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Talk by Prof. Dr. Karlheinz Meier and Dr. Eva M. Navarro-López

Kirchhoff-Institute for Physics, Heidelberg University,

School of Computer Science, The University of Manchester, UK

begin
05 Mar 2018 14:00
end
05 Mar 2018 16:00
venue
Bldg. 15.9 U, Seminar Room 4001b

Prof. Karlheinz Meier
Kirchhoff-Institute for Physics, Heidelberg University


Neuromorphic computing - From biology to user facilities


Abstract:
Neuromorphic computing holds the promise to transfer computational advantages from biological brains to artificial silicon or new material based systems. Advantages include energy efficiency, fault tolerance and, most importantly, the ability to learn continuously from unstructured data. Recent advances in artificial intelligence are based on ANNs which represent extreme simplifications of biological architectures. In particular they ignore the time domain of neural signaling which plays a key role for learning and development of neural systems.
In the talk I will deliver an overview of novel brain-inspired neuromorphic hardware architectures. Particular emphasis will be given to the role of biological principles like spike based plasticity and active dendritic trees for computation. I will also discuss the recent releases of neuromorphic systems for general use and the opportunities they offer to advance both, neuroscience and machine learning.


Dr. Eva M. Navarro-López
School of Computer Science, The University of Manchester, UK


Hybrid systems neuroscience: from Alan Turing to Ramón y Cajal, and back


Abstract:
From Alan Turing to Santiago Ramón y Cajal, and back, or the other way round: this is the trajectory of my own work. Computing and artificial intelligence have always striven to emulate the working of the brain. We aspire to advance the knowledge of the brain through computer science. After tracing the last steps of Alan Turing in Manchester, and discovering the details of the fascinating concept of morphogenesis –at first hand, from the last MSc student of Alan Turing, Bernard Richards– I found myself tracing the steps of Santiago Ramón y Cajal, the father of modern neuroscience, at Instituto Cajal and mining his legacy. These studies underline the truism that in order to advance into the future, it is necessary to understand our past. At our present, the dynamical behaviour of networks of billions of neurons in the human brain is still poorly understood, as is its relationship to the emergence of learning and memory. Pre-existing models are still fairly limited. The multi-scale complexity of the problem requires the combination of paradigms from different fields, mainly: hybrid dynamical systems –where smooth and abrupt dynamics interact– control engineering, formal methods of computer science, and network science. In this talk, we will explore how all these theories can be combined and define the field of hybrid systems neuroscience as the reformulation of hybrid system models, analysis tools and control schemes for neuronal systems. Under the hybrid systems neuroscience framework, new concepts and models are proposed to aid understanding of the adaptive dynamical processes of brain networks. All these ideas will be illustrated with examples and models that we have recently studied

Host: Prof. Markus Diesmann


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