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05/12/11
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Projects:
Neuroscience - Computation with dynamic
neural activity patterns
Complex systems - Network analysis,
systems theory and modelling
Computational intelligence
Complex nonlinear neural dynamics - CNS
/ IJCNN Workshops (past)
Neuroscience - Computation with dynamic neural activity
patterns
The project is focusing on
analysing dynamic neural activity patterns. The dynamics of such patterns is
described in terms of a probabilistic language of pattern transitions. This
has been applied to high resolution EEG data recorded. Currently I work on
the crab stomatogastric ganglion (STG), using voltage-sensitive dye imaging with the aim to
record the activity of many cells within the ganglion simultaneously. This
data describes a small, but complex neural system, which is relatively
isolated (the STG), allowing to analyse in detail a good approximation of a
functionally complete neural system. I collaborate in this work with the
groups of Dr Wolfgang Stein (University of Ulm, Germany), Prof Al Selverston
(University of California, San Diego, US), Prof Farzan Nadim (Rutgers
University, US), Prof Miles Whittington (Newcastle University), Prof Alex
Yakovlev (Newcastle University), Dr Terrence Mak (Newcastle University), Prof
Sylvie Renaud (University of Bordeaux I, France), Prof George Kemenes (University
of Sussex), Prof Alan Roberts (University of Bristol), and Prof Matt Bentley
(Newcastle University). Currently I have one PhD student, Jannetta Steyn,
working on this project, and two other PhD students, Jun Luo and Nicola
Everitt (students of Dr Mak and Prof Bentley), who also participate in the
work of the lab. PhD students of Dr Stein, Carola Staedele and Stephanie Preuss, and a postdoc of Prof
Roberts, Dr Edgar Buhl, also visited recently the lab. Video article presenting
the dye filling and recording of STG neurons: Stein, W, Städele, Andras, P
(2011). Optical imaging of
neurons in the crab stomatogastric ganglion with
voltage-sensitive dyes. Journal of Visualized Experiments, doi: 10.3791/2567.
Crab
STG (~1 x 2 mm) - low magnification
Desheated crab STG with visible neurons and a
single dye filled STG neuron
Voltage-sensitive
dye (first two) and calcium dye (last) images of desheated
crab STG (bath loading)
Voltage-sensitive
dye image of dye-loaded neurons in the desheated
crab STG The project also includes
the development of neural network models that perform computation by
employing dynamic neural activity patterns. Such models can be used to
describe the behavior of small neural systems that
perform their functions by interaction of spatio-temporal
activity patterns (e.g., olfactory bulb, crab STG). An important issue in
this context is that nonlinear dynamical models (sets of Hodgkin-Huxley
differential equations) of individual STG neurons do not always generate a
good approximation of the joint activity of such neurons when the model
neurons are joined together (e.g. models built using the Neuron language),
even if the models of individual neurons replicate the activity of these
neurons satisfactorily. The simultaneous recording of several/many STG
neurons using voltage-sensitive dye imaging allows the search for improved
models of STG neurons and also the investigation of the relationships between
parametric features of STG neurons. Earlier, this project
introduced the Sierpinski neural network. The spatio-temporal output of this network can be
characterised in terms of Sierpinski triangles. By
the interaction of such networks we can generate Sierpinski
tuning functions. Such tuning functions can be used as basis
functions to perform computational tasks with the Sierpinski
brain (a large collection of Sierpinski neural
networks).
A
Sierpinski neural network and its output activity
pattern Related papers: Städele,
C, Andras, P Stein, W (2012). Simultaneous measurement of
membrane potential changes in multiple pattern generating neurons using
voltage sensitive dye imaging. Journal of Neuroscience Methods, 203:
78-88, doi: 10.1016/ j.jneumeth.2011.09.015. Stein,
W, Städele,C, Andras, P
(2011). Single-sweep voltage sensitive dye imaging of interacting identified
neurons. Journal of Neuroscience Methods, 194:224-234, doi:
10.1016/j.jneumeth.2010.10.2007. Stein,
W, Städele, Andras, P (2011). Optical imaging of neurons in
the crab stomatogastric ganglion with
voltage-sensitive dyes. Journal of Visualized Experiments, doi: 10.3791/2567. Stein,
W, Andras, P (2010). Light-induced effects of a fluorescent voltage-sensitive
dye on neuronal activity in the crab stomatogastric
ganglion. Journal of Neuroscience Methods, 188:290-294,
–doi:10.1016/j.jneumeth.2010.03.003. Andras,
PE, Fox, D, Städele,C,
Stein, W (2010). Optical recording of detailed neural activity from single
and multiple neurons of the crab stomatogastric
ganglion. Society for Neuroscience Abstracts, 615.18. Fox,
D, Andras, P (2010). A model of endocannabinoid
2-AG-mediated depolarization-induced suppression of inhibition. BMC
Neuroscience 2010, 11(Suppl 1):P189. Abstracts of
CNS'2010. Andras,
PE (2009). Analysis of noisy voltage-sensitive dye imaging data recorded from
the crab stomatogastric ganglion. Society for
Neuroscience Abstracts, 288.10. Andras,
PE, Fourie, DL, Whittington, MA (2008). Voltage-sensitive dye imaging of the
crab stomatogastric ganglion. Society for
Neuroscience Abstracts, 693.2. Kaiser
M, Martin R, Andras P, Young MP (2007). Simulation of structural robustness
of cortical networks. European Journal of Neuroscience, 25 (10):
3185-3192. Fourie,
DL, Andras, P (2007) Open source simulation of the pyloric network. BMC
Neuroscience 2007, 8(Suppl 2):P8 (6 July 2007).
Abstracts of CNS’2007. Wennekers, T, Ay, N, Andras, P (2007). High
resolution multiple-unit EEG in cat neocortex
reveals large spatio-temporal stochastic
interactions. BioSystems , 89: 190-197. Andras, P
and Wennekers, T (2007). Cortical activity pattern
computation. BioSystems, 87: 179-185. Andras, P
& Lycett, S (2007). An advantage of chaotic
neural dynamics. In Proceedings of IJCNN’2007 (in press). Andras, P
(2006). Extraction of an activity pattern language from EEG data. Neurocomputing, 69: 1313-1316. Andras, P
(2006). The language of cortical dynamics. LNBI 4216, Berthold, MR, Glen, R
& Fischer I (eds.) CompLife 2006,
Springer-Verlag, pp.247-256. Andras, P
(2005). Pattern computation in neural communication systems. Biological
Cybernetics, 92: 452-460. Andras, P
(2005). Neural activity pattern systems. Neurocomputing,
65-66: 531-536. Andras, P.
(2004). Pattern Languages: A New Paradigm for Neurocomputation.
Neurocomputing, 58-60: 223-228. Andras, P.
(2003). A Model for Emergent Complex Order in Small Neural Networks. Journal
of Integrative Neuroscience, 2: 55-70. Andras, P.
(2003) Comparing Neurophysiological Measurements of
Simulated and Real Brains. Neurocomputing,
52-54: 677-682. Andras,
P. (2002). Computation with Chaotic Patterns. Neurocomputing,
44-46: 263-268. Andras P.
(2001) The Role of Brain Chaos. In: Wermter, S., Austin, J. & Willshaw, D. (Eds.) Emerging Neural Architectures
based on Neuroscience, Springer-Verlag,
Heidelberg, pp.296-310. Andras, P.
(2001) The Sierpinski brain. In Proceedings of
the International Joint Conference on Neural Networks 2001, vol.1., pp.654-659. Andras, P., Postma, E., and Van den Herik,
J. (2001). Natural Dynamics and Neural Networks. Journal of Intelligent
Systems, 11: 173-201. Andras, P., Postma, E., van den Herik, J.
(1999) Dealing with Environmental Dynamics. In Proceedings of BNAIC’99,
Maastricht, IKAT, pp.211-218. Complex systems - Network
analysis, systems theory and modelling
My network analysis work
aims to develop new computational methods for network analysis of various
natural and artificial systems. We developed methods to analyse cortical
networks, ecological networks (food-webs), interaction networks of software
components, and protein interaction networks. The current focus of the
project is on the analysis of run-time class and object interaction networks
in large-scale software and the development and validation of network
analysis methods in this context. We look at the software using dynamic
analysis methods in order to understand how components of the software
actually interact to deliver the software functionality. We aim to prove that
network analysis methods can be used to improve the functionality of the
software for example by helping the fixing of erroneous behaviour and
determining parts of the software that require changes for improved or
modified functionality. Currently I have one PhD student (Anjan Pakhira)
working on this project.
The
dynamic class interaction network of the JHotDraw
6.01b software (~65k lines of code) Previously, the work on
protein interaction networks aimed to help drug target discovery and drug
design. We analyzed interaction networks of proteins in various organisms,
and develop methods to determine the functionally most important parts of
these networks. We used this information to search for drug targets and
advise the drug design procedure. The protein interaction network analysis
was done in collaboration with the e-Therapeutics Ltd, a university spin-off
company. The developed methodology has been patented (Young, MP, Andras, P,
O’Neal, MA (2002). Method and apparatus for identifying components of a
network having high importance for network integrity. UK Patent (2002) GB
0225109.8, World Patent (2003) WO/2004/040497, European Patent (2003)
EP08157898.1-2416, United States Patent (2005) 20050286414.). Collaborators
in this work were: Professor Malcolm P Young, Dr Olusola
Idowu, Dr Marcus Kaiser.
The
protein interaction network of a bacterium Related papers: Pakhira,
A, Andras, P (2012). Using network analysis metrics to discover functionally
important methods in large-scale software systems. Proceedings of the 3rd
International Workshop on Emerging Trends in Software Metrics (WETSoM 2012), pp.70-76. Pakhira,
A, Andras, P (2010). Can we use network analysis methods to discover
functionally important method calls in software systems by considering
dynamic analysis data? In Proceedings of the PCODA 2010 Workshop. Andras,
P (2009). Networks of artificial social interactions. To appear in:
Proceedings of European Conference on Artificial Life – ECAL 2009. Kaiser
M, Martin R, Andras P, Young MP (2007). Simulation of structural robustness
of cortical networks. European Journal of Neuroscience, 25 (10):
3185-3192. Andras,
P, Gwyther, R, Madalinski,
AA, Lynden, SJ, Andras, A, & Young, MP (2007). Ecological network
analysis: an application to the evaluation of effects of pesticide use in an
agricultural environment. Pest Management Science, 63 (10): 943-953. Andras,
P, Idowu, O, and Periorelis,
P (2006). Fault tolerance and network integrity measures: the case of
computer-based systems. In Proceedings of AISB Convention 2006,
pp.90-97. Idowu, O.C., Lynden, S.J., Young, M.P.
and Andras, P. (2004). Bacillus Subtilis Protein
Interaction Network Analysis. In Proceedings of IEEE Computational Systems
Bioinformatics Conference, Stanford, USA, California, pp. 623-625. Periorellis, P., Idowu,
O.C., Lynden, S.J., Young, M.P., Andras, P. (2004) Dealing with complex
networks of protein interactions: A security measure. In Proceedings of
9th IEEE International Conference on Engineering of Complex Systems (ICECCS),
Bellini, P., Bohner, S.A., Steffen, B. (eds.)
pp.29-36, IEEE Computer Society. My work on systems theory
focuses on the analysis of complex social and biological systems using
methods of systems theory, building on works of Niklas
Luhmann, Francisco Varela and Humberto
Maturana. Our objective is to develop a better
understanding of how these systems function, structure themselves and evolve.
We worked on the emergence of communication inflation in these systems and on
the evolutionary adaptation processes of such systems. Recently we considered
the analysis of organisations as complex communication systems (e.g. using a
representation based on email communications) with the aim of determining
their informal structure and management decisional patterns. We also applied
the theory of abstract communication systems to biological complex systems
(protein interaction systems, neural systems) and artificial systems
(software systems). This work is done in collaboration with Dr Bruce Charlton.
I have one PhD student, Sarah Crabbe, working on a related topic (dealing
with computer anxiety).
An
abstract communication system: the system is made of communications, while
the communication units that generate these communications are not part of
the system (see more details and explanations in the papers)
Evolution
of an organisational social network Related papers: Social systems: Andras,
P (2011). Research: metrics, quality, and management implications. Accepted
for publication in Research Evaluation. Andras,
P, Charlton, BG (2009). Why are top universities losing their lead? – An
economics modelling –based approach. Science and Public Policy, 36,
317-330. Charlton
BG, Andras P (2008). ‘Down-shifting’ among top UK scientists? – The decline
of ‘revolutionary science’ and the rise of ‘normal science’ in the UK
compared with the USA. Medical Hypotheses, 70, 465-472. Charlton,
BG, Andras, P (2007). Evaluating universities using simple scientometric research-output metrics: total citation
counts per university for a retrospective seven-year rolling sample. Science
and Public Policy, 34 (8): 555-563. Andras,
P., Herald, N.D.J. and Charlton, B.G. (2007). An analysis of the dynamics of
British academic science. CS-TR-1006, School of Computing Science, University
of Newcastle, UK. Charlton BG,
Andras P (2006). Oxbridge versus the 'Ivy League": 30 year citation
trends. Oxford Magazine, 255: 16-17. Charlton BG,
Andras P (2006). Reply to May and Harvey. Oxford Magazine, 255: 18-19. Charlton BG,
Andras P (2006). Globalization in science education: An inevitable and
beneficial trend. (Editorial) Medical Hypotheses, 66: 869-873. Charlton, BG
and Andras, P (2005). Universities and social progress in modernising
societies: how educational expansion has replaced socialism as an instrument
of political reform. Critical Quarterly, 47: 30-39. Charlton, BG
and Andras, P (2005). Modernizing UK health services: ‘Short-sharp-shock’
reform, the NHS subsistence economy, and the spectre
of health care famine. Journal of Evaluation in Clinical Practice, 11:
111-119. Andras, P
& Charlton, BG (2005) Faults, errors and failures in communications: A
systems theory perspective on organisational
structure. In: Besnard, D, Gacek,
C, Jones, CB (Eds.) Structure for Dependability: Computer-Based Systems
from an Interdisciplinary Perspective, Springer-Verlag,
pp.189-216. Andras, P
& Charlton, BG (2005) Self-aware software. Will it become reality ? In: Babaoglu, O et al.
(Eds) SELF-STAR 2004, LNCS 3460, pp.229-259. Charlton BG,
Andras P (2005). The Need for a New Specialist Professional Research System
of “Pure” Medical Science. PLoS Medicine
2(8): e285. Charlton, BG
and Andras, P (2005). Medical research funding may have over-expanded and be
due for collapse. Quarterly Journal of Medicine, 98: 53-55. Andras P.
and Charlton B.G. (2004). European Science must Embrace Modernization. Nature,
429: 699. Charlton, BG
and Andras, P (2004). Campaign to revitalise
academic medicine - Is the bubble due to burst for medical research funding? British
Medical Journal, 329: 294-294. Charlton, B.
and Andras, P. (2004). The Nature and Function of Management - a perspective
from systems theory. Philosophy of Management, 3: 3-16. Andras, P.
and Charlton, B.G. (2002). Democratic Deficit and Communication Inflation in
the Health Care System. Journal of Evaluation in Clinical Practice, vol.8., no.3., pp.291-298. Charlton,
B.G. and Andras, P. (2003). Audit as a Tool of Public Policy - The Misuse of
Quality Assurance Techniques in the UK University Expansion. Accepted for
publication in: European Political Science. Andras,
P. and Charlton, B.G. (2002). Hype and Spin in Universities. Oxford
Magazine, April 2002. Andras,
P. and Charlton, B.G. (2002). Hype and Spin in the NHS. British
Journal of General Practice, vol.52., no.479.,
pp.520-521. Charlton,
B.G. and Andras, P. (2002). A System Poisoned by Deceit. The Times Higher
Education Supplement, October 4, 2002. Andras,
P. and Charlton, B.G. (2002). Unhealthy Hype. Spiked-Online, May 14, 2002, http://www.spiked-online.com/Articles/00000006D8E9.htm.
Biological systems: Andras,
P (2009). Modelling living systems. Proceedings
of European Conference on Artificial Life – ECAL 2009, LNCS 5778, pp.706-713. Charlton,
BG & Andras, P (2007). Complex biological memory conceptualized as an
abstract communication system –human long term memories grow in complexity
during sleep and undergo selection while awake. In: Kozma, R & Perlovsky, L (Eds.) Neurodynamics
of Cognition and Consciousness, Springer, pp.325-340. Andras, P,
and Andras, CD (2006). The protein interaction world hypothesis of the
origins of life. Viva Origino, 34: 40-50. Andras, P
and Andras CD (2005). Protein interaction world – an alternative hypothesis
about the origins of life. Medical Hypotheses, 64: 678-688. My recent work on agent
based modelling of complex systems aimed to analyze the evolution of
cooperation in communities of individuals using software simulations and game
theoretic analysis. We developed a simulation framework to analyze the role
of environmental uncertainty and harshness on the evolution of cooperative behavior in an agent community. The simulation
environment uses a simple parallel probabilistic formal language to describe
agent communications (see details in the papers). The results show that there
are strong relations between environmental risk and the level of cooperation
within the community. We also work on the role of communication between the
agents and the evolution of the complexity of the language that they use to
communicate. Recently we considered the use of pi-calculus for the compact
description of the agent communication language. I work on this project in
cooperation with Dr John Lazarus and Dr Gilbert Roberts.
Evolution
of the level of cooperation in function of environmental uncertainty
(the level of uncertainty is shown in the legend box) Related papers: Andras,
P (2009). Networks of artificial social
interactions. Proceedings of European Conference on Artificial Life –
ECAL 2009, LNCS 5778, pp.883-890. Andras,
P (2008). Uncertainty and communication complexity in iterated cooperation
games. In Proceedings of ALife XI, MIT
Press, pp.9-15. Andras,
P(2008). Uncertainty in iterated cooperation games.
In Proceedings of CEC 2008, pp.593-599. Andras,
P, Lazarus, J, Roberts, G (2007). Environmental adversity and uncertainty
favour cooperation. BMC Evolutionary Biology, 7:240 (30 November
2007). Andras,
P, Lazarus, J, Roberts, G, and Lynden SJ (2006). Uncertainty and Cooperation:
Analytical Results and a Simulated Agent Society. JASSS – Journal of
Artificial Societies and Social Simulation, 9:1/7. Andras,
P & Lazarus, J (2004) Cooperation, Risk and the Evolution of Teamwork.
In: Gold, N (Ed.) Teamwork: Multi-Professional Perspectives, Palgrave,
pp.56-77. Andras,
P., Roberts, G., Lazarus, J. (2003) Communication Complexity, Environmental
Risk, and Cooperation. In Alonso, E., Kudenko, D.
and Kazakov, D. (eds.) Adaptive Agents and
Multi-Agent Systems, Springer-Verlag, Berlin,
pp.49-65. Computational intelligence
I am interested in using
machine learning and computational intelligence tools to support decision
making in various contexts. In particular I am interested in particular in
using support vector machines and kernel methods in general, but also in
using various other methods as well (e.g. constrained Boltzmann machines). I
collaborate with the Social Inclusion through the Digital Economy (SIDE)
project (Prof Paul Watson, Prof Patrick Olivier, Prof
Aad van Moorsel - Newcastle University) in using such techniques to support
medical decision making and the participation in group decision making. In
the case of medical decision we look at the possibilities of objective
assessment of disease stage and progression in case of Parkinson's disease
patients using accelerometers built into wearable computing devices. The
group decision making project looks at the use of supporting the involvement
of group members in decision making using interactive touch sensitive tabletop
devices. I also collaborate with the Prof Anuar Dusembaev (Kazakh National
University, Almaty, Kazakhstan) on a project about
the use of machine learning methods to support financial investment decision
making. I also collaborate with Dr Graham Morgan (Newcastle University) on
applications of AI methods to computer games. Currently I have two PhD
students (Nils Hammerla and Kanida Sinmai) working in this area, and a PhD
student of Prof Dusembaev (Mikhail Grishko) also collaborates with my group.
I also work with Su-Yang Yu on computational intelligence applications to
computer games; he is a PhD student of Dr Jeff Yan under joint supervision.
Simultaneous
recording of acceleration data from left and right arms - the figure shows
acceleration values in the two horizontal dimensions only I am involved in a collaboration about the use of machine learning and
bioinformatics tools in mitochondrial genomics. We had recently a project
that looked at the use of support vector machines to make predictions about
localisation of proteins (in particular mitochondrial proteins) using the
combinations of various prediction methodologies. We demonstrated the
rigorous application of support vector machines to this problem highlighting
potential issues of misinterpretation of prediction results. This work showed
that simply adding more prediction methods into the pool of combined methods
does not necessarily improve the overall prediction performance of the
combined method. This work is done in collaboration with Prof Patrick
Chinnery (Newcastle University). I have one PhD student under joint
supervision, Ms Charlotte Blackburn, who works on this topic. Previously I was involved
in a project about the development of e-science tools for intracellular
imaging. This project built software tools that allow the consistent
manipulation of large volumes of microscopy data. We used these tools to
analyse the inducement and development of apoptosis in cancer cells.
Confocal image of cell nuclei
3D
rendering of a set of simulated confocal images of
nuclei Earlier I led the Newcastle
part of a large multi-university project led by the University of Edinburgh.
The Newcastle part of the project was about developing GRID middleware to
handle large neuroscience text databases and support their user in automated
organisation and management of the text collection. This work continues in
the form of collaboration with Prof Paul Watson on the sharing and management
of large volumes of neuroscience data using GRID-enabled middleware. Related papers: Yu,
S-Y, Hammerla, N, Yan, J, Andras, P (2012). A statistical aimbot
detection method for online FPS games. In proceedings of the International
Joint Conference on Neural Networks (IJCNN 2012). Lythgow,
KT, Hudson, G, Andras, P, Chinnery, PF (2011). A critical analysis of the
combined usage of protein localization prediction methods: increasing the
number of independent data sets can reduce the accuracy of predicted
mitochondrial localization. Mitochondrion, 11:444-449. Hammerla,
N, Plötz, T, Andras, P, Olivier, P (2011). Assessing motor performance with
PCA. In Proceedings of the International Workshop on Frontiers in Activity
Recognition using Pervasive Sensing. Fitch,
S, Jackson, TR, Andras, P (2008). Unsupervised segmentation of cell nuclei
using geometric models. In Proceedings of 5th IEEE International Symposium on
Biomedical Imaging – From Nano to Macro,
pp.728-731. Andras,
P and Idowu, O (2005). Kohonen
networks with graph-based augmented metrics. In Proceedings of WSOM’2005,
pp.179-186. Complex nonlinear neural dynamics - CNS / IJCNN Workshops
I was one of the organisers
of the complex neural dynamics workshops from 2001 - 2007. These workshops
brought together theoretical and experimental neuroscientists with interest
in dynamics and complex neural activity in order to facilitate the
communication and collaboration between them. Between 2001- 2004 the
workshops were organised as part of the annual Computational Neuroscience
Symposium (CNS), after 2005 we organised the workshops as part of the
International Joint Conference on Neural Networks (IJCNN). 2006 Workshop
(Vancouver, Canada) 2005 Workshop
(Montreal, Canada) 2003 Workshop
(Alicante, Spain) |
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was last updated 05/12/11