High frequency field potentials of the cerebellar cortex

The cerebellum plays a crucial role in motor coordination along with basal ganglia and the motor areas of the cerebral cortex. Both somatosensory and the cerebro-cerebral pathways bring in massive amounts of neural information to the cerebellum. The output of the cerebellar cortex projects to various motor cortices as well as down to the spinal cord to make its contributions to the motor function. The origin and function of the field potential oscillations in the cerebellum, especially in the high frequencies, have not been explored sufficiently. The primary objective of this study was to investigate the spatio-temporal characteristics of high frequency field potentials (150-350Hz) in the cerebellar cortex in a behavioral context. To this end, the paramedian lobule in rats was recorded using micro electro-corticogram (µ-ECoG) electrode arrays while the animal performed a lever press task using the forelimb. The phase synchrony analysis shows that the high frequency oscillations recorded at multiple points across the paramedian cortex episodically synchronize immediately before and desynchronize during the lever press. The electrode contacts were grouped according to their temporal course of phase synchrony around the time of lever press. Contact groups presented patches with slightly stronger synchrony values in the medio-lateral direction, and did not appear to form parasagittal zones. Spatiotemporal synchrony of high frequency field potentials has not been reported at such large-scales previously in the cerebellar cortex

Optimization of Efficient Neuron Models With Realistic Firing Dynamics. The Case of the Cerebellar Granule Cell

Biologically relevant large-scale computational models currently represent one of the main methods in neuroscience for studying information processing primitives of brain areas. However, biologically realistic neuron models tend to be computationally heavy and thus prevent these models from being part of brain-area models including thousands or even millions of neurons. The cerebellar input layer represents a canonical example of large scale networks. In particular, the cerebellar granule cells, the most numerous cells in the whole mammalian brain, have been proposed as playing a pivotal role in the creation of somato-sensorial information representations. Enhanced burst frequency (spiking resonance) in the granule cells has been proposed as facilitating the input signal transmission at the theta-frequency band (4–12 Hz), but the functional role of this cell feature in the operation of the granular layer remains largely unclear. This study aims to develop a methodological pipeline for creating neuron models that maintain biological realism and computational efficiency whilst capturing essential aspects of single-neuron processing. Therefore, we selected a light computational neuron model template (the adaptive-exponential integrate-and-fire model), whose parameters were progressively refined using an automatic parameter tuning with evolutionary algorithms (EAs). The resulting point-neuron models are suitable for reproducing the main firing properties of a realistic granule cell from electrophysiological measurements, including the spiking resonance at the theta-frequency band, repetitive firing according to a specified intensityfrequency (I-F) curve and delayed firing under current-pulse stimulation. Interestingly, the proposed model also reproduced some other emergent properties (namely, silent at rest, rheobase and negligible adaptation under depolarizing currents) even though these properties were not set in the EA as a target in the fitness function (FF), proving that these features are compatible even in computationally simple models. The proposed methodology represents a valuable tool for adjusting AdEx models according to a FF defined in the spiking regime and based on biological data. These models are appropriate for future research of the functional implication of bursting resonance at the theta band in large-scale granular layer network models.FEDER/Junta de Andalucia-Consejeria de Economia y Conocimiento under the EmbBrain project A-TIC-276-UGR18University of Granada under the Young Researchers FellowshipMinisterio de Economia y Competitividad (MINECO)-FEDER TIN2016-81041-REuropean Human Brain Project SGA2 ( H2020-RIA) 785907European Human Brain Project SGA3 (European Commission) ( H2020-RIA) 945539CEREBIO P18-FR-237

Synchronization in Primate Cerebellar Granule Cell Layer Local Field Potentials: Basic Anisotropy and Dynamic Changes During Active Expectancy

The cerebellar cortex is remarkable for its organizational regularity, out of which task-related neural networks should emerge. In Purkinje cells, both complex and simple spike network patterns are evident in sensorimotor behavior. However, task-related patterns of activity in the granule cell layer (GCL) have been less studied. We recorded local field potential (LFP) activity simultaneously in pairs of GCL sites in monkeys performing an active expectancy (lever-press) task, in passive expectancy, and at rest. LFP sites were selected when they showed strong 10–25 Hz oscillations; pair orientation was in stereotaxic sagittal and coronal (mainly), and diagonal. As shown previously, LFP oscillations at each site were modulated during the lever-press task. Synchronization across LFP pairs showed an evident basic anisotropy at rest: sagittal pairs of LFPs were better synchronized (more than double the cross-correlation coefficients) than coronal pairs, and more than diagonal pairs. On the other hand, this basic anisotropy was modifiable: during the active expectancy condition, where sagittal and coronal orientations were tested, synchronization of LFP pairs would increase just preceding movement, most notably for the coronal pairs. This lateral extension of synchronization was not observed in passive expectancy. The basic pattern of synchronization at rest, favoring sagittal synchrony, thus seemed to adapt in a dynamic fashion, potentially extending laterally to include more cerebellar cortex elements. This dynamic anisotropy in LFP synchronization could underlie GCL network organization in the context of sensorimotor tasks

Brain tissue mechanics is governed by microscale relations of the tissue constituents

Local mechanical tissue properties are a critical regulator of cell function in the central nervous system (CNS) during development and disorder. However, we still don't fully understand how the mechanical properties of individual tissue constituents, such as cell nuclei or myelin, determine tissue mechanics. Here we developed a model predicting local tissue mechanics, which induces non-affine deformations of the tissue components. Using the mouse hippocampus and cerebellum as model systems, we show that considering individual tissue components alone, as identified by immunohistochemistry, is not sufficient to reproduce the local mechanical properties of CNS tissue. Our results suggest that brain tissue shows a universal response to applied forces that depends not only on the amount and stiffness of the individual tissue constituents but also on the way how they assemble. Our model may unify current incongruences between the mechanics of soft biological tissues and the underlying constituents and facilitate the design of better biomedical materials and engineered tissues. To this end, we provide a freely-available platform to predict local tissue elasticity upon providing immunohistochemistry images and stiffness values for the constituents of the tissue

Associative memory of phase-coded spatiotemporal patterns in leaky Integrate and Fire networks

We study the collective dynamics of a Leaky Integrate and Fire network in which precise relative phase relationship of spikes among neurons are stored, as attractors of the dynamics, and selectively replayed at differentctime scales. Using an STDP-based learning process, we store in the connectivity several phase-coded spike patterns, and we find that, depending on the excitability of the network, different working regimes are possible, with transient or persistent replay activity induced by a brief signal. We introduce an order parameter to evaluate the similarity between stored and recalled phase-coded pattern, and measure the storage capacity. Modulation of spiking thresholds during replay changes the frequency of the collective oscillation or the number of spikes per cycle, keeping preserved the phases relationship. This allows a coding scheme in which phase, rate and frequency are dissociable. Robustness with respect to noise and heterogeneity of neurons parameters is studied, showing that, since dynamics is a retrieval process, neurons preserve stablecprecise phase relationship among units, keeping a unique frequency of oscillation, even in noisy conditions and with heterogeneity of internal parameters of the units

Adaptive cancelation of self-generated sensory signals in a whisking robot

Sensory signals are often caused by one's own active movements. This raises a problem of discriminating between self-generated sensory signals and signals generated by the external world. Such discrimination is of general importance for robotic systems, where operational robustness is dependent on the correct interpretation of sensory signals. Here, we investigate this problem in the context of a whiskered robot. The whisker sensory signal comprises two components: one due to contact with an object (externally generated) and another due to active movement of the whisker (self-generated). We propose a solution to this discrimination problem based on adaptive noise cancelation, where the robot learns to predict the sensory consequences of its own movements using an adaptive filter. The filter inputs (copy of motor commands) are transformed by Laguerre functions instead of the often-used tapped-delay line, which reduces model order and, therefore, computational complexity. Results from a contact-detection task demonstrate that false positives are significantly reduced using the proposed scheme

The optogenetic revolution in cerebellar investigations

The cerebellum is most renowned for its role in sensorimotor control and coordination, but a growing number of anatomical and physiological studies are demonstrating its deep involvement in cognitive and emotional functions. Recently, the development and refinement of optogenetic techniques boosted research in the cerebellar field and, impressively, revolutionized the methodological approach and endowed the investigations with entirely new capabilities. This translated into a significant improvement in the data acquired for sensorimotor tests, allowing one to correlate single-cell activity with motor behavior to the extent of determining the role of single neuronal types and single connection pathways in controlling precise aspects of movement kinematics. These levels of specificity in correlating neuronal activity to behavior could not be achieved in the past, when electrical and pharmacological stimulations were the only available experimental tools. The application of optogenetics to the investigation of the cerebellar role in higher-order and cognitive functions, which involves a high degree of connectivity with multiple brain areas, has been even more significant. It is possible that, in this field, optogenetics has changed the game, and the number of investigations using optogenetics to study the cerebellar role in non-sensorimotor functions in awake animals is growing. The main issues addressed by these studies are the cerebellar role in epilepsy (through connections to the hippocampus and the temporal lobe), schizophrenia and cognition, working memory for decision making, and social behavior. It is also worth noting that optogenetics opened a new perspective for cerebellar neurostimulation in patients (e.g., for epilepsy treatment and stroke rehabilitation), promising unprecedented specificity in the targeted pathways that could be either activated or inhibited

Function of Cerebellar Microcircuitry within a Closed-loop System during Control and Adaptation

The human motor control is both robust and stable, despite large delays and highly complex motor systems with an abundance of actuators, sensors and degrees of freedom. The cerebellum is thought help accomplish this by compensating for external loads and internal limitations and disturbances through adaptation, creating inverse models of the motor system dynamics. The cerebellum does also exhibit a generic relatively well described modular microcircuitry, making it a suitable neural circuitry to study. This thesis models a small part of the cerebellum, using detailed bio-physical models in combination with rate-based models, and uses the constructed network model to improve control of a planar double joint arm.The individual neuron models were calibrated using data from in vivo experiments. The response from the models when they were introduced to recorded primary afferent spike trains, originating from tactile stimulation, was used to validate their behaviour. Subsets of the complete network was also constructed to investigate possible functions of the granule cells and inhibitory connection patterns between interneurons within the molecular layer

DEVELOPMENT OF A CEREBELLAR MEAN FIELD MODEL: THE THEORETICAL FRAMEWORK, THE IMPLEMENTATION AND THE FIRST APPLICATION

Brain modeling constantly evolves to improve the accuracy of the simulated brain dynamics with the ambitious aim to build a digital twin of the brain. Specific models tuned on brain regions specific features empower the brain simulations introducing bottom-up physiology properties into data-driven simulators. Despite the cerebellum contains 80 % of the neurons and is deeply involved in a wide range of functions, from sensorimotor to cognitive ones, a specific cerebellar model is still missing. Furthermore, its quasi-crystalline multi-layer circuitry deeply differs from the cerebral cortical one, therefore is hard to imagine a unique general model suitable for the realistic simulation of both cerebellar and cerebral cortex. The present thesis tackles the challenge of developing a specific model for the cerebellum. Specifically, multi-neuron multi-layer mean field (MF) model of the cerebellar network, including Granule Cells, Golgi Cells, Molecular Layer Interneurons, and Purkinje Cells, was implemented, and validated against experimental data and the corresponding spiking neural network microcircuit model. The cerebellar MF model was built using a system of interdependent equations, where the single neuronal populations and topological parameters were captured by neuron-specific inter- dependent Transfer Functions. The model time resolution was optimized using Local Field Potentials recorded experimentally with high-density multielectrode array from acute mouse cerebellar slices. The present MF model satisfactorily captured the average discharge of different microcircuit neuronal populations in response to various input patterns and was able to predict the changes in Purkinje Cells firing patterns occurring in specific behavioral conditions: cortical plasticity mapping, which drives learning in associative tasks, and Molecular Layer Interneurons feed-forward inhibition, which controls Purkinje Cells activity patterns. The cerebellar multi-layer MF model thus provides a computationally efficient tool that will allow to investigate the causal relationship between microscopic neuronal properties and ensemble brain activity in health and pathological conditions. Furthermore, preliminary attempts to simulate a pathological cerebellum were done in the perspective of introducing our multi-layer cerebellar MF model in whole-brain simulators to realize patient-specific treatments, moving ahead towards personalized medicine. Two preliminary works assessed the relevant impact of the cerebellum on whole-brain dynamics and its role in modulating complex responses in causal connected cerebral regions, confirming that a specific model is required to further investigate the cerebellum-on- cerebrum influence. The framework presented in this thesis allows to develop a multi-layer MF model depicting the features of a specific brain region (e.g., cerebellum, basal ganglia), in order to define a general strategy to build up a pool of biology grounded MF models for computationally feasible simulations. Interconnected bottom-up MF models integrated in large-scale simulators would capture specific features of different brain regions, while the applications of a virtual brain would have a substantial impact on the reality ranging from the characterization of neurobiological processes, subject-specific preoperative plans, and development of neuro-prosthetic devices