AIMC Topic: Neurons

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A robust event-driven approach to always-on object recognition.

Neural networks : the official journal of the International Neural Network Society Jun 3, 2024
We propose a neuromimetic architecture capable of always-on pattern recognition, i.e. at any time during processing. To achieve this, we have extended an existing event-based algorithm (Lagorce et al., 2017), which introduced novel spatio-temporal fe...
Pattern Recognition, Visual Algorithms Humans Neurons Neural Networks, Computer Pattern Recognition, Automated
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Synergistic information supports modality integration and flexible learning in neural networks solving multiple tasks.

PLoS computational biology Jun 3, 2024
Striking progress has been made in understanding cognition by analyzing how the brain is engaged in different modes of information processing. For instance, so-called synergistic information (information encoded by a set of neurons but not by any sub...
Humans Models, Neurological Neural Networks, Computer Neurons Brain Computational Biology Learning Cognition
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Spiking generative adversarial network with attention scoring decoding.

Neural networks : the official journal of the International Neural Network Society Jun 1, 2024
Generative models based on neural networks present a substantial challenge within deep learning. As it stands, such models are primarily limited to the domain of artificial neural networks. Spiking neural networks, as the third generation of neural n...
Algorithms Brain Animals Humans Neural Networks, Computer Action Potentials Neurons Attention Deep Learning Mice Models, Neurological
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A 3D ray traced biological neural network learning model.

Nature communications Jun 1, 2024
Training large neural networks on big datasets requires significant computational resources and time. Transfer learning reduces training time by pre-training a base model on one dataset and transferring the knowledge to a new model for another datase...
Humans Models, Neurological Electroencephalography Neurons Neural Networks, Computer Machine Learning Algorithms
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SIMS: A deep-learning label transfer tool for single-cell RNA sequencing analysis.

Cell genomics May 31, 2024
Cell atlases serve as vital references for automating cell labeling in new samples, yet existing classification algorithms struggle with accuracy. Here we introduce SIMS (scalable, interpretable machine learning for single cell), a low-code data-effi...
Sequence Analysis, RNA Cell Differentiation Neurons Single-Cell Analysis Humans Organoids Brain Mice Animals Deep Learning
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How well do models of visual cortex generalize to out of distribution samples?

PLoS computational biology May 31, 2024
Unit activity in particular deep neural networks (DNNs) are remarkably similar to the neuronal population responses to static images along the primate ventral visual cortex. Linear combinations of DNN unit activities are widely used to build predicti...
Algorithms Photic Stimulation Humans Animals Visual Cortex Neural Networks, Computer Models, Neurological Computational Biology Neurons
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Estimating receptive fields of simple and complex cells in early visual cortex: A convolutional neural network model with parameterized rectification.

PLoS computational biology May 31, 2024
Neurons in the primary visual cortex respond selectively to simple features of visual stimuli, such as orientation and spatial frequency. Simple cells, which have phase-sensitive responses, can be modeled by a single receptive field filter in a linea...
Animals Computational Biology Models, Neurological Neurons Visual Cortex Visual Fields Primary Visual Cortex Neural Networks, Computer Cats Photic Stimulation
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Efficient in Vivo Neural Signal Compression Using an Autoencoder-Based Neural Network.

IEEE transactions on biomedical circuits and systems May 28, 2024
Conventional in vivo neural signal processing involves extracting spiking activity within the recorded signals from an ensemble of neurons and transmitting only spike counts over an adequate interval. However, for brain-computer interface (BCI) appli...
Electroencephalography Signal Processing, Computer-Assisted Neurons Brain-Computer Interfaces Data Compression Neural Networks, Computer Algorithms Animals
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Electrocardiography Classification with Leaky Integrate-and-Fire Neurons in an Artificial Neural Network-Inspired Spiking Neural Network Framework.

Sensors (Basel, Switzerland) May 26, 2024
Monitoring heart conditions through electrocardiography (ECG) has been the cornerstone of identifying cardiac irregularities. Cardiologists often rely on a detailed analysis of ECG recordings to pinpoint deviations that are indicative of heart anomal...
Neurons Algorithms Humans Electrocardiography Neural Networks, Computer Signal Processing, Computer-Assisted
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Spike-based dynamic computing with asynchronous sensing-computing neuromorphic chip.

Nature communications May 25, 2024
By mimicking the neurons and synapses of the human brain and employing spiking neural networks on neuromorphic chips, neuromorphic computing offers a promising energy-efficient machine intelligence. How to borrow high-level brain dynamic mechanisms t...
Neural Networks, Computer Neurons Synapses Software Algorithms Brain Humans Models, Neurological Action Potentials
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