Max-Planck-Institut für Informatik
max planck institut
mpii logo Minerva of the Max Planck Society

MPI-INF or MPI-SWS or Local Campus Event Calendar

<< Previous Entry Next Entry >> New Event Entry Edit this Entry Login to DB (to update, delete)
What and Who
Title:Updating Artificial Neural Networks: Translating Recent Discoveries about the Electrophysiology of Neurons into the Language of Computation
Speaker:Alan Schoen
coming from:International Max Planck Research School for Computer Science - IMPRS
Speakers Bio:
Event Type:PhD Application Talk
Visibility:D1, D2, D3, D4, D5, SWS, RG1, MMCI
We use this to send out email in the morning.
Level:Public Audience
Date, Time and Location
Date:Monday, 26 October 2015
Duration:120 Minutes
Building:E1 4
Artificial neural networks (ANNs) are widely used in machine learning, but they are only partially connected to biological research in neuroscience. In some cases, biological research has advanced the state of computer science. One example of this is deep learning, which was inspired by the multi-layer structure of visual cortex. On the other hand, biological research about computation within individual neurons has advanced in recent decades, but it has not transferred to ANNs. The structure of each individual neuron in an ANN is still based on a biological theory that is over 70 years old.

New experimental methods like patch-clamps, optogenetics, and two-photon imaging reveal new behaviors and computational abilities. In particular, we see that dendrites can organize and filter incoming signals in ways that greatly increase the complexity of each neuron. These findings could advance computer science, but they remain silioed in biology. I believe this is because they are expressed in a language of biology and electrophysiology that is inaccessible to computer scientists.
The goal of my masters thesis research was to translate recent biological findings into the language of computation. We used patch clamps to inject time-varying currents into neurons and study their response. Then I developed a theoretical computational framework to account for these findings which was consistent with findings from other experimental methods. The result was a mixed linear/nonlinear framework which simplified our understanding of the data.  I also proposed a computational structure that could explain the function of some neurons that process time-signals. I believe that more research in this area can strengthen the connection between biological neuroscience and theoretical computational neuroscience, to the benefit of both fields.

Name(s):Andrea Ruffing
Video Broadcast
Video Broadcast:NoTo Location:
Tags, Category, Keywords and additional notes
Attachments, File(s):

Created by:Andrea Ruffing/MPI-INF, 10/23/2015 06:50 PMLast modified by:Uwe Brahm/MPII/DE, 11/24/2016 04:13 PM
  • Andrea Ruffing, 10/23/2015 06:51 PM -- Created document.