2,588 research outputs found
Sequence learning in Associative Neuronal-Astrocytic Network
The neuronal paradigm of studying the brain has left us with limitations in
both our understanding of how neurons process information to achieve biological
intelligence and how such knowledge may be translated into artificial
intelligence and its most brain-derived branch, neuromorphic computing.
Overturning our fundamental assumptions of how the brain works, the recent
exploration of astrocytes is revealing that these long-neglected brain cells
dynamically regulate learning by interacting with neuronal activity at the
synaptic level. Following recent experimental evidence, we designed an
associative, Hopfield-type, neuronal-astrocytic network and analyzed the
dynamics of the interaction between neurons and astrocytes. We show that
astrocytes were sufficient to trigger transitions between learned memories in
the neuronal component of the network. Further, we mathematically derived the
timing of the transitions that was governed by the dynamics of the
calcium-dependent slow-currents in the astrocytic processes. Overall, we
provide a brain-morphic mechanism for sequence learning that is inspired by,
and aligns with, recent experimental findings. To evaluate our model, we
emulated astrocytic atrophy and showed that memory recall becomes significantly
impaired after a critical point of affected astrocytes was reached. This
brain-inspired and brain-validated approach supports our ongoing efforts to
incorporate non-neuronal computing elements in neuromorphic information
processing.Comment: 8 pages, 5 figure
A roadmap to integrate astrocytes into Systems Neuroscience.
Systems neuroscience is still mainly a neuronal field, despite the plethora of evidence supporting the fact that astrocytes modulate local neural circuits, networks, and complex behaviors. In this article, we sought to identify which types of studies are necessary to establish whether astrocytes, beyond their well-documented homeostatic and metabolic functions, perform computations implementing mathematical algorithms that sub-serve coding and higher-brain functions. First, we reviewed Systems-like studies that include astrocytes in order to identify computational operations that these cells may perform, using Ca2+ transients as their encoding language. The analysis suggests that astrocytes may carry out canonical computations in a time scale of subseconds to seconds in sensory processing, neuromodulation, brain state, memory formation, fear, and complex homeostatic reflexes. Next, we propose a list of actions to gain insight into the outstanding question of which variables are encoded by such computations. The application of statistical analyses based on machine learning, such as dimensionality reduction and decoding in the context of complex behaviors, combined with connectomics of astrocyte-neuronal circuits, is, in our view, fundamental undertakings. We also discuss technical and analytical approaches to study neuronal and astrocytic populations simultaneously, and the inclusion of astrocytes in advanced modeling of neural circuits, as well as in theories currently under exploration such as predictive coding and energy-efficient coding. Clarifying the relationship between astrocytic Ca2+ and brain coding may represent a leap forward toward novel approaches in the study of astrocytes in health and disease
Mammalian Brain As a Network of Networks
Acknowledgements AZ, SG and AL acknowledge support from the Russian Science Foundation (16-12-00077). Authors thank T. Kuznetsova for Fig. 6.Peer reviewedPublisher PD
Mapping the spatiotemporal dynamics of calcium signaling in cellular neural networks using optical flow
An optical flow gradient algorithm was applied to spontaneously forming net-
works of neurons and glia in culture imaged by fluorescence optical microscopy
in order to map functional calcium signaling with single pixel resolution.
Optical flow estimates the direction and speed of motion of objects in an image
between subsequent frames in a recorded digital sequence of images (i.e. a
movie). Computed vector field outputs by the algorithm were able to track the
spatiotemporal dynamics of calcium signaling pat- terns. We begin by briefly
reviewing the mathematics of the optical flow algorithm, and then describe how
to solve for the displacement vectors and how to measure their reliability. We
then compare computed flow vectors with manually estimated vectors for the
progression of a calcium signal recorded from representative astrocyte
cultures. Finally, we applied the algorithm to preparations of primary
astrocytes and hippocampal neurons and to the rMC-1 Muller glial cell line in
order to illustrate the capability of the algorithm for capturing different
types of spatiotemporal calcium activity. We discuss the imaging requirements,
parameter selection and threshold selection for reliable measurements, and
offer perspectives on uses of the vector data.Comment: 23 pages, 5 figures. Peer reviewed accepted version in press in
Annals of Biomedical Engineerin
Deep Artificial Neural Networks and Neuromorphic Chips for Big Data Analysis: Pharmaceutical and Bioinformatics Applications
[Abstract] 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.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
Astrocytes as a mechanism for meta-plasticity and contextually-guided network 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 may 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 versus
dynamically homogeneous networks and conventional non-network-based bandit
algorithms. Our results indicate how the presence of neuron-astrocyte
interaction in the brain may benefit learning over different time-scales and
the conveyance of task-relevant contextual information onto circuit dynamics.Comment: 42 pages, 14 figure
Brain edema : a valid endpoint for measuring hepatic encephalopathy?
Hepatic encephalopathy (HE) is a major complication of liver failure/disease which frequently develops during the progression of end-stage liver disease. This metabolic neuropsychiatric syndrome involves a spectrum of symptoms, including cognition impairment, attention deficits and motor dysfunction which eventually can progress to coma and death. Pathologically, HE is characterized by swelling of the astrocytes which consequently leads to brain edema, a common feature found in patients with acute liver failure (ALF) as well as in cirrhotic patients suffering from HE. The pathogenic factors involved in the onset of astrocyte swelling and brain edema in HE are unresolved. However, the role of astrocyte swelling/brain edema in the development of HE remains ambiguous and therefore measuring brain edema as an endpoint to evaluate HE is questioned. The following review will determine the effect of astrocyte swelling and brain edema on neurological function, discuss the various possible techniques to measure brain edema and lastly to propose a number of neurobehavioral tests to evaluate HE
A roadmap to integrate astrocytes into Systems Neuroscience
Systems Neuroscience is still mainly a neuronal field, despite the plethora of evidence supporting the fact that astrocytes modulate local neural circuits, networks, and complex behaviors. In this article, we sought to identify which types of studies are necessary to establish whether astrocytes, beyond their well-documented homeostatic and metabolic functions, perform computations implementing mathematical algorithms that sub-serve coding and higher-brain functions. First, we reviewed Systems-like studies that include astrocytes in order to identify computational operations that these cells may perform, using Ca transients as their encoding language. The analysis suggests that astrocytes may carry out canonical computations in time scales of sub-seconds to seconds in sensory processing, neuromodulation, brain state, memory formation, fear, and complex homeostatic reflexes. Next, we propose a list of actions to gain insight into the outstanding question of which variables are encoded by such computations. The application of statistical analyses based on machine learning, such as dimensionality reduction and decoding in the context of complex behaviors, combined with connectomics of astrocyte-neuronal circuits, are, in our view, fundamental undertakings. We also discuss technical and analytical approaches to study neuronal and astrocytic populations simultaneously, and the inclusion of astrocytes in advanced modeling of neural circuits, as well as in theories currently under exploration, such as predictive coding and energy-efficient coding. Clarifying the relationship between astrocytic Ca and brain coding may represent a leap forward towards novel approaches in the study of astrocytes in health and disease.Junior Leader Fellowhip Program by 'la Caixa' Banking Foundation, LCF/BQ/LI18/11630006
BFU2017-85936-P
BFU2016-75107-P
BFU2016-79735-P
FLAGERA-PCIN-2015-162-C02-02
HHMI 55008742
FPU13/05377
NIH R01NS099254
NSF 1604544
Agència de Gestio d’Ajuts Universitaris i de Recerca, 2017 SGR54
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