AIMC Topic: Action Potentials

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Dendritic normalisation improves learning in sparsely connected artificial neural networks.

PLoS computational biology Aug 9, 2021
Artificial neural networks, taking inspiration from biological neurons, have become an invaluable tool for machine learning applications. Recent studies have developed techniques to effectively tune the connectivity of sparsely-connected artificial n...
Nerve Net Stochastic Processes Dendrites Neural Networks, Computer Machine Learning Animals Deep Learning Computational Biology Models, Neurological Action Potentials Neurons Humans
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A database and deep learning toolbox for noise-optimized, generalized spike inference from calcium imaging.

Nature neuroscience Aug 2, 2021
Inference of action potentials ('spikes') from neuronal calcium signals is complicated by the scarcity of simultaneous measurements of action potentials and calcium signals ('ground truth'). In this study, we compiled a large, diverse ground truth da...
Databases, Factual Brain Animals Deep Learning Zebrafish Calcium Artifacts Neuroimaging Mice Models, Neurological Action Potentials Neurons
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Artificial Intelligence-Enabled ECG to Identify Silent Atrial Fibrillation in Embolic Stroke of Unknown Source.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association Jul 22, 2021
OBJECTIVES: Embolic strokes of unknown source (ESUS) are common and often suspected to be caused by unrecognized paroxysmal atrial fibrillation (AF). An AI-enabled ECG (AI-ECG) during sinus rhythm has been shown to identify patients with unrecognized...
Humans Artificial Intelligence Female Risk Factors Action Potentials Male Predictive Value of Tests Electrocardiography, Ambulatory Hospitalization Signal Processing, Computer-Assisted Middle Aged Aged Atrial Fibrillation Registries Risk Assessment Heart Rate Time Factors Embolic Stroke Aged, 80 and over
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A simple parametric representation of the Hodgkin-Huxley model.

PloS one Jul 22, 2021
The Hodgkin-Huxley model, decades after its first presentation, is still a reference model in neuroscience as it has successfully reproduced the electrophysiological activity of many organisms. The primary signal in the model represents the membrane ...
Machine Learning Action Potentials Animals Axons Models, Neurological Decapodiformes Membrane Potentials
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Partial information decomposition reveals that synergistic neural integration is greater downstream of recurrent information flow in organotypic cortical cultures.

PLoS computational biology Jul 12, 2021
The directionality of network information flow dictates how networks process information. A central component of information processing in both biological and artificial neural networks is their ability to perform synergistic integration-a type of co...
Organ Culture Techniques Nerve Net Synaptic Transmission Feedback, Physiological Somatosensory Cortex Computational Biology Models, Neurological Action Potentials Neurons Neural Networks, Computer Animals Mice
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Online Learning and Memory of Neural Trajectory Replays for Prefrontal Persistent and Dynamic Representations in the Irregular Asynchronous State.

Frontiers in neural circuits Jul 8, 2021
In the prefrontal cortex (PFC), higher-order cognitive functions and adaptive flexible behaviors rely on continuous dynamical sequences of spiking activity that constitute neural trajectories in the state space of activity. Neural trajectories subser...
Education, Distance Neuronal Plasticity Humans Models, Neurological Neural Networks, Computer Action Potentials Prefrontal Cortex Memory, Short-Term
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A deep learning algorithm to translate and classify cardiac electrophysiology.

eLife Jul 2, 2021
The development of induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) has been a critical in vitro advance in the study of patient-specific physiology, pathophysiology, and pharmacology. We designed a new deep learning multitask network ...
Models, Biological Sulfonamides Phenethylamines Myocytes, Cardiac Deep Learning Action Potentials Cell Differentiation ERG1 Potassium Channel Gene Expression Regulation Electrophysiological Phenomena Induced Pluripotent Stem Cells Electrophysiologic Techniques, Cardiac Humans Algorithms Computer Simulation
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A convolutional neural-network framework for modelling auditory sensory cells and synapses.

Communications biology Jul 1, 2021
In classical computational neuroscience, analytical model descriptions are derived from neuronal recordings to mimic the underlying biological system. These neuronal models are typically slow to compute and cannot be integrated within large-scale neu...
Humans Neural Networks, Computer Computer Simulation Synapses Cochlear Nerve Reproducibility of Results Hair Cells, Auditory, Inner Models, Neurological Action Potentials
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A convolutional neural network for estimating synaptic connectivity from spike trains.

Scientific reports Jun 8, 2021
The recent increase in reliable, simultaneous high channel count extracellular recordings is exciting for physiologists and theoreticians because it offers the possibility of reconstructing the underlying neuronal circuits. We recently presented a me...
Animals Male Models, Theoretical Temporal Lobe Models, Neurological Macaca fuscata Neural Networks, Computer Algorithms Computer Simulation Linear Models Neural Pathways Neurosciences Visual Cortex Action Potentials Signal Processing, Computer-Assisted Neurons Synapses
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Numerical Spiking Neural P Systems.

IEEE transactions on neural networks and learning systems Jun 2, 2021
Spiking neural P (SN P) systems are a class of discrete neuron-inspired computation models, where information is encoded by the numbers of spikes in neurons and the timing of spikes. However, due to the discontinuous nature of the integrate-and-fire ...
Action Potentials Neurons Linear Models Electrophysiological Phenomena Machine Learning Algorithms Reproducibility of Results Models, Neurological Neural Networks, Computer
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