AIMC Topic: Models, Neurological

Clear Filters Showing 31 to 40 of 1162 articles

Cortical-subcortical neural networks for motor learning and storing sequence memory.

Neural networks : the official journal of the International Neural Network Society
Motor sequence learning relies on the synergistic collaboration of multiple brain regions. However, most existing models for motor sequence learning primarily focus on functional-level analyses of sequence memory mechanisms, providing limited neuroph...

Efficient, Robust, and Accurate CNN Predictor for Neuronal Activation in Directional Deep Brain Stimulation.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
The programming of clinical deep brain stimulation (DBS) systems involves numerous combinations of stimulation parameters, such as stimulus amplitude, pulse width, and frequency. As more complex electrode designs, such as directional electrodes, are ...

How Can Anomalous-Diffusion Neural Networks Under Connectomics Generate Optimized Spatiotemporal Dynamics.

IEEE transactions on neural networks and learning systems
Spatiotemporal dynamics in the brain have been recognized as strongly related to the formation of perceived and cognitive diseases, such as delusions and hallucinations in Alzheimer's disease. However, two practical considerations are rarely mentione...

Linking cellular-level phenomena to brain architecture: the case of spiking cerebellar controllers.

Neural networks : the official journal of the International Neural Network Society
Linking cellular-level phenomena to brain architecture and behavior is a holy grail for theoretical and computational neuroscience. Advances in neuroinformatics have recently allowed scientists to embed spiking neural networks of the cerebellum with ...

Heterogeneous quantization regularizes spiking neural network activity.

Scientific reports
The learning and recognition of object features from unregulated input has been a longstanding challenge for artificial intelligence systems. Brains, on the other hand, are adept at learning stable sensory representations given noisy observations, a ...

SpikeCLIP: A contrastive language-image pretrained spiking neural network.

Neural networks : the official journal of the International Neural Network Society
Spiking Neural Networks (SNNs) have emerged as a promising alternative to conventional Artificial Neural Networks (ANNs), demonstrating comparable performance in both visual and linguistic tasks while offering the advantage of improved energy efficie...

Multiscroll hidden attractor in memristive autapse neuron model and its memristor-based scroll control and application in image encryption.

Neural networks : the official journal of the International Neural Network Society
In current neurodynamic studies, memristor models using polynomial or multiple nested composite functions are primarily employed to generate multiscroll attractors, but their complex mathematical form restricts both research and application. To addre...

From social effort to social behavior: An integrated neural model for social motivation.

Neuroscience and biobehavioral reviews
As humans rely on social groups for survival, social motivation is central to behavior and well-being. Here we define social motivation as the effort that initiates and directs behavior towards social outcomes, with the goal of satisfying our fundame...

Neural models for detection and classification of brain states and transitions.

Communications biology
Exploring natural or pharmacologically induced brain dynamics, such as sleep, wakefulness, or anesthesia, provides rich functional models for studying brain states. These models allow detailed examination of unique spatiotemporal neural activity patt...

Scalable Multi-FPGA HPC Architecture for Associative Memory System.

IEEE transactions on biomedical circuits and systems
Associative memory is a cornerstone of cognitive intelligence within the human brain. The Bayesian confidence propagation neural network (BCPNN), a cortex-inspired model with high biological plausibility, has proven effective in emulating high-level ...