Recurrent Inhibitory Coupling Improves Discrimination of Temporal Spike Patterns
-
1
Ludwig Maximilians Universitaet, Division of Neurobiology, Department of Biology II, Germany
-
2
Bernstein Focus Neuronal Basis of Learning: Plasticity of Neuronal Dynamics, Germany
-
3
Bernstein Center for Computational Neuroscience Munich, Germany
Inhibitory neurons are considered to play a central role as rhythm generator and in shaping feed-forward receptive fields. While much attention has been paid to such effects on excitatory neurons, little is done to study these inhibitory neurons' ability to directly process information. Here we present a linear classification model that investigates the inner workings of a recurrent inhibitory neural network.
Our work focuses on quantifying the performance of a recurrent network of inhibitory integrate-and-fire neurons in canonical classification tasks. The model begins with parallel independent excitatory Poisson inputs connected to the recurrent network. Then, the network output is feed-forwardly directed to a read-out linear classifier. The analysis is then conducted as a function of variables such as inhibitory weight, read-out delay, etc. to shed light on the principles behind the network’s computational capacity.
It is found that there is an optimum weight amongst the inhibitory neurons that yields the best performance, and this improvement could be as much as 30%. The optimum weight is where the sum of the network’s mutual information (MI) and binary MI is the highest, and it occurs when the network’s response to a stimulus is about 40%-50% silent. This illustrates how a recurrent neural network may optimize its topological parameters to obtain more computational capacity than a simple feed-forward network [1,2].
References
1. W. Maass, T. Natschläger, H. Markram: Real-time computing without stable states: a new framework for neural computation based on perturbations. Neural Comput 2002, 14: 2531-2560.
2. H. Jäger, H. Haas: Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication. Science 2004, 304: 78-80.
Keywords:
machine learning,
network dynamics
Conference:
BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011, Freiburg, Germany, 4 Oct - 6 Oct, 2011.
Presentation Type:
Poster
Topic:
neurons, networks and dynamical systems (please use "neurons, networks and dynamical systems" as keywords)
Citation:
Yuan
C and
Leibold
C
(2011). Recurrent Inhibitory Coupling Improves Discrimination of Temporal Spike Patterns.
Front. Comput. Neurosci.
Conference Abstract:
BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011.
doi: 10.3389/conf.fncom.2011.53.00211
Copyright:
The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers.
They are made available through the Frontiers publishing platform as a service to conference organizers and presenters.
The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated.
Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed.
For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions.
Received:
18 Aug 2011;
Published Online:
04 Oct 2011.
*
Correspondence:
Dr. Chun-Wei Yuan, Ludwig Maximilians Universitaet, Division of Neurobiology, Department of Biology II, Martinsried, 82152, Germany, yuan@bio.lmu.de