Analysis of Network Models with Neuron-Astrocyte Interactions

Sg-snn: a self-organizing spiking neural network based on temporal information

Neurodynamic observations indicate that the cerebral cortex evolved by self-organizing into functional networks, These networks, or distributed clusters of regions, display various degrees of attention maps based on input. Traditionally, the study of network self-organization relies predominantly on static data, overlooking temporal information in dynamic neuromorphic data. This paper proposes Temporal Self-Organizing (TSO) method for neuromorphic data processing using a spiking neural network. The TSO method incorporates information from multiple time steps into the selection strategy of the Best Matching Unit (BMU) neurons. It enables the coupled BMUs to radiate the weight across the same layer of neurons, ultimately forming a hierarchical self-organizing topographic map of concern. Additionally, we simulate real neuronal dynamics, introduce a glial cell-mediated Glial-LIF (Leaky Integrate-and-fire) model, and adjust multiple levels of BMUs to optimize the attention topological map.Experiments demonstrate that the proposed Self-organizing Glial Spiking Neural Network (SG-SNN) can generate attention topographies for dynamic event data from coarse to fine. A heuristic method based on cognitive science effectively guides the network's distribution of excitatory regions. Furthermore, the SG-SNN shows improved accuracy on three standard neuromorphic datasets: DVS128-Gesture, CIFAR10-DVS, and N-Caltech 101, with accuracy improvements of 0.3%, 2.4%, and 0.54% respectively. Notably, the recognition accuracy on the DVS128-Gesture dataset reaches 99.3%, achieving state-of-the-art (SOTA) performance.

From quiescence to self-sustained activity: How astrocytes reshape neural dynamics

Astrocytes are currently gaining attention from the neuroscience community due to their contribution to a multitude of functions, which includes but are not limited to their ability to change the neuron's spiking frequency, their capacity to alter neuron's firing activity, and their involvement in memory formation and synaptic plasticity regulation. To date, many computational models describing the dynamics of astrocytes, together with their complex interaction with neurons, were proposed, however, these could still be improved since the exploration of their functions and mechanisms was way later than neurons. Hence, in this paper, investigation of the steady state behavior of the neuron-astrocyte interaction through a presentation of phase plane analysis and one parameter bifurcation were primarily performed. Adaptive Exponential Integrate-and-Fire model was utilized to describe the firing dynamics of the neuron while the model from the work of Postnov and collaborators was used to describe the calcium dynamics of the astrocyte. The findings demonstrate that astrocytic modulation can significantly shape neuronal activity, including initiating spikes, inducing self-sustained oscillations, and exerting both inhibitory and excitatory effects depending on synaptic strength. These highlight the crucial role that the contribution of astrocytes to the synapse plays in regulating neuronal activity and producing a range of neuronal firing behaviors within the neuron-astrocyte ensemble. They may impact neuronal synchronization, an attribute of several neurological illnesses, including epilepsy, and, on the other hand, may enhance brain information processing.

A digital neuromorphic system for working memory based on spiking neuron-astrocyte network

Among various types of memory, working memory (WM) plays a crucial role in reasoning, decision-making, and behavior regulation. Neuromorphic computing is a well-established engineering approach that offers promising avenues for advancing our understanding of WM processes by mimicking the structure and operation of the human brain using electronic technology. In this work, a digital neuromorphic system is proposed and then implemented in hardware to illustrate the real-time WM process based on the spiking neuron-astrocyte network (SNAN). The implemented SNAN utilizes a bidirectional neuron-astrocyte interaction to realize the WM process, allowing for a more brain-like memory emulation. Various hardware optimization methods, including piecewise linear approximation, double buffering, and time multiplexing are recruited to minimize the area and power consumption and facilitate the implementation of the WM concept on a single field programmable gate array (FPGA) chip. The proposed neuromorphic system is evaluated by testing its capacity for multi-item memory formation, an essential characteristic of human WM. The results show that the time duration between the store and recall phases is a critical parameter for acceptable retrieval performance. Additionally, the results demonstrate that the proposed neuromorphic system for WM is resilient to noise. Finally, the design modularity of the system facilitates easy extension for implementing larger networks and adapting to real-world applications.

Neuron-Glial Interactions: Implications for Plasticity, Behavior, and Cognition

The traditional view of glial cells as mere supportive tissue has shifted, due to advances in technology and theoretical conceptualization, to include a diversity of other functions, such as regulation of complex behaviors. Astrocytes, the most abundant glial cells in the central nervous system (CNS), have been shown to modulate synaptic functions through gliotransmitter-mediated neurotransmitter reuptake, influencing neuronal signaling and behavioral functions. Contemporary studies further highlight astrocytes' involvement in complex cognitive functions. For instance, inhibiting astrocytes in the hippocampus can lead to memory deficits, suggesting their integral role in memory processes. Moreover, astrocytic calcium activity and astrocyte-neuron metabolic coupling have been linked to changes in synaptic strength and learning. Microglia, another type of glial cell, also extend beyond their supportive roles, contributing to learning and memory processes, with microglial reductions impacting these functions in a developmentally dependent manner. Oligodendrocytes, traditionally thought to have limited roles postdevelopment, are now recognized for their activity-dependent modulation of myelination and plasticity, thus influencing behavioral responses. Recent advancements in technology and computational modeling have expanded our understanding of glial functions, particularly how astrocytes influence neuronal circuits and behaviors. This review underscores the importance of glial cells in CNS functions and the need for further research to unravel the complexities of neuron-glia interactions, the impact of these interactions on brain functions, and potential implications for neurological diseases.

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.

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.

Slow ion concentration oscillations and multiple states in neuron-glia interaction-insights gained from reduced mathematical models

When potassium in the extracellular space separating neurons and glia reaches sufficient levels, neurons may fire spontaneous action potentials or even become inactivated due to membrane depolarisation, which, in turn, may lead to increased extracellular potassium levels. Under certain circumstances, this chain of events may trigger periodic bursts of neuronal activity. In the present study, reduced neuron-glia models are applied to explore the relationship between bursting behaviour and ion concentration dynamics. These reduced models are built based on a previously developed neuron-glia model, in which channel-mediated neuronal sodium and potassium currents are replaced by a function of neuronal sodium and extracellular potassium concentrations. Simulated dynamics of the resulting two reduced models display features that are qualitatively similar to those of the existing neuron-glia model. Bifurcation analyses of the reduced models show rich and interesting dynamics that include the existence of Hopf bifurcations between which the models exhibit slow ion concentration oscillations for a wide range of parameter values. The study demonstrates that even very simple models can provide insights of possible relevance to complex phenomena.