Spectral analysis of input spike trains by spike-timing-dependent plasticity

Spike-timing-dependent plasticity (STDP) has been observed in many brain areas such as sensory cortices, where it is hypothesized to structure synaptic connections between neurons. Previous studies have demonstrated how STDP can capture spiking information at short timescales using specific input configurations, such as coincident spiking, spike patterns and oscillatory spike trains. However, the corresponding computation in the case of arbitrary input signals is still unclear. This paper provides an overarching picture of the algorithm inherent to STDP, tying together many previous results for commonly used models of pairwise STDP. For a single neuron with plastic excitatory synapses, we show how STDP performs a spectral analysis on the temporal cross-correlograms between its afferent spike trains. The postsynaptic responses and STDP learning window determine kernel functions that specify how the neuron "sees" the input correlations. We thus denote this unsupervised learning scheme as 'kernel spectral component analysis' (kSCA). In particular, the whole input correlation structure must be considered since all plastic synapses compete with each other. We find that kSCA is enhanced when weight-dependent STDP induces gradual synaptic competition. For a spiking neuron with a "linear" response and pairwise STDP alone, we find that kSCA resembles principal component analysis (PCA). However, plain STDP does not isolate correlation sources in general, e.g., when they are mixed among the input spike trains. In other words, it does not perform independent component analysis (ICA). Tuning the neuron to a single correlation source can be achieved when STDP is paired with a homeostatic mechanism that reinforces the competition between synaptic inputs. Our results suggest that neuronal networks equipped with STDP can process signals encoded in the transient spiking activity at the timescales of tens of milliseconds for usual STDP.     

The Possible Role of Spike Patterns in Cortical Information Processing

When the same visual stimulus is presented across many trials, neurons in the visual cortex receive stimulus-related synaptic inputs that are reproducible across trials (S) and inputs that are not (N). The variability of spike trains recorded in the visual cortex and their apparent lack of spike-to-spike correlations beyond that implied by firing rate fluctuations, has been taken as evidence for a low S/N ratio. A recent re-analysis of in vivo cortical data revealed evidence for spike-to-spike correlations in the form of spike patterns. We examine neural dynamics at a higher S/N in order to determine what possible role spike patterns could play in cortical information processing. In vivo-like spike patterns were obtained in model simulations. Superpositions of multiple sinusoidal driving currents were especially effective in producing stable long-lasting patterns. By applying current pulses that were either short and strong or long and weak, neurons could be made to switch from one pattern to another. Cortical neurons with similar stimulus preferences are located near each other, have similar biophysical properties and receive a large number of common synaptic inputs. Hence, recordings of a single neuron across multiple trials are usually interpreted as the response of an ensemble of these neurons during one trial. In the presence of distinct spike patterns across trials there is ambiguity in what would be the corresponding ensemble, it could consist of the same spike pattern for each neuron or a set of patterns across neurons. We found that the spiking response of a neuron receiving these ensemble inputs was determined by the spike-pattern composition, which, in turn, could be modulated dynamically as a means for cortical information processing.     

Electrotonic interaction between secondary neurons of the carp olfactory bulb

Responses of secondary neurons of the carp olfactory bulb evoked by electrical stimulation of the olfactory tract were investigated by intracellular recording. In most neurons spike responses were identified as antidromic. Their latent periods varied from 2.5 to 55 msec. Two other types of responses of secondary neurons had constant latent periods: the pseudo-antidromic spike and a fast low-amplitude depolarization potential. It is concluded that these responses are generated by the antidromic spike of a neighboring neuron, connected electrotonically with the recorded neuron.M. V. Lomonosov Moscow State University. Translated from Neirofiziologiya, Vol. 8, No. 5, pp. 490–496, September–October, 1976.     

Discharges of tectal neurons evoked by electrical stimulation of single retinal ganglion cells in frogs

"EEG quanta" — extracellular monosynaptic PSPs evoked by action potentials of a single axon — were recorded in the frog thalamus at a depth of 200–700 µ during electrical stimulation of the retina by single, double, or triple pulses of current of threshold strength. With an interval of 5–25 msec between consecutive stimuli, a negative spike, increasing as the microelectrode was inserted deeper, was observed at the peak of the testing EEG quanta, which were enlarged 1.5–2.5 times. It is postulated that this spike is the synchronous discharge of neuron bodies (population spike), evoked by an action potential of a single retinotectal axon. This axon branches in layers F or G. Discharges appear in neurons of layers 6–8. The possibility that the volley discharge of one class 3–5 detector excites tectal ganglion cells, whose axons mainly form the tecto-bulbospinal tract, is discussed.Research Institute of Cardiology and Laboratories of Electroencephalography and Neurocybernetics, Medical Institute, Kaunas, Lithuania. Translated from Neirofiziologiya, Vol. 16, No. 6, pp. 829–835, November–December, 1984.     

The Limited Utility of Multiunit Data in Differentiating Neuronal Population Activity

To date, single neuron recordings remain the gold standard for monitoring the activity of neuronal populations. Since obtaining single neuron recordings is not always possible, high frequency or ‘multiunit activity’ (MUA) is often used as a surrogate. Although MUA recordings allow one to monitor the activity of a large number of neurons, they do not allow identification of specific neuronal subtypes, the knowledge of which is often critical for understanding electrophysiological processes. Here, we explored whether prior knowledge of the single unit waveform of specific neuron types is sufficient to permit the use of MUA to monitor and distinguish differential activity of individual neuron types. We used an experimental and modeling approach to determine if components of the MUA can monitor medium spiny neurons (MSNs) and fast-spiking interneurons (FSIs) in the mouse dorsal striatum. We demonstrate that when well-isolated spikes are recorded, the MUA at frequencies greater than 100Hz is correlated with single unit spiking, highly dependent on the waveform of each neuron type, and accurately reflects the timing and spectral signature of each neuron. However, in the absence of well-isolated spikes (the norm in most MUA recordings), the MUA did not typically contain sufficient information to permit accurate prediction of the respective population activity of MSNs and FSIs. Thus, even under ideal conditions for the MUA to reliably predict the moment-to-moment activity of specific local neuronal ensembles, knowledge of the spike waveform of the underlying neuronal populations is necessary, but not sufficient.     

Task-Dependent Changes in Cross-Level Coupling between Single Neurons and Oscillatory Activity in Multiscale Networks

Understanding the principles governing the dynamic coordination of functional brain networks remains an important unmet goal within neuroscience. How do distributed ensembles of neurons transiently coordinate their activity across a variety of spatial and temporal scales? While a complete mechanistic account of this process remains elusive, evidence suggests that neuronal oscillations may play a key role in this process, with different rhythms influencing both local computation and long-range communication. To investigate this question, we recorded multiple single unit and local field potential (LFP) activity from microelectrode arrays implanted bilaterally in macaque motor areas. Monkeys performed a delayed center-out reach task either manually using their natural arm (Manual Control, MC) or under direct neural control through a brain-machine interface (Brain Control, BC). In accord with prior work, we found that the spiking activity of individual neurons is coupled to multiple aspects of the ongoing motor beta rhythm (10–45 Hz) during both MC and BC, with neurons exhibiting a diversity of coupling preferences. However, here we show that for identified single neurons, this beta-to-rate mapping can change in a reversible and task-dependent way. For example, as beta power increases, a given neuron may increase spiking during MC but decrease spiking during BC, or exhibit a reversible shift in the preferred phase of firing. The within-task stability of coupling, combined with the reversible cross-task changes in coupling, suggest that task-dependent changes in the beta-to-rate mapping play a role in the transient functional reorganization of neural ensembles. We characterize the range of task-dependent changes in the mapping from beta amplitude, phase, and inter-hemispheric phase differences to the spike rates of an ensemble of simultaneously-recorded neurons, and discuss the potential implications that dynamic remapping from oscillatory activity to spike rate and timing may hold for models of computation and communication in distributed functional brain networks.     

Embedding optimization reveals long-lasting history dependence in neural spiking activity

Information processing can leave distinct footprints on the statistics of neural spiking. For example, efficient coding minimizes the statistical dependencies on the spiking history, while temporal integration of information may require the maintenance of information over different timescales. To investigate these footprints, we developed a novel approach to quantify history dependence within the spiking of a single neuron, using the mutual information between the entire past and current spiking. This measure captures how much past information is necessary to predict current spiking. In contrast, classical time-lagged measures of temporal dependence like the autocorrelation capture how long—potentially redundant—past information can still be read out. Strikingly, we find for model neurons that our method disentangles the strength and timescale of history dependence, whereas the two are mixed in classical approaches. When applying the method to experimental data, which are necessarily of limited size, a reliable estimation of mutual information is only possible for a coarse temporal binning of past spiking, a so-called past embedding. To still account for the vastly different spiking statistics and potentially long history dependence of living neurons, we developed an embedding-optimization approach that does not only vary the number and size, but also an exponential stretching of past bins. For extra-cellular spike recordings, we found that the strength and timescale of history dependence indeed can vary independently across experimental preparations. While hippocampus indicated strong and long history dependence, in visual cortex it was weak and short, while in vitro the history dependence was strong but short. This work enables an information-theoretic characterization of history dependence in recorded spike trains, which captures a footprint of information processing that is beyond time-lagged measures of temporal dependence. To facilitate the application of the method, we provide practical guidelines and a toolbox.     

Firing-rate models for neurons with a broad repertoire of spiking behaviors

Capturing the response behavior of spiking neuron models with rate-based models facilitates the investigation of neuronal networks using powerful methods for rate-based network dynamics. To this end, we investigate the responses of two widely used neuron model types, the Izhikevich and augmented multi-adapative threshold (AMAT) models, to a range of spiking inputs ranging from step responses to natural spike data. We find (i) that linear-nonlinear firing rate models fitted to test data can be used to describe the firing-rate responses of AMAT and Izhikevich spiking neuron models in many cases; (ii) that firing-rate responses are generally too complex to be captured by first-order low-pass filters but require bandpass filters instead; (iii) that linear-nonlinear models capture the response of AMAT models better than of Izhikevich models; (iv) that the wide range of response types evoked by current-injection experiments collapses to few response types when neurons are driven by stationary or sinusoidally modulated Poisson input; and (v) that AMAT and Izhikevich models show different responses to spike input despite identical responses to current injections. Together, these findings suggest that rate-based models of network dynamics may capture a wider range of neuronal response properties by incorporating second-order bandpass filters fitted to responses of spiking model neurons. These models may contribute to bringing rate-based network modeling closer to the reality of biological neuronal networks.     

Vibration receptive sensilla on the wing margins of the silkworm moth Bombyx mori

Bristles along the wing margins (wm-bristles) of the silkworm moth, Bombyx mori, were studied morphologically and electrophysiologically. The male moth has ca. 50 wm-bristles on each forewing and hindwing. Scanning electron microscopy revealed that these wm-bristles are typical mechanosensilla. Leuco-methylene blue staining demonstrated that each wm-bristle has a single receptor neuron, which is also characteristic of the mechanosensillum. The receptor neuron responded to vibrating air currents but did not respond to a constant air current. The wm-bristles showed clear directional sensitivity to vibrating air currents. The wm-bristles were classified into two types, type I and type II, by their response patterns to sinusoidal movements of the bristle. The neuron in type I discharged bursting spikes immediately following stimulation onset and also discharged a single spike for each sinusoidal cycle for frequencies less than ca. 60 Hz. The neuron in type II only responded to vibrations over 40 Hz and, specifically at 75 Hz, discharged a single spike for each sinusoidal cycle throughout the stimulation period. These results suggest that the two types of wm-bristles are highly tuned in different ways to detect vibrations due to the wing beat. The roles of the wm-bristles in the wing beat are discussed.     

Activity-dependent enhancement in the reliability of correlated spike timings in cultured cortical neurons

To study the use-dependent modification of activity in neural networks, we investigated the spike timing by simultaneously recording activity at multiple sites in a network of cultured cortical neurons. We used dynamical analysis to study the temporal structure of spike trains and the activity-dependent changes in the reliability and reproducibility of spike patterns evoked by a stimulus. We also used cross-correlation analysis to evaluate the interactions of neuron pairs. Our main conclusions are that even when no obvious change in spike numbers can be seen, use-dependent modification occurs, either enhancing or reducing in the reliability and reproducibility of spike trains evoked by a stimulus, and the fine temporal structure of stimulus-evoked spike trains and interactions between neurons are also modified by tetanic stimulation. Received: 25 February 1998 / Accepted in revised form: 24 August 1998     

Spatiotemporal Spike Coding of Behavioral Adaptation in the Dorsal Anterior Cingulate Cortex

The frontal cortex controls behavioral adaptation in environments governed by complex rules. Many studies have established the relevance of firing rate modulation after informative events signaling whether and how to update the behavioral policy. However, whether the spatiotemporal features of these neuronal activities contribute to encoding imminent behavioral updates remains unclear. We investigated this issue in the dorsal anterior cingulate cortex (dACC) of monkeys while they adapted their behavior based on their memory of feedback from past choices. We analyzed spike trains of both single units and pairs of simultaneously recorded neurons using an algorithm that emulates different biologically plausible decoding circuits. This method permits the assessment of the performance of both spike-count and spike-timing sensitive decoders. In response to the feedback, single neurons emitted stereotypical spike trains whose temporal structure identified informative events with higher accuracy than mere spike count. The optimal decoding time scale was in the range of 70–200 ms, which is significantly shorter than the memory time scale required by the behavioral task. Importantly, the temporal spiking patterns of single units were predictive of the monkeys’ behavioral response time. Furthermore, some features of these spiking patterns often varied between jointly recorded neurons. All together, our results suggest that dACC drives behavioral adaptation through complex spatiotemporal spike coding. They also indicate that downstream networks, which decode dACC feedback signals, are unlikely to act as mere neural integrators.     

Electrical Properties of the Pacemaker Neurons in the Heart Ganglion of a Stomatopod, Squilla oratoria

In the Squilla heart ganglion, the pacemaker is located in the rostral group of cells. After spontaneous firing ceased, the electrophysiological properties of these cells were examined with intracellular electrodes. Cells respond to electrical stimuli with all-or-none action potentials. Direct stimulation by strong currents decreases the size of action potentials. Comparison with action potentials caused by axonal stimulation and analysis of time relations indicate that with stronger currents the soma membrane is directly stimulated whereas with weaker currents the impulse first arises in the axon and then invades the soma. Spikes evoked in a neuron spread into all other neurons. Adjacent cells are interconnected by electrotonic connections. Histologically axons are tied with the side-junction. B spikes of adjacent cells are blocked simultaneously by hyperpolarization or by repetitive stimulation. Experiments show that under such circumstances the B spike is not directly elicited from the A spike but is evoked by invasion of an impulse or electrotonic potential from adjacent cells. On rostral stimulation a small prepotential precedes the main spike. It is interpreted as an action potential from dendrites.     

Radular mechanosensory neuron in the buccal ganglia of the terrestrial slug,Incilaria fruhstorferi

A radular mechanosensory neuron, RM, was identified in the buccal ganglia of Incilaria fruhstorferi. Fine neurites ramified bilaterally in the buccal ganglia, and main neurites entered the subradular epithelium via buccal nerve 3 (n3). When the radula was distorted by bending, RM produced an afferent spike which was preceded by an axonic spike recorded at n3. The response of RM to radular distortion was observed even in the absence of Ca²⁺, which drastically suppressed chemical synaptic interactions. Therefore, RM was concluded to be a primary radular mechanoreceptor.During rhythmic buccal motor activity induced by food or electrical stimulation of the cerebrobuccal connective, RM received excitatory input during the radular retraction phase. In the isolated buccal ganglia connected to the radula via n3s, the afferent spike, which had been evoked by electrical stimulation of the subradular epithelium, was broadened with the phasic excitatory input. Since the afferent spike was also broadened by current injection into the soma, depolarization due to the phasic input may have produced the spike broadening.Spike broadening was also observed during repetitive firing evoked by current injection. The amplitude of the excitatory postsynaptic potential in a follower neuron increased depending on the spike broadening of RM.Abbreviations CBC cerebrobuccal connective - EPSP excitatory postsynaptic potential - n1,n3 buccal nerves 1 and 3 - RBMA rhythmic buccal motor activity - RM radular mechanosensory neuron - SMT supramedian radular tensor neuron     

Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution

Signaling of information in the vertebrate central nervous system is often carried by populations of neurons rather than individual neurons. Also propagation of suprathreshold spiking activity involves populations of neurons. Empirical studies addressing cortical function directly thus require recordings from populations of neurons with high resolution. Here we describe an optical method and a deconvolution algorithm to record neural activity from up to 100 neurons with single-cell and single-spike resolution. This method relies on detection of the transient increases in intracellular somatic calcium concentration associated with suprathreshold electrical spikes (action potentials) in cortical neurons. High temporal resolution of the optical recordings is achieved by a fast random-access scanning technique using acousto-optical deflectors (AODs)¹. Two-photon excitation of the calcium-sensitive dye results in high spatial resolution in opaque brain tissue². Reconstruction of spikes from the fluorescence calcium recordings is achieved by a maximum-likelihood method. Simultaneous electrophysiological and optical recordings indicate that our method reliably detects spikes (>97% spike detection efficiency), has a low rate of false positive spike detection (< 0.003 spikes/sec), and a high temporal precision (about 3 msec) ³. This optical method of spike detection can be used to record neural activity in vitro and in anesthetized animals in vivo^3,4.     

Hair Cell Interactions in the Statocyst of Hermissenda

Hair cells in the statocyst of Hermissenda crassicornis respond to mechanical stimulation with a short latency (<2 ms) depolarizing generator potential that is followed by hyperpolarization and inhibition of spike activity. Mechanically evoked hyperpolarization and spike inhibition were abolished by cutting the static nerve, repetitive mechanical stimulation, tetrodotoxin (TTX), and Co⁺⁺. Since none of these procedures markedly altered the generator potential it was concluded that the hyperpolarization is an inhibitory synaptic potential and not a component of the mechanotransduction process. Intracellular recordings from pairs of hair cells in the same statocyst and in statocysts on opposite sides of the brain revealed that hair cells are connected by chemical and/or electrical synapses. All chemical interactions were inhibitory. Hyperpolarization and spike inhibition result from inhibitory interactions between hair cells in the same and in opposite statocysts.     

Coherence resonance for neuronal bursting with spike undershoot

Although the bursting patterns with spike undershoot are involved with the achievement of physiological or cognitive functions of brain with synaptic noise, noise induced-coherence resonance (CR) from resting state or subthreshold oscillations instead of bursting has been widely identified to play positive roles in information process. Instead, in the present paper, CR characterized by the increase firstly and then decease of peak value of power spectrum of spike trains is evoked from a bursting pattern with spike undershoot, which means that the minimal membrane potential within burst is lower than that of the subthreshold oscillations between bursts, while CR cannot be evoked from the bursting pattern without spike undershoot. With bifurcations and fast-slow variable dissection method, the bursting patterns with and without spike undershoot are classified into “Sub-Hopf/Fold” bursting and “Fold/Homoclinic” bursting, respectively. For the bursting with spike undershoot, the trajectory of the subthreshold oscillations is very close to that of the spikes within burst. Therefore, noise can induce more spikes from the subthreshold oscillations and modulate the bursting regularity, which leads to the appearance of CR. For the bursting pattern without spike undershoot, the trajectory of the quiescent state is not close to that of the spikes within burst, and noise cannot induce spikes from the quiescent state between bursts, which is cause for non-CR. The result provides a novel case of CR phenomenon and extends the scopes of CR concept, presents that noise can enhance rather than suppress information of the bursting patterns with spike undershoot, which are helpful for understanding the dynamics and the potential physiological or cognitive functions of the nerve fiber or brain neurons with such bursting patterns.     

Non-Gaussian noise optimized spiking activity of Hodgkin–Huxley neurons on random complex networks

In this paper, we numerically study how the NGN's deviation q from Gaussian noise (q = 1) affects the spike coherence and synchronization of 60 coupled Hodgkin–Huxley (HH) neurons driven by a periodic sinusoidal stimulus on random complex networks. It is found that the effect of the deviation depends on the network randomness p (the fraction of random shortcuts): for larger p (p > 0.15), the spiking regularity keeps being improved with increasing q; while, for smaller p (p < 0.15), the spiking regularity can reach the best performance at an optimal intermediate q value, indicating the occurrence of “deviation-optimized spike coherence”. The synchronization becomes enhanced with decreasing q, and the enhancing extent for a random HH neuron network is stronger than for a regular one. These behaviors show that the spike coherence and synchronization of the present HH neurons on random networks can be more strongly enhanced by various other types of external noise than by Gaussian noise, whereby the neuron firings may behave more periodically in time and more synchronously in space. Our results provide the constructive roles of the NGN on the spiking activity of the present system of HH neuron networks.     

Single-cell recordings: a method for investigating the brain's activation pattern during exercise

The precision of human movements to generate skills as accurate as the exercises performed by athletes are the consequence of a long and complex learning process. These processes involve a great amount of the nervous system’s structures. Electrophysiological techniques have been largely used to highlight brain functions related to the control of these kinds of movements. These methods cover invasive and non-invasive techniques which have been applied to humans and experimental animals. We describe here electrophysiological techniques that are used in behaving animals. Especially, we will focus on the analysis and results obtained from single-cell recording in the prefrontal cortex to explain the relationship between single neuronal activity and movement during locomotion. In addition, we will show how, analyzing these results, that we can characterize the integrative role of neurons involved in the control of locomotion. The objective is to demonstrate single-cell recording techniques as suitable methods to study, in experimental animals, the brain’s activation pattern during exercise.     

A Unified Approach to Linking Experimental,Statistical and Computational Analysis of Spike Train Data

A fundamental issue in neuroscience is how to identify the multiple biophysical mechanisms through which neurons generate observed patterns of spiking activity. In previous work, we proposed a method for linking observed patterns of spiking activity to specific biophysical mechanisms based on a state space modeling framework and a sequential Monte Carlo, or particle filter, estimation algorithm. We have shown, in simulation, that this approach is able to identify a space of simple biophysical models that were consistent with observed spiking data (and included the model that generated the data), but have yet to demonstrate the application of the method to identify realistic currents from real spike train data. Here, we apply the particle filter to spiking data recorded from rat layer V cortical neurons, and correctly identify the dynamics of an slow, intrinsic current. The underlying intrinsic current is successfully identified in four distinct neurons, even though the cells exhibit two distinct classes of spiking activity: regular spiking and bursting. This approach – linking statistical, computational, and experimental neuroscience – provides an effective technique to constrain detailed biophysical models to specific mechanisms consistent with observed spike train data.     

Dynamics and bifurcations of the adaptive exponential integrate-and-fire model

Recently, several two-dimensional spiking neuron models have been introduced, with the aim of reproducing the diversity of electrophysiological features displayed by real neurons while keeping a simple model, for simulation and analysis purposes. Among these models, the adaptive integrate-and-fire model is physiologically relevant in that its parameters can be easily related to physiological quantities. The interaction of the differential equations with the reset results in a rich and complex dynamical structure. We relate the subthreshold features of the model to the dynamical properties of the differential system and the spike patterns to the properties of a Poincaré map defined by the sequence of spikes. We find a complex bifurcation structure which has a direct interpretation in terms of spike trains. For some parameter values, spike patterns are chaotic.