AIMC Topic: Learning

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Metaplasticity and continual learning: mechanisms subserving brain computer interface proficiency.

Journal of neural engineering
Brain computer interfaces (BCIs) require substantial cognitive flexibility to optimize control performance. Remarkably, learning this control is rapid, suggesting it might be mediated by neuroplasticity mechanisms operating on very short time scales....

Unsupervised post-training learning in spiking neural networks.

Scientific reports
The human brain is a dynamic system that is constantly learning. It employs a combination of various learning strategies to facilitate complex learning processes. However, implementing biological learning mechanisms into Spiking Neural Networks (SNNs...

Modeling rapid language learning by distilling Bayesian priors into artificial neural networks.

Nature communications
Humans can learn languages from remarkably little experience. Developing computational models that explain this ability has been a major challenge in cognitive science. Existing approaches have been successful at explaining how humans generalize rapi...

Impact of pharmacology perception and learning strategies on academic achievement in undergraduate pharmacy students.

Scientific reports
Pharmacology is a cornerstone of pharmacy education, bridging biomedical sciences with clinical application. Understanding students' perceptions of pharmacology's relevance can influence their learning strategies and academic performance. Despite its...

Effects of ChatGPT on students' academic performance in Pakistan higher education classrooms.

Scientific reports
The rapid integration of cutting-edge technology is significantly transforming the higher education landscape. ChatGPT's groundbreaking technology has provided numerous advantages for higher education. This study explored students' behavioral intenti...

Elucidating the Theoretical Underpinnings of Surrogate Gradient Learning in Spiking Neural Networks.

Neural computation
Training spiking neural networks to approximate universal functions is essential for studying information processing in the brain and for neuromorphic computing. Yet the binary nature of spikes poses a challenge for direct gradient-based training. Su...

g-Distance: On the comparison of model and human heterogeneity.

Psychological review
Models are often evaluated when their behavior is at its closest to a single, sometimes averaged, set of empirical results, but this evaluation neglects the fact that both model and human behavior can be heterogeneous. Here, we develop a measure, -di...

Learning in Wilson-Cowan Model for Metapopulation.

Neural computation
The Wilson-Cowan model for metapopulation, a neural mass network model, treats different subcortical regions of the brain as connected nodes, with connections representing various types of structural, functional, or effective neuronal connectivity be...

A Layered Learning Approach to Scaling in Learning Classifier Systems for Boolean Problems.

Evolutionary computation
Evolutionary Computation (EC) often throws away learned knowledge as it is reset for each new problem addressed. Conversely, humans can learn from small-scale problems, retain this knowledge (plus functionality), and then successfully reuse them in l...

Replay as a Basis for Backpropagation Through Time in the Brain.

Neural computation
How episodic memories are formed in the brain is a continuing puzzle for the neuroscience community. The brain areas that are critical for episodic learning (e.g., the hippocampus) are characterized by recurrent connectivity and generate frequent off...