Spiking Neural P Systems with Functional Astrocytes

Spiking Neural P Systems (SN P Systems, for short) is a developing field within the universe of P Systems. New variants arise constantly as the study of their properties, such as computational completeness and computational efficiency, grows. Variants frequently incorporate new ingredients into the original model inspired by real neurophysiological structure of the brain. A singular element present within that structure is the astrocyte. Astrocytes, also known collectively as astroglia, are characteristic star-shaped glial cells in the brain and spinal cord. In this paper, a new variant of Spiking Neural P Systems incorporating astrocytes is introduced. These astrocytes are modelled as computing devices capable of performing function computation in a single computation step. In order to experimentally study the action of Spiking Neural P Systems with astrocytes, it is necessary to develop software providing the required simulation tools. Within this trend, P– Lingua offers a standard language for the definition of P Systems. Part of the same software project, pLinguaCore library provides particular implementations of parsers and simulators for the models specified in P–Lingua. Along with the new SN P System variant with astrocytes, an extension of the P–Lingua language allowing definition of these systems is presented in this paper, as well as an upgrade of pLinguaCore, including a parser and a simulator that supports the aforementioned variant.Ministerio de Ciencia e Innovación TIN2009–13192Junta de Andalucía P08-TIC-0420

Meta-Potentiation: Neuro-Astroglial Interactions Supporting Perceptual Consciousness

Conscious perceptual processing involves the sequential activation of cortical networks at several brain locations, and the onset of oscillatory synchrony affecting the same neuronal population. How do the earlier activated circuits sustain their excitation to synchronize with the later ones? We call such a sustaining process "meta-potentiation", and propose that it depends on neuro-astroglial interactions. In our proposed model, attentional cholinergic and stimulus-related glutamatergic inputs to astroglia elicit the release of astroglial glutamate to bind with neuronal NMDA receptors containing the NR2B subunit. Once calcium channels are open, slow inward currents activate the CaM/CaMKII complex to phosphorylate AMPA receptors in a population of neurons connected with the astrocyte, thus amplifying the local excitatory pattern to participate in a larger synchronized assembly that supports consciousness

Astrocytic Ion Dynamics: Implications for Potassium Buffering and Liquid Flow

We review modeling of astrocyte ion dynamics with a specific focus on the implications of so-called spatial potassium buffering, where excess potassium in the extracellular space (ECS) is transported away to prevent pathological neural spiking. The recently introduced Kirchoff-Nernst-Planck (KNP) scheme for modeling ion dynamics in astrocytes (and brain tissue in general) is outlined and used to study such spatial buffering. We next describe how the ion dynamics of astrocytes may regulate microscopic liquid flow by osmotic effects and how such microscopic flow can be linked to whole-brain macroscopic flow. We thus include the key elements in a putative multiscale theory with astrocytes linking neural activity on a microscopic scale to macroscopic fluid flow.Comment: 27 pages, 7 figure

Parallel computing for brain simulation

[Abstract] Background: The human brain is the most complex system in the known universe, it is therefore one of the greatest mysteries. It provides human beings with extraordinary abilities. However, until now it has not been understood yet how and why most of these abilities are produced. Aims: For decades, researchers have been trying to make computers reproduce these abilities, focusing on both understanding the nervous system and, on processing data in a more efficient way than before. Their aim is to make computers process information similarly to the brain. Important technological developments and vast multidisciplinary projects have allowed creating the first simulation with a number of neurons similar to that of a human brain. Conclusion: This paper presents an up-to-date review about the main research projects that are trying to simulate and/or emulate the human brain. They employ different types of computational models using parallel computing: digital models, analog models and hybrid models. This review includes the current applications of these works, as well as future trends. It is focused on various works that look for advanced progress in Neuroscience and still others which seek new discoveries in Computer Science (neuromorphic hardware, machine learning techniques). Their most outstanding characteristics are summarized and the latest advances and future plans are presented. In addition, this review points out the importance of considering not only neurons: Computational models of the brain should also include glial cells, given the proven importance of astrocytes in information processing.Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; GRC2014/049Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; R2014/039Instituto de Salud Carlos III; PI13/0028

Two-photon imaging and analysis of neural network dynamics

The glow of a starry night sky, the smell of a freshly brewed cup of coffee or the sound of ocean waves breaking on the beach are representations of the physical world that have been created by the dynamic interactions of thousands of neurons in our brains. How the brain mediates perceptions, creates thoughts, stores memories and initiates actions remains one of the most profound puzzles in biology, if not all of science. A key to a mechanistic understanding of how the nervous system works is the ability to analyze the dynamics of neuronal networks in the living organism in the context of sensory stimulation and behaviour. Dynamic brain properties have been fairly well characterized on the microscopic level of individual neurons and on the macroscopic level of whole brain areas largely with the help of various electrophysiological techniques. However, our understanding of the mesoscopic level comprising local populations of hundreds to thousands of neurons (so called 'microcircuits') remains comparably poor. In large parts, this has been due to the technical difficulties involved in recording from large networks of neurons with single-cell spatial resolution and near- millisecond temporal resolution in the brain of living animals. In recent years, two-photon microscopy has emerged as a technique which meets many of these requirements and thus has become the method of choice for the interrogation of local neural circuits. Here, we review the state-of-research in the field of two-photon imaging of neuronal populations, covering the topics of microscope technology, suitable fluorescent indicator dyes, staining techniques, and in particular analysis techniques for extracting relevant information from the fluorescence data. We expect that functional analysis of neural networks using two-photon imaging will help to decipher fundamental operational principles of neural microcircuits.Comment: 36 pages, 4 figures, accepted for publication in Reports on Progress in Physic

Dynamic image recognition in a spiking neuron network supplied by astrocytes

Mathematical model of spiking neuron network (SNN) supplied by astrocytes is investigated. The astrocytes are specific type of brain cells which are not electrically excitable but inducing chemical modulations of neuronal firing. We analyzed how the astrocytes influence on images encoded in the form of dynamic spiking pattern of the SNN. Serving at much slower time scale the astrocytic network interacting with the spiking neurons can remarkably enhance the image recognition quality. Spiking dynamics was affected by noise distorting the information image. We demonstrated that the activation of astrocyte can significantly suppress noise influence improving dynamic image representation by the SNN.Comment: arXiv admin note: text overlap with arXiv:2210.0101