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SUMMARY:LCN seminar: Our Brain plays jazz: Self-Organization of the Liquid
  State Machine
DTSTART:20110124T111500
DTSTAMP:20260405T184657Z
UID:c56860c2522961db7880433aa7fafb8a5eb27b6ac4bc7417ccc5a010
CATEGORIES:Conferences - Seminars
DESCRIPTION:Gordon PIPA\, Institute of Cognitive Science\, University of O
 snabrueck\nAbstract:\nThe liquid state (Markram and Maass 2002) and echo s
 tate machine (Jäger 2004) ('LSM') had been proposed as promising computat
 ional neuronal models. However\, the original LSM proposed a random but fi
 xed recurrent network and is therefore incompatible with the observation t
 hat biological neuronal networks are constantly changing on many different
  scales. Therefore\, we extended the original idea of the LSM and allowed 
 for self organized changes of neuronal activity and of the network itself.
  To this end we included two types of plasticity. As a first type we inclu
 ded spike timing dependent plasticity (STDP) that changes the synaptic str
 ength and has been associated with sequence learning and structure formati
 on in recurrent networks. The second type is intrinsic plasticity (IP) tha
 t changes the excitability of individual neurons to maintain homeostasis. 
 Based on extensive simulation studies we demonstrate that the combination 
 of both types first optimizes the information processing\, second leads to
  self organized criticality of the network dynamics\, and third that the i
 ntrinsic noise introduced by intrinsic plasticity increases the robustness
  of information processing in a high noise regime. We also demonstrate tha
 t using these kind of plasticity leads to activity pattern in the system t
 hat are compatible with receptive fields or place fields as known form the
  visual system or the hippocampus. This illustrates that a computational m
 achine using transient activity\, such as reservoir computing\, is well co
 mpatible with well supported coding concepts such as receptive fields. \n\
 nAcknowledgments:\nThis work was supported by the grant: EU Grant - Phocus
  - www.phocus-project.eu\n\nRelated Papers:\n1.	Lazar\, G. Pipa\, J. Tries
 ch. Fading memory and time series prediction in recurrent networks with di
 fferent forms of plasticity\, Neural Networks 20\, 312-322\, (2007)\n2.	A.
  Lazar\, G. Pipa\, and J. Triesch\, SORN: a self-organizing recurrent neur
 al network\, Frontiers Computational Neuroscience 2009\, Volume 3 page 23\
 n3.	Maass\, W.\, Natschlager\, T.\, & Markram\, H.. Real-time computing wi
 thout stable states: A new framework for neural computation based on pertu
 rbations. Neural Computation\, 14\, 2531-2560\, (2002)
LOCATION:BC 01 https://plan.epfl.ch/?room==BC%2001
STATUS:CONFIRMED
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