Search Results for « neural activity »

Artificial Intelligence and Machine learning have been widely used in various fields of mathematical computing, physical modeling, computational science, communication science, and stochastic analysis. Approaches based on Deep Artificial Neural Networks (DANN) are very popular in our days. Depending on the learning task, the exact form of DANNs is determined via their multi-layer architecture, activation functions and the so-called loss function. However, for a majority of deep learning approaches based on DANNs, the kernel structure of neural signal processing remains the same, where the node response is encoded as a linear superposition of neural activity, while the non-linearity is triggered by the activation functions. In the current paper, we suggest to analyze the neural signal processing in DANNs from the point of view of homogeneous chaos theory as known from polynomial chaos expansion (PCE). From the PCE perspective, the (linear) response on each node of a DANN could be seen as a $1^{st}$ degree multi-variate polynomial of single neurons from the previous layer, i.e. linear weighted sum of monomials. From this point of view, the conventional DANN structure relies implicitly (but erroneously) on a Gaussian distribution of neural signals. Additionally, this view revels that by design DANNs do not necessarily fulfill any orthogonality or orthonormality condition for a majority of data-driven applications. Therefore, the prevailing handling of neural signals in DANNs could lead to redundant representation as any neural signal could contain some partial information from other neural signals. To tackle that challenge, we suggest to employ the data-driven generalization of PCE theory known as arbitrary polynomial chaos (aPC) to construct a corresponding multi-variate orthonormal representations on each node of a DANN to obtain Deep arbitrary polynomial chaos neural networks.

Providing a model that achieves a strong predictive performance and is simultaneously interpretable by humans is one of the most difficult challenges in machine learning research due to the conflicting nature of these two objectives. To address this challenge, we propose a modification of the radial basis function neural network model by equipping its Gaussian kernel with a learnable precision matrix. We show that precious information is contained in the spectrum of the precision matrix that can be extracted once the training of the model is completed. In particular, the eigenvectors explain the directions of maximum sensitivity of the model revealing the active subspace and suggesting potential applications for supervised dimensionality reduction. At the same time, the eigenvectors highlight the relationship in terms of absolute variation between the input and the latent variables, thereby allowing us to extract a ranking of the input variables based on their importance to the prediction task enhancing the model interpretability. We conducted numerical experiments for regression, classification, and feature selection tasks, comparing our model against popular machine learning models, the state-of-the-art deep learning-based embedding feature selection techniques, and a transformer model for tabular data. Our results demonstrate that the proposed model does not only yield an attractive prediction performance compared to the competitors but also provides meaningful and interpretable results that potentially could assist the decision-making process in real-world applications. A PyTorch implementation of the model is available on GitHub at the following link. https://github.com/dannyzx/Gaussian-RBFNN

The presynaptic protein alpha-synuclein has a central role in Parkinson's disease (PD). However, the mechanism by which the protein contributes to neurodegeneration and its normal function remain unknown. Alpha-synuclein localizes to the nerve terminal and interacts with artificial membranes in vitro but binds weakly to native brain membranes. To characterize the membrane association of alpha-synuclein in living neurons, we used fluorescence recovery after photobleaching. Despite its enrichment at the synapse, alpha-synuclein is highly mobile, with rapid exchange between adjacent synapses. In addition, we find that alpha-synuclein disperses from the nerve terminal in response to neural activity. Dispersion depends on exocytosis, but unlike other synaptic vesicle proteins, alpha-synuclein dissociates from the synaptic vesicle membrane after fusion. Furthermore, the dispersion of alpha-synuclein is graded with respect to stimulus intensity. Neural activity thus controls the normal function of alpha-synuclein at the nerve terminal and may influence its role in PD.

An essential requirement for the representation of functional patterns in complex neural networks, such as the mammalian cerebral cortex, is the existence of stable regimes of network activation, typically arising from a limited parameter range. In this range of limited sustained activity (LSA), the activity of neural populations in the network persists between the extremes of either quickly dying out or activating the whole network. Hierarchical modular networks were previously found to show a wider parameter range for LSA than random or small-world networks not possessing hierarchical organization or multiple modules. Here we explored how variation in the number of hierarchical levels and modules per level influenced network dynamics and occurrence of LSA. We tested hierarchical configurations of different network sizes, approximating the large-scale networks linking cortical columns in one hemisphere of the rat, cat, or macaque monkey brain. Scaling of the network size affected the number of hierarchical levels and modules in the optimal networks, also depending on whether global edge density or the numbers of connections per node were kept constant. For constant edge density, only few network configurations, possessing an intermediate number of levels and a large number of modules, led to a large range of LSA independent of brain size. For a constant number of node connections, there was a trend for optimal configurations in larger-size networks to possess a larger number of hierarchical levels or more modules. These results may help to explain the trend to greater network complexity apparent in larger brains and may indicate that this complexity is required for maintaining stable levels of neural activation.

The immediate early gene c-fos has long been known as a molecular marker of neural activity. The neuron's activity is transformed into intracellular calcium influx through NMDA receptors and L-type voltage sensitive calcium channels. For the transcription of c-fos, neural activity should be strong enough to activate mitogen-activated protein kinase (MAPK) signaling pathway which shows low calcium sensitivity. Upon translation, the auto-inhibition by Fos protein regulates basal Fos expression. The pattern of external stimuli and the valence of the stimulus to the animal change Fos signal, thus the signal reflects learning and memory aspects. Understanding the features of multiple components regulating Fos signaling is necessary for the optimal generation and interpretation of Fos signal.

Random Neural Networks (RNNs) are a class of Neural Networks (NNs) that can also be seen as a specific type of queuing network. They have been successfully used in several domains during the last 25 years, as queuing networks to analyze the performance of resource sharing in many engineering areas, as learning tools and in combinatorial optimization, where they are seen as neural systems, and also as models of neurological aspects of living beings. In this article we focus on their learning capabilities, and more specifically, we present a practical guide for using the RNN to solve supervised learning problems. We give a general description of these models using almost indistinctly the terminology of Queuing Theory and the neural one. We present the standard learning procedures used by RNNs, adapted from similar well-established improvements in the standard NN field. We describe in particular a set of learning algorithms covering techniques based on the use of first order and, then, of second order derivatives. We also discuss some issues related to these objects and present new perspectives about their use in supervised learning problems. The tutorial describes their most relevant applications, and also provides a large bibliography.

BACKGROUND: In recent years, neuroimaging techniques such as functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) have played a significant role in elucidating the neural underpinnings of posttraumatic stress disorder (PTSD). However, a detailed understanding of the neural regions implicated in the disorder remains incomplete because of considerable variability in findings across studies. The aim of this meta-analysis was to identify consistent patterns of neural activity across neuroimaging study designs in PTSD to improve understanding of the neurocircuitry of PTSD. METHODS: We conducted a literature search for PET and fMRI studies of PTSD that were published before February 2011. The article search resulted in 79 functional neuroimaging PTSD studies. Data from 26 PTSD peer-reviewed neuroimaging articles reporting results from 342 adult patients and 342 adult controls were included. Peak activation coordinates from selected articles were used to generate activation likelihood estimate maps separately for symptom provocation and cognitive-emotional studies of PTSD. A separate meta-analysis examined the coupling between ventromedial prefrontal cortex and amygdala activity in patients. RESULTS: Results demonstrated that the regions most consistently hyperactivated in PTSD patients included mid- and dorsal anterior cingulate cortex, and when ROI studies were included, bilateral amygdala. By contrast, widespread hypoactivity was observed in PTSD including the ventromedial prefrontal cortex and the inferior frontal gyrus. Furthermore, decreased ventromedial prefrontal cortex activity was associated with increased amygdala activity. CONCLUSIONS: These results provide evidence for a neurocircuitry model of PTSD that emphasizes alteration in neural networks important for salience detection and emotion regulation.

Seeking high-quality representations with latent variable models (LVMs) to reveal the intrinsic correlation between neural activity and behavior or sensory stimuli has attracted much interest. In the study of the biological visual system, naturalistic visual stimuli are inherently high-dimensional and time-dependent, leading to intricate dynamics within visual neural activity. However, most work on LVMs has not explicitly considered neural temporal relationships. To cope with such conditions, we propose Time-Evolving Visual Dynamical System (TE-ViDS), a sequential LVM that decomposes neural activity into low-dimensional latent representations that evolve over time. To better align the model with the characteristics of visual neural activity, we split latent representations into two parts and apply contrastive learning to shape them. Extensive experiments on synthetic datasets and real neural datasets from the mouse visual cortex demonstrate that TE-ViDS achieves the best decoding performance on naturalistic scenes/movies, extracts interpretable latent trajectories that uncover clear underlying neural dynamics, and provides new insights into differences in visual information processing between subjects and between cortical regions. In summary, TE-ViDS is markedly competent in extracting stimulus-relevant embeddings from visual neural activity and contributes to the understanding of visual processing mechanisms. Our codes are available at https://github.com/Grasshlw/Time-Evolving-Visual-Dynamical-System.