Bidirectional coupling between astrocytes and neurons mediates learning and dynamic coordination in the brain: a multiple modeling approach

On the functions of astrocyte-mediated neuronal slow inward currents

Slow inward currents are known as neuronal excitatory currents mediated by glutamate release and activation of neuronal extrasynaptic N-methyl-D-aspartate receptors with the contribution of astrocytes. These events are significantly slower than the excitatory postsynaptic currents. Parameters of slow inward currents are determined by several factors including the mechanisms of astrocytic activation and glutamate release, as well as the diffusion pathways from the release site towards the extrasynaptic receptors. Astrocytes are stimulated by neuronal network activity, which in turn excite neurons, forming an astrocyte-neuron feedback loop. Mostly as a consequence of brain edema, astrocytic swelling can also induce slow inward currents under pathological conditions. There is a growing body of evidence on the roles of slow inward currents on a single neuron or local network level. These events often occur in synchrony on neurons located in the same astrocytic domain. Besides synchronization of neuronal excitability, slow inward currents also set synaptic strength via eliciting timing-dependent synaptic plasticity. In addition, slow inward currents are also subject to non-synaptic plasticity triggered by long-lasting stimulation of the excitatory inputs. Of note, there might be important region-specific differences in the roles and actions triggering slow inward currents. In greater networks, the pathophysiological roles of slow inward currents can be better understood than physiological ones. Slow inward currents are identified in the pathophysiological background of autism, as slow inward currents drive early hypersynchrony of the neural networks. Slow inward currents are significant contributors to paroxysmal depolarizational shifts/interictal spikes. These events are related to epilepsy, but also found in Alzheimer's disease, Parkinson's disease, and stroke, leading to the decline of cognitive functions. Events with features overlapping with slow inward currents (excitatory, N-methyl-D-aspartate-receptor mediated currents with astrocytic contribution) as ischemic currents and spreading depolarization also have a well-known pathophysiological role in worsening consequences of stroke, traumatic brain injury, or epilepsy. One might assume that slow inward currents occurring with low frequency under physiological conditions might contribute to synaptic plasticity and memory formation. However, to state this, more experimental evidence from greater neuronal networks or the level of the individual is needed. In this review, I aimed to summarize findings on slow inward currents and to speculate on the potential functions of it.

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.

Computational Modeling of Extrasynaptic NMDA Receptors: Insights into Dendritic Signal Amplification Mechanisms

Dendritic structures play a pivotal role in the computational processes occurring within neurons. Signal propagation along dendrites relies on both passive conduction and active processes related to voltage-dependent ion channels. Among these channels, extrasynaptic N-methyl-D-aspartate channels (exNMDA) emerge as a significant contributor. Prior studies have mainly concentrated on interactions between synapses and nearby exNMDA (100 nm-10 µm from synapse), activated by presynaptic membrane glutamate. This study concentrates on the correlation between synaptic inputs and distal exNMDA (>100 µm), organized in clusters that function as signal amplifiers. Employing a computational model of a dendrite, we elucidate the mechanism underlying signal amplification in exNMDA clusters. Our findings underscore the pivotal role of the optimal spatial positioning of the NMDA cluster in determining signal amplification efficiency. Additionally, we demonstrate that exNMDA subunits characterized by a large conduction decay constant. Specifically, NR2B subunits exhibit enhanced effectiveness in signal amplification compared to subunits with steeper conduction decay. This investigation extends our understanding of dendritic computational processes by emphasizing the significance of distant exNMDA clusters as potent signal amplifiers. The implications of our computational model shed light on the spatial considerations and subunit characteristics that govern the efficiency of signal amplification in dendritic structures, offering valuable insights for future studies in neurobiology and computational neuroscience.

Astrocyte- and NMDA receptor-dependent slow inward currents differently contribute to synaptic plasticity in an age-dependent manner in mouse and human neocortex

Slow inward currents (SICs) are known as excitatory events of neurons elicited by astrocytic glutamate via activation of extrasynaptic NMDA receptors. By using slice electrophysiology, we tried to provide evidence that SICs can elicit synaptic plasticity. Age dependence of SICs and their impact on synaptic plasticity was also investigated in both on murine and human cortical slices. It was found that SICs can induce a moderate synaptic plasticity, with features similar to spike timing-dependent plasticity. Overall SIC activity showed a clear decline with aging in humans and completely disappeared above a cutoff age. In conclusion, while SICs contribute to a form of astrocyte-dependent synaptic plasticity both in mice and humans, this plasticity is differentially affected by aging. Thus, SICs are likely to play an important role in age-dependent physiological and pathological alterations of synaptic plasticity.

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.

Loss of neuron network coherence induced by virus-infected astrocytes: a model study

Coherent activations of brain neuron networks underlie many physiological functions associated with various behavioral states. These synchronous fluctuations in the electrical activity of the brain are also referred to as brain rhythms. At the cellular level, rhythmicity can be induced by various mechanisms of intrinsic oscillations in neurons or the network circulation of excitation between synaptically coupled neurons. One specific mechanism concerns the activity of brain astrocytes that accompany neurons and can coherently modulate synaptic contacts of neighboring neurons, synchronizing their activity. Recent studies have shown that coronavirus infection (Covid-19), which enters the central nervous system and infects astrocytes, can cause various metabolic disorders. Specifically, Covid-19 can depress the synthesis of astrocytic glutamate and gamma-aminobutyric acid. It is also known that in the post-Covid state, patients may suffer from symptoms of anxiety and impaired cognitive functions. We propose a mathematical model of a spiking neuron network accompanied by astrocytes capable of generating quasi-synchronous rhythmic bursting discharges. The model predicts that if the release of glutamate is depressed, normal burst rhythmicity will suffer dramatically. Interestingly, in some cases, the failure of network coherence may be intermittent, with intervals of normal rhythmicity, or the synchronization can disappear.

Interplay between biochemical processes and network properties generates neuronal up and down states at the tripartite synapse

Neuronal up and down states have long been known to exist both in vitro and in vivo. A variety of functions and mechanisms have been proposed for their generation, but there has not been a clear connection between the functions and mechanisms. We explore the potential contribution of cellular-level biochemistry to the network-level mechanisms thought to underlie the generation of up and down states. We develop a neurochemical model of a single tripartite synapse, assumed to be within a network of similar tripartite synapses, to investigate possible function-mechanism links for the appearance of up and down states. We characterize the behavior of our model in different regions of parameter space and show that resource limitation at the tripartite synapse affects its ability to faithfully transmit input signals, leading to extinction-down states. Recovery of resources allows for "reignition" into up states. The tripartite synapse exhibits distinctive "regimes" of operation depending on whether ATP, neurotransmitter (glutamate), both, or neither, is limiting. Our model qualitatively matches the behavior of six disparate experimental systems, including both in vitro and in vivo models, without changing any model parameters except those related to the experimental conditions. We also explore the effects of varying different critical parameters within the model. Here we show that availability of energy, represented by ATP, and glutamate for neurotransmission at the cellular level are intimately related, and are capable of promoting state transitions at the network level as ignition and extinction phenomena. Our model is complementary to existing models of neuronal up and down states in that it focuses on cellular-level dynamics while still retaining essential network-level processes. Our model predicts the existence of a "final common pathway" of behavior at the tripartite synapse arising from scarcity of resources and may explain use dependence in the phenomenon of "local sleep." Ultimately, sleeplike behavior may be a fundamental property of networks of tripartite synapses.

A New Real-Time Analog Circuit of Ca2+ Li-Rinzel Astrocyte Model Based on Analytical Method

Different biological models are used to study physical behaviors in neural networks. So far, various models of neural network components such as neurons, synapses, and astrocytes have been proposed. An astrocyte is one of the crucial parts introduced in multiple models. A model of astrocytes used as a good reference in various papers is the Li-Rinzel calcium model. This paper presents a real-time analog circuit of the Li-Rinzel calcium model based on common-mode (CM) in 180nm CMOS technology. To the best of our knowledge, this work is the first report to introduce a real-time analog Li-Rinzel model. The careful design of equations and low power consumption are essential features of this circuit. The real-time behavior is also crucial compared with the accelerated time circuits presented so far.

Controlling the spontaneous firing behavior of a neuron with astrocyte

Mounting evidence in recent years suggests that astrocytes, a sub-type of glial cells, not only serve metabolic and structural support for neurons and synapses but also play critical roles in the regulation of proper functioning of the nervous system. In this work, we investigate the effect of astrocytes on the spontaneous firing activity of a neuron through a combined model that includes a neuron-astrocyte pair. First, we show that an astrocyte may provide a kind of multistability in neuron dynamics by inducing different firing modes such as random and bursty spiking. Then, we identify the underlying mechanism of this behavior and search for the astrocytic factors that may have regulatory roles in different firing regimes. More specifically, we explore how an astrocyte can participate in the occurrence and control of spontaneous irregular spiking activity of a neuron in random spiking mode. Additionally, we systematically investigate the bursty firing regime dynamics of the neuron under the variation of biophysical facts related to the intracellular environment of the astrocyte. It is found that an astrocyte coupled to a neuron can provide a control mechanism for both spontaneous firing irregularity and burst firing statistics, i.e., burst regularity and size.

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.

Astrocytic modulation of central pattern generating motor circuits

Central pattern generators (CPGs) generate the rhythmic and coordinated neural features necessary for the proper conduction of complex behaviors. In particular, CPGs are crucial for complex motor behaviors such as locomotion, mastication, respiration, and vocal production. While the importance of these networks in modulating behavior is evident, the mechanisms driving these CPGs are still not fully understood. On the other hand, accumulating evidence suggests that astrocytes have a significant role in regulating the function of some of these CPGs. Here, we review the location, function, and role of astrocytes in locomotion, respiration, and mastication CPGs and propose that, similarly, astrocytes may also play a significant role in the vocalization CPG.

Impact of DC-Coupled Electrophysiological Recordings for Translational Neuroscience: Case Study of Tracking Neural Dynamics in Rodent Models of Seizures

We propose that to fully understand biological mechanisms underlying pathological brain activity with transitions (e.g., into and out of seizures), wide-bandwidth electrophysiological recordings are important. We demonstrate the importance of ultraslow potential shifts and infraslow oscillations for reliable tracking of synaptic physiology, within a neural mass model, from brain recordings that undergo pathological phase transitions. We use wide-bandwidth data (direct current (DC) to high-frequency activity), recorded using epidural and penetrating graphene micro-transistor arrays in a rodent model of acute seizures. Using this technological approach, we capture the dynamics of infraslow changes that contribute to seizure initiation (active pre-seizure DC shifts) and progression (passive DC shifts). By employing a continuous-discrete unscented Kalman filter, we track biological mechanisms from full-bandwidth data with and without active pre-seizure DC shifts during paroxysmal transitions. We then apply the same methodological approach for tracking the same parameters after application of high-pass-filtering >0.3Hz to both data sets. This approach reveals that ultraslow potential shifts play a fundamental role in the transition to seizure, and the use of high-pass-filtered data results in the loss of key information in regard to seizure onset and termination dynamics.

Emulation of Astrocyte Induced Neural Phase Synchrony in Spin-Orbit Torque Oscillator Neurons

Astrocytes play a central role in inducing concerted phase synchronized neural-wave patterns inside the brain. In this article, we demonstrate that injected radio-frequency signal in underlying heavy metal layer of spin-orbit torque oscillator neurons mimic the neuron phase synchronization effect realized by glial cells. Potential application of such phase coupling effects is illustrated in the context of a temporal "binding problem." We also present the design of a coupled neuron-synapse-astrocyte network enabled by compact neuromimetic devices by combining the concepts of local spike-timing dependent plasticity and astrocyte induced neural phase synchrony.

The glutamatergic synapse: a complex machinery for information processing

Being the most abundant synaptic type, the glutamatergic synapse is responsible for the larger part of the brain's information processing. Despite the conceptual simplicity of the basic mechanism of synaptic transmission, the glutamatergic synapse shows a large variation in the response to the presynaptic release of the neurotransmitter. This variability is observed not only among different synapses but also in the same single synapse. The synaptic response variability is due to several mechanisms of control of the information transferred among the neurons and suggests that the glutamatergic synapse is not a simple bridge for the transfer of information but plays an important role in its elaboration and management. The control of the synaptic information is operated at pre, post, and extrasynaptic sites in a sort of cooperation between the pre and postsynaptic neurons which also involves the activity of other neurons. The interaction between the different mechanisms of control is extremely complicated and its complete functionality is far from being fully understood. The present review, although not exhaustively, is intended to outline the most important of these mechanisms and their complexity, the understanding of which will be among the most intriguing challenges of future neuroscience.

Use of a neuron-glia genome-scale metabolic reconstruction to model the metabolic consequences of the Arylsulphatase a deficiency through a systems biology approach

Metachromatic leukodystrophy (MLD) is a human neurodegenerative disorder characterized by progressive damage on the myelin band in the nervous system. MLD is caused by the impaired function of the lysosomal enzyme Arylsulphatase A (ARSA). The physiopathology mechanisms and the biochemical consequences in the brain of ARSA deficiency are not entirely understood. In recent years, the use of genome-scale metabolic (GEM) models has been explored as a tool for the study of the biochemical alterations in MLD. Previously, we modeled the metabolic consequences of different lysosomal storage diseases using single GEMs. In the case of MLD, using a glia GEM, we previously predicted that the metabolism of glycosphingolipids and neurotransmitters was altered. The results also suggested that mitochondrial metabolism and amino acid transport were the main reactions affected. In this study, we extended the modeling of the metabolic consequences of ARSA deficiency through the integration of neuron and glial cell metabolic models. Cell-specific models were generated from Recon2, and these were used to create a neuron-glial bi-cellular model. We propose a workflow for the integration of this type of model and its subsequent study. The results predicted the impairment pathways involved in the transport of amino acids, lipids metabolism, and catabolism of purines and pyrimidines. The use of this neuron-glial GEM metabolic reconstruction allowed to improve the prediction capacity of the metabolic consequences of ARSA deficiency, which might pave the way for the modeling of the biochemical alterations of other inborn errors of metabolism with central nervous system involvement.

Blood-brain barrier disruption in atrial fibrillation: a potential contributor to the increased risk of dementia and worsening of stroke outcomes?

Atrial fibrillation (AF) has become one of the most significant health problems worldwide, warranting urgent answers to currently pending questions on the effects of AF on brain function. Recent evidence has emerged to show an association between AF and an increased risk of developing dementia and worsening of stroke outcomes. A healthy brain is protected by the blood–brain barrier (BBB), which is formed by the endothelial cells that line cerebral capillaries. These endothelial cells are continuously exposed to shear stress (the frictional force generated by blood flow), which affects endothelial cell structure and function. Flow disturbances as experienced during AF can disrupt the BBB and leave the brain vulnerable to damage. Investigating the plausible mechanisms in detail, linking AF to cerebrovascular damage is difficult in humans, leading to paucity of available clinical data. Here, we discuss the available evidence for BBB disruption during AF due to altered cerebral blood flow, and how this may contribute to an increased risk of dementia and worsening of stroke outcomes.

Modeling Working Memory in a Spiking Neuron Network Accompanied by Astrocytes

We propose a novel biologically plausible computational model of working memory (WM) implemented by a spiking neuron network (SNN) interacting with a network of astrocytes. The SNN is modeled by synaptically coupled Izhikevich neurons with a non-specific architecture connection topology. Astrocytes generating calcium signals are connected by local gap junction diffusive couplings and interact with neurons via chemicals diffused in the extracellular space. Calcium elevations occur in response to the increased concentration of the neurotransmitter released by spiking neurons when a group of them fire coherently. In turn, gliotransmitters are released by activated astrocytes modulating the strength of the synaptic connections in the corresponding neuronal group. Input information is encoded as two-dimensional patterns of short applied current pulses stimulating neurons. The output is taken from frequencies of transient discharges of corresponding neurons. We show how a set of information patterns with quite significant overlapping areas can be uploaded into the neuron-astrocyte network and stored for several seconds. Information retrieval is organized by the application of a cue pattern representing one from the memory set distorted by noise. We found that successful retrieval with the level of the correlation between the recalled pattern and ideal pattern exceeding 90% is possible for the multi-item WM task. Having analyzed the dynamical mechanism of WM formation, we discovered that astrocytes operating at a time scale of a dozen of seconds can successfully store traces of neuronal activations corresponding to information patterns. In the retrieval stage, the astrocytic network selectively modulates synaptic connections in the SNN leading to successful recall. Information and dynamical characteristics of the proposed WM model agrees with classical concepts and other WM models.

Estimating integrated information in bidirectional neuron-astrocyte communication

There is growing evidence that suggests the importance of astrocytes as elements for neural information processing through the modulation of synaptic transmission. A key aspect of this problem is understanding the impact of astrocytes in the information carried by compound events in neurons across time. In this paper, we investigate how the astrocytes participate in the information integrated by individual neurons in an ensemble through the measurement of "integrated information." We propose a computational model that considers bidirectional communication between astrocytes and neurons through glutamate-induced calcium signaling. Our model highlights the role of astrocytes in information processing through dynamical coordination. Our findings suggest that the astrocytic feedback promotes synergetic influences in the neural communication, which is maximized when there is a balance between excess correlation and spontaneous spiking activity. The results were further linked with additional measures such as net synergy and mutual information. This result reinforces the idea that astrocytes have integrative properties in communication among neurons.

The Role of Satellite Glial Cells, Astrocytes, and Microglia in Oxaliplatin-Induced Neuropathic Pain

Oxaliplatin is a third-generation platinum-based chemotherapeutic drug. Although its efficacy against colorectal cancer is well known, peripheral neuropathy that develops during and after infusion of the agents could decrease the quality of life of the patients. Various pathways have been reported to be the cause of the oxaliplatin-induced paresthesia and dysesthesia; however, its mechanism of action has not been fully understood yet. In recent years, researchers have investigated the function of glia in pain, and demonstrated that glia in the peripheral and central nervous system could play a critical role in the development and maintenance of neuropathic pain. These results suggest that targeting the glia may be an effective therapeutic option. In the past ten years, 20 more papers focused on the role of glia in oxaliplatin-induced thermal and mechanical hypersensitivity. However, to date no review has been written to summarize and discuss their results. Thus, in this study, by reviewing 23 studies that conducted in vivo experiments in rodents, the change of satellite glial cells, astrocytes, and microglia activation in the dorsal root ganglia, spinal cord, and the brain of oxaliplatin-induced neuropathic pain animals is discussed.

Advances in Astrocyte Computational Models: From Metabolic Reconstructions to Multi-omic Approaches

The growing importance of astrocytes in the field of neuroscience has led to a greater number of computational models devoted to the study of astrocytic functions and their metabolic interactions with neurons. The modeling of these interactions demands a combined understanding of brain physiology and the development of computational frameworks based on genomic-scale reconstructions, system biology, and dynamic models. These computational approaches have helped to highlight the neuroprotective mechanisms triggered by astrocytes and other glial cells, both under normal conditions and during neurodegenerative processes. In the present review, we evaluate some of the most relevant models of astrocyte metabolism, including genome-scale reconstructions and astrocyte-neuron interactions developed in the last few years. Additionally, we discuss novel strategies from the multi-omics perspective and computational models of other glial cell types that will increase our knowledge in brain metabolism and its association with neurodegenerative diseases.

CORDIC-Astrocyte: Tripartite Glutamate-IP3-Ca 2+ Interaction Dynamics on FPGA

Real-time, large-scale simulation of biological systems is challenging due to different types of nonlinear functions describing biochemical reactions in the cells. The promise of the high speed, cost effectiveness, and power efficiency in addition to parallel processing has made application-specific hardware an attractive simulation platform. This paper proposes high-speed and low-cost digital hardware to emulate a biological-plausible astrocyte and glutamate-release mechanism. The nonlinear terms of these models were calculated using a high-precision and cost-effective algorithm. Subsequently, the modified models were simulated to study and validate their functions. We developed several hardware versions by setting different constraints to investigate trade-offs and find the best possible design. FPGA implementation results confirmed the ability of the design to emulate biological cell behaviours in detail with high accuracy. As for performance, the proposed design turned out to be faster and more efficient than previously published works that targeted digital hardware for biological-plausible astrocytes.

Calcium Microdomain Formation at the Perisynaptic Cradle Due to NCX Reversal: A Computational Study

It has recently been proposed using a multi-compartmental mathematical model that negatively fixed charged membrane-associated sites constrain the flow of cations in perisynaptic astroglial processes. This restricted movement of ions between the perisynaptic cradle (PsC), principal astroglial processes and the astrocyte soma gives rise to potassium (K+) and sodium (Na+) microdomains at the PsC. The present paper extends the above model to demonstrate that the formation of an Na+ microdomain can reverse the Na+/Ca2+ exchanger (NCX) thus providing an additional source of calcium (Ca2+) at the PsC. Results presented clearly show that reversal of the Na+/Ca2+ exchanger is instigated by a glutamate transporter coupled increase in concentration of cytoplasmic [Na+]i at the PsC, which and instigates Ca2+ influx through the NCX. As the flow of Ca2+ along the astrocyte process and away from the PsC is also constrained by Ca2+ binding proteins, then a Ca2+ microdomain forms at the PsC. The paper also serves to demonstrate that the EAAT, NKA, and NCX represent the minimal requirement necessary and sufficient for the development of a Ca2+ microdomain and that these mechanisms directly link neuronal activity and glutamate release to the formation of localized Na+ and Ca2+ microdomains signals at the PsC. This local source of Ca2+ can provide a previously underexplored form of astroglial Ca2+ signaling.

Digital Multiplierless Realization of Coupled Wilson Neuron Model

The human brain is composed of 10¹¹ neurons with a switching speed of about 1 ms. Studying spiking neural networks, including the modeling, simulation, and implementation of the biological neuron models, helps us to learn about the brain and the related diseases, or to design more efficient bio-mimic processors and smarter robots. Such applications have made this part of neuromorphic research works very popular. In this paper, the Wilson neuron model has been implemented as an approximation of the Hodgkin-Huxley biological model that is adjusted for the efficient digital realization on the platforms. Results show that the proposed model can adequately reproduce neuron dynamical behaviors. The hardware implementation on the field-programmable gate array (FPGA) shows that our modifications on the Wilson original model imitate the biological behavior of neurons, besides using feasibility, targeting a low cost and high efficiency. The modifications raised a 15% speed-up compared with the original model. The mean normalized root-mean-square error, root-mean-square error, and the mean absolute error parameters are 6.43, 0.44, and 0.31, respectively.

A Compartmental Model to Investigate Local and Global Ca2+ Dynamics in Astrocytes

Intracellular Ca²⁺ dynamics in astrocytes can be triggered by neuronal activity and in turn regulate a variety of downstream processes that modulate neuronal function. In this fashion, astrocytic Ca²⁺ signaling is regarded as a processor of neural network activity by means of complex spatial and temporal Ca²⁺ dynamics. Accordingly, a key step is to understand how different patterns of neural activity translate into spatiotemporal dynamics of intracellular Ca²⁺ in astrocytes. Here, we introduce a minimal compartmental model for astrocytes that can qualitatively reproduce essential hierarchical features of spatiotemporal Ca²⁺ dynamics in astrocytes. We find that the rate of neuronal firing determines the rate of Ca²⁺ spikes in single individual processes as well as in the soma of the cell, while correlations of incoming neuronal activity are important in determining the rate of “global” Ca²⁺ spikes that can engulf soma and the majority of processes. Significantly, our model predicts that whether the endoplasmic reticulum is shared between soma and processes or not determines the relationship between the firing rate of somatic Ca²⁺ events and the rate of neural network activity. Together these results provide intuition about how neural activity in combination with inherent cellular properties shapes spatiotemporal astrocytic Ca²⁺ dynamics, and provide experimentally testable predictions.

Challenges in Reproducibility, Replicability, and Comparability of Computational Models and Tools for Neuronal and Glial Networks, Cells, and Subcellular Structures

The possibility to replicate and reproduce published research results is one of the biggest challenges in all areas of science. In computational neuroscience, there are thousands of models available. However, it is rarely possible to reimplement the models based on the information in the original publication, let alone rerun the models just because the model implementations have not been made publicly available. We evaluate and discuss the comparability of a versatile choice of simulation tools: tools for biochemical reactions and spiking neuronal networks, and relatively new tools for growth in cell cultures. The replicability and reproducibility issues are considered for computational models that are equally diverse, including the models for intracellular signal transduction of neurons and glial cells, in addition to single glial cells, neuron-glia interactions, and selected examples of spiking neuronal networks. We also address the comparability of the simulation results with one another to comprehend if the studied models can be used to answer similar research questions. In addition to presenting the challenges in reproducibility and replicability of published results in computational neuroscience, we highlight the need for developing recommendations and good practices for publishing simulation tools and computational models. Model validation and flexible model description must be an integral part of the tool used to simulate and develop computational models. Constant improvement on experimental techniques and recording protocols leads to increasing knowledge about the biophysical mechanisms in neural systems. This poses new challenges for computational neuroscience: extended or completely new computational methods and models may be required. Careful evaluation and categorization of the existing models and tools provide a foundation for these future needs, for constructing multiscale models or extending the models to incorporate additional or more detailed biophysical mechanisms. Improving the quality of publications in computational neuroscience, enabling progressive building of advanced computational models and tools, can be achieved only through adopting publishing standards which underline replicability and reproducibility of research results.

A computational study of astrocytic glutamate influence on post-synaptic neuronal excitability

The ability of astrocytes to rapidly clear synaptic glutamate and purposefully release the excitatory transmitter is critical in the functioning of synapses and neuronal circuits. Dysfunctions of these homeostatic functions have been implicated in the pathology of brain disorders such as mesial temporal lobe epilepsy. However, the reasons for these dysfunctions are not clear from experimental data and computational models have been developed to provide further understanding of the implications of glutamate clearance from the extracellular space, as a result of EAAT2 downregulation: although they only partially account for the glutamate clearance process. In this work, we develop an explicit model of the astrocytic glutamate transporters, providing a more complete description of the glutamate chemical potential across the astrocytic membrane and its contribution to glutamate transporter driving force based on thermodynamic principles and experimental data. Analysis of our model demonstrates that increased astrocytic glutamate content due to glutamine synthetase downregulation also results in increased postsynaptic quantal size due to gliotransmission. Moreover, the proposed model demonstrates that increased astrocytic glutamate could prolong the time course of glutamate in the synaptic cleft and enhances astrocyte-induced slow inward currents, causing a disruption to the clarity of synaptic signalling and the occurrence of intervals of higher frequency postsynaptic firing. Overall, our work distilled the necessity of a low astrocytic glutamate concentration for reliable synaptic transmission of information and the possible implications of enhanced glutamate levels as in epilepsy.

The role of astrocytes in the excitability and hyperexcitability of neurons is a subject which has gained a lot of attention, particularly in the pathology of neurological disorders including epilepsy. Although not completely understood, the control of glutamate homeostasis is believed to play a role in paroxysmal neuronal hyperexcitability known to precede seizure activity. We have developed a computational model which explores two of the astrocytic homeostatic mechanisms, namely glutamate clearance and gliotransmission, and connect them with a common controlling factor, astrocytic cytoplasmic glutamate concentration. In our model simulations we demonstrate both a slower clearance rate of synaptic glutamate and enhanced astrocytic glutamate release where cytoplasmic glutamate is elevated, both of which contribute to high frequency neuronal firing and conditions for seizure generation. We also describe a viable role for astrocytes as a “high pass” filter, where astrocytic activation in the form of intracellular calcium oscillations is possible for only a certain range of presynaptic neuronal firing rates, the lower bound of the range being reduced where astrocytic glutamate is elevated. In physiological terms this perhaps indicates not only neuronal but also astrocytic glutamate-mediated excitation in the neural-astrocytic network.

Computational Models for Calcium-Mediated Astrocyte Functions

The computational neuroscience field has heavily concentrated on the modeling of neuronal functions, largely ignoring other brain cells, including one type of glial cell, the astrocytes. Despite the short history of modeling astrocytic functions, we were delighted about the hundreds of models developed so far to study the role of astrocytes, most often in calcium dynamics, synchronization, information transfer, and plasticity in vitro, but also in vascular events, hyperexcitability, and homeostasis. Our goal here is to present the state-of-the-art in computational modeling of astrocytes in order to facilitate better understanding of the functions and dynamics of astrocytes in the brain. Due to the large number of models, we concentrated on a hundred models that include biophysical descriptions for calcium signaling and dynamics in astrocytes. We categorized the models into four groups: single astrocyte models, astrocyte network models, neuron-astrocyte synapse models, and neuron-astrocyte network models to ease their use in future modeling projects. We characterized the models based on which earlier models were used for building the models and which type of biological entities were described in the astrocyte models. Features of the models were compared and contrasted so that similarities and differences were more readily apparent. We discovered that most of the models were basically generated from a small set of previously published models with small variations. However, neither citations to all the previous models with similar core structure nor explanations of what was built on top of the previous models were provided, which made it possible, in some cases, to have the same models published several times without an explicit intention to make new predictions about the roles of astrocytes in brain functions. Furthermore, only a few of the models are available online which makes it difficult to reproduce the simulation results and further develop the models. Thus, we would like to emphasize that only via reproducible research are we able to build better computational models for astrocytes, which truly advance science. Our study is the first to characterize in detail the biophysical and biochemical mechanisms that have been modeled for astrocytes.

Computational modeling of opioid-induced synaptic plasticity in hippocampus

According to a broad range of research, opioids consumption can lead to pathological memory formation. Experimental observations suggested that hippocampal glutamatergic synapses play an indispensable role in forming such a pathological memory. It has been suggested that memory formation at the synaptic level is developed through LTP induction. Here, we attempt to computationally indicate how morphine induces pathological LTP at hippocampal CA3-CA1 synapses. Then, based on simulations, we will suggest how one can prevent this type of pathological LTP. To this purpose, a detailed computational model is presented, which consists of one pyramidal neuron and one interneuron both from CA3, one CA1 pyramidal neuron, and one astrocyte. Based on experimental findings morphine affects the hippocampal neurons in three primary ways: 1) disinhibitory mechanism of interneurons in CA3, 2) enhancement of NMDARs current by μ Opioid Receptor (μOR) activation and 3) by attenuation of astrocytic glutamate reuptake ability. By utilizing these effects, simulations were implemented. Our results indicate that morphine can induce LTP by all aforementioned possible mechanisms. Based on our simulation results, attenuation of pathologic LTP achieved mainly by stimulation of astrocytic glutamate transporters, down-regulation of the astrocytic metabotropic glutamate receptors (mGlurs) or by applying NMDAR’s antagonist. Based on our observations, we suggest that astrocyte has a dominant role in forming addiction-related memories. This finding may help researchers in exploring drug actions for preventing relapse.

Gap junctional signaling in pattern regulation: Physiological network connectivity instructs growth and form

Gap junctions (GJs) are aqueous channels that allow cells to communicate via physiological signals directly. The role of gap junctional connectivity in determining single-cell functions has long been recognized. However, GJs have another important role: the regulation of large-scale anatomical pattern. GJs are not only versatile computational elements that allow cells to control which small molecule signals they receive and emit, but also establish connectivity patterns within large groups of cells. By dynamically regulating the topology of bioelectric networks in vivo, GJs underlie the ability of many tissues to implement complex morphogenesis. Here, a review of recent data on patterning roles of GJs in growth of the zebrafish fin, the establishment of left-right patterning, the developmental dysregulation known as cancer, and the control of large-scale head-tail polarity, and head shape in planarian regeneration has been reported. A perspective in which GJs are not only molecular features functioning in single cells, but also enable global neural-like dynamics in non-neural somatic tissues has been proposed. This view suggests a rich program of future work which capitalizes on the rapid advances in the biophysics of GJs to exploit GJ-mediated global dynamics for applications in birth defects, regenerative medicine, and morphogenetic bioengineering. © 2016 Wiley Periodicals, Inc. Develop Neurobiol 77: 643-673, 2017.

Mathematical investigation of IP3-dependent calcium dynamics in astrocytes

We study evoked calcium dynamics in astrocytes, a major cell type in the mammalian brain. Experimental evidence has shown that such dynamics are highly variable between different trials, cells, and cell subcompartments. Here we present a qualitative analysis of a recent mathematical model of astrocyte calcium responses. We show how the major response types are generated in the model as a result of the underlying bifurcation structure. By varying key channel parameters, mimicking blockers used by experimentalists, we manipulate this underlying bifurcation structure and predict how the distributions of responses can change. We find that store-operated calcium channels, plasma membrane bound channels with little activity during calcium transients, have a surprisingly strong effect, underscoring the importance of considering these channels in both experiments and mathematical settings. Variation in the maximum flow in different calcium channels is also shown to determine the range of stable oscillations, as well as set the range of frequencies of the oscillations. Further, by conducting a randomized search through the parameter space and recording the resulting calcium responses, we create a database that can be used by experimentalists to help estimate the underlying channel distribution of their cells.

From in silico astrocyte cell models to neuron-astrocyte network models: A review

The idea that astrocytes may be active partners in synaptic information processing has recently emerged from abundant experimental reports. Because of their spatial proximity to neurons and their bidirectional communication with them, astrocytes are now considered as an important third element of the synapse. Astrocytes integrate and process synaptic information and by doing so generate cytosolic calcium signals that are believed to reflect neuronal transmitter release. Moreover, they regulate neuronal information transmission by releasing gliotransmitters into the synaptic cleft affecting both pre- and postsynaptic receptors. Concurrent with the first experimental reports of the astrocytic impact on neural network dynamics, computational models describing astrocytic functions have been developed. In this review, we give an overview over the published computational models of astrocytic functions, from single-cell dynamics to the tripartite synapse level and network models of astrocytes and neurons.

Feedback control stabilization of critical dynamics via resource transport on multilayer networks: How glia enable learning dynamics in the brain

Learning and memory are acquired through long-lasting changes in synapses. In the simplest models, such synaptic potentiation typically leads to runaway excitation, but in reality there must exist processes that robustly preserve overall stability of the neural system dynamics. How is this accomplished? Various approaches to this basic question have been considered. Here we propose a particularly compelling and natural mechanism for preserving stability of learning neural systems. This mechanism is based on the global processes by which metabolic resources are distributed to the neurons by glial cells. Specifically, we introduce and study a model composed of two interacting networks: a model neural network interconnected by synapses that undergo spike-timing-dependent plasticity; and a model glial network interconnected by gap junctions that diffusively transport metabolic resources among the glia and, ultimately, to neural synapses where they are consumed. Our main result is that the biophysical constraints imposed by diffusive transport of metabolic resources through the glial network can prevent runaway growth of synaptic strength, both during ongoing activity and during learning. Our findings suggest a previously unappreciated role for glial transport of metabolites in the feedback control stabilization of neural network dynamics during learning.

Deep Artificial Neural Networks and Neuromorphic Chips for Big Data Analysis: Pharmaceutical and Bioinformatics Applications

Over the past decade, Deep Artificial Neural Networks (DNNs) have become the state-of-the-art algorithms in Machine Learning (ML), speech recognition, computer vision, natural language processing and many other tasks. This was made possible by the advancement in Big Data, Deep Learning (DL) and drastically increased chip processing abilities, especially general-purpose graphical processing units (GPGPUs). All this has created a growing interest in making the most of the potential offered by DNNs in almost every field. An overview of the main architectures of DNNs, and their usefulness in Pharmacology and Bioinformatics are presented in this work. The featured applications are: drug design, virtual screening (VS), Quantitative Structure-Activity Relationship (QSAR) research, protein structure prediction and genomics (and other omics) data mining. The future need of neuromorphic hardware for DNNs is also discussed, and the two most advanced chips are reviewed: IBM TrueNorth and SpiNNaker. In addition, this review points out the importance of considering not only neurons, as DNNs and neuromorphic chips should also include glial cells, given the proven importance of astrocytes, a type of glial cell which contributes to information processing in the brain. The Deep Artificial Neuron-Astrocyte Networks (DANAN) could overcome the difficulties in architecture design, learning process and scalability of the current ML methods.

Modulation of Synaptic Plasticity by Glutamatergic Gliotransmission: A Modeling Study

Glutamatergic gliotransmission, that is, the release of glutamate from perisynaptic astrocyte processes in an activity-dependent manner, has emerged as a potentially crucial signaling pathway for regulation of synaptic plasticity, yet its modes of expression and function in vivo remain unclear. Here, we focus on two experimentally well-identified gliotransmitter pathways, (i) modulations of synaptic release and (ii) postsynaptic slow inward currents mediated by glutamate released from astrocytes, and investigate their possible functional relevance on synaptic plasticity in a biophysical model of an astrocyte-regulated synapse. Our model predicts that both pathways could profoundly affect both short- and long-term plasticity. In particular, activity-dependent glutamate release from astrocytes could dramatically change spike-timing-dependent plasticity, turning potentiation into depression (and vice versa) for the same induction protocol.

A Digital Realization of Astrocyte and Neural Glial Interactions

The implementation of biological neural networks is a key objective of the neuromorphic research field. Astrocytes are the largest cell population in the brain. With the discovery of calcium wave propagation through astrocyte networks, now it is more evident that neuronal networks alone may not explain functionality of the strongest natural computer, the brain. Models of cortical function must now account for astrocyte activities as well as their relationships with neurons in encoding and manipulation of sensory information. From an engineering viewpoint, astrocytes provide feedback to both presynaptic and postsynaptic neurons to regulate their signaling behaviors. This paper presents a modified neural glial interaction model that allows a convenient digital implementation. This model can reproduce relevant biological astrocyte behaviors, which provide appropriate feedback control in regulating neuronal activities in the central nervous system (CNS). Accordingly, we investigate the feasibility of a digital implementation for a single astrocyte constructed by connecting a two coupled FitzHugh Nagumo (FHN) neuron model to an implementation of the proposed astrocyte model using neuron-astrocyte interactions. Hardware synthesis, physical implementation on FPGA, and theoretical analysis confirm that the proposed neuron astrocyte model, with significantly low hardware cost, can mimic biological behavior such as the regulation of postsynaptic neuron activity and the synaptic transmission mechanisms.

Re-membering the body: applications of computational neuroscience to the top-down control of regeneration of limbs and other complex organs

A major goal of regenerative medicine and bioengineering is the regeneration of complex organs, such as limbs, and the capability to create artificial constructs (so-called biobots) with defined morphologies and robust self-repair capabilities. Developmental biology presents remarkable examples of systems that self-assemble and regenerate complex structures toward their correct shape despite significant perturbations. A fundamental challenge is to translate progress in molecular genetics into control of large-scale organismal anatomy, and the field is still searching for an appropriate theoretical paradigm for facilitating control of pattern homeostasis. However, computational neuroscience provides many examples in which cell networks - brains - store memories (e.g., of geometric configurations, rules, and patterns) and coordinate their activity towards proximal and distant goals. In this Perspective, we propose that programming large-scale morphogenesis requires exploiting the information processing by which cellular structures work toward specific shapes. In non-neural cells, as in the brain, bioelectric signaling implements information processing, decision-making, and memory in regulating pattern and its remodeling. Thus, approaches used in computational neuroscience to understand goal-seeking neural systems offer a toolbox of techniques to model and control regenerative pattern formation. Here, we review recent data on developmental bioelectricity as a regulator of patterning, and propose that target morphology could be encoded within tissues as a kind of memory, using the same molecular mechanisms and algorithms so successfully exploited by the brain. We highlight the next steps of an unconventional research program, which may allow top-down control of growth and form for numerous applications in regenerative medicine and synthetic bioengineering.

Artificial Neuron–Glia Networks Learning Approach Based on Cooperative Coevolution

Artificial Neuron–Glia Networks (ANGNs) are a novel bio-inspired machine learning approach. They extend classical Artificial Neural Networks (ANNs) by incorporating recent findings and suppositions about the way information is processed by neural and astrocytic networks in the most evolved living organisms. Although ANGNs are not a consolidated method, their performance against the traditional approach, i.e. without artificial astrocytes, was already demonstrated on classification problems. However, the corresponding learning algorithms developed so far strongly depends on a set of glial parameters which are manually tuned for each specific problem. As a consequence, previous experimental tests have to be done in order to determine an adequate set of values, making such manual parameter configuration time-consuming, error-prone, biased and problem dependent. Thus, in this paper, we propose a novel learning approach for ANGNs that fully automates the learning process, and gives the possibility of testing any kind of reasonable parameter configuration for each specific problem. This new learning algorithm, based on coevolutionary genetic algorithms, is able to properly learn all the ANGNs parameters. Its performance is tested on five classification problems achieving significantly better results than ANGN and competitive results with ANN approaches.

Computational simulation: astrocyte-induced depolarization of neighboring neurons mediates synchronous UP states in a neural network

Although recent reports have suggested that synchronous neuronal UP states are mediated by astrocytic activity, the mechanism responsible for this remains unknown. Astrocytic glutamate release synchronously depolarizes adjacent neurons, while synaptic transmissions are blocked. The purpose of this study was to confirm that astrocytic depolarization, propagated through synaptic connections, can lead to synchronous neuronal UP states. We applied astrocytic currents to local neurons in a neural network consisting of model cortical neurons. Our results show that astrocytic depolarization may generate synchronous UP states for hundreds of milliseconds in neurons even if they do not directly receive glutamate release from the activated astrocyte.

Astrocytes: Orchestrating synaptic plasticity?

Synaptic plasticity is the capacity of a preexisting connection between two neurons to change in strength as a function of neural activity. Because synaptic plasticity is the major candidate mechanism for learning and memory, the elucidation of its constituting mechanisms is of crucial importance in many aspects of normal and pathological brain function. In particular, a prominent aspect that remains debated is how the plasticity mechanisms, that encompass a broad spectrum of temporal and spatial scales, come to play together in a concerted fashion. Here we review and discuss evidence that pinpoints to a possible non-neuronal, glial candidate for such orchestration: the regulation of synaptic plasticity by astrocytes.

An astrocyte neuromorphic circuit that influences neuronal phase synchrony

Neuromorphic circuits are designed and simulated to emulate the role of astrocytes in phase synchronization of neuronal activity. We emulate, to a first order, the ability of slow inward currents (SICs) evoked by the astrocyte, acting on extrasynaptic N-methyl-D-aspartate receptors (NMDAR) of adjacent neurons, as a mechanism for phase synchronization. We run a simulation test incorporating two small networks of neurons interacting with astrocytic microdomains. These microdomains are designed using a resistive and capacitive ladder network and their interactions occur through pass transistors. Upon enough synaptic activity, the astrocytic microdomains interact with each other, generating SIC events on synapses of adjacent neurons. Since the amplitude of SICs is several orders of magnitude larger compared to synaptic currents, a SIC event drastically enhances the excitatory postsynaptic potential (EPSP) on adjacent neurons simultaneously. This causes neurons to fire synchronously in phase. Phase synchrony holds for a duration of time proportional to the time constant of the SIC decay. Once the SIC decay has completed, the neurons are able to go back to their natural phase difference, inducing desynchronization of their firing of spikes. This paper incorporates some biological aspects observed by recent experiments showing astrocytic influence on neuronal synchronization, and intends to offer a circuit view on the hypothesis of astrocytic role on synchronous activity that could potentially lead to the binding of neuronal information.

Conceptual Network Model From Sensory Neurons to Astrocytes of the Human Nervous System

From a single-cell animal like paramecium to vertebrates like ape, the nervous system plays an important role in responding to the variations of the environment. Compared to animals, the nervous system in the human body possesses more intricate organization and utility. The nervous system anatomy has been understood progressively, yet the explanation at the cell level regarding complete information transmission is still lacking. Along the signal pathway toward the brain, an external stimulus first activates action potentials in the sensing neuron and these electric pulses transmit along the spinal nerve or cranial nerve to the neurons in the brain. Second, calcium elevation is triggered in the branch of astrocyte at the tripartite synapse. Third, the local calcium wave expands to the entire territory of the astrocyte. Finally, the calcium wave propagates to the neighboring astrocyte via gap junction channel. In our study, we integrate the existing mathematical model and biological experiments in each step of the signal transduction to establish a conceptual network model for the human nervous system. The network is composed of four layers and the communication protocols of each layer could be adapted to entities with different characterizations. We verify our simulation results against the available biological experiments and mathematical models and provide a test case of the integrated network. As the production of conscious episode in the human nervous system is still under intense research, our model serves as a useful tool to facilitate, complement and verify current and future study in human cognition.

Neuron-glia networks: integral gear of brain function

Astrocytes, the most abundant glial cell in the brain, play critical roles in metabolic and homeostatic functions of the Nervous System; however, their participation in coding information and cognitive processes has been largely ignored. The strategic position of astrocyte processes facing synapses and the astrocyte ability to uptake neurotransmitters and release neuroactive substances, so-called “gliotransmitters”, provide the scenario for prolific neuron-astrocyte signaling. From studies at single-cell level to animal behavior, recent advances in technology and genetics have revealed the impact of astrocyte activity in brain function from cellular and synaptic physiology, neuronal circuits to behavior. The present review critically discusses the consequences of astrocyte signaling on synapses and networks, as well as its impact on neuronal information processing, showing that some crucial brain functions arise from the coordinated activity of neuron-glia networks.

Morphology, input-output relations and synaptic connectivity of Cajal-Retzius cells in layer 1 of the developing neocortex of CXCR4-EGFP mice

Layer 1 (L1) neurons, in particular Cajal-Retzius (CR) cells are among the earliest generated neurons in the neocortex. However, their role and that of L1 GABAergic interneurons in the establishment of an early cortical microcircuit are still poorly understood. Thus, the morphology of whole-cell recorded and biocytin-filled CR cells was investigated in postnatal day (P) 7-11 old CXCR4-EGFP mice where CR cells can be easily identified by their fluorescent appearance. Confocal-, light- and subsequent electron microscopy was performed to investigate their developmental regulation, morphology, synaptic input-output relationships and electrophysiological properties. CR cells reached their peak in occurrence between P4 to P7 and from thereon declined to almost complete disappearance at P14 by undergoing selective cell death through apoptosis. CR cells formed a dense and long-range horizontal network in layer 1 with a remarkable high density of synaptic boutons along their axons. They received dense GABAergic and non-GABAergic synaptic input and in turn provided synaptic output preferentially with spines or shafts of terminal tuft dendrites of pyramidal neurons. Interestingly, no dye-coupling between CR cells with other cortical neurons was observed as reported for other species, however, biocytin-labeling of individual CR cells leads to co-staining of L1 end foot astrocytes. Electrophysiologically, CR cells are characterized by a high input resistance and a characteristic firing pattern. Increasing depolarizing currents lead to action potential of decreasing amplitude and increasing half width, often terminated by a depolarization block. The presence of membrane excitability, the high density of CR cells in layer 1, their long-range horizontal axonal projection together with a high density of synaptic boutons and their synaptic input-output relationship suggest that they are an integral part of an early cortical network important not only in layer 1 but also for the establishment and formation of the cortical column.

Stochastic nanoroughness modulates neuron-astrocyte interactions and function via mechanosensing cation channels

Extracellular soluble signals are known to play a critical role in maintaining neuronal function and homeostasis in the CNS. However, the CNS is also composed of extracellular matrix macromolecules and glia support cells, and the contribution of the physical attributes of these components in maintenance and regulation of neuronal function is not well understood. Because these components possess well-defined topography, we theorize a role for topography in neuronal development and we demonstrate that survival and function of hippocampal neurons and differentiation of telencephalic neural stem cells is modulated by nanoroughness. At roughnesses corresponding to that of healthy astrocytes, hippocampal neurons dissociated and survived independent from astrocytes and showed superior functional traits (increased polarity and calcium flux). Furthermore, telencephalic neural stem cells differentiated into neurons even under exogenous signals that favor astrocytic differentiation. The decoupling of neurons from astrocytes seemed to be triggered by changes to astrocyte apical-surface topography in response to nanoroughness. Blocking signaling through mechanosensing cation channels using GsMTx4 negated the ability of neurons to sense the nanoroughness and promoted decoupling of neurons from astrocytes, thus providing direct evidence for the role of nanotopography in neuron-astrocyte interactions. We extrapolate the role of topography to neurodegenerative conditions and show that regions of amyloid plaque buildup in brain tissue of Alzheimer's patients are accompanied by detrimental changes in tissue roughness. These findings suggest a role for astrocyte and ECM-induced topographical changes in neuronal pathologies and provide new insights for developing therapeutic targets and engineering of neural biomaterials.