Artificial neural networks have been used as a powerful processing tool in various areas
such as pattern recognition, control, robotics, and bioinformatics. Their wide applicability has …

Top-down feedback in cortex is critical for guiding sensory processing, which has
prominently been formalized in the theory of hierarchical predictive coding (hPC). However …

Synaptic plasticity is believed to be a key physiological mechanism for learning. It is well
established that it depends on pre-and postsynaptic activity. However, models that rely …

A vast majority of computation in the brain is performed by spiking neural networks. Despite
the ubiquity of such spiking, we currently lack an understanding of how biological spiking …

Background Rapidly changing excitability states in an oscillating neuronal network can
explain response variability to external stimulation, but if repetitive stimulation of always the …

What happens in our brain when we make a decision? What triggers a neuron to send out a
signal? What is the neural code? This textbook for advanced undergraduate and beginning …

In spike-timing-dependent plasticity (STDP), the order and precise temporal interval
between presynaptic and postsynaptic spikes determine the sign and magnitude of long …

Synapses are highly plastic and are modified by changes in patterns of neural activity or
sensory experience. Plasticity of cortical excitatory synapses is thought to be important for …

Synaptic plasticity, the putative basis of learning and memory formation, manifests in various
forms and across different timescales. Here we show that the interaction of Hebbian …

In this paper we demonstrate in the intact human the possibility of a non-invasive modulation
of motor cortex excitability by the application of weak direct current through the scalp …