Evidence for spatiotemporal firing patterns within the auditory thalamus of the cat

On the semantics of ecoacoustic codes

Ecological codes have been defined as every biological code integrated by factors originated by the environmental context that participates in the codepoiesis process. Ecological codes create a strict relationship between the inner world of organsims and the external relational world, and represent the mechanism with which the vivo-scape is realized. Acoustic codes are used in nature to decode acoustic signals between individuals of the same or different species and belong to the category of biological codes. Ecoacoustic codes are the outcome of the evolution of acoustic codes, and results as the interplay between acoustic codes and environmental factors. Soundtope codes represent the results of emerging properties of the acoustic communities.

Multimodal cortico-cortical associations induced by fear and sensory conditioning in the guinea pig

Sensory cortices are defined by responses to physical stimulation in specific modalities. Recently, additional associatively induced responses have been reported for stimuli other than the main specific modality for each cortex in the human and mammalian brain. In this study, to investigate a type of consolidation, associative responses in the guinea pig cortices (auditory, visual, and somatosensory) were simultaneously measured using optical imaging after first- or second-order conditioning comprising foot shock as an aversive stimulus and tone and light as sensory stimuli. Our findings indicated that (1) after the first- and second-order conditioning, associative responses in each cortical area were additionally induced to stimulate the other specific modality; (2) an associative response to sensory conditioning with tone and light was also seen as a change in the response at the neuronal level without behavioral phenomena; and (3) when fear conditioning with light and foot shock was applied before sensory conditioning with tone and light, the associative response to foot shock in the primary visual cortex (V1) was decreased (extinction) compared with the response after the first-order fear conditioning, whereas the associative response was increased (facilitation) for fear conditioning after sensory conditioning. Our results suggest that various types of bottom-up information are consolidated as associative responses induced in the cortices, which are traced repetitively or alternatively by a change in plasticity involving facilitation and extinction in the cortical network. This information-combining process of cortical responses may play a crucial role in the dynamic linking of memory in the brain.

Corticothalamic Pathways in Auditory Processing: Recent Advances and Insights From Other Sensory Systems

The corticothalamic (CT) pathways emanate from either Layer 5 (L5) or 6 (L6) of the neocortex and largely outnumber the ascending, thalamocortical pathways. The CT pathways provide the anatomical foundations for an intricate, bidirectional communication between thalamus and cortex. They act as dynamic circuits of information transfer with the ability to modulate or even drive the response properties of target neurons at each synaptic node of the circuit. L6 CT feedback pathways enable the cortex to shape the nature of its driving inputs, by directly modulating the sensory message arriving at the thalamus. L5 CT pathways can drive the postsynaptic neurons and initiate a transthalamic corticocortical circuit by which cortical areas communicate with each other. For this reason, L5 CT pathways place the thalamus at the heart of information transfer through the cortical hierarchy. Recent evidence goes even further to suggest that the thalamus via CT pathways regulates functional connectivity within and across cortical regions, and might be engaged in cognition, behavior, and perceptual inference. As descending pathways that enable reciprocal and context-dependent communication between thalamus and cortex, we venture that CT projections are particularly interesting in the context of hierarchical perceptual inference formulations such as those contemplated in predictive processing schemes, which so far heavily rely on cortical implementations. We discuss recent proposals suggesting that the thalamus, and particularly higher order thalamus via transthalamic pathways, could coordinate and contextualize hierarchical inference in cortical hierarchies. We will explore these ideas with a focus on the auditory system.

Complex temporal patterns processing by a neural mass model of a cortical column

It is well known that neuronal networks are capable of transmitting complex spatiotemporal information in the form of precise sequences of neuronal discharges characterized by recurrent patterns. At the same time, the synchronized activity of large ensembles produces local field potentials that propagate through highly dynamic oscillatory waves, such that, at the whole brain scale, complex spatiotemporal dynamics of electroencephalographic (EEG) signals may be associated to sensorimotor decision making processes. Despite these experimental evidences, the link between highly temporally organized input patterns and EEG waves has not been studied in detail. Here, we use a neural mass model to investigate to what extent precise temporal information, carried by deterministic nonlinear attractor mappings, is filtered and transformed into fluctuations in phase, frequency and amplitude of oscillatory brain activity. The phase shift that we observe, when we drive the neural mass model with specific chaotic inputs, shows that the local field potential amplitude peak appears in less than one full cycle, thus allowing traveling waves to encode temporal information. After converting phase and amplitude changes obtained into point processes, we quantify input-output similarity following a threshold-filtering algorithm onto the amplitude wave peaks. Our analysis shows that the neural mass model has the capacity for gating the input signal and propagate selected temporal features of that signal. Finally, we discuss the effect of local excitatory/inhibitory balance on these results and how excitability in cortical columns, controlled by neuromodulatory innervation of the cerebral cortex, may contribute to set a fine tuning and gating of the information fed to the cortex.

Attractor dynamics of a Boolean model of a brain circuit controlled by multiple parameters

Studies of Boolean recurrent neural networks are briefly introduced with an emphasis on the attractor dynamics determined by the sequence of distinct attractors observed in the limit cycles. We apply this framework to a simplified model of the basal ganglia-thalamocortical circuit where each brain area is represented by a "neuronal" node in a directed graph. Control parameters ranging from neuronal excitability that affects all cells to targeted local connections modified by a new adaptive plasticity rule, and the regulation of the interactive feedback affecting the external input stream of information, allow the network dynamics to switch between stable domains delimited by highly discontinuous boundaries and reach very high levels of complexity with specific configurations. The significance of this approach with regard to brain circuit studies is briefly discussed.

The cortical modulation of stimulus-specific adaptation in the auditory midbrain and thalamus: a potential neuronal correlate for predictive coding

To follow an ever-changing auditory scene, the auditory brain is continuously creating a representation of the past to form expectations about the future. Unexpected events will produce an error in the predictions that should “trigger” the network’s response. Indeed, neurons in the auditory midbrain, thalamus and cortex, respond to rarely occurring sounds while adapting to frequently repeated ones, i.e., they exhibit stimulus specific adaptation (SSA). SSA cannot be explained solely by intrinsic membrane properties, but likely involves the participation of the network. Thus, SSA is envisaged as a high order form of adaptation that requires the influence of cortical areas. However, present research supports the hypothesis that SSA, at least in its simplest form (i.e., to frequency deviants), can be transmitted in a bottom-up manner through the auditory pathway. Here, we briefly review the underlying neuroanatomy of the corticofugal projections before discussing state of the art studies which demonstrate that SSA present in the medial geniculate body (MGB) and inferior colliculus (IC) is not inherited from the cortex but can be modulated by the cortex via the corticofugal pathways. By modulating the gain of neurons in the thalamus and midbrain, the auditory cortex (AC) would refine SSA subcortically, preventing irrelevant information from reaching the cortex.

An attractor-based complexity measurement for Boolean recurrent neural networks

We provide a novel refined attractor-based complexity measurement for Boolean recurrent neural networks that represents an assessment of their computational power in terms of the significance of their attractor dynamics. This complexity measurement is achieved by first proving a computational equivalence between Boolean recurrent neural networks and some specific class of -automata, and then translating the most refined classification of -automata to the Boolean neural network context. As a result, a hierarchical classification of Boolean neural networks based on their attractive dynamics is obtained, thus providing a novel refined attractor-based complexity measurement for Boolean recurrent neural networks. These results provide new theoretical insights to the computational and dynamical capabilities of neural networks according to their attractive potentialities. An application of our findings is illustrated by the analysis of the dynamics of a simplified model of the basal ganglia-thalamocortical network simulated by a Boolean recurrent neural network. This example shows the significance of measuring network complexity, and how our results bear new founding elements for the understanding of the complexity of real brain circuits.

On the precision of neural computation with interaural level differences in the lateral superior olive

Interaural level difference (ILD) is one of the basic binaural clues in the spatial localization of a sound source. Due to the acoustic shadow cast by the head, a sound source out of the medial plane results in an increased sound level at the nearer ear and a decreased level at the distant ear. In the mammalian auditory brainstem, the ILD is processed by a neuronal circuit of binaural neurons in the lateral superior olive (LSO). These neurons receive major excitatory projections from the ipsilateral side and major inhibitory projections from the contralateral side. As the sound level is encoded predominantly by the neuronal discharge rate, the principal function of LSO neurons is to estimate and encode the difference between the discharge rates of the excitatory and inhibitory inputs. Two general mechanisms of this operation are biologically plausible: (1) subtraction of firing rates integrated over longer time intervals, and (2) detection of coincidence of individual spikes within shorter time intervals. However, the exact mechanism of ILD evaluation is not known. Furthermore, given the stochastic nature of neuronal activity, it is not clear how the circuit achieves the remarkable precision of ILD assessment observed experimentally. We employ a probabilistic model and complementary computer simulations to investigate whether the two general mechanisms are capable of the desired performance. Introducing the concept of an ideal observer, we determine the theoretical ILD accuracy expressed by means of the just-noticeable difference (JND) in dependence on the statistics of the interacting spike trains, the overall firing rate, detection time, the number of converging fibers, and on the neural mechanism itself. We demonstrate that the JNDs rely on the precision of spike timing; however, with an appropriate parameter setting, the lowest theoretical values are similar or better than the experimental values. Furthermore, a mechanism based on excitatory and inhibitory coincidence detection may give better results than the subtraction of firing rates. This article is part of a Special Issue entitled Neural Coding 2012.

How synaptic weights determine stability of synchrony in networks of pulse-coupled excitatory and inhibitory oscillators

Under which conditions can a network of pulse-coupled oscillators sustain stable collective activity states? Previously, it was shown that stability of the simplest pattern conceivable, i.e., global synchrony, in networks of symmetrically pulse-coupled oscillators can be decided in a rigorous mathematical fashion, if interactions either all advance or all retard oscillation phases ("mono-interaction network"). Yet, many real-world networks-for example neuronal circuits-are asymmetric and moreover crucially feature both types of interactions. Here, we study complex networks of excitatory (phase-advancing) and inhibitory (phase-retarding) leaky integrate-and-fire (LIF) oscillators. We show that for small coupling strength, previous results for mono-interaction networks also apply here: pulse time perturbations eventually decay if they are smaller than a transmission delay and if all eigenvalues of the linear stability operator have absolute value smaller or equal to one. In this case, the level of inhibition must typically be significantly stronger than that of excitation to ensure local stability of synchrony. For stronger coupling, however, network synchrony eventually becomes unstable to any finite perturbation, even if inhibition is strong and all eigenvalues of the stability operator are at most unity. This new type of instability occurs when any oscillator, inspite of receiving inhibitory input from the network on average, can by chance receive sufficient excitatory input to fire a pulse before all other pulses in the system are delivered, thus breaking the near-synchronous perturbation pattern.

Effect of auditory cortex deactivation on stimulus-specific adaptation in the medial geniculate body

An animal's survival may depend on detecting new events or objects in its environment, and it is likely that the brain has evolved specific mechanisms to detect such changes. In sensory systems, neurons often exhibit stimulus-specific adaptation (SSA) whereby they adapt to frequently occurring stimuli, but resume firing when "surprised" by rare or new ones. In the auditory system, SSA has been identified in the midbrain, thalamus, and auditory cortex (AC). It has been proposed that the SSA observed subcortically originates in the AC as a higher-order property that is transmitted to the subcortical nuclei via corticofugal pathways. Here we report that SSA in the auditory thalamus of the rat remains intact when the AC is deactivated by cooling, thus demonstrating that the AC is not necessary for the generation of SSA in the thalamus. The AC does, however, modulate the responses of thalamic neurons in a way that strongly indicates a gain modulation mechanism. The changes imposed by the AC in thalamic neurons depend on the level of SSA that they exhibit.

Cortical inactivation by cooling in small animals

Reversible inactivation of the cortex by surface cooling is a powerful method for studying the function of a particular area. Implanted cooling cryoloops have been used to study the role of individual cortical areas in auditory processing of awake-behaving cats. Cryoloops have also been used in rodents for reversible inactivation of the cortex, but recently there has been a concern that the cryoloop may also cool non-cortical structures either directly or via the perfusion of blood, cooled as it passed close to the cooling loop. In this study we have confirmed that the loop can inactivate most of the auditory cortex without causing a significant reduction in temperature of the auditory thalamus or other subcortical structures. We placed a cryoloop on the surface of the guinea pig cortex, cooled it to 2°C and measured thermal gradients across the neocortical surface. We found that the temperature dropped to 20–24°C among cells within a radius of about 2.5 mm away from the loop. This temperature drop was sufficient to reduce activity of most cortical cells and led to the inactivation of almost the entire auditory region. When the temperature of thalamus, midbrain, and middle ear were measured directly during cortical cooling, there was a small drop in temperature (about 4°C) but this was not sufficient to directly reduce neural activity. In an effort to visualize the extent of neural inactivation we measured the uptake of thallium ions following an intravenous injection. This confirmed that there was a large reduction of activity across much of the ipsilateral cortex and only a small reduction in subcortical structures.

Neural codes in the thalamocortical auditory system: from artificial stimuli to communication sounds

Over the last 15 years, an increasing number of studies have described the responsiveness of thalamic and cortical neurons to communication sounds. Whereas initial studies have simply looked for neurons exhibiting higher firing rate to conspecific vocalizations over their modified, artificially synthesized versions, more recent studies determine the relative contribution of "rate coding" and "temporal coding" to the information transmitted by spike trains. In this article, we aim at reviewing the different strategies employed by thalamic and cortical neurons to encode information about acoustic stimuli, from artificial to natural sounds. Considering data obtained with simple stimuli, we first illustrate that different facets of temporal code, ranging from a strict correspondence between spike-timing and stimulus temporal features to more complex coding strategies, do already exist with artificial stimuli. We then review lines of evidence indicating that spike-timing provides an efficient code for discriminating communication sounds from thalamus, primary and non-primary auditory cortex up to frontal areas. As the neural code probably developed, and became specialized, over evolution to allow precise and reliable processing of sounds that are of survival value, we argue that spike-timing based coding strategies might set the foundations of our perceptive abilities.

Recurrent spatiotemporal firing patterns in large spiking neural networks with ontogenetic and epigenetic processes

Neural development and differentiation are characterized by an overproduction of cells and a transient exuberant number of connections followed by cell death and selective synaptic pruning. We simulated large spiking neural networks (10,000 units at its maximum size) with and without an ontogenetic process corresponding to a brief initial phase of apoptosis driven by an excessive firing rate mimicking cell death due to glutamatergic neurotoxicity and glutamate-triggered apoptosis. This phase was followed by the onset of spike timing dependent synaptic plasticity (STDP), driven by spatiotemporal patterns of stimulation. Despite the reduction in cell counts the apoptosis tended to increase the excitatory/inhibitory ratio because the inhibitory cells were affected at first. Recurrent spatiotemporal firing patterns emerged in both developmental condition but they differed in dynamics. They were less numerous but repeated more often after apoptosis. The results suggest that initial cell death may be necessary for the emergence of stable cell assemblies, able to sustain and process temporal information, from the initially randomly connected networks.

Reconstruction of underlying nonlinear deterministic dynamics embedded in noisy spike trains

An experimentally recorded time series formed by the exact times of occurrence of the neuronal spikes (spike train) is likely to be affected by observational noise that provokes events mistakenly confused with neuronal discharges, as well as missed detection of genuine neuronal discharges. The points of the spike train may also suffer a slight jitter in time due to stochastic processes in synaptic transmission and to delays in the detecting devices. This study presents a procedure aimed at filtering the embedded noise (denoising the spike trains) the spike trains based on the hypothesis that recurrent temporal patterns of spikes are likely to represent the robust expression of a dynamic process associated with the information carried by the spike train. The rationale of this approach is tested on simulated spike trains generated by several nonlinear deterministic dynamical systems with embedded observational noise. The application of the pattern grouping algorithm (PGA) to the noisy time series allows us to extract a set of points that form the reconstructed time series. Three new indices are defined for assessment of the performance of the denoising procedure. The results show that this procedure may indeed retrieve the most relevant temporal features of the original dynamics. Moreover, we observe that additional spurious events affect the performance to a larger extent than the missing of original points. Thus, a strict criterion for the detection of spikes under experimental conditions, thus reducing the number of spurious spikes, may raise the possibility to apply PGA to detect endogenous deterministic dynamics in the spike train otherwise masked by the observational noise.

Deterministic neural dynamics transmitted through neural networks

Precise spatiotemporal sequences of neuronal discharges (i.e., intervals between epochs repeating more often than expected by chance), have been observed in a large set of experimental electrophysiological recordings. Sensitivity to temporal information, by itself, does not demonstrate that dynamics embedded in spike trains can be transmitted through a neural network. This study analyzes how synaptic transmission through three archetypical types of neurons (regular-spiking, thalamo-cortical and resonator), simulated by a simple spiking model, can affect the transmission of precise timings generated by a nonlinear deterministic system (i.e., the Zaslavskii mapping in the present study). The results show that cells with subthreshold oscillations (resonators) are very sensitive to stochastic inputs, and are not a good candidate for transmitting temporally coded information. Thalamo-cortical neurons may transmit very well temporal patterns in the absence of background activity, but jitter accumulates along the synaptic chain. Conversely, we observed that cortical regular-spiking neurons can propagate filtered temporal information in a reliable way through the network, and with high temporal accuracy. We discuss the results in the general framework of neural dynamics and brain theories.

Inevitable evolutionary temporal elements in neural processing: a study based on evolutionary simulations

Recent studies have suggested that some neural computational mechanisms are based on the fine temporal structure of spiking activity. However, less effort has been devoted to investigating the evolutionary aspects of such mechanisms. In this paper we explore the issue of temporal neural computation from an evolutionary point of view, using a genetic simulation of the evolutionary development of neural systems. We evolve neural systems in an environment with selective pressure based on mate finding, and examine the temporal aspects of the evolved systems. In repeating evolutionary sessions, there was a significant increase during evolution in the mutual information between the evolved agent's temporal neural representation and the external environment. In ten different simulated evolutionary sessions, there was an increased effect of time -related neural ablations on the agents' fitness. These results suggest that in some fitness landscapes the emergence of temporal elements in neural computation is almost inevitable. Future research using similar evolutionary simulations may shed new light on various biological mechanisms.

Spike timing and reliability in cortical pyramidal neurons: effects of EPSC kinetics, input synchronization and background noise on spike timing

In vivo studies have shown that neurons in the neocortex can generate action potentials at high temporal precision. The mechanisms controlling timing and reliability of action potential generation in neocortical neurons, however, are still poorly understood. Here we investigated the temporal precision and reliability of spike firing in cortical layer V pyramidal cells at near-threshold membrane potentials. Timing and reliability of spike responses were a function of EPSC kinetics, temporal jitter of population excitatory inputs, and of background synaptic noise. We used somatic current injection to mimic population synaptic input events and measured spike probability and spike time precision (STP), the latter defined as the time window (Δt) holding 80% of response spikes. EPSC rise and decay times were varied over the known physiological spectrum. At spike threshold level, EPSC decay time had a stronger influence on STP than rise time. Generally, STP was highest (≤2.45 ms) in response to synchronous compounds of EPSCs with fast rise and decay kinetics. Compounds with slow EPSC kinetics (decay time constants>6 ms) triggered spikes at lower temporal precision (≥6.58 ms). We found an overall linear relationship between STP and spike delay. The difference in STP between fast and slow compound EPSCs could be reduced by incrementing the amplitude of slow compound EPSCs. The introduction of a temporal jitter to compound EPSCs had a comparatively small effect on STP, with a tenfold increase in jitter resulting in only a five fold decrease in STP. In the presence of simulated synaptic background activity, precisely timed spikes could still be induced by fast EPSCs, but not by slow EPSCs.

Temporally precise cortical firing patterns are associated with distinct action segments

Despite many reports indicating the existence of precise firing sequences in cortical activity, serious objections have been raised regarding the statistics used to detect them and the relations of these sequences to behavior. We show that in behaving monkeys, pairs of spikes from different neurons tend to prefer certain time delays when measured in relation to a specific behavior. Single-unit activity was recorded from eight microelectrodes inserted into the motor and premotor cortices of two monkeys while they were performing continuous drawinglike hand movements. Repeated scribbling paths, termed drawing components, were extracted by data-mining techniques. The set of the least predictable relations between drawing components and pairs of neurons was determined and represented by one statistic termed the relations score. The chance probability of the relations score was evaluated by teetering the spike times: 1,000 surrogates were generated by randomly teetering the original time of each spike in a small window. In nine of 13 experimental days the precision was better than 12 ms and, in the best case, spike precision reached 0.5 ms.

Cortical microstimulation in auditory cortex of rat elicits best-frequency dependent behaviors

Electrical activation of the auditory cortex has been shown to elicit an auditory sensation; however, the perceptual effects of auditory cortical microstimulation delivered through penetrating microelectrodes have not been clearly elucidated. This study examines the relationship between electrical microstimulus location within the adult rat auditory cortex and the subsequent behavior induced. Four rats were trained on an auditory frequency discrimination task and their lever-pressing behavior in response to stimuli of intermediate auditory frequencies was quantified. Each trained rat was then implanted with a microwire array in the auditory cortex of the left hemisphere. Best frequencies (BFs) of each electrode in the array were determined by both local field potential and multi-unit spike-rate activity evoked by pure tone stimuli. A cross-dimensional psychophysical generalization paradigm was used to evaluate cortical microstimulation-induced behavior. Using the BFs of each electrode, the microstimulation-induced behavior was evaluated relative to the auditory-induced behavior. Microstimulation resulted in behavior that was dependent on the BFs of the electrodes used for stimulation. These results are consistent with recent reports indicating that electrophysiological recordings of neural responses to sensory stimuli may provide insight into the sensation generated by electrical stimulation of the same sensory neural tissue.

Unitary events in multiple single-neuron spiking activity: I. Detection and significance

It has been proposed that cortical neurons organize dynamically into functional groups (cell assemblies) by the temporal structure of their joint spiking activity. Here, we describe a novel method to detect conspicuous patterns of coincident joint spike activity among simultaneously recorded single neurons. The statistical significance of these unitary events of coincident joint spike activity is evaluated by the joint-surprise. The method is tested and calibrated on the basis of simulated, stationary spike trains of independently firing neurons, into which coincident joint spike events were inserted under controlled conditions. The sensitivity and specificity of the method are investigated for their dependence on physiological parameters (firing rate, coincidence precision, coincidence pattern complexity) and temporal resolution of the analysis. In the companion article in this issue, we describe an extension of the method, designed to deal with nonstationary firing rates.

Detecting precise firing sequences in experimental data

A precise firing sequence (PFS) is defined here as a sequence of three spikes with fixed delays (up to some time accuracy Delta), that repeat excessively. This paper provides guidelines for detecting PFSs, verifying their significance through surrogate spike trains, and identifying existing PFSs. The method is based on constructing a three-fold correlation among spikes, estimating the expected shape of the correlation by smoothing, and detecting points for which the correlations significantly protrude above the expected correlation. Validation is achieved by generating surrogate spike trains in which the time of each of the real spikes is randomly jittered within a small time window. The method is extensively tested through application to simulated spike trains, and the results are illustrated with recordings of single units in the frontal cortex of behaving monkeys. Pitfalls which may cause false detection of PFSs, or loss of existing PFSs, include searching for PFSs in which the same neuron participates more than once, and attempting to produce a surrogate with some fixed statistical property.

A pattern grouping algorithm for analysis of spatiotemporal patterns in neuronal spike trains. 2. Application to simultaneous single unit recordings

This study demonstrates the practical application of the pattern grouping algorithm (PGA), presented in the companion paper (Tetko IV, Villa AEP. A pattern grouping algorithm for analysis of spatiotemporal patterns in neuronal spike trains. 1. Detection of repeated patterns. J. Neurosci. Methods 2000; accompanying article), to data sets including up to 30 simultaneously recorded spike trains. The analysis of a large network of simulated neurons shows that the incidence of patterns cannot be simply related to an increase in firing rates obtained after Hebbian learning. Patterns that disappeared and reappeared in the thalamus of anesthetized rats when the cerebral cortex was reversibly inactivated suggest that widespread cell assemblies contribute to the generation and propagation of precisely timed activity. In an another experiment multiple spike trains were recorded from the temporal cortex of freely moving rats performing a complex two-choice discrimination task. The presence or absence of particular patterns in the period preceding the cue was associated with changes in reaction time. In conclusion, neuronal network interactions may generate spatiotemporal firing patterns detectable by PGA. We provide evidence of such patterned activity associated with specific animal's behavior, thus suggesting the existence of complex temporal coding schemes in the higher nervous centers of the brain.

A comparative analysis of the morphology of corticothalamic projections in mammals

Recent anatomical tracing methods have revealed new principles underlying the organization of corticothalamic connections in the mammalian nervous system. These data demonstrated the distribution of two types of synaptic contacts in the corticothalamic projection: small (<1 microm) and giant (2-10 microm) axon terminals. We compare the organization of corticothalamic projections in the auditory, somatosensory, visual, and motor systems of a variety of mammalian species, including the monkey. In all these systems and species, both types of corticothalamic terminals have been observed. Small endings formed the major corticothalamic terminal field, whereas giant terminals were less numerous and formed additional terminal fields together with small terminals. After comparing their spatial distribution, as well as the degree of reciprocity between the corticothalamic and thalamocortical projections, different roles are proposed for small and giant endings. Small terminals are typically present in the projection serving the feed-back control of the cerebral cortex on the thalamic nucleus from which it receives its main projection. In contrast, giant terminals are involved in feed-forward projections by which activity from a cortical area is distributed, via the thalamus, to other parts of the cerebral cortex. The cross-species and cross-systems comparison reveals differences in the mode of feed-forward projection, which may be involved in the activation of other parts of the same cortical area or form part of a projection that activates other cortical areas.

Precise spatiotemporal repeating patterns in monkey primary and supplementary motor areas occur at chance levels

Precise spatiotemporal patterns in neural discharge are a possible mechanism for information encoding in the brain. Previous studies have found that such patterns repeat and appear to relate to key behavioral events. Whether these patterns occur above chance levels remains controversial. To address this question, we have made simultaneous recordings from between two and nine neurons in the primary motor cortex and supplementary motor area of three monkeys while they performed a precision grip task. Out of a total of 67 neurons, 46 were antidromically identified as pyramidal tract neurons. Sections of recordings 60 s long were searched for patterns involving three or more spikes that repeated at least twice. The allowed jitter for pattern repetition was 3 ms, and the pattern length was limited to 192 ms. In all 11 recordings analyzed, large numbers of repeating patterns were found. To assess the expected chance level of patterns, "surrogate" datasets were generated. These had the same moment-by-moment modulation in firing rate as the experimental spike trains, and matched their interspike interval distribution, but did not preserve the precise timing of individual spikes. The number of repeating patterns in 10 randomly generated surrogates was used to form 99% confidence limits on the repeating pattern count expected by chance. There was close agreement between these confidence limits and the number of patterns seen in the experimental data. Analysis of high complexity patterns was carried out in four long recordings (mean duration 23.2 min, mean number of neurons simultaneously recorded 7.5). This analysis logged only patterns composed of a larger number (7-11) of spikes. The number of patterns seen in the surrogate datasets showed a small but significant excess over those seen in the original experimental data; this is discussed in the context of surrogate generation. The occurrence of repeating patterns in the experimental data were strongly associated with particular phases of the precision grip task; however, a similar task dependence was seen for the surrogate data. When a repeating pattern was used as a template to find inexact matches, in which up to half of the component spikes could be missing, similar numbers of matches were found in experimental and surrogate data, and the time of occurrence of such matches showed the same task dependence. We conclude that the existence of precise repeating patterns in our data are not due to cortical mechanisms that favor this form of coding, since as many, if not more, patterns are produced by spike trains constructed only to modulate their firing rate in the same way as the experimental data, and to match the interspike interval histograms. The task dependence of pattern occurrence is explicable as an artifact of the modulation of neural firing rate. The consequences for theories of temporal coding in the cortex are discussed.

Neural ensemble coding and statistical periodicity: speculations on the operation of the mind's eye

Statistical periodicity is a statistical property of densities which arises in the description of retarded dynamical systems. This property is particularly attractive as a possible mechanism for the ensemble coding of information in the nervous system because it operates rapidly and has high storage capacity. For a population of neurons which exhibits statistical periodicity, information would not be encoded by the periodicity, but rather by the spatio-temporal distributions of neural activity. Statistical periodicity is discussed in relation to the temporal binding hypothesis and to the occurrence of multistability in neural systems.

Multistability in recurrent neural loops arising from delay

The dynamics of a recurrent inhibitory neural loop composed of a periodically spiking Aplysia motoneuron reciprocally connected to a computer are investigated as a function of the time delay, tau, for propagation around the loop. It is shown that for certain choices of tau, multiple qualitatively different neural spike trains co-exist. A mathematical model is constructed for the dynamics of this pulsed-coupled recurrent loop in which all parameters are readily measured experimentally: the phase resetting curve of the neuron for a given simulated postsynaptic current and tau. For choices of the parameters for which multiple spiking patterns co-exist in the experimental paradigm, the model exhibits multistability. Numerical simulations suggest that qualitatively similar results will occur if the motoneuron is replaced by several other types of neurons and that once tau becomes sufficiently long, multistability will be the dominant form of dynamical behavior. These observations suggest that great care must be taken in determining the etiology of qualitative changes in neural spiking patterns, particularly when propagation times around polysynaptic loops are long.

Cellular mechanisms contributing to response variability of cortical neurons in vivo

Cortical neurons recorded in vivo exhibit highly variable responses to the repeated presentation of the same stimulus. To further understand the cellular mechanisms underlying this phenomenon, we performed intracellular recordings from neurons in cat striate cortex in vivo and examined the relationships between spontaneous activity and visually evoked responses. Activity was assessed on a trial-by-trial basis by measuring the membrane potential (Vm) fluctuations and spike activity during brief epochs immediately before and after the onset of an evoked response. We found that the response magnitude, expressed as a change in Vm relative to baseline, was linearly correlated with the preceding spontaneous Vm. This correlation was enhanced when the cells were hyperpolarized to reduce the activation of voltage-gated conductances. The output of the cells, expressed as spike counts and latencies, was only moderately correlated with fluctuations in the preceding spontaneous Vm. Spike-triggered averaging of Vm revealed that visually evoked action potentials arise from transient depolarizations having a rise time of approximately 10 msec. Consistent with this, evoked spike count was found to be linearly correlated with the magnitude of Vm fluctuations in the gamma (20-70 Hz) frequency band. We also found that the threshold of visually evoked action potentials varied over a range of approximately 10 mV. Examination of simultaneously recorded intracellular and extracellular activity revealed a correlation between Vm depolarization and spike discharges in adjacent cells. Together these results demonstrate that response variability is attributable largely to coherent fluctuations in cortical activity preceding the onset of a stimulus, but also to variations in action potential threshold and the magnitude of high-frequency fluctuations evoked by the stimulus.

Spatiotemporal activity patterns of rat cortical neurons predict responses in a conditioned task

Precise and repeated spike-train timings within and across neurons define spatiotemporal patterns of activity. Although the existence of these patterns in the brain is well established in several species, there has been no direct evidence of their influence on behavioral output. To address this question, up to 15 neurons were recorded simultaneously in the auditory cortex of freely moving rats while animals waited for acoustic cues in a Go/NoGo task. A total of 235 significant patterns were detected during this interval from an analysis of 13 hr of recording involving over 1 million spikes. Of particular interest were 129 (55%) patterns that were significantly associated with the type of response the animal made later, independent of whether the response was that prompted by the cue because the response occurred later and the cue was chosen randomly. Of these behavior-predicting patterns, half (59/129) were associated with an enhanced tendency to go in response to the stimulus, and for 11 patterns of this subset, trials including the pattern were followed by significantly faster reaction time than those lacking the pattern. The remaining behavior-predicting patterns were associated with an enhanced NoGo tendency. Overall mean discharge rates did not vary across trials. Hence, these data demonstrate that particular spatiotemporal patterns predict future behavioral responses. Such presignal activity could form templates for extracting specific sensory information, motor programs prespecifying preference for a particular act, and/or some intermediate, associative brain process.

Learning-induced physiological plasticity in the thalamo-cortical sensory systems: a critical evaluation of receptive field plasticity, map changes and their potential mechanisms

The goal of this review is to give a detailed description of the main results obtained in the field of learning-induced plasticity. The review is focused on receptive field and map changes observed in the auditory, somatosensory and visual thalamo-cortical system as a result of an associative training performed in waking animals. Receptive field (RF) plasticity, 2DG and map changes obtained in the auditory and somatosensory system are reviewed. In the visual system, as there is no RF and map analysis during learning per se, the evidence presented are from increased neuronal responsiveness, and from the effects of perceptual learning in human and non human primates. Across sensory modalities, the re-tuning of neurons to a significant stimulus or map reorganizations in favour of the significant stimuli were observed at the thalamic and/or cortical level. The analysis of the literature in each sensory modality indicates that relationships between learning-induced sensory plasticity and behavioural performance can, or cannot, be found depending on the tasks that were used. The involvement (i) of Hebbian synaptic plasticity in the described neuronal changes and (ii) of neuromodulators as "gating" factors of the neuronal changes, is evaluated. The weakness of the Hebbian schema to explain learning-induced changes and the need to better define what the word "learning" means are stressed. It is suggested that future research should focus on the dynamic of information processing in sensory systems, and the concept of "effective connectivity" should be useful in that matter.

Corticofugal modulation of functional connectivity within the auditory thalamus of rat, guinea pig and cat revealed by cooling deactivation

Microelectrode recordings were simultaneously performed at multiple sites in the medial geniculate body (MGB) of anesthetized cats, rats and guinea pigs. We studied the effect of cortical deactivation on the association of neural activity within the thalamus during spontaneous activity. The corticofugal influence was suppressed by temporary cooling of the auditory cortex. Pairs of spike trains recorded from the same electrode were distinguished from cases where units were in MGB but recorded with different electrodes. Time domain analyses included crosscorrelations and search for precise repetition of complex spatiotemporal firing patterns of reverberating thalamic circuits. As a complementary approach we performed bispectral analyses of simultaneously recorded local field potentials in order to uncover the frequency components of their power spectra which are non linearly coupled. All results suggest that new functional neuronal circuits might appear at the thalamic level in the absence of input from the cortex. The newly active intrathalamic connections would provide the necessary input to sustain the reverberating activity of thalamic cell assemblies and generate low frequency non-linear interactions. The dynamic control exerted by the cortex over the functional segregation of information processing carried out in the thalamus conforms with theoretical neural network studies and with the functional selectivity-adaptive filtering theory of thalamic neuronal assemblies. Although this general conclusion remains valid across species, specific differences are discussed in the frame of known differences of the microcircuitry elements.

Dynamical cell assemblies in the rat auditory cortex in a reaction-time task

Simultaneous single unit spike trains were recorded in the auditory cortex of freely moving rats performing a complex cognitive task. The experimental paradigm is based on a two-choice task (Go/Nogo) with a two-component (pitch and location) auditory stimulus lasting 500 ms. We report evidence that firstly functional interactions, measured by cross-correlation analysis, between single units in the auditory cortex are dynamically modified in the period preceding the onset of the auditory stimulation, referred to as the 'waiting period'. We secondly observed that spatio-temporal firing patterns both within, and across cell spike trains also tended to appear in the waiting period, several seconds before the actual stimulus delivery. These patterns indicate a very precise repetition of spike discharges separated by long intervals (up to several hundreds of milliseconds). No consistent changes in mean rate were observed. These results suggest that network activity in the auditory cortex is selectively modified in rate independent ways before the actual sensory stimulation. These modifications may reflect participation of recurrent neuronal networks in processes anticipating the expected sensory input.

Spatiotemporal structure of cortical activity: properties and behavioral relevance

The study was designed to reveal occurrences of precise firing sequences (PFSs) in cortical activity and to test their behavioral relevance. Two monkeys were trained to perform a delayed-response paradigm and to open puzzle boxes. Extracellular activity was recorded from neurons in premotor and prefrontal areas with an array of six microelectrodes. An algorithm was developed to detect PFSs, defined as a set of three spikes and two intervals with a precision of +/-1 ms repeating significantly more than expected by chance. The expected level of repetition was computed based on the firing rate and the pairwise correlation of the participating units, assuming a Poisson distribution of event counts. Accordingly, the search for PFSs was corrected for rate modulations. PFSs were found in 24/25 recording sessions. Most PFSs (76%) were composed of spikes of more than one unit but usually not more than two units (67%). The PFSs spanned hundreds of milliseconds, and the average interval between two events within the PFSs was 200 ms. No traces of periodic oscillations were found in the PFS intervals. The bins of the matrix that were defined as PFSs were isolated temporally: the spikes that generated PFSs were not associated with high-frequency bursts or rapid coherent rate fluctuations. A given PFS tended to be correlated with the animal's behavior. Furthermore, for 19% of the PFS pairs that shared the same unit composition, each member of the pair was associated with a different type of behavior. The PFSs often appeared in clusters that were associated with particular phases of the behavior. The firing rate of single units did not provide a full explanation for the timing and structure of these clusters. A reduced spike train (RST) was defined for each unit by taking all spikes of that unit that were part of any PFS. In 88% of the cases the degree of modulation of the RST was higher than that of the complete spike train. The results suggest that relevant information is carried by the fine temporal structure of cortical activity. A coding scheme that involves such temporal structures is rich and sufficiently flexible to facilitate a rapid organization of cortical neurons into functional groups. The results can be accounted for by the synfire chain model, which suggests that cortical activity is mediated by synchronous activation of neural groups in a reverberatory mode.

Measure and statistical test for cross-correlation between paired neuronal spike trains with small sample size

Recent development of multi-unit recording techniques such as optical recording and multi-electrode arrays makes it possible to record neuronal activities from tens or hundreds of neurons simultaneously. To analyze functional connections between these neurons, cross-correlation analysis has been most commonly applied to the hundreds to thousands of pairs of these neurons. However, conventional cross-correlation data needs statistical tests for significance especially when the sample size of recorded spike trains is small. Here, a multiple hypergeometric model based on a transformation of the cross-correlogram data to a 2 x J table has been suggested. The exact p value for significance can be obtained by the generalized Fisher's method with small sample size and a cross-correlation coefficient for the strength of cross-correlation can be obtained based on the R-square analogue for nominal data. For large sample size, chi 2 test can be applied based on the same transformation. Examples of real spike train data set and simulation show that the methods are applicable to the data of multi-unit activity with only tens of spikes. These methods are especially useful when thousands of cross-correlograms need to be screened quickly and automatically.

Investigating dynamic aspects of brain function in slice preparations: spatiotemporal stimulus patterns generated with an easy-to-build multi-electrode array

Electrical stimulation of nervous tissue with single stimulating electrodes is a technique widely used for the investigation of nervous system function. While it has proved to be useful in all kinds of experiments, single electrode stimuli are, however, far from being 'natural'. In most parts of the living brain, incoming activity results from the firing of a large number of presynaptic neurons, thus reflecting a complex combination of space and time aspects of neural activity. In this paper, a multi-electrode stimulating system is introduced which allows for the generation of fast space-time stimulus patterns. An example for the application of dynamic input patterns to the cerebellar cortex in vitro is given. The corresponding experiments revealed aspects of cerebellar function which cannot be seen using static or single electrode stimulation.

Physiological differentiation within the auditory part of the thalamic reticular nucleus of the cat

Spike trains of 153 single units were recorded in the caudoventral part of the thalamic reticular nucleus (RE) of 7 nitrous oxide anaesthetized cats. Functional properties defined by spontaneous activity pattern, studied by mean of auto renewal density histograms, were used to subdivide the units into 4 groups. Types I (18%), II (56%) and III (15%) were defined by an increasing bursting activity and Type IV (11%) by firing no bursts spontaneously. The responses to auditory stimuli confirmed that the caudoventral part of RE is tightly related to central auditory pathways. Responses to white noise bursts (200 ms duration) significantly let appear that Type I units responded in a high proportion (greater than 70%) until 80 ms after the stimulus onset, Type II units where mostly affected during the entire stimulus duration, and Type III units showed preferentially late responses. The units responsive to high frequencies (greater than 8 kHz) were mostly located in the dorsal and the units responsive to low frequencies (less than 2 kHz) in the anteroventral sector of auditory RE. However, only a loosely tonotopy is supported by this study. The neuronal circuitry within RE was shown to be stable when white noise bursts were delivered. Cross-correlograms indicated a large proportion of interconnected units (64%) and signs of mutual inhibition between neighboring RE units (11%). The hypothesis is discussed that the auditory RE exerts a fine control on the time-dependent analysis of the incoming auditory input to the cerebral cortex. The complex intranuclear connectivity suggests that the cell types correspond to distinct patterns of functional connections.