The patch-clamp technique has been the gold standard for analysis of excitable cells. Since its development in the 1980s, it has contributed immensely to our understanding of neurons, muscle cells, and cardiomyocytes and the ion channels and receptor...
This article reviews existing work and future opportunities in neuroevolution, an area of machine learning in which evolutionary optimization methods such as genetic algorithms are used to construct neural networks to achieve desired behavior. The ar...
Theoretical neuroscientists and machine learning researchers have proposed a variety of learning rules to enable artificial neural networks to effectively perform both supervised and unsupervised learning tasks. It is not always clear, however, how t...
Teravoxel-scale, cellular-resolution images of cleared rodent brains acquired with light-sheet fluorescence microscopy have transformed the way we study the brain. Realizing the potential of this technology requires computational pipelines that gener...
Artificial neurons with bio-inspired firing patterns have the potential to significantly improve the performance of neural network computing. The most significant component of an artificial neuron circuit is a large amount of energy consumption. Rece...
Higher cortical areas carry a wide range of sensory, cognitive and motor signals mixed in heterogeneous responses of single neurons tuned to multiple task variables. Dimensionality reduction methods that rely on correlations between neural activity a...
Biological neurons use diverse temporal expressions of spikes to achieve efficient communication and modulation of neural activities. Nonetheless, existing neuromorphic computing systems mainly use simplified neuron models with limited spiking behavi...
Current artificial systems suffer from catastrophic forgetting during continual learning, a limitation absent in biological systems. Biological mechanisms leverage the dual representation of specific and generalized memories within corticohippocampal...
The brain's ability to learn and distinguish rapid sequences of events is essential for timing-dependent tasks, such as those in sports and music. However, the mechanisms underlying this ability remain an active area of research. Here, we present a P...
. Deep learning tools applied to high-resolution neurophysiological data have significantly progressed, offering enhanced decoding, real-time processing, and readability for practical applications. However, the design of artificial neural networks to...