A roadmap to integrate astrocytes into Systems Neuroscience.

Systems neuroscience is still mainly a neuronal field, despite the plethora of evidence supporting the fact that astrocytes modulate local neural circuits, networks, and complex behaviors. In this article, we sought to identify which types of studies are necessary to establish whether astrocytes, beyond their well-documented homeostatic and metabolic functions, perform computations implementing mathematical algorithms that sub-serve coding and higher-brain functions. First, we reviewed Systems-like studies that include astrocytes in order to identify computational operations that these cells may perform, using Ca2+ transients as their encoding language. The analysis suggests that astrocytes may carry out canonical computations in a time scale of subseconds to seconds in sensory processing, neuromodulation, brain state, memory formation, fear, and complex homeostatic reflexes. Next, we propose a list of actions to gain insight into the outstanding question of which variables are encoded by such computations. The application of statistical analyses based on machine learning, such as dimensionality reduction and decoding in the context of complex behaviors, combined with connectomics of astrocyte-neuronal circuits, is, in our view, fundamental undertakings. We also discuss technical and analytical approaches to study neuronal and astrocytic populations simultaneously, and the inclusion of astrocytes in advanced modeling of neural circuits, as well as in theories currently under exploration such as predictive coding and energy-efficient coding. Clarifying the relationship between astrocytic Ca2+ and brain coding may represent a leap forward toward novel approaches in the study of astrocytes in health and disease

Overview on cognitive impairment in psychotic disorders: from impaired microcircuits to dysconnectivity

Schizophrenia's cognitive deficits, often overshadowed by positive symptoms, significantly contribute to the disorder's morbidity. Increasing attention highlights these deficits as reflections of neural circuit dysfunction across various cortical regions. Numerous connectivity alterations linked to cognitive symptoms in psychotic disorders have been reported, both at the macroscopic and microscopic level, emphasizing the potential role of plasticity and microcircuits impairment during development and later stages. However, the heterogeneous clinical presentation of cognitive impairment and diverse connectivity findings pose challenges in summarizing them into a cohesive picture. This review aims to synthesize major cognitive alterations, recent insights into network structural and functional connectivity changes and proposed mechanisms and microcircuit alterations underpinning these symptoms, particularly focusing on neurodevelopmental impairment, E/I balance, and sleep disturbances. Finally, we will also comment on some of the most recent and promising therapeutic approaches that aim to target these mechanisms to address cognitive symptoms. Through this comprehensive exploration, we strive to provide an updated and nuanced overview of the multiscale connectivity impairment underlying cognitive impairment in psychotic disorders.The project that gave rise to these results received the support of a fellowship from “la Caixa” foundation “(ID 100010434)”. The fellowship code is: “(LCF/BQ/DI19/11730048)”, and financed L.M. work. M.S. is a fellow of Eurecat's “Vicente Lopez ´ ” PhD grant program. M.V.V. was supported by the Grant/Award Number: PID2020-119072RA-I00/AEI/ 10.13039/501100011033 financed by the Spanish Ministry of Science, Innovation and Universities (MCIU), State Research Agency (AEI). This last institution also financed M.V.V trough the Grant/Award Number: PID2020-119072RA-I00/AEI/10.13039/501100011033. G.D. was supported by the project NEurological MEchanismS of Injury, and Sleep-like cellular dynamics (NEMESIS) (ref. 101071900) funded by the EU ERC Synergy Horizon Europe, by the NODYN Project PID2022-136216NBI00 financed by the MCIN/AEI/10.13039/501100011033/FEDER, UE., the Ministry of Science and Innovation, the Spanish State Research Agency and the European Regional Development Fund, by the AGAUR research support grant (ref. 2021 SGR 00917) funded by the Department of Research and Universities of the Generalitat de Catalunya, and by the project eBRAIN-Health - Actionable Multilevel Health Data (id 101058516), funded by the EU Horizon Europe.Postprint (published version

The influence of dopamine on prediction, action and learning

In this thesis I explore functions of the neuromodulator dopamine in the context of autonomous learning and behaviour. I first investigate dopaminergic influence within a simulated agent-based model, demonstrating how modulation of synaptic plasticity can enable reward-mediated learning that is both adaptive and self-limiting. I describe how this mechanism is driven by the dynamics of agentenvironment interaction and consequently suggest roles for both complex spontaneous neuronal activity and specific neuroanatomy in the expression of early, exploratory behaviour. I then show how the observed response of dopamine neurons in the mammalian basal ganglia may also be modelled by similar processes involving dopaminergic neuromodulation and cortical spike-pattern representation within an architecture of counteracting excitatory and inhibitory neural pathways, reflecting gross mammalian neuroanatomy. Significantly, I demonstrate how combined modulation of synaptic plasticity and neuronal excitability enables specific (timely) spike-patterns to be recognised and selectively responded to by efferent neural populations, therefore providing a novel spike-timing based implementation of the hypothetical ‘serial-compound’ representation suggested by temporal difference learning. I subsequently discuss more recent work, focused upon modelling those complex spike-patterns observed in cortex. Here, I describe neural features likely to contribute to the expression of such activity and subsequently present novel simulation software allowing for interactive exploration of these factors, in a more comprehensive neural model that implements both dynamical synapses and dopaminergic neuromodulation. I conclude by describing how the work presented ultimately suggests an integrated theory of autonomous learning, in which direct coupling of agent and environment supports a predictive coding mechanism, bootstrapped in early development by a more fundamental process of trial-and-error learning

The Dynamic Brain: From Spiking Neurons to Neural Masses and Cortical Fields

The cortex is a complex system, characterized by its dynamics and architecture, which underlie many functions such as action, perception, learning, language, and cognition. Its structural architecture has been studied for more than a hundred years; however, its dynamics have been addressed much less thoroughly. In this paper, we review and integrate, in a unifying framework, a variety of computational approaches that have been used to characterize the dynamics of the cortex, as evidenced at different levels of measurement. Computational models at different space–time scales help us understand the fundamental mechanisms that underpin neural processes and relate these processes to neuroscience data. Modeling at the single neuron level is necessary because this is the level at which information is exchanged between the computing elements of the brain; the neurons. Mesoscopic models tell us how neural elements interact to yield emergent behavior at the level of microcolumns and cortical columns. Macroscopic models can inform us about whole brain dynamics and interactions between large-scale neural systems such as cortical regions, the thalamus, and brain stem. Each level of description relates uniquely to neuroscience data, from single-unit recordings, through local field potentials to functional magnetic resonance imaging (fMRI), electroencephalogram (EEG), and magnetoencephalogram (MEG). Models of the cortex can establish which types of large-scale neuronal networks can perform computations and characterize their emergent properties. Mean-field and related formulations of dynamics also play an essential and complementary role as forward models that can be inverted given empirical data. This makes dynamic models critical in integrating theory and experiments. We argue that elaborating principled and informed models is a prerequisite for grounding empirical neuroscience in a cogent theoretical framework, commensurate with the achievements in the physical sciences

29th Annual Computational Neuroscience Meeting: CNS*2020

Meeting abstracts This publication was funded by OCNS. The Supplement Editors declare that they have no competing interests. Virtual | 18-22 July 202

Optical tracking of nerve activity using intrinsic changes in birefringence

Changes in birefringence (or dynamic birefringence) provide an arguably cleaner method of measuring IOS as compared to scattering methods. Other imaging methods have substantial limitations. Nerves inherently exhibit a static (rest condition) birefringence that is associated with the structural anisotropies of axonal protein filaments, membrane phospholipids and proteins, as well as surrounding tissues, which include Schwann cells and axon sheaths. The dynamic birefringence, or “crossed-polarized signal” (XPS), in neurons arises from activity in axons and occurs with a rapid momentary change, typically a decrease, in the birefringence when action potentials (APs) propagate along them. We improved the signal-to-noise to make detecting this signal an easier task, and present the XPS as a viable candidate for detecting AP activity in anisotropic nervous tissue. Our data collectively serves as a strong indication that there is a capacitive-charging-like effect directly inducing a gradual recovery (long tail) of the XPS to baseline, and also causing a smoothing of the XPS trace. A setup was constructed that successfully demonstrated the feasibility of tracking propagating compound APs in a peripheral nerve using the XPS. We made significant progress in the attempt to investigate birefringence of myelination. For the first time, the XPS in a myelinated tissue was detected, and it appears to be bipolar in nature. Further work in investigating the nature of this signal is needed, and is currently underway. Since changes in birefringence in neurons are associated instantaneously with electrophysiological phenomena, they are well-suited for fast imaging of propagating action potentials in neuronal tissue. In summary, imaging based on polarization sensing of changes in birefringence offers promise for an improved noninvasive method of detecting and tracking AP activity in myelinated and unmyelinated nerves and could be designed for pre-clinical and surgical applications

Dynamics of biologically informed neural mass models of the brain

This book contributes to the development and analysis of computational models that help brain function to be understood. The mean activity of a brain area is mathematically modeled in such a way as to strike a balance between tractability and biological plausibility. Neural mass models (NMM) are used to describe switching between qualitatively different regimes (such as those due to pharmacological interventions, epilepsy, sleep, or context-induced state changes), and to explain resonance phenomena in a photic driving experiment. The description of varying states in an ordered sequence gives a principle scheme for the modeling of complex phenomena on multiple time scales. The NMM is matched to the photic driving experiment routinely applied in the diagnosis of such diseases as epilepsy, migraine, schizophrenia and depression. The model reproduces the clinically relevant entrainment effect and predictions are made for improving the experimental setting.Die vorliegende Arbeit stellt einen Beitrag zur Entwicklung und Analyse von Computermodellen zum Verständnis von Hirnfunktionen dar. Es wird die mittlere Aktivität eines Hirnareals analytisch einfach und dabei biologisch plausibel modelliert. Auf Grundlage eines Neuronalen Massenmodells (NMM) werden die Wechsel zwischen Oszillationsregimen (z.B. durch pharmakologisch, epilepsie-, schlaf- oder kontextbedingte Zustandsänderungen) als geordnete Folge beschrieben und Resonanzphänomene in einem Photic-Driving-Experiment erklärt. Dieses NMM kann sehr komplexe Dynamiken (z.B. Chaos) innerhalb biologisch plausibler Parameterbereiche hervorbringen. Um das Verhalten abzuschätzen, wird das NMM als Funktion konstanter Eingangsgrößen und charakteristischer Zeitenkonstanten vollständig auf Bifurkationen untersucht und klassifiziert. Dies ermöglicht die Beschreibung wechselnder Regime als geordnete Folge durch spezifische Eingangstrajektorien. Es wird ein Prinzip vorgestellt, um komplexe Phänomene durch Prozesse verschiedener Zeitskalen darzustellen. Da aufgrund rhythmischer Stimuli und der intrinsischen Rhythmen von Neuronenverbänden die Eingangsgrößen häufig periodisch sind, wird das Verhalten des NMM als Funktion der Intensität und Frequenz einer periodischen Stimulation mittels der zugehörigen Lyapunov-Spektren und der Zeitreihen charakterisiert. Auf der Basis der größten Lyapunov-Exponenten wird das NMM mit dem Photic-Driving-Experiment überein gebracht. Dieses Experiment findet routinemäßige Anwendung in der Diagnostik verschiedener Erkrankungen wie Epilepsie, Migräne, Schizophrenie und Depression. Durch die Anwendung des vorgestellten NMM wird der für die Diagnostik entscheidende Mitnahmeeffekt reproduziert und es werden Vorhersagen für eine Verbesserung der Indikation getroffen