Brian 2 allows scientists to simply and efficiently simulate spiking neural network models.
These models can feature novel dynamical equations, their interactions with the …

Single spikes can trigger repeatable firing sequences in cortical networks. The mechanisms
that support reliable propagation of activity from such small events and their functional …

INTRODUCTION Accumulation and interaction of amyloid‐beta (Aβ) and tau proteins during
progression of Alzheimer's disease (AD) are shown to tilt neuronal circuits away from …

Transcranial direct current stimulation (tDCS) is a variant of noninvasive neuromodulation,
which promises treatment for brain diseases like major depressive disorder. In experiments …

The electroencephalogram (EEG) is a major tool for non-invasively studying brain function
and dysfunction. Comparing experimentally recorded EEGs with neural network models is …

Reinforcement learning is a paradigm that can account for how organisms learn to adapt
their behavior in complex environments with sparse rewards. To partition an environment …

Brain networks store new memories using functional and structural synaptic plasticity.
Memory formation is generally attributed to Hebbian plasticity, while homeostatic plasticity is …

Neuromorphic hardware is based on emulating the natural biological structure of the brain.
Since its computational model is similar to standard neural models, it could serve as a …

Modern computational neuroscience strives to develop complex network models to explain
dynamics and function of brains in health and disease. This process goes hand in hand with …

Plasticity is the mechanistic basis of development, aging, learning, and memory, both in
healthy and pathological brains. Structural plasticity is rarely accounted for in computational …