BACKGROUND: BrainAge models estimate the biological age of the brain using neuroimaging or clinical features, making them promising tools for studying neurodegenerative diseases like Alzheimer's disease. However, the reliance of BrainAge models on ne...
Object recognition requires flexible and robust information processing, especially in view of the challenges posed by naturalistic visual settings. The ventral stream in visual cortex is provided with this robustness by its recurrent connectivity. Re...
Continuous bump attractor networks (CBANs) are a prevailing model for how neural circuits represent continuous variables. CBANs maintain these representations by temporally integrating inputs that encode differential (i.e., incremental) changes to a ...
Computational models of neural processing in the auditory cortex usually ignore that neurons have an internal memory: they characterize their responses from simple convolutions with a finite temporal window. To circumvent this limitation, we propose ...
The Journal of neuroscience : the official journal of the Society for Neuroscience
Oct 8, 2025
The integration of multiple sensory inputs is essential for human perception and action in uncertain environments. This process includes reference frame transformations as different sensory signals are encoded in different coordinate systems. Studies...
Proceedings of the National Academy of Sciences of the United States of America
Oct 7, 2025
Understanding the computational principles of the brain and translating them into neuromorphic hardware and modern deep learning architectures is critical for advancing neuro-inspired AI (NeuroAI). Here, we develop an experimentally constrained, biop...
Current machine learning systems consume vastly more energy than biological brains. Neuromorphic systems aim to overcome this difference by mimicking the brain's information coding via discrete voltage spikes. However, it remains unclear how both art...
In this work, a supervised learning rule based on Temporal Single Spike Coding for Effective Transfer Learning (TS4TL) is presented, an efficient approach for training multilayer fully connected Spiking Neural Networks (SNNs) as classifier blocks wit...
The brain exhibits rich dynamical properties that underpin its remarkable temporal processing capabilities. However, spiking neural networks (SNNs) inspired by the brain have not yet matched their biological counterparts in temporal processing and re...
While many mechanisms have been proposed to drive Alzheimer's disease, particularly the accumulation of amyloid plaques and hyperphosphorylation of tau proteins, emerging evidence suggests that they may be the byproducts of earlier damage rather than...
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