Interspike Interval Distribution Research Articles

Axonal Conduction Delays, Brain State, and Corticogeniculate Communication.

Thalamocortical conduction times are short, but layer 6 corticothalamic axons display an enormous range of conduction times, some exceeding 40-50 ms. Here, we investigate (1) how axonal conduction times of corticogeniculate (CG) neurons are related to the visual information conveyed to the thalamus, and (2) how alert versus nonalert awake brain states affect visual processing across the spectrum of CG conduction times. In awake female Dutch-Belted rabbits, we found 58% of CG neurons to be visually responsive, and 42% to be unresponsive. All responsive CG neurons had simple, orientation-selective receptive fields, and generated sustained responses to stationary stimuli. CG axonal conduction times were strongly related to modulated firing rates (F1 values) generated by drifting grating stimuli, and their associated interspike interval distributions, suggesting a continuum of visual responsiveness spanning the spectrum of axonal conduction times. CG conduction times were also significantly related to visual response latency, contrast sensitivity (C-50 values), directional selectivity, and optimal stimulus velocity. Increasing alertness did not cause visually unresponsive CG neurons to become responsive and did not change the response linearity (F1/F0 ratios) of visually responsive CG neurons. However, for visually responsive CG neurons, increased alertness nearly doubled the modulated response amplitude to optimal visual stimulation (F1 values), significantly shortened response latency, and dramatically increased response reliability. These effects of alertness were uniform across the broad spectrum of CG axonal conduction times.SIGNIFICANCE STATEMENT Corticothalamic neurons of layer 6 send a dense feedback projection to thalamic nuclei that provide input to sensory neocortex. While sensory information reaches the cortex after brief thalamocortical axonal delays, corticothalamic axons can exhibit conduction delays of <2 ms to 40-50 ms. Here, in the corticogeniculate visual system of awake rabbits, we investigate the functional significance of this axonal diversity, and the effects of shifting alert/nonalert brain states on corticogeniculate processing. We show that axonal conduction times are strongly related to multiple visual response properties, suggesting a continuum of visual responsiveness spanning the spectrum of corticogeniculate axonal conduction times. We also show that transitions between awake brain states powerfully affect corticogeniculate processing, in some ways more strongly than in layer 4.

Motor Units Discriminated From High-density, Surface Electromyography Require Careful Scrutiny.

High-density, surface electromyography (EMG) allows concurrent recording of motor unit (MU) activity by increasing the recording area and the number of recording sites. The recorded signal comprises overlapping MU action potentials, some of which can be identified with a decomposition algorithm. However, such analyses often produce results that contradict the classical findings on motor unit discharge characteristics. PURPOSE: To evaluate the quality of the interspike intervals (ISIs) extracted from a high-density surface EMG recording by a decomposition algorithm. METHODS: Muscle activity was recorded using high-density, surface EMG (4x8 grid with a 10 mm interelectrode distance) in 10 persons with multiple sclerosis during steady, isometric dorsiflexor contractions on their less-affected limb. The target force was 10% of maximum. Custom Matlab software was used to perform a convolution kernel compensation model and a blind-source separation algorithm to discriminate individual MUs from EMG recordings. The discharge times (DT) from the discriminated MUs were used to calculate the ISIs (ISI = DTn-DTn-1). For each MU, the average ISI, average coefficient of variation for ISI (ISICV), kurtosis for the distribution of ISIs, and skewness for the distribution of ISIs was calculated. ISIs outside of the range of 25 ms - 400 ms were deemed inaccurate and removed from the data and the same analyses were repeated. Data are reported as mean [95% CI]. RESULTS: The ISIs (n = 33,980; 303 [285-321] per MU) of 113 MUs were examined. Before removing ISIs that did not meet the inclusion criteria (25 - 400 ms), the average ISI was 136 ms [123 - 151 ms], the average ISICV was 72% [50 - 95%], the average kurtosis was 53.5 [39.1 - 67.8], and the average skewness was 4.4 [3.5 - 4.3]. On average, 3.34% (range 0 - 31%) of the ISIs per MU were outside the acceptable range. After ISI removal, the average ISI was 120 ms [114 - 125 ms], the average ISICV was 28% [26 - 31%], the average kurtosis was 11.0 [8.9 - 13.2], and the average skewness was 1.95 [1.70 - 2.20]. Most removed ISIs were <25 ms. CONCLUSION: The findings quantify the extent to which MU discharge times extracted from high-density, surface EMG recordings with an automatic decomposition algorithm can be confounded by discrimination errors.

Effects of Neuropeptide Y on Neuron Spike Activity in the Rat Suprachiasmatic Nucleus in Vitro

Experiments on sagittal hippocampus slices from male Wistar rats addressed the effects of 10 nM neuropeptide Y on the level of electrical activity in neurons in the suprachiasmatic nucleus and parameters of spike-based information encoding. Application of neuropeptide Y led to a decrease in the action potential generation frequency in 35 of the 81 neurons recorded; eight cells showed increases in this parameter, and the remaining 38 neurons showed no change in the level of spike activity. The decrease in the spike generation frequency in suprachiasmatic nucleus neurons was accompanied by increases in the entropy of the distribution of interspike intervals and the mutual information between adjacent interspike intervals, which is evidence for increases in the degree of irregularity in these intervals and the patterning of spike information in response to neuropeptide Y. These results demonstrate the ability of neuropeptide Y to modulate the activity level and affect the spike code in the relatively numerous population of neurons in the circadian oscillator of the suprachiasmatic nucleus.

Neurons in Primate Visual Cortex Alternate between Responses to Multiple Stimuli in Their Receptive Field.

A fundamental question concerning representation of the visual world in our brain is how a cortical cell responds when presented with more than a single stimulus. We find supportive evidence that most cells presented with a pair of stimuli respond predominantly to one stimulus at a time, rather than a weighted average response. Traditionally, the firing rate is assumed to be a weighted average of the firing rates to the individual stimuli (response-averaging model) (Bundesen et al., 2005). Here, we also evaluate a probability-mixing model (Bundesen et al., 2005), where neurons temporally multiplex the responses to the individual stimuli. This provides a mechanism by which the representational identity of multiple stimuli in complex visual scenes can be maintained despite the large receptive fields in higher extrastriate visual cortex in primates. We compare the two models through analysis of data from single cells in the middle temporal visual area (MT) of rhesus monkeys when presented with two separate stimuli inside their receptive field with attention directed to one of the two stimuli or outside the receptive field. The spike trains were modeled by stochastic point processes, including memory effects of past spikes and attentional effects, and statistical model selection between the two models was performed by information theoretic measures as well as the predictive accuracy of the models. As an auxiliary measure, we also tested for uni- or multimodality in interspike interval distributions, and performed a correlation analysis of simultaneously recorded pairs of neurons, to evaluate population behavior.

Serial Spike Time Correlations Affect Probability Distribution of Joint Spike Events.

Detecting the existence of temporally coordinated spiking activity, and its role in information processing in the cortex, has remained a major challenge for neuroscience research. Different methods and approaches have been suggested to test whether the observed synchronized events are significantly different from those expected by chance. To analyze the simultaneous spike trains for precise spike correlation, these methods typically model the spike trains as a Poisson process implying that the generation of each spike is independent of all the other spikes. However, studies have shown that neural spike trains exhibit dependence among spike sequences, such as the absolute and relative refractory periods which govern the spike probability of the oncoming action potential based on the time of the last spike, or the bursting behavior, which is characterized by short epochs of rapid action potentials, followed by longer episodes of silence. Here we investigate non-renewal processes with the inter-spike interval distribution model that incorporates spike-history dependence of individual neurons. For that, we use the Monte Carlo method to estimate the full shape of the coincidence count distribution and to generate false positives for coincidence detection. The results show that compared to the distributions based on homogeneous Poisson processes, and also non-Poisson processes, the width of the distribution of joint spike events changes. Non-renewal processes can lead to both heavy tailed or narrow coincidence distribution. We conclude that small differences in the exact autostructure of the point process can cause large differences in the width of a coincidence distribution. Therefore, manipulations of the autostructure for the estimation of significance of joint spike events seem to be inadequate.

Skellam process with resetting: a neural spike train model.

This paper introduces the Skellam process with resetting. Resetting is a modification that accommodates the modeling of neural spike trains. We show this as a biologically plausible model, which codes the information content of neural spike trains with three, potentially, time-varying functions. We show that the interspike interval distribution under this model follows a mixture of gamma distributions, a flexible class covering a wide range of commonly used models. Through simulation studies and the analyses of connected retinal ganglion and lateral geniculate nucleus cells, we evaluate the performance of this model. Copyright © 2016 John Wiley & Sons, Ltd.

Spike timing in auditory-nerve fibers during spontaneous activity and phase locking.

In vertebrates, all acoustic information transmitted from the inner ear to the central auditory system is relayed by primary auditory afferents (auditory-nerve fibers; ANFs). These neurons are also the most peripheral elements to use action potentials (spikes) to encode the acoustic information. Here, we review what is known about the spiking of ANFs during spontaneous activity, when spike timing might be regarded as largely random, and during stimulation by low-frequency sounds, when spikes are phase locked to the stimulus waveform, a phenomenon generally considered a hallmark of temporal precision and speed in the auditory system. We focus on mammals, in which each ANF is driven by a single ribbon synapse in a single receptor cell, but also cover relevant research on ANFs of vertebrates from other classes. For spontaneous activity, we highlight several spike-history effects in interspike interval distributions, hazard-rate functions, serial interval correlations, and spike-count statistics. We also review models that have attempted to account for these properties. For phase locking, we focus on the responses to low-frequency tones, rather than to low-frequency components of broadband signals such as noise or clicks. We critically review the measures commonly used to quantify phase locking and urge caution when interpreting such measures with respect to spike-timing precision. We also review the dependence of phase locking on stimulus amplitude and frequency. Finally, we identify some open questions.

A Parametric Simulation of Neuronal Noise From Microelectrode Recordings.

In this paper we present an efficient model of microelectrode recordings (MER) from the subthalamic nucleus acquired during deep brain stimulation (DBS) surgery. The model shows how changes in the "noise" relate to the neuronal spike time statistics. A top-down approach is used with analysis-by-synthesis of the MER power spectra. The model is built around a sum of filtered point processes consisting of thousands of neurons and including extracellular filtering. The quality of the model is demonstrated through comparisons to recordings from eight individuals (both hemispheres in six) who have undergone DBS implantation for the treatment of Parkinson's disease. The simulated recordings were compared using their voltage amplitude distributions, power spectral density estimates and phase synchrony while varying only one free parameter (the shape of the inter-spike interval distribution). Through this simple model, we show that the noise present in a DBS MER contains properties that match that of patient recordings when a Weibull distribution with shape parameter of 0.8 is used for the inter-spike interval.

The rat suprachiasmatic nucleus: the master clock ticks at 30Hz.

Key points Light‐responsive neurones in the rat suprachiasmatic nucleus discharge with a harmonic distribution of interspike intervals, whereas unresponsive neurones seldom do.This harmonic patterning has a fundamental frequency of close to 30 Hz, and is the same in light‐on cells as in light‐off cells, and is unaffected by exposure to light.Light‐on cells are more active than light‐off cells in both subjective day and subjective night, and both light‐on cells and light‐off cells respond more strongly to changes in light intensity during the subjective night than during the subjective day.Paired recordings indicate that the discharge of adjacent light‐responsive cells is very tightly synchronized.The gap junction inhibitor carbenoxolone increases the spontaneous activity of suprachiasmatic nucleus neurones but does not block the harmonic discharge patterning. The suprachiasmatic nucleus (SCN) of the hypothalamus has an essential role in orchestrating circadian rhythms of behaviour and physiology. In the present study, we recorded from single SCN neurons in urethane‐anaesthetized rats, categorized them by the statistical features of their electrical activity and by their responses to light, and examined how activity in the light phase differs from activity in the dark phase. We classified cells as light‐on cells or light‐off cells according to how their firing rate changed in acute response to light, or as non‐responsive cells. In both sets of light‐responsive neurons, responses to light were stronger at subjective night than in subjective day. Neuronal firing patterns were analysed by constructing hazard functions from interspike interval data. For most light‐responsive cells, the hazard functions showed a multimodal distribution, with a harmonic sequence of modes, indicating that spike activity was driven by an oscillatory input with a fundamental frequency of close to 30 Hz; this harmonic pattern was rarely seen in non‐responsive SCN cells. The frequency of the rhythm was the same in light‐on cells as in light‐off cells, was the same in subjective day as at subjective night, and was unaffected by exposure to light. Paired recordings indicated that the discharge of adjacent light‐responsive neurons was very tightly synchronized, consistent with electrical coupling.

Statistics of a leaky integrate-and-fire model of neurons driven by dichotomous noise.

The behavior of a stochastic leaky integrate-and-fire model of neurons is considered. The effect of temporally correlated random neuronal input is modeled as a colored two-level (dichotomous) Markovian noise. Relying on the Riemann method, exact expressions for the output interspike interval density and for the serial correlation coefficient are derived, and their dependence on noise parameters (such as correlation time and amplitude) is analyzed. Particularly, noise-induced sign reversal and a resonancelike amplification of the kurtosis of the interspike interval distribution are established. The features of spike statistics, analytically revealed in our study, are compared with recently obtained results for a perfect integrate-and-fire neuron model.

Distribution of transition times in a stochastic model of excitable cell: Insights into the cell-intrinsic mechanisms of randomness in neuronal interspike intervals.

In this paper, we develop an analytical approach to studying random patterns of activity in excitable cells. Our analytical approach uses a two-state stochastic model of excitable system based on the electrophysiological properties of refractoriness and restitution, which characterize cell recovery after excitation. By applying the notion of probability density flux, we derive the distributions of transition times between states and the distribution of interspike interval (ISI) durations for a constant applied stimulus. The derived ISI distribution is unimodal and, provided that the time spent in the excited state is constant, can be approximated by a Rayleigh peak followed by an exponential tail. We then explore the role of the model parameters in determining the shape of the derived distributions and the ISI coefficient of variation. Finally, we use our analytical results to study simulation results from the stochastic Morris-Lecar neuron and from a three-state extension of the proposed stochastic model, which is capable of reproducing multimodal ISI histograms.

Impact of substance P on the correlation of spike train evoked by electro acupuncture

Substance P (SP) participates in the neural signal transmission evoked by electro-acupuncture (EA). This paper investigates the impact of SP on the correlation of spike train in the median nerve evoked by EA at 'Neiguan' acupoint (PC6). It shows that the spiking rate and interspike interval (ISI) distribution change obviously after inhibiting SP. This variation of spiking activity indicates that SP affects the temporal structure of spike train through modulating the action potential on median nerve filaments. Furtherly, the correlation coefficient and scaling exponent are considered to measure the correlation of spike train. Scaled Windowed Variance (SWV) method is applied to calculate scaling exponent which quantifies the long-range correlation of the neural electrical signals. It is found that the correlation coefficients of ISI increase after inhibiting SP released. In addition, the scaling exponents of neuronal spike train have significant differences between before and after inhibiting SP. These findings demonstrate that SP has an influence on the long-range correlation of spike train. Our results indicate that SP may play an important role in EA-induced neural spiking and encoding.

Oxytocin Neurones: Intrinsic Mechanisms Governing the Regularity of Spiking Activity.

Oxytocin neurones of the rat supraoptic nucleus are osmoresponsive and, with all other things being equal, they fire at a mean rate that is proportional to the plasma sodium concentration. However, individual spike times are governed by highly stochastic events, namely the random occurrences of excitatory synaptic inputs, the probability of which is increased by increasing extracellular osmotic pressure. Accordingly, interspike intervals (ISIs) are very irregular. In the present study, we show, by statistical analyses of firing patterns in oxytocin neurones, that the mean firing rate as measured in bins of a few seconds is more regular than expected from the variability of ISIs. This is consistent with an intrinsic activity‐dependent negative‐feedback mechanism. To test this, we compared observed neuronal firing patterns with firing patterns generated by a leaky integrate‐and‐fire model neurone, modified to exhibit activity‐dependent mechanisms known to be present in oxytocin neurones. The presence of a prolonged afterhyperpolarisation (AHP) was critical for the ability to mimic the observed regularisation of mean firing rate, although we also had to add a depolarising afterpotential (DAP; sometimes called an afterdepolarisation) to the model to match the observed ISI distributions. We tested this model by comparing its behaviour with the behaviour of oxytocin neurones exposed to apamin, a blocker of the medium AHP. Good fits indicate that the medium AHP actively contributes to the firing patterns of oxytocin neurones during non‐bursting activity, and that oxytocin neurones generally express a DAP, even though this is usually masked by superposition of a larger AHP.

Activity-Dependent Plasticity of Spike Pauses in Cerebellar Purkinje Cells.

The plasticity of intrinsic excitability has been described in several types of neurons, but the significance of non-synaptic mechanisms in brain plasticity and learning remains elusive. Cerebellar Purkinje cells are inhibitory neurons that spontaneously fire action potentials at high frequencies and regulate activity in their target cells in the cerebellar nuclei by generating a characteristic spike burst-pause sequence upon synaptic activation. Using patch-clamp recordings from mouse Purkinje cells, we find that depolarization-triggered intrinsic plasticity enhances spike firing and shortens the duration of spike pauses. Pause plasticity is absent from mice lacking SK2-type potassium channels (SK2(-/-) mice) and in occlusion experiments using the SK channel blocker apamin, while apamin wash-in mimics pause reduction. Our findings demonstrate that spike pauses can be regulated through an activity-dependent, exclusively non-synaptic, SK2 channel-dependent mechanism and suggest that pause plasticity-by altering the Purkinje cell output-may be crucial to cerebellar information storage and learning.

Entracking as a Brain Stem Code for Pitch: The Butte Hypothesis.

The basic nature of pitch is much debated. A robust code for pitch exists in the auditory nerve in the form of an across-fiber pooled interspike interval (ISI) distribution, which resembles the stimulus autocorrelation. An unsolved question is how this representation can be "read out" by the brain. A new view is proposed in which a known brain-stem property plays a key role in the coding of periodicity, which I refer to as "entracking", a contraction of "entrained phase-locking". It is proposed that a scalar rather than vector code of periodicity exists by virtue of coincidence detectors that code the dominant ISI directly into spike rate through entracking. Perfect entracking means that a neuron fires one spike per stimulus-waveform repetition period, so that firing rate equals the repetition frequency. Key properties are invariance with SPL and generalization across stimuli. The main limitation in this code is the upper limit of firing (~ 500 Hz). It is proposed that entracking provides a periodicity tag which is superimposed on a tonotopic analysis: at low SPLs and fundamental frequencies > 500 Hz, a spectral or place mechanism codes for pitch. With increasing SPL the place code degrades but entracking improves and first occurs in neurons with low thresholds for the spectral components present. The prediction is that populations of entracking neurons, extended across characteristic frequency, form plateaus ("buttes") of firing rate tied to periodicity.

Burst Firing in a Motion-Sensitive Neural Pathway Correlates with Expansion Properties of Looming Objects that Evoke Avoidance Behaviors.

The locust visual system contains a well-defined motion-sensitive pathway that transfers visual input to motor centers involved in predator evasion and collision avoidance. One interneuron in this pathway, the descending contralateral movement detector (DCMD), is typically described as using rate coding; edge expansion of approaching objects causes an increased rate of neuronal firing that peaks after a certain retinal threshold angle is exceeded. However, evidence of intrinsic DCMD bursting properties combined with observable oscillations in mean firing rates and tight clustering of spikes in raw traces, suggest that bursting may be important for motion detection. Sensory neuron bursting provides important timing information about dynamic stimuli in many model systems, yet no studies have rigorously investigated if bursting occurs in the locust DCMD during object approach. We presented repetitions of 30 looming stimuli known to generate behavioral responses to each of 20 locusts in order to identify and quantify putative bursting activity in the DCMD. Overall, we found a bimodal distribution of inter-spike intervals (ISI) with peaks of more frequent and shorter ISIs occurring from 1–8 ms and longer less frequent ISIs occurring from 40–50 ms. Subsequent analysis identified bursts and isolated single spikes from the responses. Bursting frequency increased in the latter phase of an approach and peaked at the time of collision, while isolated spiking was predominant during the beginning of stimulus approach. We also found that the majority of inter-burst intervals (IBIs) occurred at 40–50 ms (or 20–25 bursts/s). Bursting also occurred across varied stimulus parameters and suggests that burst timing may be a key component of looming detection. Our findings suggest that the DCMD uses two modes of coding to transmit information about looming stimuli and that these modes change dynamically with a changing stimulus at a behaviorally-relevant time.

The role of microcircuits in the pre-frontal cortex in detecting and encoding temporally patterned information

Working memory is the capacity of the brain to hold information temporarily for immediate use. The neural correlate of this type of short-term memory is persistent spiking activity (PA) of both excitatory and inhibitory neurons, mainly in the pre-frontal cortex (PFC). Neurons embedded in PFC microcircuits integrate widespread information from various brain regions. PFC microcircuits of ~ten neurons are characterized by highly reciprocal connections and non-linear integration in their dendrites. Understanding how dendrites integrate information from multiple sources is crucial to explain their functional role. Spatial and temporal integration of signals occurs at dendrites before propagating to the soma, and plays a role in coding of information [1,2]. The main question is whether and how pyramidal neurons in such circuits can detect temporally structured information in order to timely adjust behavior. Dendrites of cortical pyramidal neurons have been shown to exhibit temporal sensitivity [1]. To address this question, we constructed a detailed PFC microcircuit, implemented in the NEURON simulation environment. We used reconstructed morphologies of layer 5 PFC pyramidal neurons, validated against experimental findings. The microcircuit consisted of nine pyramidal neurons and two interneurons, all interconnected [3]. To investigate temporal coding, we delivered temporally structured input to four out of the nine pyramidal neurons and assessed, given persistent activity emergence, a) the time-to-first-spike (ttfs) of each pyramidal cell and b) the Inter-Spike Intervals (ISIs) during PA. We find that temporally patterned inputs (simulated as different temporal orders of activated neurons) induce different responses, in both the ttfs and ISI distributions. To investigate the mechanisms that mediate this type of coding, we varied both the stimulus frequency and the pyramidal neuron morphologies in the microcircuit. We found that lower stimulus frequencies resulted in increased differences between various temporal orders of activation. The same result was observed when using neurons with morphologically complex dendritic trees, indicating that dendritic morphology may play a key role in the ability of PFC microcircuits to detect and encode temporally patterned inputs. Further, we investigated whether this type of temporal coding exhibits specificity, i.e. whether the PFC can consistently interpret similar temporally patterned inputs in a spectrum of background activity environments. Overall, this study seeks to understand at what level and how neuronal circuits implement the timely firing of PFC pyramidal neurons. Improving our understanding of temporal coding in complex areas like the PFC is essential for disentangling how the brain detects, encodes and transmits information. While dendrites of cortical neurons have been shown to detect differences in the order of incoming signals, our study breaks new ground in finding whether such a temporal code is preserved at the microcircuit level. These findings can be tested experimentally to further investigate the role of temporal coding in higher-level areas of the brain.

Nonparametric estimation of characteristics of the interspike interval distribution

We address the problem of non-parametric estimation of the probability density function as a description of the probability distribution of noncorrelated interspike intervals (ISI) in records of neuronal activity. We also continue our previous effort [1,2] to propose alternative estimators of the variability measures. Kernel density estimators are probably the most frequently used non-parametric estimators of the probability distribution. However, there are also other non-parametric approaches. We focus on non-parametric methods based on a principle of extrema of the Fisher information. Specifically, we focus on the maximum penalized likelihood estimation of the probability density function proposed by Good and Gaskins [3], which can be understood as a kernel estimator with a particular kernel function [4]. Other non-parametric approach we would like to address is the spline interpolation proposed by Huber [5] which can uniquely estimate the ISI distribution.

Application of generalized linear models to investigate functional synaptic coupling and synchrony in an animal model of schizophrenia

Current hypotheses about the pathophysiology of schizophrenia suggest that the disease results from disordered connectivity in the brain. Human functional imaging studies have lent support to this idea by demonstrating reduced temporal correlations between cortical areas in schizophrenic patients [1]; however, functional evidence for disconnectivity at the synaptic level is limited. Here we describe a study in which we applied computational modeling techniques to assess functional connectivity between spiking neurons in a pharmacological nonhuman primate model of schizophrenia [2]. Neural data were obtained from multielectrode recording arrays inserted into the parietal and prefrontal cortices of macaque monkeys while the animals performed a cognitive control task that measures a specific cognitive impairment in human patients with schizophrenia. Phencyclidine, an NMDA receptor (NMDAR) antagonist with well-described schizomimetic properties [3], was administered systemically on alternating days with injections of saline. To characterize changes in synaptic communication between neurons in the disease state, we evaluated timing relationships in spike trains of simultaneously recorded neurons. We employed a Generalized Linear Model (GLM) approach [4] to infer patterns of functional connectivity between neurons. The result of the GLM fit is a set of coupling functions that estimate the change in the probability of a spike in one neuron in response to a spike in another simultaneously recorded neuron. Unlike traditional cross-correlation approaches used for inferring functional connectivity, GLMs parse out the proportion of variance in spike timing attributable to synaptic interactions between neurons from other sources of variance in spike timing such as stimulus input, the intrinsic spike patterns of the cells, and the effects of other simultaneously recorded cells in the network [4]. After identifying functionally coupled pairs of neurons using GLMs, we sought to evaluate the effects of phencyclidine on functional coupling and synchrony between coupled cells. We found a dramatic decrease in the proportion of cells that were functionally coupled in the prefrontal cortex in the phencyclidine condition as compared to the control condition. In order to investigate hypothesized alterations in spike timing in the disease state, we identified the distribution of interspike intervals between putative pre- and postsynaptic neurons. In our analysis we found a prominent zero-lag peak representing a large number of coincident action potentials between coupled cells in the saline condition, but not in the phencyclidine condition. This suggests a change in the timing of action potentials which was apparent in the absence of a change in firing rates, precluding overall decreased activity as an explanation for reduced synchrony. In summary, these results suggest that phencyclidine induces a functional disconnection between synaptically coupled cortical neurons which may be related to a Hebbian reduction in synaptic strength resulting from desynchronization of spiking activity.

Surround suppression and normalization in a model of coupled balanced cortical networks with short-term synaptic plasticity

The input currents to neurons in the cortex are under many circumstances highly variable with a mean intensity considerably below the firing threshold. As a consequence, the spiking activity of cortical neurons is strongly fluctuating such that the coefficient of variation of the inter-spike interval distribution of individual neurons is approximately equal to 1, implying almost Poisson-like spiking. In addition to this characteristic activity, the connectivity of excitatory and inhibitory neurons in cortex is sparse and irregular. It has been shown in models that super-threshold excitatory and strong inhibitory input currents, which nearly cancel for individual neurons, can lead to this irregular spiking activity [1]. However, balanced networks of excitatory and inhibitory neurons are characterized by a strictly linear relation between stimulus strength and network firing rate, making it hard to perform more complex computational tasks like the generation of receptive fields, multiple stable activity states or normalization, which for example has been measured in visual cortex (eg. [2,3]). Synapses displaying activity dependent short-term plasticity (STP) have been previously reported to give rise to a non-linear network response with potentially multiple stable states for a given stimulus [4]. In this study, we analyze analytically and numerically the computational properties of two interconnected balanced networks, receiving independent stationary stimuli. This situation can be viewed as a simple instantiation of two interconnected cortical columns. For an illustration of the network topology, see Figure ​Figure1A.1A. We demonstrate that these stimuli are normalized by the system and that increasing the stimulus to one network, suppresses the activity of the neighboring network (see Figure ​Figure1B).1B). Thereby, normalization and suppression are linear in stimulus strength when STP is disabled and becomes non-linear with activity dependent synapses. Figure 1 A. Schematic illustration of the network topology. E1 and I1 represent excitatory and inhibitory neuronal populations of network 1, receiving input from an external population X1. Same for network 2. Coupling of neuronal populations is indicated by the ... References van Vreeswijk C, Sompolinsky H. Chaotic Balanced State in a Model of Cortical Circuits. Neural Comput. 1998;10(6):1321–1371. [PubMed] Carandini M, Heeger DJ, Movshon JA. Linearity and normalization in simple cells of the macaque primary visual cortex. J Neurosci. 1997;17(21):8621–8644. [PubMed] Levitt JB, Kiper DC, Movshon JA. Receptive fields and functional architecture of macaque V2. J Neurophysiol. 1994;71(6):2517–2542. [PubMed] Mongillo G, Hansel D, van Vreeswijk C. Bistability and spatiotemporal irregularity in neuronal networks with nonlinear synaptic transmission. Phys Rev Lett. 2012;108(15):158101. [PubMed] Articles from BMC Neuroscience are provided here courtesy of BioMed Central Formats: Article | PubReader | ePub (beta) | PDF (246K) | Citation Share Facebook Twitter Google+ You are here: NCBI > Literature > PubMed Central (PMC) Write to the Help Desk External link. Please review our privacy policy. NLM NIH DHHS USA.gov Copyright | Disclaimer | Privacy | Browsers | Accessibility | Contact National Center for Biotechnology Information, U.S. National Library of Medicine 8600 Rockville Pike, Bethesda MD, 20894 USA