Gap junction permeability modulated by dopamine exerts effects on spatial and temporal correlation of retinal ganglion cells’ firing activities

Synchronized activities among retinal ganglion cells (RGCs) via gap junctions can be increased by exogenous dopamine (DA). During DA application, single neurons’ firing activities become more synchronized with its adjacent neighbors. One intriguing question is how the enhanced spatial synchronization alters the temporal firing structure of single neurons. In the present study, firing activities of bullfrog’s dimming detectors in response to binary pseudo-random checker-board flickering were recorded via a multi-channel recording system. DA was applied in the retina to modulate synchronized activities between RGCs, and the effect of DA on firing activities of single neurons was examined. It was found that, during application of DA, synchronized activities between single neuron and its neighboring neurons was enhanced. At the meantime, the temporal structures of single neuron spike train changed significantly, and the temporal correlation in single neuron’s response decreased. The pharmacological study results indicated that the activation of D1 receptor might have effects on gap junction permeability between RGCs. Our results suggested that the dopaminergic pathway participated in the modulation of spatial and temporal correlation of RGCs’ firing activities, and may exert critical effects on visual information processing in the retina.     

Coemergence of regularity and complexity during neural network development

With the growing recognition that rhythmic and oscillatory patterns are widespread in the brain and play important roles in all aspects of the function of our nervous system, there has been a resurgence of interest in neuronal synchronized bursting activity. Here, we were interested in understanding the development of synchronized bursts as information-bearing neuronal activity patterns. For that, we have monitored the morphological organization and spontaneous activity of neuronal networks cultured on multielectrode-arrays during their self-executed evolvement from a mixture of dissociated cells into an active network. Complex collective network electrical activity evolved from sporadic firing patterns of the single neurons. On the system (network) level, the activity was marked by bursting events with interneuronal synchronization and nonarbitrary temporal ordering. We quantified these individual-to-collective activity transitions using newly-developed system level quantitative measures of time series regularity and complexity. We found that individual neuronal activity before synchronization was characterized by high regularity and low complexity. During neuronal wiring, there was a transient period of reorganization marked by low regularity, which then leads to coemergence of elevated regularity and functional (nonstochastic) complexity. We further investigated the morphology-activity interplay by modeling artificial neuronal networks with different topological organizations and connectivity schemes. The simulations support our experimental results by showing increased levels of complexity of neuronal activity patterns when neurons are wired up and organized in clusters (similar to mature real networks), as well as network-level activity regulation once collective activity forms.     

The analytical characteristics of the dynamics of interneuronal functional connections during conditioned-reflex activity]

The dynamics of functional relations between neurons was studied in the frontal cortex of dogs performing reversal conditioning task. To reveal the functionally relevant relationships between the temporal patterns of correlated firing and behavioral events, we developed an original processing technique. The technique included the following procedures: a) isolation of the "coupled spikes" (CS) from simultaneously recorded impulse trains: b) search for the temporal patterns of correlated firings and their classification by clustering single trials with similar temporal distribution of CS; c) assessment of behavioral significance of the identified patterns by evaluation of the probabilities of coincidence of behavioral events and different CS patterns. Significant correlations between impulse trains were revealed in 38 neuronal pairs of 456 analyzed. The effects of change in behavioral context on the CS dynamics during the task performance were found in 87% of neuronal pairs with correlated activity. In 17 pairs the behavioral conditions were identified, under which potentially connected neurons fired independently during all the periods of the behavioral task. The potentialities of the advanced processing technique are discussed. We suggest that this analysis can provide useful information about the temporal distribution of correlated firings under conditions of nonstereotyped behavior, when an animal reacts in the dynamically organized experimental context.     

A cross-interval spike train analysis: the correlation between spike generation and temporal integration of doublets

A stochastic spike train analysis technique is introduced to reveal the correlation between the firing of the next spike and the temporal integration period of two consecutive spikes (i.e., a doublet). Statistics of spike firing times between neurons are established to obtain the conditional probability of spike firing in relation to the integration period. The existence of a temporal integration period is deduced from the time interval between two consecutive spikes fired in a reference neuron as a precondition to the generation of the next spike in a compared neuron. This analysis can show whether the coupled spike firing in the compared neuron is correlated with the last or the second-to-last spike in the reference neuron. Analysis of simulated and experimentally recorded biological spike trains shows that the effects of excitatory and inhibitory temporal integration are extracted by this method without relying on any subthreshold potential recordings. The analysis also shows that, with temporal integration, a neuron driven by random firing patterns can produce fairly regular firing patterns under appropriate conditions. This regularity in firing can be enhanced by temporal integration of spikes in a chain of polysynaptically connected neurons. The bandpass filtering of spike firings by temporal integration is discussed. The results also reveal that signal transmission delays may be attributed not just to conduction and synaptic delays, but also to the delay time needed for temporal integration. Received: 3 March 1997 / Accepted in revised form: 6 November 1997     

Neuron classification based on temporal firing patterns by the dynamical analysis with changing time resolution (DCT) method

Spike train data of many neurons can be obtained by multirecording techniques; however, the data make it difficult to estimate the connective structure in a large network. Neuron classification should be helpful in that regard, assuming that multiple neurons having similar connections with other neurons show a similar temporal firing pattern. We propose a novel method for classifying neurons based on temporal firing patterns of spike train data called the dynamical analysis with changing time resolution (DCT) method. The DCT method can evaluate temporal firing patterns by a simple algorithm with few arbitrary factors and automatically classify neurons by similarity of temporal firing patterns. In the DCT method, temporal firing patterns were objectively evaluated by analyzing their dependence on temporal resolution. We confirmed the effectiveness of the DCT method using actual spike train data.     

The chronotron: a neuron that learns to fire temporally precise spike patterns

In many cases, neurons process information carried by the precise timings of spikes. Here we show how neurons can learn to generate specific temporally precise output spikes in response to input patterns of spikes having precise timings, thus processing and memorizing information that is entirely temporally coded, both as input and as output. We introduce two new supervised learning rules for spiking neurons with temporal coding of information (chronotrons), one that provides high memory capacity (E-learning), and one that has a higher biological plausibility (I-learning). With I-learning, the neuron learns to fire the target spike trains through synaptic changes that are proportional to the synaptic currents at the timings of real and target output spikes. We study these learning rules in computer simulations where we train integrate-and-fire neurons. Both learning rules allow neurons to fire at the desired timings, with sub-millisecond precision. We show how chronotrons can learn to classify their inputs, by firing identical, temporally precise spike trains for different inputs belonging to the same class. When the input is noisy, the classification also leads to noise reduction. We compute lower bounds for the memory capacity of chronotrons and explore the influence of various parameters on chronotrons' performance. The chronotrons can model neurons that encode information in the time of the first spike relative to the onset of salient stimuli or neurons in oscillatory networks that encode information in the phases of spikes relative to the background oscillation. Our results show that firing one spike per cycle optimizes memory capacity in neurons encoding information in the phase of firing relative to a background rhythm.     

Long-term recording on multi-electrode array reveals degraded inhibitory connection in neuronal network development

Spontaneous neuronal activity plays an important role in development. However, the mechanism that underlies the long-term spontaneous developmental change of cultured neuronal networks in vitro is not well understood. To investigate the contribution of inhibitory and excitatory connections to the development of neuronal networks, dissociated neurons from an embryonic rat hippocampal formation were cultured on a multi-electrode array plate and spontaneous activities were recorded by multi-channel system. These spontaneous activities were compared to bicuculline-induced firings, which were recorded by 60 electrodes simultaneously from 1 to 14 weeks in vitro (WIV). The phenomena showed that the spontaneous firing activities changed from an initial pattern of synchronized bursts to a later pattern of high frequency random spikes. The bicuculline-induced firing activities transformed from a pattern of synchronized bursts throughout all active sites in 3 WIV, to a pattern of local synchronized or random spikes appearing in the intervals of synchronized bursts after 11 WIV, while the firing rate hardly changed. Kynurenic acid, a broad-spectrum glutamate receptor antagonist, blocked all activities while CNQX inhibited only the local synchronized or random spikes. These suggest that the inhibitory connection was age-dependent degraded in vitro and the developmental spontaneous firing pattern was built by the homeostatic balance of the excitatory-inhibitory connection networks. Long-term cultures on MEA provided a useful tool to measure the relationship between spontaneous developmental change and pharmacological influence in vitro.     

Distinct neuronal types contribute to hybrid temporal encoding strategies in primate auditory cortex

Studies of the encoding of sensory stimuli by the brain often consider recorded neurons as a pool of identical units. Here, we report divergence in stimulus-encoding properties between subpopulations of cortical neurons that are classified based on spike timing and waveform features. Neurons in auditory cortex of the awake marmoset (Callithrix jacchus) encode temporal information with either stimulus-synchronized or nonsynchronized responses. When we classified single-unit recordings using either a criteria-based or an unsupervised classification method into regular-spiking, fast-spiking, and bursting units, a subset of intrinsically bursting neurons formed the most highly synchronized group, with strong phase-locking to sinusoidal amplitude modulation (SAM) that extended well above 20 Hz. In contrast with other unit types, these bursting neurons fired primarily on the rising phase of SAM or the onset of unmodulated stimuli, and preferred rapid stimulus onset rates. Such differentiating behavior has been previously reported in bursting neuron models and may reflect specializations for detection of acoustic edges. These units responded to natural stimuli (vocalizations) with brief and precise spiking at particular time points that could be decoded with high temporal stringency. Regular-spiking units better reflected the shape of slow modulations and responded more selectively to vocalizations with overall firing rate increases. Population decoding using time-binned neural activity found that decoding behavior differed substantially between regular-spiking and bursting units. A relatively small pool of bursting units was sufficient to identify the stimulus with high accuracy in a manner that relied on the temporal pattern of responses. These unit type differences may contribute to parallel and complementary neural codes.

Neurons in auditory cortex show highly diverse responses to sounds. This study suggests that neuronal type inferred from baseline firing properties accounts for much of this diversity, with a subpopulation of bursting units being specialized for precise temporal encoding.     

A role of burst firings in encoding of spatiotemporally-varying stimulus

To investigate a role of burst firings of neurons in encoding of spatiotemporally-varying stimulus, we focus on electrosensory system of a weakly electric fish. Weakly electric fish generates electric field around its body using electric organ discharge and can accurately detect the location of an object using the modulation of electric field induced by the object. We developed a model of fish body by which we numerically describe the spatiotemporal patterns of electric field around the fish body. We also made neural models of electroreceptor distributed on the fish body and of electrosensory lateral-line lobe (ELL) to investigate what kinds of information of electric field distorted by an object they detect. Here we show that the spatiotemporal features of electric field around the fish body are encoded by the timing of burst firings of ELL neurons. The information of object distance is extracted by the area of synchronous firings of neurons in a higher nucleus, torus semicircularis.     

Visual pattern discrimination by population retinal ganglion cells’ activities during natural movie stimulation

In the visual system, neurons often fire in synchrony, and it is believed that synchronous activities of group neurons are more efficient than single cell response in transmitting neural signals to down-stream neurons. However, whether dynamic natural stimuli are encoded by dynamic spatiotemporal firing patterns of synchronous group neurons still needs to be investigated. In this paper we recorded the activities of population ganglion cells in bullfrog retina in response to time-varying natural images (natural scene movie) using multi-electrode arrays. In response to some different brief section pairs of the movie, synchronous groups of retinal ganglion cells (RGCs) fired with similar but different spike events. We attempted to discriminate the movie sections based on temporal firing patterns of single cells and spatiotemporal firing patterns of the synchronous groups of RGCs characterized by a measurement of subsequence distribution discrepancy. The discrimination performance was assessed by a classification method based on Support Vector Machines. Our results show that different movie sections of the natural movie elicited reliable dynamic spatiotemporal activity patterns of the synchronous RGCs, which are more efficient in discriminating different movie sections than the temporal patterns of the single cells’ spike events. These results suggest that, during natural vision, the down-stream neurons may decode the visual information from the dynamic spatiotemporal patterns of the synchronous group of RGCs’ activities.     

Neural coding for the retrieval of multiple memory patterns

We investigate the retrieval dynamics in a feature-based semantic memory model, in which the features are coded by neurons of the Hindmarsh–Rose type in the chaotic regime. We consider the retrieval process as consisting of the synchronized firing activity of the neurons coding for the same memory pattern. The retrieval dynamics is investigated for multiple patterns, with particular attention to the case of overlapping memories. In this case, we hypothesize a dynamical nontransitive mechanism based on synchronization, that allows for a shared feature to participate in multiple memory representations. The problem of the choice of a cognitive plausible time-scale for the retrieval analysis is investigated by analyzing the information that can be inferred from finite-time analyses. Different types of indicators are proposed in order to evaluate the temporal dynamics of the neurons engaged in the retrieval process. We interpret the simulation results as suggestive of a role for chaotic dynamics in allowing for flexible composition of elementary meaningful units in memory representations.     

Spontaneous firings of carnivorous aquatic Utricularia traps: temporal patterns and mechanical oscillations

Aquatic species of Utricularia are carnivorous plants living in environments poor in nutrients. Their trapping mechanism has fascinated generations of scientists and is still debated today. It was reported recently that Utricularia traps can fire spontaneously. We show here that these spontaneous firings follow an unexpected diversity of temporal patterns, from "metronomic" traps which fire at fixed time intervals to "random" patterns, displaying more scattered firing times. Some "bursting" traps even combine both aspects, with groups of fast regular firings separated by a variable amount of time. We propose a physical model to understand these very particular behaviors, showing that a trap of Utricularia accomplishes mechanical oscillations, based on continuous pumping and sudden opening of the trap door (buckling). We isolate the key parameters governing these oscillations and discuss the effect of their fluctuations.     

Neural coding for the retrieval of multiple memory patterns

We investigate the retrieval dynamics in a feature-based semantic memory model, in which the features are coded by neurons of the Hindmarsh-Rose type in the chaotic regime. We consider the retrieval process as consisting of the synchronized firing activity of the neurons coding for the same memory pattern. The retrieval dynamics is investigated for multiple patterns, with particular attention to the case of overlapping memories. In this case, we hypothesize a dynamical nontransitive mechanism based on synchronization, that allows for a shared feature to participate in multiple memory representations. The problem of the choice of a cognitive plausible time-scale for the retrieval analysis is investigated by analyzing the information that can be inferred from finite-time analyses. Different types of indicators are proposed in order to evaluate the temporal dynamics of the neurons engaged in the retrieval process. We interpret the simulation results as suggestive of a role for chaotic dynamics in allowing for flexible composition of elementary meaningful units in memory representations.     

Neural responses in the primary auditory cortex of freely behaving cats while discriminating fast and slow click-trains

Repeated acoustic events are ubiquitous temporal features of natural sounds. To reveal the neural representation of the sound repetition rate, a number of electrophysiological studies have been conducted on various mammals and it has been proposed that both the spike-time and firing rate of primary auditory cortex (A1) neurons encode the repetition rate. However, previous studies rarely examined how the experimental animals perceive the difference in the sound repetition rate, and a caveat to these experiments is that they compared physiological data obtained from animals with psychophysical data obtained from humans. In this study, for the first time, we directly investigated acoustic perception and the underlying neural mechanisms in the same experimental animal by examining spike activities in the A1 of free-moving cats while performing a Go/No-go task to discriminate the click-trains at different repetition rates (12.5-200 Hz). As reported by previous studies on passively listening animals, A1 neurons showed both synchronized and non-synchronized responses to the click-trains. We further found that the neural performance estimated from the precise temporal information of synchronized units was good enough to distinguish all 16.7-200 Hz from the 12.5 Hz repetition rate; however, the cats showed declining behavioral performance with the decrease of the target repetition rate, indicating an increase of difficulty in discriminating two slower click-trains. Such behavioral performance was well explained by the firing rate of some synchronized and non-synchronized units. Trial-by-trial analysis indicated that A1 activity was not affected by the cat's judgment of behavioral response. Our results suggest that the main function of A1 is to effectively represent temporal signals using both spike timing and firing rate, while the cats may read out the rate-coding information to perform the task in this experiment.     

Spectral Characteristics of Phase Sensitivity and Discharge Rate of Neurons in the Ascending Tectofugal Visual System

Drifting gratings can modulate the activity of visual neurons at the temporal frequency of the stimulus. In order to characterize the temporal frequency modulation in the cat’s ascending tectofugal visual system, we recorded the activity of single neurons in the superior colliculus, the suprageniculate nucleus, and the anterior ectosylvian cortex during visual stimulation with drifting sine-wave gratings. In response to such stimuli, neurons in each structure showed an increase in firing rate and/or oscillatory modulated firing at the temporal frequency of the stimulus (phase sensitivity). To obtain a more complete characterization of the neural responses in spatiotemporal frequency domain, we analyzed the mean firing rate and the strength of the oscillatory modulations measured by the standardized Fourier component of the response at the temporal frequency of the stimulus. We show that the spatiotemporal stimulus parameters that elicit maximal oscillations often differ from those that elicit a maximal discharge rate. Furthermore, the temporal modulation and discharge-rate spectral receptive fields often do not overlap, suggesting that the detection range for visual stimuli provided jointly by modulated and unmodulated response components is larger than the range provided by a one response component.     

When inhibition not excitation synchronizes neural firing

Excitatory and inhibitory synaptic coupling can have counter-intuitive effects on the synchronization of neuronal firing. While it might appear that excitatory coupling would lead to synchronization, we show that frequently inhibition rather than excitation synchronizes firing. We study two identical neurons described by integrate-and-fire models, general phase-coupled models or the Hodgkin-Huxley model with mutual, non-instantaneous excitatory or inhibitory synapses between them. We find that if the rise time of the synapse is longer than the duration of an action potential, inhibition not excitation leads to synchronized firing.     

Interactive responses of a thalamic neuron to formalin induced lasting pain in behaving mice

Thalamocortical (TC) neurons are known to relay incoming sensory information to the cortex via firing in tonic or burst mode. However, it is still unclear how respective firing modes of a single thalamic relay neuron contribute to pain perception under consciousness. Some studies report that bursting could increase pain in hyperalgesic conditions while others suggest the contrary. However, since previous studies were done under either neuropathic pain conditions or often under anesthesia, the mechanism of thalamic pain modulation under awake conditions is not well understood. We therefore characterized the thalamic firing patterns of behaving mice in response to nociceptive pain induced by inflammation. Our results demonstrated that nociceptive pain responses were positively correlated with tonic firing and negatively correlated with burst firing of individual TC neurons. Furthermore, burst properties such as intra-burst-interval (IntraBI) also turned out to be reliably correlated with the changes of nociceptive pain responses. In addition, brain stimulation experiments revealed that only bursts with specific bursting patterns could significantly abolish behavioral nociceptive responses. The results indicate that specific patterns of bursting activity in thalamocortical relay neurons play a critical role in controlling long-lasting inflammatory pain in awake and behaving mice.     

Impulse pattern in bi-directionally coupled model neurons of different dynamics

The effects of bi-directional gap junction coupling of two model neurons with subthreshold oscillations have been examined when the individual neurons are operating at different dynamical states either in the tonic or bursting firing mode. Our simulations indicate that intermediate coupling strengths mostly lead to highly variable, often chaotic impulse patterns whereas transition to completely synchronized activity at high coupling strengths is generally going along with transitions to regular limit cycle activity. The synchronized activity pattern, however, can be completely different from the original pattern of the uncoupled neurons.     

Leptin counteracts the hypoxia-induced inhibition of spontaneously firing hippocampal neurons: a microelectrode array study

Besides regulating energy balance and reducing body-weight, the adipokine leptin has been recently shown to be neuroprotective and antiapoptotic by promoting neuronal survival after excitotoxic and oxidative insults. Here, we investigated the firing properties of mouse hippocampal neurons and the effects of leptin pretreatment on hypoxic damage (2 hours, 3% O(2)). Experiments were carried out by means of the microelectrode array (MEA) technology, monitoring hippocampal neurons activity from 11 to 18 days in vitro (DIV). Under normoxic conditions, hippocampal neurons were spontaneously firing, either with prevailing isolated and randomly distributed spikes (11 DIV), or with patterns characterized by synchronized bursts (18 DIV). Exposure to hypoxia severely impaired the spontaneous activity of hippocampal neurons, reducing their firing frequency by 54% and 69%, at 11 and 18 DIV respectively, and synchronized their firing activity. Pretreatment with 50 nM leptin reduced the firing frequency of normoxic neurons and contrasted the hypoxia-induced depressive action, either by limiting the firing frequency reduction (at both ages) or by increasing it to 126% (in younger neurons). In order to find out whether leptin exerts its effect by activating large conductance Ca(2+)-activated K(+) channels (BK), as shown on rat hippocampal neurons, we applied the BK channel blocker paxilline (1 μM). Our data show that paxilline reversed the effects of leptin, both on normoxic and hypoxic neurons, suggesting that the adipokine counteracts hypoxia through BK channels activation in mouse hippocampal neurons.     

The nature of the relationships between the dynamics of local interneuronal connections and of the total bioelectrical activity of the frontal cortex in the dog]

Temporal patterns of correlated firings were studied in small groups of neurons simultaneously recorded in the frontal cortex of a behaving dog. To identify the character of relations between the patterns of interneuronal connections in local ensembles and event-related potentials (ERP), the following procedure was applied. At the first step, single trials of behavioral task were classified by clustering temporal distribution of correlated firings in pairs of units. Then the surface ERP were averaged separately for each cluster and parameters of the ERP components were compared between the clusters. The analysis revealed 3 of 7 recording sites, where significant differences were observed between the ERP sampled from the clusters with different structures of the local interneuronal connections. The results suggest that temporal modulation of interactions between neurons in local ensembles reflects a large-scale reorganization of cortical networks.