AIMC Topic: Models, Neurological

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A robust Parkinson's disease detection model based on time-varying synaptic efficacy function in spiking neural network.

BMC neurology Dec 30, 2024
Parkinson's disease (PD) is a neurodegenerative disease affecting millions of people around the world. Conventional PD detection algorithms are generally based on first and second-generation artificial neural network (ANN) models which consume high e...
Parkinson Disease Neurons Algorithms Models, Neurological Humans Neural Networks, Computer Action Potentials
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Free-space optical spiking neural network.

PloS one Dec 30, 2024
Neuromorphic engineering has emerged as a promising avenue for developing brain-inspired computational systems. However, conventional electronic AI-based processors often encounter challenges related to processing speed and thermal dissipation. As an...
Action Potentials Humans Algorithms Neural Networks, Computer Brain Models, Neurological
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Chaotic recurrent neural networks for brain modelling: A review.

Neural networks : the official journal of the International Neural Network Society Dec 27, 2024
Even in the absence of external stimuli, the brain is spontaneously active. Indeed, most cortical activity is internally generated by recurrence. Both theoretical and experimental studies suggest that chaotic dynamics characterize this spontaneous ac...
Models, Neurological Nonlinear Dynamics Recurrent Neural Networks Computer Simulation Animals Humans Algorithms Neural Networks, Computer Brain
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Decoding neuronal networks: A Reservoir Computing approach for predicting connectivity and functionality.

Neural networks : the official journal of the International Neural Network Society Dec 26, 2024
In this study, we address the challenge of analyzing electrophysiological measurements in neuronal networks. Our computational model, based on the Reservoir Computing Network (RCN) architecture, deciphers spatio-temporal data obtained from electrophy...
Animals Models, Neurological Nerve Net Optogenetics Neurons Neural Networks, Computer Algorithms Computer Simulation
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Intrinsic plasticity coding improved spiking actor network for reinforcement learning.

Neural networks : the official journal of the International Neural Network Society Dec 19, 2024
Deep reinforcement learning (DRL) exploits the powerful representational capabilities of deep neural networks (DNNs) and has achieved significant success. However, compared to DNNs, spiking neural networks (SNNs), which operate on binary signals, mor...
Action Potentials Neurons Models, Neurological Reinforcement, Psychology Computer Simulation Humans Deep Learning Neural Networks, Computer Neuronal Plasticity
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Enhanced accuracy in first-spike coding using current-based adaptive LIF neuron.

Neural networks : the official journal of the International Neural Network Society Dec 16, 2024
First spike timings are crucial for decision-making in spiking neural networks (SNNs). A recently introduced first-spike (FS) coding method demonstrates comparable accuracy to firing-rate (FR) coding in processing complex temporal information through...
Neurons Models, Neurological Action Potentials Humans Neural Networks, Computer
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Damage explains function in spiking neural networks representing central pattern generator.

Journal of neural engineering Dec 16, 2024
Complex biological systems have evolved to control movement dynamics despite noisy and unpredictable inputs and processing delays that necessitate forward predictions. The staple example in vertebrates is the locomotor control emerging from interacti...
Neural Networks, Computer Computer Simulation Animals Locomotion Nerve Net Central Pattern Generators Action Potentials Cats Models, Neurological
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Adapting to time: Why nature may have evolved a diverse set of neurons.

PLoS computational biology Dec 13, 2024
Brains have evolved diverse neurons with varying morphologies and dynamics that impact temporal information processing. In contrast, most neural network models use homogeneous units that vary only in spatial parameters (weights and biases). To explor...
Brain Animals Computational Biology Humans Neural Networks, Computer Nerve Net Adaptation, Physiological Biological Evolution Action Potentials Neurons Models, Neurological
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Neural networks with optimized single-neuron adaptation uncover biologically plausible regularization.

PLoS computational biology Dec 13, 2024
Neurons in the brain have rich and adaptive input-output properties. Features such as heterogeneous f-I curves and spike frequency adaptation are known to place single neurons in optimal coding regimes when facing changing stimuli. Yet, it is still u...
Nerve Net Adaptation, Physiological Animals Computational Biology Action Potentials Models, Neurological Brain Algorithms Humans Neurons Computer Simulation Neural Networks, Computer
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Similarity-based context aware continual learning for spiking neural networks.

Neural networks : the official journal of the International Neural Network Society Dec 12, 2024
Biological brains have the capability to adaptively coordinate relevant neuronal populations based on the task context to learn continuously changing tasks in real-world environments. However, existing spiking neural network-based continual learning ...
Algorithms Learning Action Potentials Neurons Models, Neurological Humans Neural Networks, Computer
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