Network-State Modulation of Power-Law Frequency-Scaling in Visual Cortical Neurons

Various types of neural-based signals, such as EEG, local field potentials and intracellular synaptic potentials, integrate multiple sources of activity distributed across large assemblies. They have in common a power-law frequency-scaling structure at high frequencies, but it is still unclear whether this scaling property is dominated by intrinsic neuronal properties or by network activity. The latter case is particularly interesting because if frequency-scaling reflects the network state it could be used to characterize the functional impact of the connectivity. In intracellularly recorded neurons of cat primary visual cortex in vivo, the power spectral density of Vm activity displays a power-law structure at high frequencies with a fractional scaling exponent. We show that this exponent is not constant, but depends on the visual statistics used to drive the network. To investigate the determinants of this frequency-scaling, we considered a generic recurrent model of cortex receiving a retinotopically organized external input. Similarly to the in vivo case, our in computo simulations show that the scaling exponent reflects the correlation level imposed in the input. This systematic dependence was also replicated at the single cell level, by controlling independently, in a parametric way, the strength and the temporal decay of the pairwise correlation between presynaptic inputs. This last model was implemented in vitro by imposing the correlation control in artificial presynaptic spike trains through dynamic-clamp techniques. These in vitro manipulations induced a modulation of the scaling exponent, similar to that observed in vivo and predicted in computo. We conclude that the frequency-scaling exponent of the Vm reflects stimulus-driven correlations in the cortical network activity. Therefore, we propose that the scaling exponent could be used to read-out the “effective” connectivity responsible for the dynamical signature of the population signals measured at different integration levels, from Vm to LFP, EEG and fMRI

EXPLORATION PAR DES INTERFACES HYBRIDES DU CODE NEURONAL ET DES MÉCANISMES DE RÉGULATION DE L'INFORMATION SENSORIELLE DANS LE SYSTÈME VISUEL

Determining the neural code in the thalamus and cerebral cortex, especially in the primary visual area, is hindered by the complexity of the neural network which is based on an astonishing diversity of neuronal types exhibiting varying properties at the morphological, biochemical, electrical and synaptic levels. This diversity is amplified by the numerous functional properties of each neuron which are reflecting the highly recurrent synaptic connections in cortical circuits as well as the corticothalamocortical loop. In other words, the response specificity of each neuron is affected through thousands of excitatory and inhibitory synapses, by the overall computation performed in the thalamocortical network. In the first part, we developed a model of contextual synaptic bombardment reproducing the dynamics of thousands of excitatory and inhibitory synapses converging to a single cortical neuron. A major advantage of this model is the possibility to control the amount of synchronization among the afferent synapses contacting the cortical neuron. We show in the visual cortex of cats that the amount of synaptic synchronization is related to the sub-threshold neuronal activity correlation level. Classically used artificial stimulations such as drifting gratings led the cerebral cortex into a redundant and correlated state while natural stimulations produced a richer neural code with less correlations. These results indicate that the sub-threshold neuronal activity correlation level is an indicator of the functional state in which the cerebral cortex is engaged. In the second part, we further investigated the neural code by extending our study to the thalamus, the major gateway for the flow of sensory information from the periphery to the cerebral cortex. The thalamus receives a strong corticothalamic feedback which results from the overall computation performed by the cortical areas. In order to study the impact of the corticothalamic feedack, we modeled a retinothalamocortical pathway mixing artificial and biological neurons recorded in the slice and we mimicked in these neurons a synaptic bombardment of cortical origin through the injection of mixed excitatory and inhibitory stochastic inputs in dynamic-clamp. This approach allowed us to control independently every thalamic neurons involved in the artificial pathway. We show that the sensory information transfer from the retina to the primary visual cortex is regulated by both a stochastic facilitation process across the population and the classical gain control a the cellular level. The stochastic facilitation process which could not be seen at the single-cell level is governed by the level of inter-neuronal correlation of the neuronal activity in the thalamus. Unlike conventional views, -a highly decorrelated neuronal activity- optimizes the sensory information transfer from the retina to the cortex by promoting the synchronization of synaptic inputs. We suggest that a cortically-induced decorrelation could increase the transfer efficiency for specific cell assemblies in the thalamus, constituting an attentional mechanism at the level of the thalamocortical circuits. At the same time, we developed a method to extract synaptic conductance fluctuations from single-trial intracellular recordings. We expect this method will help refining our understanding of the synaptic contexts in which the neurons are immersed with potential benefits on the development of new synaptic bombardment models. To conclude, our work confirms the hypothesis of a neural code based on synaptic synchronization governed by the level of correlation of the neuronal activity. Our results are consistent with numerous studies on attentional processes and suggest that active correlation and decorrelation mechanisms as well as oscillatory activities may regulate the information transfer between sensory organs and cortical areas.L'identification du codage neuronal dans le thalamus et le cortex cérébral, et en particulier dans l'aire visuelle primaire, se heurte à la complexité du réseau neuronal qui repose sur une diversité étonnante des neurones, sur les plans morphologique, biochimique et électrique, et de leurs connexions synaptiques. À cela s'ajoute une importante diversité des propriétés fonctionnelles de ces neurones reflétant en grande partie la forte récurrence des connexions synaptiques au sein des réseaux corticaux ainsi que la boucle cortico-thalamo-corticale. En d'autres termes, le calcul global effectué dans le réseau thalamo-cortical influence, via des milliers de connexions synaptiques excitatrices et inhibitrices, la spécificité de la réponse de chaque neurone. Dans une première partie, nous avons développé un modèle de bombardement synaptique contextuel reproduisant la dynamique de milliers de synapses excitatrices et inhibitrices convergeant vers un neurone cortical avec l'avantage de pouvoir paramétrer le niveau de synchronisation des synapses afférentes. Nous montrons que le niveau de synchronisation synaptique est relié au taux de corrélation de l'activité neuronale sous-liminaire dans le cortex visuel du chat, avec d'une part un régime où le codage neuronal est très redondant pour des stimulations artificielles classiquement utilisées du type réseau de luminance sinusoïdale, et d'autre part un régime où le codage neuronal est beaucoup plus riche présentant moins de corrélation neuronale pour des stimulations naturelles. Ces résultats indiquent que le taux de corrélation de l'activité neuronale sous-liminaire est un indicateur fonctionnel du régime de codage dans lequel est engagé le cortex cérébral. Dans une seconde partie, nous avons étendu l'exploration du codage neuronal au thalamus, passerelle principale qui transmet les informations sensorielles en provenance de la périphérie vers le cortex cérébral. Le thalamus reçoit un fort retour cortico-thalamique qui résulte du calcul global effectué par les aires corticales. Nous avons étudié son influence en modélisant une voie retino-thalamo-corticale mixant neurones artificiels et neurones biologiques in vitro dans laquelle un bombardement synaptique d'origine corticale est mimé via l'injection de conductances stochastiques excitatrices et inhibitrices en clamp dynamique. Cette approche confère l'avantage de pouvoir contrôller individuellement chacun des neurones thalamiques dans la voie artificielle. Nous montrons qu'un processus de facilitation stochastique à l'échelle de la population s'adjoint au gain cellulaire classique pour contrôler le transfert de l'information sensorielle de la rétine au cortex visuel primaire. Ce processus de facilitation stochastique, qui n'aurait pas pu être discerné à l'échelle de la cellule individuelle, est gouverné par le taux de corrélation inter-neuronale de l'activité neuronale dans le thalamus. À l'inverse des conceptions classiques, -un fort taux de décorrélation- optimise le transfert sensoriel de la rétine au cortex en favorisant la synchronisation des afférences synaptiques. Nous suggérons qu'une décorrélation induite par les aires corticales pourrait augmenter l'efficacité du transfert pour certaines assemblées cellulaires dans le thalamus, constituant ainsi un mécanisme attentionnel à l'échelle des circuits thalamo-corticaux. En parallèle, nous avons développé une méthode d'extraction des fluctuations des conductances synaptiques des neurones à partir d'enregistrements intracellulaires unitaires. Cette méthode devrait permettre de raffiner nos connaissances sur la nature des contextes synaptiques dans lesquels sont immergés les neurones avec des retombées potentielles sur le développement de nouveaux modèles de bombardements synaptiques. En conclusion, nos travaux confirment l'hypothèse d'un codage neuronal basé sur la synchronisation synaptique conditionnée par le niveau de corrélation de l'activité neuronale. Nos travaux sont cohérents avec de nombreuses études sur les processus attentionnels et suggèrent que des mécanismes de corrélation et décorrélation actives, ainsi que des activités oscillatoires, pourraient réguler le transfert de l'information entre les organes sensoriels et les aires corticales

Corticothalamic Synaptic Noise as a Mechanism for Selective Attention in Thalamic Neurons

International audienceA reason why the thalamus is more than a passive gateway for sensory signals is that two-third of the synapses of thalamocortical neurons are directly or indirectly related to the activity of corticothalamic axons. While the responses of thalamocortical neurons evoked by sensory stimuli are well characterized, with ON-and OFF-center receptive field structures, the prevalence of synaptic noise resulting from neocortical feedback in intracellularly recorded thalamocortical neurons in vivo has attracted little attention. However, in vitro and modeling experiments point to its critical role for the integration of sensory signals. Here we combine our recent findings in a unified framework suggesting the hypothesis that corticothalamic synaptic activity is adapted to modulate the transfer efficiency of thalamocortical neurons during selective attention at three different levels: First, on ionic channels by interacting with intrinsic membrane properties, second at the neuron level by impacting on the input-output gain, and third even more effectively at the cell assembly level by boosting the information transfer of sensory features encoded in thalamic subnetworks. This top-down population control is achieved by tuning the correlations in subthreshold membrane potential fluctuations and is adapted to modulate the transfer of sensory features encoded by assemblies of thalamocortical relay neurons. We thus propose that cortically-controlled (de-)correlation of subthreshold noise is an efficient and swift dynamic mechanism for selective attention in the thalamus

Cortically-Controlled Population Stochastic Facilitation as a Plausible Substrate for Guiding Sensory Transfer across the Thalamic Gateway

<div><p>The thalamus is the primary gateway that relays sensory information to the cerebral cortex. While a single recipient cortical cell receives the convergence of many principal relay cells of the thalamus, each thalamic cell in turn integrates a dense and distributed synaptic feedback from the cortex. During sensory processing, the influence of this functional loop remains largely ignored. Using dynamic-clamp techniques in thalamic slices <i>in vitro</i>, we combined theoretical and experimental approaches to implement a realistic hybrid retino-thalamo-cortical pathway mixing biological cells and simulated circuits. The synaptic bombardment of cortical origin was mimicked through the injection of a stochastic mixture of excitatory and inhibitory conductances, resulting in a gradable correlation level of afferent activity shared by thalamic cells. The study of the impact of the simulated cortical input on the global retinocortical signal transfer efficiency revealed a novel control mechanism resulting from the collective resonance of all thalamic relay neurons. We show here that the transfer efficiency of sensory input transmission depends on three key features: i) the number of thalamocortical cells involved in the many-to-one convergence from thalamus to cortex, ii) the statistics of the corticothalamic synaptic bombardment and iii) the level of correlation imposed between converging thalamic relay cells. In particular, our results demonstrate counterintuitively that the retinocortical signal transfer efficiency increases when the level of correlation across thalamic cells decreases. This suggests that the transfer efficiency of relay cells could be selectively amplified when they become simultaneously desynchronized by the cortical feedback. When applied to the intact brain, this network regulation mechanism could direct an attentional focus to specific thalamic subassemblies and select the appropriate input lines to the cortex according to the descending influence of cortically-defined “priors”.</p></div

Impact of thalamocortical oscillations on the retinocortical information transfer efficiency <i>in computo</i>.

<p>A and B. Sine-wave currents of varying amplitude and frequency were injected to every model TC cells in addition to retinal inputs and uncorrelated synaptic bombardment. The current oscillations were either coherent (same phase for every TC cells) or desynchronized (phase evenly distributed in the thalamic population). C and D. Transfer efficiency for both conditions shown in A and B, respectively.</p

Network topology affects the retinocortical transfer of sensory information <i>in computo</i>.

<p>A. Transfer efficiency as a function of the thalamic population size. Each point represents the simulation of a modeled convergent circuit for three predefined TC AMPA synaptic weights in addition to a special case (dashed line) where the synaptic weight was adjusted to the thalamic population size on a per-simulation basis (see text for more details). The thickness of the curves represent the standard deviation across ten repetitions of the same retinal sensory simulation realized each time in the context of a different realization of the cortical synaptic bombardment. B. Transfer efficiency as a function of the TC AMPA synaptic weight for three predefined thalamic population size. C. Influence of the level of retinal input synchronization. The TE was measured for varying retinothalamic spike-time mean jitters and retinal synchronization levels (see text for more details). D. Transfer efficiency measured as a function of the thalamocortical spike-time mean jitter.</p

Decorrelation of the corticothalamic synaptic noise boosts retinocortical signal transfer in BICNs.

<p>A. Top. Illustration of voltage traces for a small single-cell BICN (indicated by an arrow in B) receiving uncorrelated synaptic bombardment. Insets. Zoomed sections of the biological TC cells membrane voltage fluctuations. Bottom. Same BICN as above receiving a correlated synaptic bombardment. Numerous spike failures are observed compared to the uncorrelated synaptic bombardment. The lower left bar graph shows the mean (± SEM across all spikes) retinocortical spike transmission probability for both the uncorrelated and correlated conditions. B. Transfer efficiency as a function of the synaptic noise correlation strength in small single-cell BICNs (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003401#s4" target="_blank">Methods</a>) normalized relative to the respective uncorrelated condition of each BICN ( = 0). Each curve represents a different BICN with varying synaptic bombardment correlation strength. The correlation was varied using the heterogeneous schema. Curves with similar colors represent BICNs built from the same biological TC neuron. C. Average TE drop for all small single-cell BICNs (± SEM across all BICNs) as a function of the synaptic bombardment correlation strength. D. TE measurements for large mixed-cell BICNs and average TE for large single-cell BICNs of varying size for both correlated and partially decorrelated conditions (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003401#s4" target="_blank">Methods</a>).</p

<i>In vitro</i> and <i>In computo</i> reconstructions of convergent thalamocortical networks.

<p>A. Hybrid convergent circuit model. Biological or model TC cells synaptically converge to a model cortical neuron. The population of TC cells is fed with modeled retinal inputs and receives a corticothalamic input mimicked through the injection of stochastically fluctuating mixed excitatory and inhibitory conductances. Inset. Retinal and thalamic synaptic inputs elicit somatic conductance-based events in the target neurons (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003401#s4" target="_blank">Methods</a>). i, ii, iii, iv. Critical parameters of the circuit explored in this study. See text for more details. B. BICNs consisted in at least one biological TC cell recorded multiple times with identical retinal inputs and varying patterns of corticothalamic synaptic noise injected in real time through dynamic-clamp. The obtained response patterns were then simultaneously replayed in the hybrid circuit thus mimicking the functional impact produced by thalamocortical convergence. C. Membrane potential traces for a BICN. A single TC cell was recorded sequentially using the same model retinal inputs but adding different realizations of a synaptic stochastic bombardment each sharing the same conductance mean and variance (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003401#s4" target="_blank">Methods</a>; this is the uncorrelated condition in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003401#pcbi-1003401-g004" target="_blank">Fig. 4</a> and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003401#pcbi-1003401-g005" target="_blank">5</a>). Only five out of the ten thalamic voltage traces are shown. Spikes were truncated to −15 mV. D. Same as C with model TC cells.</p