Consequences of Converting Graded to Action Potentials upon Neural Information Coding and Energy Efficiency

Information is encoded in neural circuits using both graded and action potentials, converting between them within single neurons and successive processing layers. This conversion is accompanied by information loss and a drop in energy efficiency. We investigate the biophysical causes of this loss of information and efficiency by comparing spiking neuron models, containing stochastic voltage-gated Na+ and K+ channels, with generator potential and graded potential models lacking voltage-gated Na+ channels. We identify three causes of information loss in the generator potential that are the by-product of action potential generation: (1) the voltage-gated Na+ channels necessary for action potential generation increase intrinsic noise and (2) introduce non-linearities, and (3) the finite duration of the action potential creates a `footprint' in the generator potential that obscures incoming signals. These three processes reduce information rates by similar to 50% in generator potentials, to similar to 3 times that of spike trains. Both generator potentials and graded potentials consume almost an order of magnitude less energy per second than spike trains. Because of the lower information rates of generator potentials they are substantially less energy efficient than graded potentials. However, both are an order of magnitude more efficient than spike trains due to the higher energy costs and low information content of spikes, emphasizing that there is a two-fold cost of converting analogue to digital; information loss and cost inflation.

Determining feature relevance for the grouping of motor unit action potentials through generative topographic mapping

The study of motor unit action potential (MUAP) activity from electrornyographic signals is an important stage on neurological investigations that aim to understand the state of the neuromuscular system. In this context, the identification and clustering of MUAPs that exhibit common characteristics, and the assessment of which data features are most relevant for the definition of such cluster structure are central issues. In this paper, we propose the application of an unsupervised Feature Relevance Determination (FRD) method to the analysis of experimental MUAPs obtained from healthy human subjects. In contrast to approaches that require the knowledge of a priori information from the data, this FRD method is embedded on a constrained mixture model, known as Generative Topographic Mapping, which simultaneously performs clustering and visualization of MUAPs. The experimental results of the analysis of a data set consisting of MUAPs measured from the surface of the First Dorsal Interosseous, a hand muscle, indicate that the MUAP features corresponding to the hyperpolarization period in the physisiological process of generation of muscle fibre action potentials are consistently estimated as the most relevant and, therefore, as those that should be paid preferential attention for the interpretation of the MUAP groupings.

Generative topographic mapping applied to clustering and visualization of motor unit action potentials

The identification and visualization of clusters formed by motor unit action potentials (MUAPs) is an essential step in investigations seeking to explain the control of the neuromuscular system. This work introduces the generative topographic mapping (GTM), a novel machine learning tool, for clustering of MUAPs, and also it extends the GTM technique to provide a way of visualizing MUAPs. The performance of GTM was compared to that of three other clustering methods: the self-organizing map (SOM), a Gaussian mixture model (GMM), and the neural-gas network (NGN). The results, based on the study of experimental MUAPs, showed that the rate of success of both GTM and SOM outperformed that of GMM and NGN, and also that GTM may in practice be used as a principled alternative to the SOM in the study of MUAPs. A visualization tool, which we called GTM grid, was devised for visualization of MUAPs lying in a high-dimensional space. The visualization provided by the GTM grid was compared to that obtained from principal component analysis (PCA). (c) 2005 Elsevier Ireland Ltd. All rights reserved.

Classification of neural action potentials using mean shift clustering

 Understanding neural functions requires the observation of the activities of single neurons that are represented via electrophysiological data. Processing and understanding these data are challenging problems in biomedical engineering. A microelectrode commonly records the activity of multiple neurons. Spike sorting is a process of classifying every single action potential (spike) to a particular neuron. This paper proposes a combination between diffusion maps (DM) and mean shift clustering method for spike sorting. DM is utilized to extract spike features, which are highly capable of discriminating different spike shapes. Mean shift clustering provides an automatic unsupervised clustering, which takes extracted features from DM as inputs. Experimental results show a noticeable dominance of the features extracted by DM compared to those selected by wavelet transformation (WT). Accordingly, the proposed integrated method is significantly superior to the popular existing combination of WT and superparamagnetic clustering regarding spike sorting accuracy.

Mathematical Modeling to Investigate Antiarrhythmic Drug Side Effects: Rate-Dependence Role in Ionic Currents and Action Potentials Shape in the O’Hara Model

Sudden cardiac death due to ventricular arrhythmia is one of the leading causes of mortality in the world. In the last decades, it has proven that anti-arrhythmic drugs, which prolong the refractory period by means of prolongation of the cardiac action potential duration (APD), play a good role in preventing of relevant human arrhythmias. However, it has long been observed that the “class III antiarrhythmic effect” diminish at faster heart rates and that this phenomenon represent a big weakness, since it is the precise situation when arrhythmias are most prone to occur. It is well known that mathematical modeling is a useful tool for investigating cardiac cell behavior. In the last 60 years, a multitude of cardiac models has been created; from the pioneering work of Hodgkin and Huxley (1952), who first described the ionic currents of the squid giant axon quantitatively, mathematical modeling has made great strides. The O’Hara model, that I employed in this research work, is one of the modern computational models of ventricular myocyte, a new generation began in 1991 with ventricular cell model by Noble et al. Successful of these models is that you can generate novel predictions, suggest experiments and provide a quantitative understanding of underlying mechanism. Obviously, the drawback is that they remain simple models, they don’t represent the real system. The overall goal of this research is to give an additional tool, through mathematical modeling, to understand the behavior of the main ionic currents involved during the action potential (AP), especially underlining the differences between slower and faster heart rates. In particular to evaluate the rate-dependence role on the action potential duration, to implement a new method for interpreting ionic currents behavior after a perturbation effect and to verify the validity of the work proposed by Antonio Zaza using an injected current as a perturbing effect.

Velocity recovery cycles of human muscle action potentials in chronic renal failure

To test the hypothesis that muscle fibers are depolarized in patients with chronic renal failure, by measuring velocity recovery cycles of muscle action potentials as indicators of muscle membrane potential.

Velocity recovery cycles of human muscle action potentials: Repeatability and variability

Velocity recovery cycles (VRCs) of human muscle action potentials have been proposed as a new technique for assessing muscle membrane function in myopathies. This study was undertaken to determine the variability and repeatability of VRC measures such as supernormality, to help guide future clinical use of the method.

Microneurography in rats: a minimally invasive method to record single C-fiber action potentials from peripheral nerves in vivo

Microneurography is a method suitable for recording intraneural single or multiunit action potentials in conscious subjects. Microneurography has rarely been applied to animal experiments, where more invasive methods, like the teased fiber recording technique, are widely used. We have tested the feasibility of microneurographic recordings from the peripheral nerves of rats. Tungsten microelectrodes were inserted into the sciatic nerve at mid-thigh level. Single or multiunit action potentials evoked by regular electrical stimulation were recorded, digitized and displayed as a raster plot of latencies. The method allows unambiguous recording and recognition of single C-fiber action potentials from an in vivo preparation, with minimal disruption of the nerve being recorded. Multiple C-fibers can be recorded simultaneously for several hours, and if the animal is allowed to recover, repeated recording sessions can be obtained from the same nerve at the same level over a period of weeks or months. Also, single C units can be functionally identified by their changes in latency to natural stimuli, and insensitive units can be recognized as 'silent' nociceptors or sympathetic efferents by their distinctive profiles of activity-dependent slowing during repetitive electrical stimulation, or by the effect on spontaneous efferent activity of a proximal anesthetic block. Moreover, information about the biophysical properties of C axons can be obtained from their latency recovery cycles. Finally, we show that this preparation is potentially suitable for the study of C-fiber behavior in models of neuropathies and nerve lesions, both under resting conditions and in response to drug administration.

The time window for generation of dendritic spikes by coincidence of action potentials and EPSPs is layer specific in somatosensory cortex

The precise timing of events in the brain has consequences for intracellular processes, synaptic plasticity, integration and network behaviour. Pyramidal neurons, the most widespread excitatory neuron of the neocortex have multiple spike initiation zones, which interact via dendritic and somatic spikes actively propagating in all directions within the dendritic tree. For these neurons, therefore, both the location and timing of synaptic inputs are critical. The time window for which the backpropagating action potential can influence dendritic spike generation has been extensively studied in layer 5 neocortical pyramidal neurons of rat somatosensory cortex. Here, we re-examine this coincidence detection window for pyramidal cell types across the rat somatosensory cortex in layers 2/3, 5 and 6. We find that the time-window for optimal interaction is widest and shifted in layer 5 pyramidal neurons relative to cells in layers 6 and 2/3. Inputs arriving at the same time and locations will therefore differentially affect spike-timing dependent processes in the different classes of pyramidal neurons.

Velocity recovery cycles of human muscle action potentials and their sensitivity to ischemia

This study was undertaken to test whether recovery cycle measurements can provide useful information about the membrane potential of human muscle fibers. Multifiber responses to direct muscle stimulation through needle electrodes were recorded from the brachioradialis of healthy volunteers, and the latency changes measured as conditioning stimuli were applied at interstimulus intervals of 2-1000 ms. In all subjects, the relative refractory period (RRP), which lasted 3.27 +/- 0.45 ms (mean +/- SD, n = 12), was followed by a phase of supernormality, in which the velocity increased by 9.3 +/- 3.4% at 6.1 +/- 1.3 ms, and recovered over 1 s. A broad hump of additional supernormality was seen at around 100 ms. Extra conditioning stimuli had little effect on the early supernormality but increased the later component. The two phases of supernormality resembled early and late afterpotentials, attributable respectively to the passive decay of membrane charge and potassium accumulation in the t-tubules. Five minutes of ischemia progressively prolonged the RRP and reduced supernormality, confirming that these parameters are sensitive to membrane depolarization. Velocity recovery cycles may provide useful information about altered muscle membrane potential and t-tubule function in muscle disease. Muscle Nerve, 2008.

Calcium electrogenesis in distal apical dendrites of layer 5 pyramidal cells at a critical frequency of back-propagating action potentials

Action potentials in juvenile and adult rat layer-5 neocortical pyramidal neurons can be initiated at both axonal and distal sites of the apical dendrite. However, little is known about the interaction between these two initiation sites. Here, we report that layer 5 pyramidal neurons are very sensitive to a critical frequency of back-propagating action potentials varying between 60 and 200 Hz in different neurons. Bursts of four to five back-propagating action potentials above the critical frequency elicited large regenerative potentials in the distal dendritic initiation zone. The critical frequency had a very narrow range (10–20 Hz), and the dendritic regenerative activity led to further depolarization at the soma. The dendritic frequency sensitivity was suppressed by blockers of voltage-gated calcium channels, and also by synaptically mediated inhibition. Calcium-fluorescence imaging revealed that the site of largest transient increase in intracellular calcium above the critical frequency was located 400–700 μm from the soma at the site for initiation of calcium action potentials. Thus, the distal dendritic initiation zone can interact with the axonal initiation zone, even when inputs to the neuron are restricted to regions close to the soma, if the output of the neuron exceeds a critical frequency.