Modeling Working Memory in a Spiking Neuron Network Accompanied by Astrocytes

Bridging metabolic syndrome and cognitive dysfunction: role of astrocytes

Metabolic syndrome (MetS) and cognitive dysfunction pose significant challenges to global health and the economy. Systemic inflammation, endocrine disruption, and autoregulatory impairment drive neurodegeneration and microcirculatory damage in MetS. Due to their unique anatomy and function, astrocytes sense and integrate multiple metabolic signals, including peripheral endocrine hormones and nutrients. Astrocytes and synapses engage in a complex dialogue of energetic and immunological interactions. Astrocytes act as a bridge between MetS and cognitive dysfunction, undergoing diverse activation in response to metabolic dysfunction. This article summarizes the alterations in astrocyte phenotypic characteristics across multiple pathological factors in MetS. It also discusses the clinical value of astrocytes as a critical pathologic diagnostic marker and potential therapeutic target for MetS-associated cognitive dysfunction.

Astrocytes as a mechanism for contextually-guided network dynamics and function

Astrocytes are a ubiquitous and enigmatic type of non-neuronal cell and are found in the brain of all vertebrates. While traditionally viewed as being supportive of neurons, it is increasingly recognized that astrocytes play a more direct and active role in brain function and neural computation. On account of their sensitivity to a host of physiological covariates and ability to modulate neuronal activity and connectivity on slower time scales, astrocytes may be particularly well poised to modulate the dynamics of neural circuits in functionally salient ways. In the current paper, we seek to capture these features via actionable abstractions within computational models of neuron-astrocyte interaction. Specifically, we engage how nested feedback loops of neuron-astrocyte interaction, acting over separated time-scales, may endow astrocytes with the capability to enable learning in context-dependent settings, where fluctuations in task parameters may occur much more slowly than within-task requirements. We pose a general model of neuron-synapse-astrocyte interaction and use formal analysis to characterize how astrocytic modulation may constitute a form of meta-plasticity, altering the ways in which synapses and neurons adapt as a function of time. We then embed this model in a bandit-based reinforcement learning task environment, and show how the presence of time-scale separated astrocytic modulation enables learning over multiple fluctuating contexts. Indeed, these networks learn far more reliably compared to dynamically homogeneous networks and conventional non-network-based bandit algorithms. Our results fuel the notion that neuron-astrocyte interactions in the brain benefit learning over different time-scales and the conveyance of task-relevant contextual information onto circuit dynamics.

Computational modeling of neuron-glia signaling interactions to unravel cellular and neural circuit functioning

Glial cells have been shown to be vital for various brain functions, including homeostasis, information processing, and cognition. Over the past 30 years, various signaling interactions between neuronal and glial cells have been shown to underlie these functions. This review summarizes the interactions, particularly between neurons and astrocytes, which are types of glial cells. Some of the interactions remain controversial in part due to the nature of experimental methods and preparations used. Based on the accumulated data, computational models of the neuron-astrocyte interactions have been developed to explain the complex functions of astrocytes in neural circuits and to test conflicting hypotheses. This review presents the most significant recent models, modeling methods and simulation tools for neuron-astrocyte interactions. In the future, we will especially need more experimental research on awake animals in vivo and new computational models of neuron-glia interactions to advance our understanding of cellular dynamics and the functioning of neural circuits in different brain regions.

Random fluctuations and synaptic plasticity enhance working memory activities in the neuron-astrocyte network

Random fluctuations are inescapable feature in biological systems, but appropriate intensity of randomness can effectively facilitate information transfer and memory encoding within the nervous system. In the study, a modified spiking neuron-astrocyte network model with excitatory-inhibitory balance and synaptic plasticity is established. This model considers external input noise, and allows investigating the effects of intrinsic random fluctuations on working memory tasks. It is found that the astrocyte network, acting as a low-pass filter, reduces the noise component of the total input currents and improves the recovered images. The memory performance is enhanced by selecting appropriate intensity of random fluctuations, while excessive intensity can inhibit signal transmission of network. As the intensity of random fluctuations gradually increases, there exists a maximum value of the working memory performance. The cued recall of the network markedly decreases excessive input noise relative to test images. Meanwhile, a greater contrast effect is observed as the external input noise increases. In addition, synaptic plasticity reduces the firing rates and firing peaks of neurons, thus stabilizing the working memory activity during the test. The outcomes of this study may provide some inspirations for comprehending the role of random fluctuations in working memory mechanisms and neural information processing within the cerebral cortex.

The "psychiatric" neuron: the psychic neuron of the cerebral cortex, revisited

Nearly 25 years ago, Dr. Patricia Goldman-Rakic published her review paper, "The 'Psychic' Neuron of the Cerebral Cortex," outlining the circuit-level dynamics, neurotransmitter systems, and behavioral correlates of pyramidal neurons in the cerebral cortex, particularly as they relate to working memory. In the decades since the release of this paper, the existing literature and our understanding of the pyramidal neuron have increased tremendously, and research is still underway to better characterize the role of the pyramidal neuron in both healthy and psychiatric disease states. In this review, we revisit Dr. Goldman-Rakic's characterization of the pyramidal neuron, focusing on the pyramidal neurons of the prefrontal cortex (PFC) and their role in working memory. Specifically, we examine the role of PFC pyramidal neurons in the intersection of working memory and social function and describe how deficits in working memory may actually underlie the pathophysiology of social dysfunction in psychiatric disease states. We briefly describe the cortico-cortical and corticothalamic connections between the PFC and non-PFC brain regions, as well the microcircuit dynamics of the pyramidal neuron and interneurons, and the role of both these macro- and microcircuits in the maintenance of the excitatory/inhibitory balance of the cerebral cortex for working memory function. Finally, we discuss the consequences to working memory when pyramidal neurons and their circuits are dysfunctional, emphasizing the resulting social deficits in psychiatric disease states with known working memory dysfunction.

Involvement of Astrocytes in the Formation, Maintenance, and Function of the Blood-Brain Barrier

The blood-brain barrier (BBB) is a fundamental structure that protects the composition of the brain by determining which ions, metabolites, and nutrients are allowed to enter the brain from the blood or to leave it towards the circulation. The BBB is structurally composed of a layer of brain capillary endothelial cells (BCECs) bound to each other through tight junctions (TJs). However, its development as well as maintenance and properties are controlled by the other brain cells that contact the BCECs: pericytes, glial cells, and even neurons themselves. Astrocytes seem, in particular, to have a very important role in determining and controlling most properties of the BBB. Here, we will focus on these latter cells, since the comprehension of their roles in brain physiology has been continuously expanding, even including the ability to participate in neurotransmission and in complex functions such as learning and memory. Accordingly, pathological conditions that alter astrocytic functions can alter the BBB's integrity, thus compromising many brain activities. In this review, we will also refer to different kinds of in vitro BBB models used to study the BBB's properties, evidencing its modifications under pathological conditions.

Astrocyte's self-repairing characteristics improve working memory in spiking neuronal networks

Astrocytes play a significant role in the working memory (WM) mechanism, yet their contribution to spiking neuron-astrocyte networks (SNAN) is underexplored. This study proposes a non-probabilistic SNAN incorporating a self-repairing (SR) mechanism through endocannabinoid pathways to facilitate WM function. Four experiments were conducted with different damaging patterns, replicating close-to-realistic synaptic impairments. Simulation results suggest that the SR process enhances WM performance by improving the consistency of neuronal firing. Moreover, the intercellular astrocytic [Ca]2+ transmission via gap junctions improves WM and SR processes. With increasing damage, WM and SR activities initially fail for non-matched samples and then for smaller and minimally overlapping matched samples. Simulation results also indicate that the inclusion of the SR mechanism in both random and continuous forms of damage improves the resilience of the WM by approximately 20%. This study highlights the importance of astrocytes in synaptically impaired networks.

Breathing cluster in complex neuron-astrocyte networks

Brain activities are featured by spatially distributed neural clusters of coherent firings and a spontaneous slow switching of the clusters between the coherent and incoherent states. Evidences from recent in vivo experiments suggest that astrocytes, a type of glial cell regarded previously as providing only structural and metabolic supports to neurons, participate actively in brain functions by regulating the neural firing activities, yet the underlying mechanism remains unknown. Here, introducing astrocyte as a reservoir of the glutamate released from the neuron synapses, we propose the model of the complex neuron-astrocyte network, and investigate the roles of astrocytes in regulating the cluster synchronization behaviors of networked chaotic neurons. It is found that a specific set of neurons on the network are synchronized and form a cluster, while the remaining neurons are kept as desynchronized. Moreover, during the course of network evolution, the cluster is switching between the synchrony and asynchrony states in an intermittent fashion, henceforth the phenomenon of "breathing cluster." By the method of symmetry-based analysis, we conduct a theoretical investigation on the synchronizability of the cluster. It is revealed that the contents of the cluster are determined by the network symmetry, while the breathing of the cluster is attributed to the interplay between the neural network and the astrocyte. The phenomenon of breathing cluster is demonstrated in different network models, including networks with different sizes, nodal dynamics, and coupling functions. The findings shed light on the cellular mechanism of astrocytes in regulating neural activities and give insights into the state-switching of the neocortex.

Astrocytes: new evidence, new models, new roles

Astrocytes have been in the limelight of active research for about 3 decades now. Over this period, ideas about their function and role in the nervous system have evolved from simple assistance in energy supply and homeostasis maintenance to a complex informational and metabolic hub that integrates data on local neuronal activity, sensory and arousal context, and orchestrates many crucial processes in the brain. Rapid progress in experimental techniques and data analysis produces a growing body of data, which can be used as a foundation for formulation of new hypotheses, building new refined mathematical models, and ultimately should lead to a new level of understanding of the contribution of astrocytes to the cognitive tasks performed by the brain. Here, we highlight recent progress in astrocyte research, which we believe expands our understanding of how low-level signaling at a cellular level builds up to processes at the level of the whole brain and animal behavior. We start our review with revisiting data on the role of noradrenaline-mediated astrocytic signaling in locomotion, arousal, sensory integration, memory, and sleep. We then briefly review astrocyte contribution to the regulation of cerebral blood flow regulation, which is followed by a discussion of biophysical mechanisms underlying astrocyte effects on different brain processes. The experimental section is closed by an overview of recent experimental techniques available for modulation and visualization of astrocyte dynamics. We then evaluate how the new data can be potentially incorporated into the new mathematical models or where and how it already has been done. Finally, we discuss an interesting prospect that astrocytes may be key players in important processes such as the switching between sleep and wakefulness and the removal of toxic metabolites from the brain milieu.

Building transformers from neurons and astrocytes

Glial cells account for between 50% and 90% of all human brain cells, and serve a variety of important developmental, structural, and metabolic functions. Recent experimental efforts suggest that astrocytes, a type of glial cell, are also directly involved in core cognitive processes such as learning and memory. While it is well established that astrocytes and neurons are connected to one another in feedback loops across many timescales and spatial scales, there is a gap in understanding the computational role of neuron-astrocyte interactions. To help bridge this gap, we draw on recent advances in AI and astrocyte imaging technology. In particular, we show that neuron-astrocyte networks can naturally perform the core computation of a Transformer, a particularly successful type of AI architecture. In doing so, we provide a concrete, normative, and experimentally testable account of neuron-astrocyte communication. Because Transformers are so successful across a wide variety of task domains, such as language, vision, and audition, our analysis may help explain the ubiquity, flexibility, and power of the brain's neuron-astrocyte networks.

Meta-SpikePropamine: learning to learn with synaptic plasticity in spiking neural networks

We propose that in order to harness our understanding of neuroscience toward machine learning, we must first have powerful tools for training brain-like models of learning. Although substantial progress has been made toward understanding the dynamics of learning in the brain, neuroscience-derived models of learning have yet to demonstrate the same performance capabilities as methods in deep learning such as gradient descent. Inspired by the successes of machine learning using gradient descent, we introduce a bi-level optimization framework that seeks to both solve online learning tasks and improve the ability to learn online using models of plasticity from neuroscience. We demonstrate that models of three-factor learning with synaptic plasticity taken from the neuroscience literature can be trained in Spiking Neural Networks (SNNs) with gradient descent via a framework of learning-to-learn to address challenging online learning problems. This framework opens a new path toward developing neuroscience inspired online learning algorithms.

Information Encoding in Bursting Spiking Neural Network Modulated by Astrocytes

We investigated a mathematical model composed of a spiking neural network (SNN) interacting with astrocytes. We analysed how information content in the form of two-dimensional images can be represented by an SNN in the form of a spatiotemporal spiking pattern. The SNN includes excitatory and inhibitory neurons in some proportion, sustaining the excitation-inhibition balance of autonomous firing. The astrocytes accompanying each excitatory synapse provide a slow modulation of synaptic transmission strength. An information image was uploaded to the network in the form of excitatory stimulation pulses distributed in time reproducing the shape of the image. We found that astrocytic modulation prevented stimulation-induced SNN hyperexcitation and non-periodic bursting activity. Such homeostatic astrocytic regulation of neuronal activity makes it possible to restore the image supplied during stimulation and lost in the raster diagram of neuronal activity due to non-periodic neuronal firing. At a biological point, our model shows that astrocytes can act as an additional adaptive mechanism for regulating neural activity, which is crucial for sensory cortical representations.

Shining the Light on Astrocytic Ensembles

While neurons have traditionally been considered the primary players in information processing, the role of astrocytes in this mechanism has largely been overlooked due to experimental constraints. In this review, we propose that astrocytic ensembles are active working groups that contribute significantly to animal conduct and suggest that studying the maps of these ensembles in conjunction with neurons is crucial for a more comprehensive understanding of behavior. We also discuss available methods for studying astrocytes and argue that these ensembles, complementarily with neurons, code and integrate complex behaviors, potentially specializing in concrete functions.

Analysis of Network Models with Neuron-Astrocyte Interactions

Neural networks, composed of many neurons and governed by complex interactions between them, are a widely accepted formalism for modeling and exploring global dynamics and emergent properties in brain systems. In the past decades, experimental evidence of computationally relevant neuron-astrocyte interactions, as well as the astrocytic modulation of global neural dynamics, have accumulated. These findings motivated advances in computational glioscience and inspired several models integrating mechanisms of neuron-astrocyte interactions into the standard neural network formalism. These models were developed to study, for example, synchronization, information transfer, synaptic plasticity, and hyperexcitability, as well as classification tasks and hardware implementations. We here focus on network models of at least two neurons interacting bidirectionally with at least two astrocytes that include explicitly modeled astrocytic calcium dynamics. In this study, we analyze the evolution of these models and the biophysical, biochemical, cellular, and network mechanisms used to construct them. Based on our analysis, we propose how to systematically describe and categorize interaction schemes between cells in neuron-astrocyte networks. We additionally study the models in view of the existing experimental data and present future perspectives. Our analysis is an important first step towards understanding astrocytic contribution to brain functions. However, more advances are needed to collect comprehensive data about astrocyte morphology and physiology in vivo and to better integrate them in data-driven computational models. Broadening the discussion about theoretical approaches and expanding the computational tools is necessary to better understand astrocytes' roles in brain functions.

Astrocyte strategies in the energy-efficient brain

Astrocytes generate ATP through glycolysis and mitochondrion respiration, using glucose, lactate, fatty acids, amino acids, and ketone bodies as metabolic fuels. Astrocytic mitochondria also participate in neuronal redox homeostasis and neurotransmitter recycling. In this essay, we aim to integrate the multifaceted evidence about astrocyte bioenergetics at the cellular and systems levels, with a focus on mitochondrial oxidation. At the cellular level, the use of fatty acid β-oxidation and the existence of molecular switches for the selection of metabolic mode and fuels are examined. At the systems level, we discuss energy audits of astrocytes and how astrocytic Ca2+ signaling might contribute to the higher performance and lower energy consumption of the brain as compared to engineered circuits. We finish by examining the neural-circuit dysregulation and behavior impairment associated with alterations of astrocytic mitochondria. We conclude that astrocytes may contribute to brain energy efficiency by coupling energy, redox, and computational homeostasis in neural circuits.

Working memory depends on the excitatory-inhibitory balance in neuron-astrocyte network

Previous studies have shown that astrocytes are involved in information processing and working memory (WM) in the central nervous system. Here, the neuron-astrocyte network model with biological properties is built to study the effects of excitatory-inhibitory balance and neural network structures on WM tasks. It is found that the performance metrics of WM tasks under the scale-free network are higher than other network structures, and the WM task can be successfully completed when the proportion of excitatory neurons in the network exceeds 30%. There exists an optimal region for the proportion of excitatory neurons and synaptic weight that the memory performance metrics of the WM tasks are higher. The multi-item WM task shows that the spatial calcium patterns for different items overlap significantly in the astrocyte network, which is consistent with the formation of cognitive memory in the brain. Moreover, complex image tasks show that cued recall can significantly reduce systematic noise and maintain the stability of the WM tasks. The results may contribute to understand the mechanisms of WM formation and provide some inspirations into the dynamic storage and recall of memory.

Rapid astrocyte-dependent facilitation amplifies multi-vesicular release in hippocampal synapses

Synaptic facilitation is a major form of short-term plasticity typically driven by an increase in residual presynaptic calcium. Using near-total internal reflection fluorescence (near-TIRF) imaging of single vesicle release in cultured hippocampal synapses, we demonstrate a distinctive, release-dependent form of facilitation in which probability of vesicle release is higher following a successful glutamate release event than following a failure. This phenomenon has an onset of ≤500 ms and lasts several seconds, resulting in clusters of successful release events. The release-dependent facilitation requires neuronal contact with astrocytes and astrocytic glutamate uptake by EAAT1. It is not observed in neurons grown alone or in the presence of astrocyte-conditioned media. This form of facilitation dynamically amplifies multi-vesicular release. Facilitation-evoked release events exhibit spatial clustering and have a preferential localization toward the active zone center. These results uncover a rapid astrocyte-dependent form of facilitation acting via modulation of multi-vesicular release and displaying distinctive spatiotemporal properties.

Multiple forms of working memory emerge from synapse-astrocyte interactions in a neuron-glia network model

Persistent activity in populations of neurons, time-varying activity across a neural population, or activity-silent mechanisms carried out by hidden internal states of the neural population have been proposed as different mechanisms of working memory (WM). Whether these mechanisms could be mutually exclusive or occur in the same neuronal circuit remains, however, elusive, and so do their biophysical underpinnings. While WM is traditionally regarded to depend purely on neuronal mechanisms, cortical networks also include astrocytes that can modulate neural activity. We propose and investigate a network model that includes both neurons and glia and show that glia-synapse interactions can lead to multiple stable states of synaptic transmission. Depending on parameters, these interactions can lead in turn to distinct patterns of network activity that can serve as substrates for WM.

Modulation of working memory duration by synaptic and astrocytic mechanisms

Short-term synaptic plasticity and modulations of the presynaptic vesicle release rate are key components of many working memory models. At the same time, an increasing number of studies suggests a potential role of astrocytes in modulating higher cognitive function such as WM through their influence on synaptic transmission. Which influence astrocytic signaling could have on the stability and duration of WM representations, however, is still unclear. Here, we introduce a slow, activity-dependent astrocytic regulation of the presynaptic release probability in a synaptic attractor model of WM. We compare and analyze simulations of a simple WM protocol in firing rate and spiking networks with and without astrocytic regulation, and underpin our observations with analyses of the phase space dynamics in the rate network. We find that the duration and stability of working memory representations are altered by astrocytic signaling and by noise. We show that astrocytic signaling modulates the mean duration of WM representations. Moreover, if the astrocytic regulation is strong, a slow presynaptic timescale introduces a 'window of vulnerability', during which WM representations are easily disruptable by noise before being stabilized. We identify two mechanisms through which noise from different sources in the network can either stabilize or destabilize WM representations. Our findings suggest that (i) astrocytic regulation can act as a crucial determinant for the duration of WM representations in synaptic attractor models of WM, and (ii) that astrocytic signaling could facilitate different mechanisms for volitional top-down control of WM representations and their duration.

In vivo spatiotemporal dynamics of astrocyte reactivity following neural electrode implantation

Brain computer interfaces (BCIs), including penetrating microelectrode arrays, enable both recording and stimulation of neural cells. However, device implantation inevitably causes injury to brain tissue and induces a foreign body response, leading to reduced recording performance and stimulation efficacy. Astrocytes in the healthy brain play multiple roles including regulating energy metabolism, homeostatic balance, transmission of neural signals, and neurovascular coupling. Following an insult to the brain, they are activated and gather around the site of injury. These reactive astrocytes have been regarded as one of the main contributors to the formation of a glial scar which affects the performance of microelectrode arrays. This study investigates the dynamics of astrocytes within the first 2 weeks after implantation of an intracortical microelectrode into the mouse brain using two-photon microscopy. From our observation astrocytes are highly dynamic during this period, exhibiting patterns of process extension, soma migration, morphological activation, and device encapsulation that are spatiotemporally distinct from other glial cells, such as microglia or oligodendrocyte precursor cells. This detailed characterization of astrocyte reactivity will help to better understand the tissue response to intracortical devices and lead to the development of more effective intervention strategies to improve the functional performance of neural interfacing technology.

A perspective on astrocyte regulation of neural circuit function and animal behavior

Studies over the past two decades have demonstrated that astrocytes are tightly associated with neurons and play pivotal roles in neural circuit development, operation, and adaptation in health and disease. Nevertheless, precisely how astrocytes integrate diverse neuronal signals, modulate neural circuit structure and function at multiple temporal and spatial scales, and influence animal behavior or disease through aberrant excitation and molecular output remains unclear. This Perspective discusses how new and state-of-the-art approaches, including fluorescence indicators, opto- and chemogenetic actuators, genetic targeting tools, quantitative behavioral assays, and computational methods, might help resolve these longstanding questions. It also addresses complicating factors in interpreting astrocytes' role in neural circuit regulation and animal behavior, such as their heterogeneity, metabolism, and inter-glial communication. Research on these questions should provide a deeper mechanistic understanding of astrocyte-neuron assemblies' role in neural circuit function, complex behaviors, and disease.

Toward an integrative neurovascular framework for studying brain networks

Brain functional connectivity based on the measure of blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) signals has become one of the most widely used measurements in human neuroimaging. However, the nature of the functional networks revealed by BOLD fMRI can be ambiguous, as highlighted by a recent series of experiments that have suggested that typical resting-state networks can be replicated from purely vascular or physiologically driven BOLD signals. After going through a brief review of the key concepts of brain network analysis, we explore how the vascular and neuronal systems interact to give rise to the brain functional networks measured with BOLD fMRI. This leads us to emphasize a view of the vascular network not only as a confounding element in fMRI but also as a functionally relevant system that is entangled with the neuronal network. To study the vascular and neuronal underpinnings of BOLD functional connectivity, we consider a combination of methodological avenues based on multiscale and multimodal optical imaging in mice, used in combination with computational models that allow the integration of vascular information to explain functional connectivity.

Toward Reflective Spiking Neural Networks Exploiting Memristive Devices

The design of modern convolutional artificial neural networks (ANNs) composed of formal neurons copies the architecture of the visual cortex. Signals proceed through a hierarchy, where receptive fields become increasingly more complex and coding sparse. Nowadays, ANNs outperform humans in controlled pattern recognition tasks yet remain far behind in cognition. In part, it happens due to limited knowledge about the higher echelons of the brain hierarchy, where neurons actively generate predictions about what will happen next, i.e., the information processing jumps from reflex to reflection. In this study, we forecast that spiking neural networks (SNNs) can achieve the next qualitative leap. Reflective SNNs may take advantage of their intrinsic dynamics and mimic complex, not reflex-based, brain actions. They also enable a significant reduction in energy consumption. However, the training of SNNs is a challenging problem, strongly limiting their deployment. We then briefly overview new insights provided by the concept of a high-dimensional brain, which has been put forward to explain the potential power of single neurons in higher brain stations and deep SNN layers. Finally, we discuss the prospect of implementing neural networks in memristive systems. Such systems can densely pack on a chip 2D or 3D arrays of plastic synaptic contacts directly processing analog information. Thus, memristive devices are a good candidate for implementing in-memory and in-sensor computing. Then, memristive SNNs can diverge from the development of ANNs and build their niche, cognitive, or reflective computations.

Controlling synchronization of gamma oscillations by astrocytic modulation in a model hippocampal neural network

Recent in vitro and in vivo experiments demonstrate that astrocytes participate in the maintenance of cortical gamma oscillations and recognition memory. However, the mathematical understanding of the underlying dynamical mechanisms remains largely incomplete. Here we investigate how the interplay of slow modulatory astrocytic signaling with fast synaptic transmission controls coherent oscillations in the network of hippocampal interneurons that receive inputs from pyramidal cells. We show that the astrocytic regulation of signal transmission between neurons improves the firing synchrony and extends the region of coherent oscillations in the biologically relevant values of synaptic conductance. Astrocyte-mediated potentiation of inhibitory synaptic transmission markedly enhances the coherence of network oscillations over a broad range of model parameters. Astrocytic regulation of excitatory synaptic input improves the robustness of interneuron network gamma oscillations induced by physiologically relevant excitatory model drive. These findings suggest a mechanism, by which the astrocytes become involved in cognitive function and information processing through modulating fast neural network dynamics.

Bistability and Chaos Emergence in Spontaneous Dynamics of Astrocytic Calcium Concentration

In this work, we consider a mathematical model describing spontaneous calcium signaling in astrocytes. Based on biologically relevant principles, this model simulates experimentally observed calcium oscillations and can predict the emergence of complicated dynamics. Using analytical and numerical analysis, various attracting sets were found and investigated. Employing bifurcation theory analysis, we examined steady state solutions, bistability, simple and complicated periodic limit cycles and also chaotic attractors. We found that astrocytes possess a variety of complex dynamical modes, including chaos and multistability, that can further provide different modulations of neuronal circuits, enhancing their plasticity and flexibility.

Astrocytes mediate analogous memory in a multi-layer neuron–astrocyte network

Modeling the neuronal processes underlying short-term working memory remains the focus of many theoretical studies in neuroscience. In this paper, we propose a mathematical model of a spiking neural network (SNN) which simulates the way a fragment of information is maintained as a robust activity pattern for several seconds and the way it completely disappears if no other stimuli are fed to the system. Such short-term memory traces are preserved due to the activation of astrocytes accompanying the SNN. The astrocytes exhibit calcium transients at a time scale of seconds. These transients further modulate the efficiency of synaptic transmission and, hence, the firing rate of neighboring neurons at diverse timescales through gliotransmitter release. We demonstrate how such transients continuously encode frequencies of neuronal discharges and provide robust short-term storage of analogous information. This kind of short-term memory can store relevant information for seconds and then completely forget it to avoid overlapping with forthcoming patterns. The SNN is inter-connected with the astrocytic layer by local inter-cellular diffusive connections. The astrocytes are activated only when the neighboring neurons fire synchronously, e.g., when an information pattern is loaded. For illustration, we took grayscale photographs of people’s faces where the shades of gray correspond to the level of applied current which stimulates the neurons. The astrocyte feedback modulates (facilitates) synaptic transmission by varying the frequency of neuronal firing. We show how arbitrary patterns can be loaded, then stored for a certain interval of time, and retrieved if the appropriate clue pattern is applied to the input.