Cross-trial correlation analysis of evoked potentials reveals arousal-related attenuation of thalamo-cortical coupling

We describe a computational method for assessing functional connectivity in sensory neuronal networks. The method, which we term cross-trial correlation, can be applied to signals representing local field potentials (LFPs) evoked by sensory stimulations and utilizes their trial-to-trial variability. A set of single trial samples of a given post-stimulus latency from consecutive evoked potentials (EPs) recorded at a given site is correlated with such sets for all other latencies and recording sites. The results of this computation reveal how neuronal activities at various sites and latencies correspond to activation of other sites at other latencies. The method was used to investigate the functional connectivity of thalamo-cortical network of somatosensory system in behaving rats at two levels of alertness: habituated and aroused. We analyzed potentials evoked by vibrissal deflections recorded simultaneously from the ventrobasal thalamus and barrel cortex. The cross-trial correlation analysis applied to the early post-stimulus period (<25 ms) showed that the magnitude of the population spike recorded in the thalamus at 5 ms post-stimulus correlated with the cortical activation at 6–13 ms post-stimulus. This correlation value was reduced at 6–9 ms, i.e. at early postsynaptic cortical response, with increased level of the animals’ arousal. Similarly, the aroused state diminished positive thalamo-cortical correlation for subsequent early EP waves, whereas the efficacy of an indirect cortico-fugal inhibition (over 15 ms) did not change significantly. Thus we were able to characterize the state related changes of functional connections within the thalamo-cortical network of behaving animals.     

Internally generated preactivation of single neurons in human medial frontal cortex predicts volition

Understanding how self-initiated behavior is encoded by neuronal circuits in the human brain remains elusive. We recorded the activity of 1019 neurons while twelve subjects performed self-initiated finger movement. We report progressive neuronal recruitment over ～1500 ms before subjects report making the decision to move. We observed progressive increase or decrease in neuronal firing rate, particularly in the supplementary motor area (SMA), as the reported time of decision was approached. A population of 256 SMA neurons is sufficient to predict in single trials the impending decision to move with accuracy greater than 80% already 700 ms prior to subjects' awareness. Furthermore, we predict, with a precision of a few hundred ms, the actual time point of this voluntary decision to move. We implement a computational model whereby volition emerges once a change in internally generated firing rate of neuronal assemblies crosses a threshold.     

Prefrontal phase locking to hippocampal theta oscillations

The interactions between cortical and hippocampal circuits are critical for memory formation, yet their basic organization at the neuronal network level is not well understood. Here, we demonstrate that a significant portion of neurons in the medial prefrontal cortex of freely behaving rats are phase locked to the hippocampal theta rhythm. In addition, we show that prefrontal neurons phase lock best to theta oscillations delayed by approximately 50 ms and confirm this hippocampo-prefrontal directionality and timing at the level of correlations between single cells. Finally, we find that phase locking of prefrontal cells is predicted by the presence of significant correlations with hippocampal cells at positive delays up to 150 ms. The theta-entrained activity across cortico-hippocampal circuits described here may be important for gating information flow and guiding the plastic changes that are believed to underlie the storage of information across these networks.     

Unconsciously Perceived Fear in Peripheral Vision Alerts the Limbic System: A MEG Study

Background

In ecological situations, threatening stimuli often come out from the peripheral vision. Such aggressive messages must trigger rapid attention to the periphery to allow a fast and adapted motor reaction. Several clues converge to hypothesize that peripheral danger presentation can trigger off a fast arousal network potentially independent of the consciousness spot.

Methodology/Principal Findings

In the present MEG study, spatio-temporal dynamics of the neural processing of danger related stimuli were explored as a function of the stimuli position in the visual field. Fearful and neutral faces were briefly presented in the central or peripheral visual field, and were followed by target faces stimuli. An event-related beamformer source analysis model was applied in three time windows following the first face presentations: 80 to 130 ms, 140 to 190 ms, and 210 to 260 ms. The frontal lobe and the right internal temporal lobe part, including the amygdala, reacted as soon as 80 ms of latency to fear occurring in the peripheral vision. For central presentation, fearful faces evoked the classical neuronal activity along the occipito-temporal visual pathway between 140 and 190 ms.

Conclusions

Thus, the high spatio-temporal resolution of MEG allowed disclosing a fast response of a network involving medial temporal and frontal structures in the processing of fear related stimuli occurring unconsciously in the peripheral visual field. Whereas centrally presented stimuli are precisely processed by the ventral occipito-temporal cortex, the related-to-danger stimuli appearing in the peripheral visual field are more efficient to produce a fast automatic alert response possibly conveyed by subcortical structures.     

Modelling studies on the computational function of fast temporal structure in cortical circuit activity

The interplay between modelling and experimental studies can support the exploration of the function of neuronal circuits in the cortex. We exemplify such an approach with a study on the role of spike timing and gamma-oscillations in associative memory in strongly connected circuits of cortical neurones. It is demonstrated how associative memory studies on different levels of abstraction can specify the functionality to be expected in real cortical neuronal circuits. In our model overlapping random configurations of sparse cell populations correspond to memory items that are stored by simple Hebbian coincidence learning. This associative memory task will be implemented with biophysically well tested compartmental neurones developed by Pinsky and Rinzel . We ran simulation experiments to study memory recall in two network architectures: one interconnected pool of cells, and two reciprocally connected pools. When recalling a memory by stimulating a spatially overlapping set of cells, the completed pattern is coded by an event of synchronized single spikes occurring after 25-60 ms. These fast associations are performed even at a memory load corresponding to the memory capacity of optimally tuned formal associative networks (>0.1 bit/synapse). With tonic stimulation or feedback loops in the network the neurones fire periodically in the gamma-frequency range (20-80 Hz). With fast changing inputs memory recall can be switched between items within a single gamma cycle. Thus, oscillation is not a primary coding feature necessary for associative memory. However, it accompanies reverberatory feedback providing an improved iterative memory recall completed after a few gamma cycles (60-260 ms). In the bidirectional architecture reverberations do not express in a rigid phase locking between the pools. For small stimulation sets bursting occurred in these cells acting as a supportive mechanism for associative memory.     

Limited plasticity of the rabbit visual cortex and hippocampal neurons in the oddball test

The activity of 41 visual cortex and 20 hippocampal neurons from field CA1 was registered in experiments using oddball-stimulation with different color stimuli varied in intensity. 34% cortical and 37% hippocampal neurons demonstrated plasticity reactions. The significant increase of latest phases of neuronal activity (200-500 and 200-1000 ms after stimulation for cortical neurons and 300-550 ms for hippocampal neurons) was shown in responses to rare deviant stimuli, which had a less intensity than frequently standards. The quantity of the earliest neuronal phase of activity (40-120 ms after stimulation) was stabilized in responses to deviants and standards during the experiment. We propose that such increase of the latest phases of neuronal activity (the limited plasticity) may reflect the mechanisms of orienting reaction.     

Synchronization study in ring-like and grid-like neuronal networks

In this paper, we study the synchronization status of both two gap-junction coupled neurons and neuronal network with two different network connectivity patterns. One of the network connectivity patterns is a ring-like neuronal network, which only considers nearest-neighbor neurons. The other is a grid-like neuronal network, with all nearest neighbor couplings. We show that by varying some key parameters, such as the coupling strength and the external current injection, the neuronal network will exhibit various patterns of firing synchronization. Different types of firing synchronization are diagnosed by means of a mean field potential, a bifurcation diagram, a correlation coefficient and the ISI-distance method. Numerical simulations demonstrate that the synchronization status of multiple neurons is much dependent on the network patters, when the number of neurons is the same. It is also demonstrated that the synchronization status of two coupled neurons is similar with the grid-like neuronal network, but differs radically from that of the ring-like neuronal network. These results may be instructive in understanding synchronization transitions in neuronal systems.     

Distributed Fading Memory for Stimulus Properties in the Primary Visual Cortex

It is currently not known how distributed neuronal responses in early visual areas carry stimulus-related information. We made multielectrode recordings from cat primary visual cortex and applied methods from machine learning in order to analyze the temporal evolution of stimulus-related information in the spiking activity of large ensembles of around 100 neurons. We used sequences of up to three different visual stimuli (letters of the alphabet) presented for 100 ms and with intervals of 100 ms or larger. Most of the information about visual stimuli extractable by sophisticated methods of machine learning, i.e., support vector machines with nonlinear kernel functions, was also extractable by simple linear classification such as can be achieved by individual neurons. New stimuli did not erase information about previous stimuli. The responses to the most recent stimulus contained about equal amounts of information about both this and the preceding stimulus. This information was encoded both in the discharge rates (response amplitudes) of the ensemble of neurons and, when using short time constants for integration (e.g., 20 ms), in the precise timing of individual spikes (≤∼20 ms), and persisted for several 100 ms beyond the offset of stimuli. The results indicate that the network from which we recorded is endowed with fading memory and is capable of performing online computations utilizing information about temporally sequential stimuli. This result challenges models assuming frame-by-frame analyses of sequential inputs.     

The dynamics of a neuronal culture of dissociated cortical neurons of neonatal rats

Neuronal networks of dissociated cortical neurons from neonatal rats were cultured over a multielectrode dish with 64 active sites, which were used both for recording the electrical activity and for stimulation. After about 4 weeks of culture, a dense network of neurons had developed and their electrical activity was studied. When a brief voltage pulse was applied to one extracellular electrode, a clear electrical response was evoked over almost the entire network. When a strong voltage pulse was used, the response was composed of an early phase, terminating within 25 ms, and a late phase which could last several hundreds of milliseconds. Action potentials evoked during the early phase occurred with a precise timing with a small jitter and the electrical activity initiated by a localized stimulation diffused significantly over the network. In contrast, the late phase was characterized by the occurrence of clusters of electrical activity with significant spatio-temporal fluctuations. The late phase was suppressed by adding small amounts of d(−)-2-amino-5-phosphonovaleric acid to the extracellular medium, or by increasing the amount of extracellular Mg²⁺. The electrical activity of the network was substantially increased by the addition of bicuculline to the extracellular medium. The results presented here show that the neuronal network may exist in two different dynamical states: one state in which the neuronal network behaves as a non-chaotic deterministic system and another state where the system exhibits large spatio-temporal fluctuations, characteristic of stochastic or chaotic systems. Received: 8 June 1999 / Accepted in revised form: 10 January 2000     

Latency-Related Development of Functional Connections in Cultured Cortical Networks

To study plasticity, we cultured cortical networks on multielectrode arrays, enabling simultaneous recording from multiple neurons. We used conditional firing probabilities to describe functional network connections by their strength and latency. These are abstract representations of neuronal pathways and may arise from direct pathways between two neurons or from a common input. Functional connections based on direct pathways should reflect synaptic properties. Therefore, we searched for long-term potentiation (this mechanism occurs in vivo when presynaptic action potentials precede postsynaptic ones with interspike intervals up to ∼20 ms) in vitro. To investigate if the strength of functional connections showed a similar latency-related development, we selected periods of monotonously increasing or decreasing strength. We observed increased incidence of short latencies (5-30 ms) during strengthening, whereas these rarely occurred during weakening. Furthermore, we saw an increased incidence of 40-65 ms latencies during weakening. Conversely, functional connections tended to strengthen in periods with short latency, whereas strengthening was significantly less than average during long latency. Our data suggest that functional connections contain information about synaptic connections, that conditional firing probability analysis is sensitive enough to detect it and that a substantial fraction of all functional connections is based on direct pathways.     

Effects of acoustic stimuli on neuronal activity in the auditory cortex of the rat

Spontaneous activity of cortical neurons exhibits alternative fluctuations of membrane potential consisting of phased depolarization called "up-state" and persistent hyperpolarization called "down-state" during slow wave sleep and anesthesia. Here, we examined the effects of sound stimuli (noise bursts) on neuronal activity by intracellular recording in vivo from the rat auditory cortex (AC). Noise bursts increased the average time in the up-state by 0.81+/-0.65 s (range, 0.27-1.74 s) related to a 10 s recording duration. The rise times of the spontaneous up-events averaged 69.41+/-18.04 ms (range, 40.10-119.21 ms), while those of the sound-evoked up-events were significantly shorter (p<0.001) averaging only 22.54+/-8.81 ms (range, 9.31-45.74 ms). Sound stimulation did not influence ongoing spontaneous up-events. Our data suggest that a sound stimulus does not interfere with ongoing spontaneous neuronal activity in auditory cortex but can evoke new depolarizations in addition to the spontaneous ones.     

Recurrent Network Models for Perfect Temporal Integration of Fluctuating Correlated Inputs

Temporal integration of input is essential to the accumulation of information in various cognitive and behavioral processes, and gradually increasing neuronal activity, typically occurring within a range of seconds, is considered to reflect such computation by the brain. Some psychological evidence suggests that temporal integration by the brain is nearly perfect, that is, the integration is non-leaky, and the output of a neural integrator is accurately proportional to the strength of input. Neural mechanisms of perfect temporal integration, however, remain largely unknown. Here, we propose a recurrent network model of cortical neurons that perfectly integrates partially correlated, irregular input spike trains. We demonstrate that the rate of this temporal integration changes proportionately to the probability of spike coincidences in synaptic inputs. We analytically prove that this highly accurate integration of synaptic inputs emerges from integration of the variance of the fluctuating synaptic inputs, when their mean component is kept constant. Highly irregular neuronal firing and spike coincidences are the major features of cortical activity, but they have been separately addressed so far. Our results suggest that the efficient protocol of information integration by cortical networks essentially requires both features and hence is heterotic.     

Convergence of Signals and Reorganization of Neuronal Activity in a Model of Neuronal Network and in Striatum of the Monkey Brain

To elucidate principles of neuronal organization providing preservation of informational content of converging impulse flows in afferent impulsation of neurons, a comparison is performed of results obtained in the previously carried out experiments on a model of neuronal network and in a study of correlates of behavior in the neuronal network of the monkey brain neostriatum (putamen). This comparison has shown that responses of the neuronal network model to different ratio of input impulse flows and changes of the neostriatal neuronal activity, which accompany different behavioral actions, are seen the most clearly in reorganization of composition of the most active neurons. Each combination of input signals and each behavioral action of the animal correspond to a non-repeated mosaic of neuronal activity. The data obtained indicate that the neuronal network, both real and in the simplest model variant, is able to transform the converging input signals into the mosaic equivalent to their entire combination and thereby to transmit the result of generalization of the input signals of the network to the innervated brain structures.     

Neuronal Adaptation Translates Stimulus Gaps into a Population Code

Neurons in sensory pathways exhibit a vast multitude of adaptation behaviors, which are assumed to aid the encoding of temporal stimulus features and provide the basis for a population code in higher brain areas. Here we study the transition to a population code for auditory gap stimuli both in neurophysiological recordings and in a computational network model. Independent component analysis (ICA) of experimental data from the inferior colliculus of Mongolian gerbils reveals that the network encodes different gap sizes primarily with its population firing rate within 30 ms after the presentation of the gap, where longer gap size evokes higher network activity. We then developed a computational model to investigate possible mechanisms of how to generate the population code for gaps. Phenomenological (ICA) and functional (discrimination performance) analyses of our simulated networks show that the experimentally observed patterns may result from heterogeneous adaptation, where adaptation provides gap detection at the single neuron level and neuronal heterogeneity ensures discriminable population codes for the whole range of gap sizes in the input. Furthermore, our work suggests that network recurrence additionally enhances the network''s ability to provide discriminable population patterns.     

Spike-timing-dependent BDNF secretion and synaptic plasticity

In acute hippocampal slices, we found that the presence of extracellular brain-derived neurotrophic factor (BDNF) is essential for the induction of spike-timing-dependent long-term potentiation (tLTP). To determine whether BDNF could be secreted from postsynaptic dendrites in a spike-timing-dependent manner, we used a reduced system of dissociated hippocampal neurons in culture. Repetitive pairing of iontophoretically applied glutamate pulses at the dendrite with neuronal spikes could induce persistent alterations of glutamate-induced responses at the same dendritic site in a manner that mimics spike-timing-dependent plasticity (STDP)—the glutamate-induced responses were potentiated and depressed when the glutamate pulses were applied 20 ms before and after neuronal spiking, respectively. By monitoring changes in the green fluorescent protein (GFP) fluorescence at the dendrite of hippocampal neurons expressing GFP-tagged BDNF, we found that pairing of iontophoretic glutamate pulses with neuronal spiking resulted in BDNF secretion from the dendrite at the iontophoretic site only when the glutamate pulses were applied within a time window of approximately 40 ms prior to neuronal spiking, consistent with the timing requirement of synaptic potentiation via STDP. Thus, BDNF is required for tLTP and BDNF secretion could be triggered in a spike-timing-dependent manner from the postsynaptic dendrite.     

Automatic Adaptation to Fast Input Changes in a Time-Invariant Neural Circuit

Neurons must faithfully encode signals that can vary over many orders of magnitude despite having only limited dynamic ranges. For a correlated signal, this dynamic range constraint can be relieved by subtracting away components of the signal that can be predicted from the past, a strategy known as predictive coding, that relies on learning the input statistics. However, the statistics of input natural signals can also vary over very short time scales e.g., following saccades across a visual scene. To maintain a reduced transmission cost to signals with rapidly varying statistics, neuronal circuits implementing predictive coding must also rapidly adapt their properties. Experimentally, in different sensory modalities, sensory neurons have shown such adaptations within 100 ms of an input change. Here, we show first that linear neurons connected in a feedback inhibitory circuit can implement predictive coding. We then show that adding a rectification nonlinearity to such a feedback inhibitory circuit allows it to automatically adapt and approximate the performance of an optimal linear predictive coding network, over a wide range of inputs, while keeping its underlying temporal and synaptic properties unchanged. We demonstrate that the resulting changes to the linearized temporal filters of this nonlinear network match the fast adaptations observed experimentally in different sensory modalities, in different vertebrate species. Therefore, the nonlinear feedback inhibitory network can provide automatic adaptation to fast varying signals, maintaining the dynamic range necessary for accurate neuronal transmission of natural inputs.     

Time-Dependent Increase in Network Response to Stimulation

In vitro neuronal cultures have become a popular method with which to probe network-level neuronal dynamics and phenomena in controlled laboratory settings. One of the key dynamics of interest in these in vitro studies has been the extent to which cultured networks display properties indicative of learning. Here we demonstrate the effects of a high frequency electrical stimulation signal in training cultured networks of cortical neurons. Networks receiving this training signal displayed a time-dependent increase in the response to a low frequency probing stimulation, particularly in the time window of 20–50 ms after stimulation. This increase was found to be statistically significant as compared to control networks that did not receive training. The timing of this increase suggests potentiation of synaptic mechanisms. To further investigate this possibility, we leveraged the powerful Cox statistical connectivity method as previously investigated by our group. This method was used to identify and track changes in network connectivity strength.     

Oscillations and synchrony in a cortical neural network

In this paper, the oscillations and synchronization status of two different network connectivity patterns based on Izhikevich model are studied. One of the connectivity patterns is a randomly connected neuronal network, the other one is a small-world neuronal network. This Izhikevich model is a simple model which can not only reproduce the rich behaviors of biological neurons but also has only two equations and one nonlinear term. Detailed investigations reveal that by varying some key parameters, such as the connection weights of neurons, the external current injection, the noise of intensity and the neuron number, this neuronal network will exhibit various collective behaviors in randomly coupled neuronal network. In addition, we show that by changing the number of nearest neighbor and connection probability in small-world topology can also affect the collective dynamics of neuronal activity. These results may be instructive in understanding the collective dynamics of mammalian cortex.     

Organization of neocortical neuronal networks

Multiple unit activity in deep layers of the frontal and motor cortices was recorded by chronically implanted semimicroelectrodes in waking cats with different levels of food motivation. From four to seven neuronal spike trains were selected from the recorded multiunit activity. Interactions between neighbouring neurons in the motor and frontal areas of the neocortex (within the local neuronal networks) and between the neurons of these areas (distributed neuronal networks) were estimated by means of statistical crosscorrelation analysis of spike trains within the range of delays from 0 to 100 ms. Neurons in the local networks were divided in two subgroups: the neurons with higher spike amplitudes with the dominance of divergent connections and neurons with lower spike amplitudes with the dominance of convergent connections. Strong monosynaptic connections (discharges with a delay of less than 2 ms) between the neurons with high- and low-amplitude spikes formed the background of the local networks. Connections between low-amplitude neurons in the frontal cortex and high-amplitude neurons in the motor cortex dominated in the distributed networks. A 24-hour food deprivation predominantly altered the late interneuronal crosscorrelations with time delays within the range of 2-100 ms in both local and distributed networks.     

体外培养的神经元网络可塑性

神经元网络是大脑执行高级认知行为的结构基础,研究证明学习记忆及神经退行性疾病与神经元网络可塑性密切相关。因此,揭示调控和改变神经元网络可塑性的机制对理解神经系统信息交互以及疾病治疗具有重大意义。目前,基于微电极阵列（microelectrode array, MEA）培养的神经元网络是体外探究学习和记忆机制的理想模型,同时针对该模型的研究为预防和治疗神经退行性疾病提供了独特的视角。本文综述了基于MEA采集体外培养神经元网络的放电信号来构建功能网络的相关研究,分别从二维神经元网络和三维脑类器官发育,以及开环和闭环电刺激对神经元网络可塑性影响的角度,总结了体外培养神经元网络可塑性的相关研究,最后对该方向的应用前景进行了展望。