Search results for "NEURAL NETWORK"

Auxiliary α2δ1 and α2δ3 Subunits of Calcium Channels Drive Excitatory and Inhibitory Neuronal Network Development

2020

VGCCs are multisubunit complexes that play a crucial role in neuronal signaling. Auxiliary α2δ subunits of VGCCs modulate trafficking and biophysical properties of the pore-forming α1 subunit and trigger excitatory synaptogenesis. Alterations in the expression level of α2δ subunits were implicated in several syndromes and diseases, including chronic neuropathic pain, autism, and epilepsy. However, the contribution of distinct α2δ subunits to excitatory/inhibitory imbalance and aberrant network connectivity characteristic for these pathologic conditions remains unclear. Here, we show that α2δ1 overexpression enhances spontaneous neuronal network activity in developing and mature cultures of …

Tuning neural circuits by turning the interneuron knob

2017

Interneurons play a critical role in sculpting neuronal circuit activity and their dysfunction can result in neurological and neuropsychiatric disorders. To temporally structure and balance neuronal activity in the adult brain interneurons display a remarkable degree of subclass-specific plasticity, of which the underlying molecular mechanisms have recently begun to be elucidated. Grafting new interneurons to pre-existing neuronal networks allows for amelioration of circuit dysfunction in rodent models of neurological disease and can reopen critical windows for circuit plasticity. The crucial contribution of specific classes of interneurons to circuit homeostasis and plasticity in health an…

A stable brain from unstable components: Emerging concepts and implications for neural computation.

2017

Neuroscientists have often described the adult brain in similar terms to an electronic circuit board- dependent on fixed, precise connectivity. However, with the advent of technologies allowing chronic measurements of neural structure and function, the emerging picture is that neural networks undergo significant remodeling over multiple timescales, even in the absence of experimenter-induced learning or sensory perturbation. Here, we attempt to reconcile the parallel observations that critical brain functions are stably maintained, while synapse- and single-cell properties appear to be reformatted regularly throughout adult life. In this review, we discuss experimental evidence at multiple …

2020

Human movements are characterized by highly non-linear and multi-dimensional interactions within the motor system. Recently, an increasing emphasis on machine-learning applications has led to a significant contribution to the field of gait analysis, e.g., in increasing the classification performance. In order to ensure the generalizability of the machine-learning models, different data preprocessing steps are usually carried out to process the measured raw data before the classifications. In the past, various methods have been used for each of these preprocessing steps. However, there are hardly any standard procedures or rather systematic comparisons of these different methods and their im…

Optogenetics: a new method for the causal analysis of neuronal networks in vivo

2012

Abstract The causal analysis of neuronal network function requires selective manipulations of ge­netically defined neuronal subpopulations in the intact living brain. Here, we highlight the method of optogenetics, which meets those needs. We cover methodological aspects, limitations, and practical applications in the field of neurosciences. The fundamentals of optogenetics are light-sensitive transmembrane channels and light-driven ion pumps, which can be genetically encoded, without requir­ing the application of exogenous cofactors. These opsins are expressed in neurons by means of viral gene transfer and cell-specific promoters. Light for stimulation can be non- or minimally invasively de…

The quality of cortical network function recovery depends on localization and degree of axonal demyelination

2016

AbstractMyelin loss is a severe pathological hallmark common to a number of neurodegenerative diseases, including multiple sclerosis (MS). Demyelination in the central nervous system appears in the form of lesions affecting both white and gray matter structures. The functional consequences of demyelination on neuronal network and brain function are not well understood. Current therapeutic strategies for ameliorating the course of such diseases usually focus on promoting remyelination, but the effectiveness of these approaches strongly depends on the timing in relation to the disease state. In this study, we sought to characterize the time course of sensory and behavioral alterations induced…

Automatic sleep scoring: A deep learning architecture for multi-modality time series

2020

Background: Sleep scoring is an essential but time-consuming process, and therefore automatic sleep scoring is crucial and urgent to help address the growing unmet needs for sleep research. This paper aims to develop a versatile deep-learning architecture to automate sleep scoring using raw polysomnography recordings. Method: The model adopts a linear function to address different numbers of inputs, thereby extending model applications. Two-dimensional convolution neural networks are used to learn features from multi-modality polysomnographic signals, a “squeeze and excitation” block to recalibrate channel-wise features, together with a long short-term memory module to exploit long-range co…

On the structural connectivity of large-scale models of brain networks at cellular level

2021

AbstractThe brain’s structural connectivity plays a fundamental role in determining how neuron networks generate, process, and transfer information within and between brain regions. The underlying mechanisms are extremely difficult to study experimentally and, in many cases, large-scale model networks are of great help. However, the implementation of these models relies on experimental findings that are often sparse and limited. Their predicting power ultimately depends on how closely a model’s connectivity represents the real system. Here we argue that the data-driven probabilistic rules, widely used to build neuronal network models, may not be appropriate to represent the dynamics of the …

Recurrent Deep Neural Networks for Nucleosome Classification

2020

Nucleosomes are the fundamental repeating unit of chromatin. A nucleosome is an 8 histone proteins complex, in which approximately 147–150 pairs of DNA bases bind. Several biological studies have clearly stated that the regulation of cell type-specific gene activities are influenced by nucleosome positioning. Bioinformatic studies have improved those results showing proof of sequence specificity in nucleosomes’ DNA fragment. In this work, we present a recurrent neural network that uses nucleosome sequence features representation for their classification. In particular, we implement an architecture which stacks convolutional and long short-term memory layers, with the main purpose to avoid t…

Retrieving infinite numbers of patterns in a spin-glass model of immune networks

2013

The similarity between neural and immune networks has been known for decades, but so far we did not understand the mechanism that allows the immune system, unlike associative neural networks, to recall and execute a large number of memorized defense strategies {\em in parallel}. The explanation turns out to lie in the network topology. Neurons interact typically with a large number of other neurons, whereas interactions among lymphocytes in immune networks are very specific, and described by graphs with finite connectivity. In this paper we use replica techniques to solve a statistical mechanical immune network model with `coordinator branches' (T-cells) and `effector branches' (B-cells), a…