Adaptation Reduces Variability of the Neuronal Population Code

Sequences of events in noise-driven excitable systems with slow variables often show serial correlations among their intervals of events. Here, we employ a master equation for general non-renewal processes to calculate the interval and count statistics of superimposed processes governed by a slow adaptation variable. For an ensemble of spike-frequency adapting neurons this results in the regularization of the population activity and an enhanced post-synaptic signal decoding. We confirm our theoretical results in a population of cortical neurons.Comment: 4 pages, 2 figure

Efficient spike-sorting of multi-state neurons using inter-spike intervals information

We demonstrate the efficacy of a new spike-sorting method based on a Markov Chain Monte Carlo (MCMC) algorithm by applying it to real data recorded from Purkinje cells (PCs) in young rat cerebellar slices. This algorithm is unique in its capability to estimate and make use of the firing statistics as well as the spike amplitude dynamics of the recorded neurons. PCs exhibit multiple discharge states, giving rise to multimodal interspike interval (ISI) histograms and to correlations between successive ISIs. The amplitude of the spikes generated by a PC in an "active" state decreases, a feature typical of many neurons from both vertebrates and invertebrates. These two features constitute a major and recurrent problem for all the presently available spike-sorting methods. We first show that a Hidden Markov Model with 3 log-Normal states provides a flexible and satisfying description of the complex firing of single PCs. We then incorporate this model into our previous MCMC based spike-sorting algorithm (Pouzat et al, 2004, J. Neurophys. 91, 2910-2928) and test this new algorithm on multi-unit recordings of bursting PCs. We show that our method successfully classifies the bursty spike trains fired by PCs by using an independent single unit recording from a patch-clamp pipette.Comment: 25 pages, to be published in Journal of Neurocience Method

Neural spike train synchronization indices: Definitions, interpretations, and applications

A comparison of previously defined spike train synchronization indices is undertaken within a stochastic point process framework. The second-order cumulant density (covariance density) is shown to be common to all the indices. Simulation studies were used to investigate the sampling variability of a single index based on the second-order cumulant. The simulations used a paired motoneurone model and a paired regular spiking cortical neurone model. The sampling variability of spike trains generated under identical conditions from the paired motoneurone model varied from 50% to 160% of the estimated value. On theoretical grounds, and on the basis of simulated data a rate dependence is present in all synchronization indices. The application of coherence and pooled coherence estimates to the issue of synchronization indices is considered. This alternative frequency domain approach allows an arbitrary number of spike train pairs to be evaluated for statistically significant differences, and combined into a single population measure. The pooled coherence framework allows pooled time domain measures to be derived, application of this to the simulated data is illustrated. Data from the cortical neurone model is generated over a wide range of firing rates (1-250 spikes/s). The pooled coherence framework correctly characterizes the sampling variability as not significant over this wide operating range. The broader applicability of this approach to multielectrode array data is briefly discussed

Impact of photon noise on the reliability of a motion-sensitive neuron in the fly's visual system

Grewe J, Kretzberg J, Warzecha A-K, Egelhaaf M. Impact of photon noise on the reliability of a motion-sensitive neuron in the fly's visual system. The journal of neuroscience. 2003;23(34):10776-10783.Variable behavioral responses to identical visual stimuli can, in part, be traced back to variable neuronal signals that provide unreliable information about the outside world. This unreliability in encoding of visual information is caused by several noise sources such as photon noise, synaptic noise, or the stochastic nature of ion channels. Neurons of the fly's visual motion pathway have been claimed to represent perfect encoders, with photon noise as the main noise source limiting their performance. Other studies on the fly's visual system suggest, however, that internal noise emerging within the nervous system also affects the reliability of motion vision. To resolve these contradictory interpretations, we performed an electrophysiological investigation, inspired by the "equivalent noise" paradigm applied in psychophysics, on the fly's motion-sensitive H1 neuron. Noise-like brightness fluctuations of different strength were superimposed on the motion stimuli. Because the noise level found to affect the temporal properties of the spike responses is much larger than the estimate of photon noise under the experimental conditions, our results indicate that motion vision is more likely to be limited by internal sources of variability than by photon noise

Transient Information Flow in a Network of Excitatory and Inhibitory Model Neurons: Role of Noise and Signal Autocorrelation

We investigate the performance of sparsely-connected networks of integrate-and-fire neurons for ultra-short term information processing. We exploit the fact that the population activity of networks with balanced excitation and inhibition can switch from an oscillatory firing regime to a state of asynchronous irregular firing or quiescence depending on the rate of external background spikes. We find that in terms of information buffering the network performs best for a moderate, non-zero, amount of noise. Analogous to the phenomenon of stochastic resonance the performance decreases for higher and lower noise levels. The optimal amount of noise corresponds to the transition zone between a quiescent state and a regime of stochastic dynamics. This provides a potential explanation on the role of non-oscillatory population activity in a simplified model of cortical micro-circuits.Comment: 27 pages, 7 figures, to appear in J. Physiology (Paris) Vol. 9

Relationship between wave processes in sunspots and quasi-periodic pulsations in active region flares

A phenomenological relationship between oscillations in a sunspot and quasi-periodic pulsations (QPP) in flaring energy releases at an active region (AR) above the sunspot is established. The analysis of the microwave emission recorded by the Nobeyama Radioheliograph at 17 GHz shows a gradual increase in the power of the 3-min oscillation train in the sunspot associated with AR 10756 before flares in this AR. The flaring light curves are found to be bursty with a period of 3 min. Our analysis of the spatial distribution of the 3-min oscillation power implies that the oscillations follow from sunspots along coronal loops towards the flaring site. It is proposed that QPP in the flaring energy releases can be triggered by 3-min slow magnetoacoustic waves leaking from sunspots

Enhancement of synchronization in a hybrid neural circuit by spike timing dependent plasticity

Synchronization of neural activity is fundamental for many functions of the brain. We demonstrate that spike-timing dependent plasticity (STDP) enhances synchronization (entrainment) in a hybrid circuit composed of a spike generator, a dynamic clamp emulating an excitatory plastic synapse, and a chemically isolated neuron from the Aplysia abdominal ganglion. Fixed-phase entrainment of the Aplysia neuron to the spike generator is possible for a much wider range of frequency ratios and is more precise and more robust with the plastic synapse than with a nonplastic synapse of comparable strength. Further analysis in a computational model of HodgkinHuxley-type neurons reveals the mechanism behind this significant enhancement in synchronization. The experimentally observed STDP plasticity curve appears to be designed to adjust synaptic strength to a value suitable for stable entrainment of the postsynaptic neuron. One functional role of STDP might therefore be to facilitate synchronization or entrainment of nonidentical neurons

ELECTROPHYSIOLOGY OF BASAL GANGLIA (BG) CIRCUITRY AND DYSTONIA AS A MODEL OF MOTOR CONTROL DYSFUNCTION

The basal ganglia (BG) is a complex set of heavily interconnected nuclei located in the central part of the brain that receives inputs from the several areas of the cortex and projects via the thalamus back to the prefrontal and motor cortical areas. Despite playing a significant part in multiple brain functions, the physiology of the BG and associated disorders like dystonia remain poorly understood. Dystonia is a devastating condition characterized by ineffective, twisting movements, prolonged co-contractions and contorted postures. Evidences suggest that it occurs due to abnormal discharge patterning in BG-thalamocortocal (BGTC) circuitry. The central purpose of this study was to understand the electrophysiology of BGTC circuitry and its role in motor control and dystonia. Toward this goal, an advanced multi-target multi-unit recording and analysis system was utilized, which allows simultaneous collection and analysis of multiple neuronal units from multiple brain nuclei. Over the cause of this work, neuronal data from the globus pallidus (GP), subthalamic nucleus (STN), entopenduncular nucleus (EP), pallidal receiving thalamus (VL) and motor cortex (MC) was collected from normal, lesioned and dystonic rats under awake, head restrained conditions. The results have shown that the neuronal population in BG nuclei (GP, STN and EP) were characterized by a dichotomy of firing patterns in normal rats which remains preserved in dystonic rats. Unlike normals, neurons in dystonic rat exhibit reduced mean firing rate, increased irregularity and burstiness at resting state. The chaotic changes that occurs in BG leads to inadequate hyperpolarization levels within the VL thalamic neurons resulting in a shift from the normal bursting mode to an abnormal tonic firing pattern. During movement, the dystonic EP generates abnormally synchronized and elongated burst duration which further corrupts the VL motor signals. It was finally concluded that the loss of specificity and temporal misalignment between motor neurons leads to corrupted signaling to the muscles resulting in dystonic behavior. Furthermore, this study reveals the importance of EP output in controlling firing modes occurring in the VL thalamus