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Neurons

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Inference of network connectivity from temporally binned spike trains.

Journal of neuroscience methods
BACKGROUND: Processing neural activity to reconstruct network connectivity is a central focus of neuroscience, yet the spatiotemporal requisites of biological nervous systems are challenging for current neuronal sensing modalities. Consequently, meth...

Large-Scale Bio-Inspired FPGA Models for Path Planning.

IEEE transactions on biomedical circuits and systems
The hippocampus provides significant inspiration for spatial navigation and memory in both humans and animals. Constructing large-scale spiking neural network (SNN) models based on the biological neural systems is an important approach to comprehend ...

Scalable Multi-Hierarchy Embedded Platform for Neural Population Simulations.

IEEE transactions on biomedical circuits and systems
Brain-inspired structured neural circuits are the cornerstones of both computational and perceived intelligence. Real-time simulations of large-scale high-dimensional neural populations with complex nonlinearities pose a significant challenge. Taking...

Using deep learning to quantify neuronal activation from single-cell and spatial transcriptomic data.

Nature communications
Neuronal activity-dependent transcription directs molecular processes that regulate synaptic plasticity, brain circuit development, behavioral adaptation, and long-term memory. Single cell RNA-sequencing technologies (scRNAseq) are rapidly developing...

MedGAN: optimized generative adversarial network with graph convolutional networks for novel molecule design.

Scientific reports
Generative Artificial Intelligence can be an important asset in the drug discovery process to meet the demand for novel medicines. This work outlines the optimization and fine-tuning steps of MedGAN, a deep learning model based on Wasserstein Generat...

Achieving High Core Neuron Density in a Neuromorphic Chip Through Trade-off Among Area, Power Consumption, and Data Access Bandwidth.

IEEE transactions on biomedical circuits and systems
As a crucial component of neuromorphic chips, on-chip memory usually occupies most of the on-chip resources and limits the improvement of neuron density. The alternative of using off-chip memory may result in additional power consumption or even a bo...

Topological data analysis of the firings of a network of stochastic spiking neurons.

Frontiers in neural circuits
Topological data analysis is becoming more and more popular in recent years. It has found various applications in many different fields, for its convenience in analyzing and understanding the structure and dynamic of complex systems. We used topologi...

Memristor-Based Neuromorphic Chips.

Advanced materials (Deerfield Beach, Fla.)
In the era of information, characterized by an exponential growth in data volume and an escalating level of data abstraction, there has been a substantial focus on brain-like chips, which are known for their robust processing power and energy-efficie...

Neural networks from biological to artificial and vice versa.

Bio Systems
In this paper, we examine how deep learning can be utilized to investigate neural health and the difficulties in interpreting neurological analyses within algorithmic models. The key contribution of this paper is the investigation of the impact of a ...

Subconfluent ARPE-19 Cells Display Mesenchymal Cell-State Characteristics and Behave like Fibroblasts, Rather Than Epithelial Cells, in Experimental HCMV Infection Studies.

Viruses
Human cytomegalovirus (HCMV) has a broad cellular tropism and epithelial cells are important physiological targets during infection. The retinal pigment epithelial cell line ARPE-19 has been used to model HCMV infection in epithelial cells for decade...