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

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.

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.

Complex Regulation of Protocadherin Epigenetics on Aging-Related Brain Health

Life expectancy continues to increase in the high-income world due to advances in medical care; however, quality of life declines with increasing age due to normal aging processes. Current research suggests that various aspects of aging are genetically modulated and thus may be slowed via genetic modification. Here, we show evidence for epigenetic modulation of the aging process in the brain from over 1800 individuals as part of the Framingham Heart Study. We investigated the methylation of genes in the protocadherin (PCDH) clusters, including the alpha (PCHDA), beta (PCDHB), and gamma (PCDHG) clusters. Reduced PCDHG, elevated PCDHA, and elevated PCDHB methylation levels were associated with substantial reductions in the rate of decline of regional white matter volume as well as certain cognitive skills, independent of overall accelerated or retarded aging as estimated by a DNA clock. These results are likely due to the different effects of the expression of genes in the alpha, beta, and gamma PCHD clusters and suggest that experience-based aging processes related to a decline in regional brain volume and select cognitive skills may be slowed via targeted epigenetic modifications.

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.

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.

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.

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.

Unraveling the mysteries of dendritic spine dynamics: Five key principles shaping memory and cognition

Recent research extends our understanding of brain processes beyond just action potentials and chemical transmissions within neural circuits, emphasizing the mechanical forces generated by excitatory synapses on dendritic spines to modulate presynaptic function. From in vivo and in vitro studies, we outline five central principles of synaptic mechanics in brain function: P1: Stability - Underpinning the integral relationship between the structure and function of the spine synapses. P2: Extrinsic dynamics - Highlighting synapse-selective structural plasticity which plays a crucial role in Hebbian associative learning, distinct from pathway-selective long-term potentiation (LTP) and depression (LTD). P3: Neuromodulation - Analyzing the role of G-protein-coupled receptors, particularly dopamine receptors, in time-sensitive modulation of associative learning frameworks such as Pavlovian classical conditioning and Thorndike's reinforcement learning (RL). P4: Instability - Addressing the intrinsic dynamics crucial to memory management during continual learning, spotlighting their role in "spine dysgenesis" associated with mental disorders. P5: Mechanics - Exploring how synaptic mechanics influence both sides of synapses to establish structural traces of short- and long-term memory, thereby aiding the integration of mental functions. We also delve into the historical background and foresee impending challenges.