PRINCIPLES OF INFORMATION PROCESSING IN NEURONAL AVALANCHES

How the brain processes information is poorly understood. It has been suggested that the imbalance of excitation and inhibition (E/I) can significantly affect information processing in the brain. Neuronal avalanches, a type of spontaneous activity recently discovered, have been ubiquitously observed in vitro and in vivo when the cortical network is in the E/I balanced state. In this dissertation, I experimentally demonstrate that several properties regarding information processing in the cortex, i.e. the entropy of spontaneous activity, the information transmission between stimulus and response, the diversity of synchronized states and the discrimination of external stimuli, are optimized when the cortical network is in the E/I balanced state, exhibiting neuronal avalanche dynamics. These experimental studies not only support the hypothesis that the cortex operates in the critical state, but also suggest that criticality is a potential principle of information processing in the cortex. Further, we study the interaction structure in population neuronal dynamics, and discovered a special structure of higher order interactions that are inherent in the neuronal dynamics

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

Translational pipelines for closed-loop neuromodulation

Closed-loop neuromodulation systems have shown significant potential for addressing unmet needs in the treatment of disorders of the central nervous system, yet progress towards clinical adoption has been slow. Advanced technological developments often stall in the preclinical stage by failing to account for the constraints of implantable medical devices, and due to the lack of research platforms with a translational focus. This thesis presents the development of three clinically relevant research systems focusing on refinements of deep brain stimulation therapies. First, we introduce a system for synchronising implanted and external stimulation devices, allowing for research into multi-site stimulation paradigms, cross-region neural plasticity, and questions of phase coupling. The proposed design aims to sidestep the limited communication capabilities of existing commercial implant systems in providing a stimulation state readout without reliance on telemetry, creating a cross-platform research tool. Next, we present work on the Picostim-DyNeuMo adaptive neuromodulation platform, focusing on expanding device capabilities from activity and circadian adaptation to bioelectric marker--based responsive stimulation. Here, we introduce a computationally optimised implementation of a popular band power--estimation algorithm suitable for deployment in the DyNeuMo system. The new algorithmic capability was externally validated to establish neural state classification performance in two widely-researched use cases: Parkinsonian beta bursts and seizures. For in vivo validation, a pilot experiment is presented demonstrating responsive neurostimulation to cortical alpha-band activity in a non-human primate model for the modulation of attention state. Finally, we turn our focus to the validation of a recently developed method to provide computationally efficient real-time phase estimation. Following theoretical analysis, the method is integrated into the commonly used Intan electrophysiological recording platform, creating a novel closed-loop optogenetics research platform. The performance of the research system is characterised through a pilot experiment, targeting the modulation of cortical theta-band activity in a transgenic mouse model

Modeling and Analysis of Neural Spike Trains

open

Cortical circuits for visual processing and epileptic activity propagation

The thesis focuses on the relationship between cortical connectivity and cortical function. The first part investigates how the fine scale connectivity between visual neurons determines their functional responses during physiological sensory processing. The second part ascertains how the mesoscopic scale connectivity between brain areas constrains the spread of abnormal activity during the propagation of focal cortical seizures. Part 1: Neurons in the primary visual cortex (V1) are tuned to retinotopic location, orientation and direction of motion. Such selectivity stems from the integration of inputs from hundreds of presynaptic neurons distributed across cortical layers. Yet, the functional principles that organize such presynaptic networks have only begun to be understood. To uncover them, I used monosynaptic rabies virus tracing to target a single pyramidal neuron in L2/3 (starter neuron) and trace its presynaptic partners. I combined this approach with two-photon microscopy in V1 to investigate the relationship between the activity of the starter cell, its presynaptic neurons and the surrounding excitatory population across cortical layers in awake animals. Part 2: Focal epilepsy involves excessive and synchronous cortical activity that propagates both locally and distally. Does this propagation follow the same functional circuits as normal cortical activity? I induced focal seizures in primary visual cortex (V1) of awake mice, and compared their propagation to the retinotopic organization of V1 and higher visual areas. I measured activity through simultaneous local field potential recordings and widefield calcium imaging, and observed prolonged seizures that were orders of magnitude larger than normal visual responses. I demonstrate that seizure start as standing waves (synchronous elevated activity in the focal V1 region and in corresponding retinotopic locations in higher areas) and then propagate both locally and into distal regions. These regions matched each other in retinotopy. I conclude that seizure propagation respects the connectivity underlying normal visual processing

Modelling human choices: MADeM and decision‑making

Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)

Micro-, Meso- and Macro-Dynamics of the Brain

Neurosciences, Neurology, Psychiatr