muscular strength was determined by isokinetic maximal peak force (PF) of the leg extensors, both raw and normalized to body mass. Normalized, but not raw, muscular strength was shown to be significantly inversely associated with CVD risk factor characterization for both males and females (Phttps://digitalcommons.odu.edu/gradposters2022_education/1002/thumbnail.jp
Non-equilibrium dynamics of stochastic point processes with refractoriness
Stochastic point processes with refractoriness appear frequently in the
quantitative analysis of physical and biological systems, such as the
generation of action potentials by nerve cells, the release and reuptake of
vesicles at a synapse, and the counting of particles by detector devices. Here
we present an extension of renewal theory to describe ensembles of point
processes with time varying input. This is made possible by a representation in
terms of occupation numbers of two states: Active and refractory. The dynamics
of these occupation numbers follows a distributed delay differential equation.
In particular, our theory enables us to uncover the effect of refractoriness on
the time-dependent rate of an ensemble of encoding point processes in response
to modulation of the input. We present exact solutions that demonstrate generic
features, such as stochastic transients and oscillations in the step response
as well as resonances, phase jumps and frequency doubling in the transfer of
periodic signals. We show that a large class of renewal processes can indeed be
regarded as special cases of the model we analyze. Hence our approach
represents a widely applicable framework to define and analyze non-stationary
renewal processes.Comment: 8 pages, 4 figure
Generalized Rate-Code Model for Neuron Ensembles with Finite Populations
We have proposed a generalized Langevin-type rate-code model subjected to
multiplicative noise, in order to study stationary and dynamical properties of
an ensemble containing {\it finite} neurons. Calculations using the
Fokker-Planck equation (FPE) have shown that owing to the multiplicative noise,
our rate model yields various kinds of stationary non-Gaussian distributions
such as gamma, inverse-Gaussian-like and log-normal-like distributions, which
have been experimentally observed. Dynamical properties of the rate model have
been studied with the use of the augmented moment method (AMM), which was
previously proposed by the author with a macroscopic point of view for
finite-unit stochastic systems. In the AMM, original -dimensional stochastic
differential equations (DEs) are transformed into three-dimensional
deterministic DEs for means and fluctuations of local and global variables.
Dynamical responses of the neuron ensemble to pulse and sinusoidal inputs
calculated by the AMM are in good agreement with those obtained by direct
simulation. The synchronization in the neuronal ensemble is discussed.
Variabilities of the firing rate and of the interspike interval (ISI) are shown
to increase with increasing the magnitude of multiplicative noise, which may be
a conceivable origin of the observed large variability in cortical neurons.Comment: 19 pages, 9 figures, accepted in Phys. Rev. E after minor
modification
Comparison of Langevin and Markov channel noise models for neuronal signal generation
The stochastic opening and closing of voltage-gated ion channels produces
noise in neurons. The effect of this noise on the neuronal performance has been
modelled using either approximate or Langevin model, based on stochastic
differential equations or an exact model, based on a Markov process model of
channel gating. Yet whether the Langevin model accurately reproduces the
channel noise produced by the Markov model remains unclear. Here we present a
comparison between Langevin and Markov models of channel noise in neurons using
single compartment Hodgkin-Huxley models containing either and
, or only voltage-gated ion channels. The performance of the
Langevin and Markov models was quantified over a range of stimulus statistics,
membrane areas and channel numbers. We find that in comparison to the Markov
model, the Langevin model underestimates the noise contributed by voltage-gated
ion channels, overestimating information rates for both spiking and non-spiking
membranes. Even with increasing numbers of channels the difference between the
two models persists. This suggests that the Langevin model may not be suitable
for accurately simulating channel noise in neurons, even in simulations with
large numbers of ion channels
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