The impact of spike timing variability on the signal-encoding performance of neural spiking models

It remains unclear whether the variability of neuronal spike trains in vivo arises due to biological noise sources or represents highly precise encoding of temporally varying synaptic input signals. Determining the variability of spike timing can provide fundamental insights into the nature of strategies used in the brain to represent and transmit information in the form of discrete spike trains. In this study, we employ a signal estimation paradigm to determine how variability in spike timing affects encoding of random time-varying signals. We assess this for two types of spiking models: an integrate-and-fire model with random threshold and a more biophysically realistic stochastic ion channel model. Using the coding fraction and mutual information as information-theoretic measures, we quantify the efficacy of optimal linear decoding of random inputs from the model outputs and study the relationship between efficacy and variability in the output spike train. Our findings suggest that variability does not necessarily hinder signal decoding for the biophysically plausible encoders examined and that the functional role of spiking variability depends intimately on the nature of the encoder and the signal processing task; variability can either enhance or impede decoding performance

Channel noise in excitable neuronal membranes

Stochastic fluctuations of voltage-gated ion channels generate current and voltage noise in neuronal membranes. This noise may be a critical determinant of the efficacy of information processing within neural systems. Using Monte-Carlo simulations, we carry out a systematic investigation of the relationship between channel kinetics and the resulting membrane voltage noise using a stochastic Markov version of the Mainen-Sejnowski model of dendritic excitability in cortical neurons. Our simulations show that kinetic parameters which lead to an increase in membrane excitability (increasing channel densities, decreasing temperature) also lead to an increase in the magnitude of the sub-threshold voltage noise. Noise also increases as the membrane is depolarized from rest towards threshold. This suggests that channel fluctuations may interfere with a neuron’s ability to function as an integrator of its synaptic inputs and may limit the reliability and precision of neural information processing

Variability and coding efficiency of noisy neural spike encoders

Encoding synaptic inputs as a train of action potentials is a fundamental function of nerve cells. Although spike trains recorded in vivo have been shown to be highly variable, it is unclear whether variability in spike timing represents faithful encoding of temporally varying synaptic inputs or noise inherent in the spike encoding mechanism. It has been reported that spike timing variability is more pronounced for constant, unvarying inputs than for inputs with rich temporal structure. This could have significant implications for the nature of neural coding, particularly if precise timing of spikes and temporal synchrony between neurons is used to represent information in the nervous system. To study the potential functional role of spike timing variability, we estimate the fraction of spike timing variability which conveys information about the input for two types of noisy spike encoders — an integrate and fire model with randomly chosen thresholds and a model of a patch of neuronal membrane containing stochastic Na+ and K+ channels obeying Hodgkin–Huxley kinetics. The quality of signal encoding is assessed by reconstructing the input stimuli from the output spike trains using optimal linear mean square estimation. A comparison of the estimation performance of noisy neuronal models of spike generation enables us to assess the impact of neuronal noise on the efficacy of neural coding. The results for both models suggest that spike timing variability reduces the ability of spike trains to encode rapid time-varying stimuli. Moreover, contrary to expectations based on earlier studies, we find that the noisy spike encoding models encode slowly varying stimuli more effectively than rapidly varying ones

Gender Differences in Human Single Neuron Responses to Male Emotional Faces

Well-documented differences in the psychology and behavior of men and women have spurred extensive exploration of gender€™s role within the brain, particularly regarding emotional processing. While neuroanatomical studies clearly show differences between the sexes, the functional effects of these differences are less understood. Neuroimaging studies have shown inconsistent locations and magnitudes of gender differences in brain hemodynamic responses to emotion. To better understand the neurophysiology of these gender differences, we analyzed recordings of single neuron activity in the human brain as subjects of both genders viewed emotional expressions. This study included recordings of single-neuron activity of 14 (6 male) epileptic patients in four brain areas: amygdala (236 neurons), hippocampus (n = 270), anterior cingulate cortex (n = 256), and ventromedial prefrontal cortex (n = 174). Neural activity was recorded while participants viewed a series of avatar male faces portraying positive, negative or neutral expressions. Significant gender differences were found in the left amygdala, where 23% (n = 15/66) of neurons in men were significantly affected by facial emotion, vs. 8% (n = 6/76) of neurons in women. A Fisher€™s exact test comparing the two ratios found a highly significant difference between the two (p \u3c 0.01). These results show specific differences between genders at the single-neuron level in the human amygdala. These differences may reflect gender-based distinctions in evolved capacities for emotional processing and also demonstrate the importance of including subject gender as an independent factor in future studies of emotional processing by single neurons in the human amygdala

Subthreshold Voltage Noise Due to Channel Fluctuations in Active Neuronal Membranes

Voltage-gated ion channels in neuronal membranes fluctuate randomly between different conformational states due to thermal agitation. Fluctuations between conducting and nonconducting states give rise to noisy membrane currents and subthreshold voltage fluctuations and may contribute to variability in spike timing. Here we study subthreshold voltage fluctuations due to active voltage-gated Na+ and K+ channels as predicted by two commonly used kinetic schemes: the Mainen et al. (1995) (MJHS) kinetic scheme, which has been used to model dendritic channels in cortical neurons, and the classical Hodgkin-Huxley (1952) (HH) kinetic scheme for the squid giant axon. We compute the magnitudes, amplitude distributions, and power spectral densities of the voltage noise in isopotential membrane patches predicted by these kinetic schemes. For both schemes, noise magnitudes increase rapidly with depolarization from rest. Noise is larger for smaller patch areas but is smaller for increased model temperatures. We contrast the results from Monte Carlo simulations of the stochastic nonlinear kinetic schemes with analytical, closed-form expressions derived using passive and quasi-active linear approximations to the kinetic schemes. For all subthreshold voltage ranges, the quasi-active linearized approximation is accurate within 8% and may thus be used in large-scale simulations of realistic neuronal geometries

Rate Limitations of Unitary Event Analysis

Unitary event analysis is a new method for detecting episodes of synchronized neural activity (Riehle, Grüun, Diesmann, & Aertsen, 1997). It detects time intervals that contain coincident firing at higher rates than would be expected if the neurons fired as independent inhomogeneous Poisson processes; all coincidences in such intervals are called unitary events (UEs). Changes in the frequency of UEs that are correlated with behavioral states may indicate synchronization of neural firing that mediates or represents the behavioral state. We show that UE analysis is subject to severe limitations due to the underlying discrete statistics of the number of coincident events. These limitations are particularly stringent for low (0–10 spikes/s) firing rates. Under these conditions, the frequency of UEs is a random variable with a large variation relative to its mean. The relative variation decreases with increasing firing rate, and we compute the lowest firing rate, at which the 95% confidence interval around the mean frequency of UEs excludes zero. This random variation in UE frequency makes interpretation of changes in UEs problematic for neurons with low firing rates. As a typical example, when analyzing 150 trials of an experiment using an averaging window 100 ms wide and a 5ms coincidence window, firing rates should be greater than 7 spikes per second

Model-free detection of synchrony in neuronal spike trains, with an application to primate somatosensory cortex

Synchronized neuronal firing has been reported in many neural systems and may play a role in the representation of sensory stimuli and the modification of sensory representations by both experience and attention. In this report we describe a bootstrap procedure for computing the statistical significance of changes in the degree of synchrony and apply it to recordings from the second somatosensory (SII) cortex of Macaques performing tactile and visual discrimination tasks. A majority (68%) of neuron pairs in SII fire synchronously in response to a tactile stimulus. In a fraction of those pairs (17.5%), the degree of synchrony covaries with the focus of attention

Aircraft engine with inter-turbine engine frame supported counter rotating low pressure turbine rotors

An aircraft gas turbine engine assembly includes an inter-turbine frame axially located between high and low pressure turbines. Low pressure turbine has counter rotating low pressure inner and outer rotors with low pressure inner and outer shafts which are at least in part rotatably disposed co-axially within a high pressure rotor. Inter-turbine frame includes radially spaced apart radially outer first and inner second structural rings disposed co-axially about a centerline and connected by a plurality of circumferentially spaced apart struts. Forward and aft sump members having forward and aft central bores are fixedly joined to axially spaced apart forward and aft portions of the inter-turbine frame. Low pressure inner and outer rotors are rotatably supported by a second turbine frame bearing mounted in aft central bore of aft sump member. A mount for connecting the engine to an aircraft is located on first structural ring

MUSE crowded field 3D spectroscopy in NGC 300 III. Characterizing extremely faint HII regions and diffuse ionized gas

There are known differences between the physical properties of HII and diffuse ionized gas (DIG), but most of the studied regions in the literature are relatively bright. We compiled a faint sample of 390 HII regions with median $\log_{10}H\alpha$=34.7 in the spiral galaxy NGC300, derived their physical properties in terms of metallicity, density, extinction, and kinematics, and performed a comparative analysis of the properties of the DIG. We used MUSE data of nine fields in NGC300, covering a galactocentric distance of zero to ~450 arcsec (~4 projected kpc), including spiral arm and inter-arm regions. We binned the data in dendrogram leaves and extracted all strong nebular emission lines. We identified HII and DIG regions and compared their electron densities, metallicity, extinction, and kinematic properties. We also tested the effectiveness of unsupervised machine-learning algorithms in distinguishing between the HII and DIG regions. The gas density in the HII and DIG regions is close to the low-density limit in all fields. The average velocity dispersion in the DIG is higher than in the HII regions, which can be explained by the DIG being 1.8 kK hotter than HII gas. The DIG manifests a lower ionization parameter than HII gas, and the DIG fractions vary between 15-77%, with strong evidence of a contribution by hot low-mass evolved stars and shocks to the DIG ionization. Most of the DIG is consistent with no extinction and an oxygen metallicity that is indistinguishable from that of the HII gas.We observe a flat metallicity profile in the central region, without a sign of a gradient. The differences between extremely faint HII and DIG regions follow the same trends and correlations as their much brighter cousins. HII and DIG are so heterogeneous, however, that the differences within each class are larger than the differences between the two classes.Comment: Accepted in A&

Sparse and Distributed Coding of Episodic Memory in Neurons of the Human Hippocampus

Neurocomputational models hold that sparse distributed coding is the most efficient way for hippocampal neurons to encode episodic memories rapidly. We investigated the representation of episodic memory in hippocampal neurons of nine epilepsy patients undergoing intracranial monitoring as they discriminated between recently studied words (targets) and new words (foils) on a recognition test. On average, single units and multiunits exhibited higher spike counts in response to targets relative to foils, and the size of this effect correlated with behavioral performance. Further analyses of the spike-count distributions revealed that (i) a small percentage of recorded neurons responded to any one target and (ii ) a small percentage of targets elicited a strong response in any one neuron. These findings are consistent with the idea that in the human hippocampus episodic memory is supported by a sparse distributed neural code