Pau V. Aceituno - Emergent resonances in Recurrent Neural Networks and their effects on learning
Channel: LiSNN.channel
Date: 2021-09-01

Learning in Spiking Neural Networks seminar series presents:

Speaker: Pau Vilimelis Aceituno - ETH Zürich [1]

Title: Emergent resonances in Recurrent Neural Networks and their effects on learning

Abstract: Reservoir Computing (RC) is one of the rare computing paradigms which can be used both as a theoretical neuroscience model and as a machine learning tool. The key feature of the RC paradigm is its reservoir, a recurrent neural network that projects the input time series into a high-dimensional space. Although some computational features such as memory capacity have been studied in depth, the adaptation of reservoirs to specific problems is still not understood. 
Here we explore this fundamental question and show, both analytically and computationally, how structural features determine the type of tasks that these recurrent neural networks can perform. Then we evaluate the biological plausibility of such structures by showing how they can emerge from known biological learning rules such as Spike-timing dependent plasticity (STDP).

Host: Learning in Spiking Neural Networks Initiative [2]

Please note that this has been our first recorded hybrid event. We apologize for the few technical glitches.

[1] https://scholar.google.com/citations?user=dahpSB8AAAAJ&hl=en
[2] https://www.uni-frankfurt.de/103796621/Learning_in_Spiking_Neural_Networks