AIMC Topic:
Neurons

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Improving Recall in Sparse Associative Memories That Use Neurogenesis.

Neural computation Feb 14, 2025
The creation of future low-power neuromorphic solutions requires specialist spiking neural network (SNN) algorithms that are optimized for neuromorphic settings. One such algorithmic challenge is the ability to recall learned patterns from their nois...
Algorithms Animals Models, Neurological Humans Neural Networks, Computer Neurogenesis Mental Recall Association Learning Neurons Memory
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Contrastive Neuron Pruning for Backdoor Defense.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society Jan 1, 2025
Recent studies have revealed that deep neural networks (DNNs) are susceptible to backdoor attacks, in which attackers insert a pre-defined backdoor into a DNN model by poisoning a few training samples. A small subset of neurons in DNN is responsible ...
Animals Deep Learning Image Processing, Computer-Assisted Neural Networks, Computer Algorithms Neurons Humans
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Intelligent Control to Suppress Epileptic Seizures in the Amygdala: In Silico Investigation Using a Network of Izhikevich Neurons.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Jan 1, 2025
Closed-loop electricalstimulation of brain structures is one of the most promising techniques to suppress epileptic seizures in drug-resistant refractory patients who are also ineligible to ablative neurosurgery. In this work, an intelligent controll...
Neural Networks, Computer Algorithms Computer Simulation Humans Seizures Electroencephalography Nerve Net Models, Neurological Neurons Epilepsy Amygdala Action Potentials Nonlinear Dynamics
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Sparse-Coding Variational Autoencoders.

Neural computation Nov 19, 2024
The sparse coding model posits that the visual system has evolved to efficiently code natural stimuli using a sparse set of features from an overcomplete dictionary. The original sparse coding model suffered from two key limitations; however: (1) com...
Algorithms Animals Neurons Models, Neurological Humans Neural Networks, Computer
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Fault-tolerant neural networks from biological error correction codes.

Physical review. E Nov 1, 2024
It has been an open question in deep learning if fault-tolerant computation is possible: can arbitrarily reliable computation be achieved using only unreliable neurons? In the grid cells of the mammalian cortex, analog error correction codes have bee...
Action Potentials Neurons Animals Neural Networks, Computer Models, Neurological
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Trainable Reference Spikes Improve Temporal Information Processing of SNNs With Supervised Learning.

Neural computation Sep 17, 2024
Spiking neural networks (SNNs) are the next-generation neural networks composed of biologically plausible neurons that communicate through trains of spikes. By modifying the plastic parameters of SNNs, including weights and time delays, SNNs can be t...
Models, Neurological Neural Networks, Computer Neurons Action Potentials Supervised Machine Learning Humans Time Factors
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Inference on the Macroscopic Dynamics of Spiking Neurons.

Neural computation Sep 17, 2024
The process of inference on networks of spiking neurons is essential to decipher the underlying mechanisms of brain computation and function. In this study, we conduct inference on parameters and dynamics of a mean-field approximation, simplifying th...
Humans Bayes Theorem Neurons Animals Models, Neurological Neural Networks, Computer Action Potentials Algorithms
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Learning meaningful representation of single-neuron morphology via large-scale pre-training.

Bioinformatics (Oxford, England) Sep 1, 2024
SUMMARY: Single-neuron morphology, the study of the structure, form, and shape of a group of specialized cells in the nervous system, is of vital importance to define the type of neurons, assess changes in neuronal development and aging and determine...
Cell Shape Humans Animals Neurons Deep Learning Datasets as Topic Rats Drosophila melanogaster
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Comparison of Regression Methods to Predict the First Spike Latency in Response to an External Stimulus in Intracellular Recordings for Cerebellar Cells.

Studies in health technology and informatics Aug 22, 2024
The significance of intracellular recording in neurophysiology is emphasized in this article, with considering the functions of neurons, particularly the role of first spike latency in response to external stimuli. The study employs advanced machine ...
Regression Analysis Models, Neurological Machine Learning Neurons Cerebellum Animals Action Potentials
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Self-organization toward 1/f noise in deep neural networks.

Chaos (Woodbury, N.Y.) Aug 1, 2024
In biological neural networks, it has been well recognized that a healthy brain exhibits 1/f noise patterns. However, in artificial neural networks that are increasingly matching or even out-performing human cognition, this phenomenon has yet to be e...
Models, Neurological Neurons Humans Neural Networks, Computer
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