AIMC Topic: Action Potentials

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Design of an experimental setup for delivering intracortical microstimulation in vivo via Spiking Neural Network.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2023
Electroceutical approaches for the treatment of neurological disorders, such as stroke, can take advantage of neuromorphic engineering, to develop devices able to achieve a seamless interaction with the neural system. This paper illustrates the devel...
Neurons Action Potentials Stroke Neural Networks, Computer Computers Animals Rats
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Real-time Neural Connectivity Inference with Presynaptic Spike-driven Spike Timing-Dependent Plasticity.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2023
Brain-like artificial intelligence in electronics can be built efficiently by understanding the connectivity of neuronal circuitry. The concept of neural connectivity inference with a two-dimensional cross-bar structure memristor array is indicated i...
Neural Networks, Computer Action Potentials Artificial Intelligence Neurons Neuronal Plasticity
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Stochastic photonic spiking neuron for Bayesian inference with unsupervised learning.

Optics letters Mar 15, 2023
Stochasticity is an inherent feature of biological neural activities. We propose a noise-injection scheme to implement a GHz-rate stochastic photonic spiking neuron (S-PSN). The firing-probability encoding is experimentally demonstrated and exploited...
Bayes Theorem Neurons Action Potentials Unsupervised Machine Learning Neural Networks, Computer
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NeuroAI: If grid cells are the answer, is path integration the question?

Current biology : CB Mar 13, 2023
Spatially modulated neurons known as grid cells are thought to play an important role in spatial cognition. A new study has found that units with grid-cell-like properties can emerge within artificial neural networks trained to path integrate, and de...
Cognition Models, Neurological Neural Networks, Computer Learning Space Perception Entorhinal Cortex Action Potentials Neurons
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Macroscopic dynamics of neural networks with heterogeneous spiking thresholds.

Physical review. E Feb 1, 2023
Mean-field theory links the physiological properties of individual neurons to the emergent dynamics of neural population activity. These models provide an essential tool for studying brain function at different scales; however, for their application ...
Action Potentials Neurons Adaptation, Physiological Neural Networks, Computer Models, Neurological
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Spiking Neural Networks and Mathematical Models.

Advances in experimental medicine and biology Jan 1, 2023
Neural networks are applied in various scientific fields such as medicine, engineering, pharmacology, etc. Investigating operations of neural networks refers to estimating the relationship among single neurons and their contributions to the network a...
Neurons Neural Networks, Computer Brain Models, Neurological Action Potentials
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Scalability of Large Neural Network Simulations via Activity Tracking With Time Asynchrony and Procedural Connectivity.

Neural computation Aug 16, 2022
We present a new algorithm to efficiently simulate random models of large neural networks satisfying the property of time asynchrony. The model parameters (average firing rate, number of neurons, synaptic connection probability, and postsynaptic dura...
Humans Neural Networks, Computer Computer Simulation Animals Mammals Models, Neurological Synapses Action Potentials
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A machine-learning approach for long-term prediction of experimental cardiac action potential time series using an autoencoder and echo state networks.

Chaos (Woodbury, N.Y.) Jun 1, 2022
Computational modeling and experimental/clinical prediction of the complex signals during cardiac arrhythmias have the potential to lead to new approaches for prevention and treatment. Machine-learning (ML) and deep-learning approaches can be used fo...
Time Factors Action Potentials Machine Learning Computer Simulation Neural Networks, Computer
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Coherent oscillations in balanced neural networks driven by endogenous fluctuations.

Chaos (Woodbury, N.Y.) Feb 1, 2022
We present a detailed analysis of the dynamical regimes observed in a balanced network of identical quadratic integrate-and-fire neurons with sparse connectivity for homogeneous and heterogeneous in-degree distributions. Depending on the parameter va...
Neural Networks, Computer Models, Neurological Nerve Net Action Potentials Neurons
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Surrogate gradients for analog neuromorphic computing.

Proceedings of the National Academy of Sciences of the United States of America Jan 25, 2022
To rapidly process temporal information at a low metabolic cost, biological neurons integrate inputs as an analog sum, but communicate with spikes, binary events in time. Analog neuromorphic hardware uses the same principles to emulate spiking neural...
Models, Biological Brain Neurons Models, Neurological Models, Theoretical Neural Networks, Computer Algorithms Action Potentials Computers
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