[Comp-neuro] CFP: Advances in Biologically Inspired Reservoir Computing

John Butcher j.b.butcher at keele.ac.uk
Fri Jul 1 10:13:52 CEST 2016


Dear all,

We are seeking papers for a special issue on Reservoir Computing, paper
submission deadline is 31st September 2016.  For more details see below and
http://ispac.diet.uniroma1.it/cognitive-computation-special-issue/

Many thanks

*Scope and motivation*

*Reservoir computing is a family of techniques for training and analyzing
recurrent neural networks, wherein the recurrent portion of the network is
assigned before the training process, typically via stochastic assignment
of its weights. The non-linear reservoir acts as a high-dimensional kernel
space, which generates complex dynamics characterized by sharp transitions
between ordered and chaotic regimes. The behavior of this model emulates
the functioning of many biological (complex) systems, among which the
brain.*

*Driven by the conceptual simplicity of the reservoir and by links with
neuroscience, computer science and systems’ theory, researchers have
achieved remarkable breakthroughs, both in theory and in practice. These
include dynamical models for explaining the working behavior of reservoirs,
unsupervised strategies for the adaptation of the network, and the design
of unconventional computing architectures for its execution.*

*The recent upsurge of interest in fully adaptable recurrent networks, far
from shifting the attention from the field, has brought renewed interest in
reservoir computing models. In our era of extreme computational power and
sophisticated problems, it is essential to understand the limits and the
potentialities of simple (both deterministic and random) collections of
processing units. For this reason, many fundamental questions remain open,
including the design of optimal task-dependent reservoirs in a stable
fashion, novel investigations on the memory and power capabilities of
reservoir devices, and their applicability in an ever-increasing range of
domains.*

*In light of this, the aim of this special issue is to provide a unified
platform for bringing forth and advancing the state-of-the-art in reservoir
computing approaches. Researchers are invited to submit innovative works on
the theory and implementation of this family of techniques, in order to
provide an up-to-date overview on the field.*

*Topics*

*The topics of interest to be covered by this Special Issue include, but
are not limited to:*

*-Theoretical analyses on the computational power of reservoir computing.*
*-Deep reservoir models.*
*-Techniques for the automatic adaptation of the reservoir and the readout.*
*-Supervised, unsupervised and semi-supervised training criteria.*
*-Non-conventional substrates for the implementations of reservoirs.*
*-Parallel and distributed algorithms for reservoir computing.*
*-Comparisons between reservoir computing and standard (deep) neural
networks.*
*-Reservoir computing for reinforcement learning problems.*
*-Fundamental links between reservoir computing and neuroscientific
findings.*
*-Investigation of reservoir dynamic in a phase space of reduced
dimensionality.*
*-Applicative papers in all areas (including robotics, industrial control,
etc.) are welcome, as well as outstanding surveys on specific aspects of
the field.*

-- 
Dr John Butcher
Neuroscience Research Associate
School of Life Sciences
Huxley Building
Keele University
UK
ST5 5BG

http://www.keele.ac.uk/lifesci/people/researchassistants/johnbutcher/
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