Computational neuroscience continues to be a broad and dynamic discipline that transforms
itself as new experimental methods make the collection of new, often multi-modal data types …

Artificial neural networks (ANNs) are at the core of most Deep learning (DL) algorithms that
successfully tackle complex problems like image recognition, autonomous driving, and …

Rhythmic activity is ubiquitous in neural systems, with theta-resonant pyramidal neurons
integrating rhythmic inputs in many cortical structures. Impedance analysis has been widely …

Cellular traffic forecasting is an essential task that enables network operators to perform
resource allocation and anomaly mitigation in fast-paced modern environments. However …

Multiple neurophysiological experiments have shown that dendritic non-linearities can have
a strong influence on synaptic input integration. In this work we model a single neuron as a …