Electrodiffusion in neural tissue at long timescales

Learning Multiple Timescales in Recurrent Neural Networks

Recurrent Neural Networks (RNNs) are powerful architectures for sequence learning. Recent advances on the vanishing gradient problem have led to improved results and an increased research interest. Among recent proposals are architectural innovations that allow the emergence of multiple timescales during training. This paper explores a number of architectures for sequence generation and predict...

Prediction at Different Timescales

Neural timescales or lack thereof.

This article aims at making readers, experimentalists and theorists, more aware of the abstractions made by an observer when measuring and reporting behavioral and neural timescales. These abstractions stow away the fact that, above lower boundaries that reflect fairly well understood physical constraints, observed and reported timescales are often not intrinsic to the biological system; rather...

Short and Long Timescales in Recycles

A new awareness on modelling is growing in the control-oriented community recognising the fact that control is dominantly model based. Since control is about manipulating certain characteristics of the plant, it is no surprise that modelling for control focuses on extracting exactly those characteristics of the plant that are to be controlled. This invariably induces the use of time scale assum...

The neuronal response at extended timescales: long-term correlations without long-term memory

Long term temporal correlations frequently appear at many levels of neural activity. We show that when such correlations appear in isolated neurons, they indicate the existence of slow underlying processes and lead to explicit conditions on the dynamics of these processes. Moreover, although these slow processes can potentially store information for long times, we demonstrate that this does not...