NEW: Two Papers related to the
C-MANTEC
Constructive Neural Network have been recently accepted (See refs A28
& A29)
Versión en
Castellano
I am Associate
Professor at the Department of Computer Science, of
the University of Málaga, Spain.
Born in Tucumán,
North
West
of
Argentina,
I
did
my
secondary
school
at
Gymnasium
UNT. I finished my undergraduate studies in Physics and also a PhD at FaMAF (Universidad Nacional de Córdoba,
Argentina), where I studied the learning and
generalization properties of feedforward neural networks.
After obtaining my PhD Thesis in August 2000, I went
as a postdoctoral
fellow to the Cognitive Neuroscience Sector at SISSA, Trieste (Italy), where I worked
with Prof. Alessandro Treves
on
neural
networks
models
for
facial
expression
recognition
and
also
on
methods
based
on
information
theory applied to the analysis of neuronal
recordings.
In February 2002, I joined the Oxford group in computational
neuroscience where my research focused on understanding the coding
used by neurons in the inferior temporal visual cortex. In this area of
the brain, neurons have a clear and invariant response to objects and
faces.
Since May 2005, I am in Málaga, Spain
within the group
of Computational Intelligence
and Vision doing research on the three following areas described
in detail below. I started working in Spain
as a fellow
of
the
"Ramón
y
Cajal"
Programme of the Spanish Ministry of Education and Science, to become
Associate professor in 2010.
Neural
networks: I am particularly interested on the ability
of neural networks to generalize to novel inputs. I recently
proposed a measure for the complexity
of
the
Boolean
functions related to the generalization ability that
can be obtained when the functions are implemented on neural networks
and with some collaborators (Dr. J.M. Jerez (Málaga) , Dr. M. Anthony
(LSE) and Dr. S.A.
Cannas (Córdoba) we are also exploring interesting extensions and links
with physics. We analyze the computational capabilities of
different Neural Networks architectures, developing methods that will
help to improve and understand the process of generalization. We
are actually working on constructive
learning algorithms that permit
to build almost optimal neural architectures, avoiding the problem of
selecting the proper architecture for a given problem. Also these
constructive algorithms facilitate the knowledge (or rule) extraction
procedure that helps to understand how the network is solving the
problem. We are also developing
algorithms that extract a linearly separable subset of examples from a
non-linearly separable problem, as this is related to the constructive
algorithms. We have organized in collaboration with Dr. D.
Elizondo a
special session on Constructive Neural Networks algorithms
in ICANN`08
in Prague in September 2008. We have recently edited a book about
Constructive
Neural Networks, published by Springer
in the Computational Intelligence Series Collection. Constructive
Neural Networks are an alternative to the problem of selecting a proper
neural architecture for a given problem and we have recently developed
two algorithms: DASG & C-Mantec. DASG is a decomposition algorithm
that gets as input a Boolean function (or data) and decompose it
according to the influence of its variables to obtain a set of linearly
separable functions, that put altogetether in a neural network can
compute the original function. This work was published in IEEE TCAS-I
in 2008 [23]. C-Mantec, our latest development, is a very powerful
constructive method that utilizes competition between neurons to create
very compact architectures with good generalization capabilities. The
main work related to the algorithm has been recently accepted in the
NEURAL NETWORKS journal (a beta-version of C-mantec can be
available upon request).
Survival Analysis:
Another line of research is the application of neural networks,
statistics and artificial intelligence techniques to the problem
of prognosis in Medicine. In collaboration with Dr. J.M. Jerez
(Málaga), we organized a special session entitled "ANN
and
Prognosis
in
Medicine" at the European Symposium on Artificial
Neural Networks (ESANN, 2005).
Recently,
one
of
our
works
in
collaboration
with
Dr.
E.
Alba
has
been
accepted
for
publication in the journal Breast Cancer
Research and Treatment. In this line of research, We are also
collaborating with Dr. V. Hadjianastiassiou (Oxford, London) applying
neural networks to the problem of survival prediction in Abdominal
Aortic Aneurism. In a recent work (2010) we have analyzed the
imputation of missing data using several machine learning and
statistical techniques. We are currently moving towards the
analysis of microarray data as the Málaga Hospital plans to obtain this
kind of data in the near future.
Neuroscience: My area of
research lies within the field of Computational Neuroscience,
where my interest is in neuronal coding, i.e., how the
information is encoded and transmitted in the brain. I analyze
recordings
using information
theory, developing
new methods and performing simulations to test for different
hypothesis. We analyze the coding of information under natural
conditions to analyze whether most of the information is in the "rate
code" or whether correlations play an important role. We have
also analyzed the role of
correlation between the responses of the cells and also quantified the
sparseness of the representation in the inferior temporal visual cortex
cells [A20]. A more recent work, [A24], analyzes how the information
is encoded in the voxels recorded from fmri signals in a decision task,
and a comparison to neurophysiological data is carried out.
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Books:
[B2] 
Constructive
neural
networks. L.Franco,
D.
Elizondo and J.M. Jerez (Eds.), Springer (2010). Series: Studies in Computational
Intelligence,
Vol. 258, ISBN:
978-3-642-04511-0. [Springer
site] [More
Info]
[B1] Escenario de Emisiones
de Gases de Efecto Invernadero en la Ciudad de Málaga. I.
Molina
Conde,
J.M.
Jerez
Aragonés,
L.
Franco,
E.M.
Sánchez
Teba
and
P.
Marín
Cots.
Ediciones
del
Ayuntamiento
de Málaga, España. ISBN:
978-84-692-0913-4. (2009).
Journals :
[A29] D.
Urda, E. Cañete, J.L. Subirats, L. Franco, L. Llopis, and J.M. Jerez. Energy efficient reprogramming in WSN
using constructive neural networks. International
Journal of Innovative Computing, Information and Control,
8.
(2012). [pdf] (Accepted)
[A28] J.L. Subirats, L. Franco and J.M. Jerez. C-Mantec: a novel constructive neural
network algorithm incorporating competition between neurons. Neural
Networks, 26, pp 130-140, (2012). [pdf]
[A27] J.P. Neirotti and L. Franco. Computational capabilities of multilayer
committee machines. Journal of Physics A: Mathematical &
Theoretical, 43, 445103, (2010). [pdf]
[A26] J.M. Jerez, I. Molina, P.J. García-Laencina, E.
Alba, N. Ribelles, M. Martín and L. Franco. Missing Data Imputation Using Statistical
and Machine Learning Methods in a Real Breast Cancer Problem. Artificial
Intelligence
in
Medicine, 50, pp.105-115, (2010). [pdf]
[A25] I. Gómez, L. Franco and J.M. Jerez. Neural Network Architecture
Selection: Can function complexity help?. Neural
Processing
Letters, 30, pp. 71-87, (2009).
[pdf]
[A24] E.T. Rolls, F. Grabenhorst and L.
Franco. Prediction of Subjective
Affective State
from Brain Activations . Journal
of
Neurophysiology, 101, pp.1294-1308, (2009). [pdf]
[A23] J.L. Subirats, J.M.
Jerez and L. Franco. A New Decomposition Algorithm for
Threshold Synthesis and Generalization of Boolean Functions. IEEE Transactions on Circuits and
Systems I, 55, pp. 3188-3196, (2008). [pdf]
[A22] L. Franco, J.L. Subirats and
J.M.
Jerez. MaxSet: An Algorithm for Finding a Good
Approximation for the Largest Linearly Separable Set. Lecture
Notes in Computer Science, 4668, pp. 648-656, (2007). [pdf]
[A21] L. Franco, J.L. Subirats, I. Molina, E. Alba
and
J.M.
Jerez. Early breast cancer prognosis
prediction and rule extraction using a new constructive neural
network algorithm. Lecture
Notes
in
Computer
Science, 4507, pp. 1004-1011, (2007). [pdf]
[A20] L. Franco, E.T. Rolls, N.C. Aggelopoulos and
J.M.
Jerez. Neuronal selectivity,
population sparseness, and ergodicity in the inferior temporal
visual
cortex. Biological
Cybernetics, 96, pp. 547-560, (2007). [pdf]
[A19] E.T. Rolls, L. Franco, N.C. Aggelopoulos and J.M.
Jerez. Information in the first
spike, the order of spikes, and the
number of spikes provided by neurons in the inferior temporal visual
cortex. Vision
Research, 46, pp. 4193-4205, (2006). [pdf]
[A18] J.L. Subirats, I. Gomez, J.M. Jerez and L. Franco. Optimal Synthesis of Boolean Functions by
Threshold Functions. Lecture Notes in Computer Science,
4131, pp. 983-992, (2006). [pdf]
[A17] I. Gómez, L. Franco, J.L. Subirats and J.M. Jerez. Neural Networks Architecture Selection:
Size Depends on Function Complexity. Lecture Notes in
Computer Science, 4131, pp. 122-129,
(2006). [pdf]
[A16] L. Franco. Generalization
ability of Boolean functions implemented in feedforward neural
networks. Neurocomputing, 70, pp. 351-361,
(2006). [pdf]
[A15] L. Franco and M. Anthony. The influence of oppositely classified
examples on the generalization complexity of Boolean functions. IEEE
Transactions
on
Neural
Networks, 17, pp. 578--590, (2006). [pdf]
[A14] V.G. Hadjianastassiou, L. Franco, J.M.
Jerez, I.E. Evangelou, D.R. Goldhill, P.P. Tekkis, L.J. Hands. Optimal prediction of
mortality after abdominal aortic aneurism repair with
statistical models. Journal
of
Vascular
Surgery, 43, pp. 467--473. (2006). [pdf]
[A13]
J. Jerez, L. Franco, E. Alba, A. Llombart-Cussac, A.
Lluch, N. Ribelles, B. Munárriz and M. Martín. Improvement of Breast Cancer Relapse
Prediction in High Risk Intervals Using Artificial Neural Networks. Breast Cancer Research and
Treatment, 94, pp. 265--272. (2005). [pdf]
[A12] E.T. Rolls, L. Franco and S.M.
Stringer. The perirhinal cortex and long term familiarity memory. Quarterly
Journal
of
Experimental
Psychology,
Section
B, 58, pp. 234--245.
(2005). [pdf]
[A11] E.T. Rolls, J-Z. Xiang and L. Franco. Object,
space,
and
object-space
representations
in
the
human
hippocampus. Journal
of
Neurophysiology, 94, pp. 833--844. (2005). [pdf]
[A10] L. Franco, J.M. Jerez, and J.M. Bravo-Montoya. Role of Function Complexity and Network
Size in the Generalization Ability of Feedforward Networks.
Lecture Notes in Computer
Science, 3512, pp. 1--8. (2005). [pdf]
[A9] N.C. Aggelopoulos, L. Franco and E.T. Rolls. Object
perception in natural scenes: encoding by inferior temporal cortex
simultaneously recorded neurons. Journal of Neurophysiology,
93,
pp.
1342--1357.
(2005).
[pdf]
[A8] L. Franco, E.T. Rolls, A. Treves and N.C.
Aggelopoulos. The use of decoding to analyze the contribution to
the information of the correlations between the firing of
simultaneously recorded neurons. Experimental Brain Research,
155,
pp.
370--384.
(2004).
[pdf]
[A7] E.T. Rolls, N.C. Aggelopoulos, L. Franco, and A.
Treves. Information encoding in the
inferior temporal visual cortex: contributions of the firing rates and
the correlations between the firing of neurons. Biological
Cybernetics, 90, pp. 19--32. (2004). [pdf]
[A6] L. Franco and S.A. Cannas. Non-glassy ground
state in a long-range antiferromagnetic frustrated model in the
hypercubic cell. Physica A, 332, pp. 337--348. (2004). [pdf]
[A5] J.M. Jerez, L. Franco and I. Molina. CBA
generated receptive fields implemented in a Facial expression
recognition task. Lecture Notes in Computer Science,
2686, pp. 734--741, (2003). [pdf]
[A4] E.T. Rolls, L. Franco, N.C. Aggelopoulos, and S. Reece.
An information theoretic approach to the contributions of the
firing rates and correlations between the firing of neurons. Journal
of
Neurophysiology, 89, pp. 2810--2822. (2003). [pdf]
[A3] L. Franco and S.A. Cannas. Generalization
Properties of Modular Networks: Implementing the Parity Function. IEEE
Transactions
on
Neural
Networks, 12, pp. 1306--1313. (2001). [pdf]
[A2] L. Franco and S.A. Cannas. Generalization and
Selection of Examples in Feed-Forward Neural Networks. Neural
Computation, 12, pp. 2405-- 2426. (2000). [pdf]
[A1] L. Franco and S.A. Cannas. Solving Arithmetic
Problems Using Feed-Forward Neural Networks. Neurocomputing,
18,
pp.
61--79.
(1998). [pdf]
Proceedings, Lecture Notes, Book chapters & other publications
[B15] Y. Couce, L.
Franco, D. Urda, J.L. Subirats and J.M. Jerez. Hybrid (Generalization-Correlation)
method for feature selection in high dimensional DNA microarray
prediction problems. Lecture
Notes in Artificial
Intelligence, 6692, pp. 202-209.
Proceedings of the IWANN
2011 conference, Torremolinos, Spain, June 8-10, (2011). ISBN: 978-3-642-21498-1
[B14] D. Urda, J.L. Subirats, L.
Franco, and J.M. Jerez.
Constructive neural networks to predict breast cancer outcome by using
gene expression profiles. Lecture Notes in Artificial
Intelligence, 6096, pp. 317-326. Proceedings of the IEA-AIE,
Córdoba, Spain, June 1-3, (2010). ISBN: 978-3-642-13021-2. [pdf]
[B13] I. Gómez, L. Franco, J.M. Jerez & J.L. Subirats. Extension of the generalization complexity
measure to real valued input data sets. Lecture Notes in Computer Science,
6063,
pp.
86-94.
Proceedings
of
the
ISNN'2010,
Shanghai,
China,
June
6-9, (2010). ISBN: 978-3-642-13277-3. [pdf]
[B12] N. Ribelles, J.M. Jerez, D.
Urda, J.L. Subirats, A. Marquez, C. Quero, E. Torres, L. Franco
& E. Alba. Galén:
Sistema de Información para la gestión y
coordinación de procesos en un servicio de Oncología. RevistaeSalud,
6 (21) (2010). [pdf]
[B11] J.L. Subirats, L. Franco,
I.
Molina and J.M. Jerez. Active
learning using a constructive neural network algorithm. Lecture Notes in Computer Science,
5164,
pp.
803-811.
Proceedings
of the International Conference in Artificial Neural Networks
(ICANN), Praga, República Checa, 3-6 September. (2008). [pdf]
[B10] J.L. Subirats, L. Franco, I.
Gómez and J.M. Jerez. Computational
capabilities of feedforward neural networks: the role of the output
function. Proceedings
of the XII CAEPIA'07,
Salamanca, Spain, vol. II, pp. 231-238. ISBN:
978-84-611-8848-2.
November 12-16
(2007). [pdf]
[B9] L. Franco, J.L. Subirats, M.
Anthony and J.M. Jerez. A New
Constructive Approach for the Set of Linearly Separable Functions.
Proceedings of the IEEE
International Joint Conference on Neural Networks (IJCNN'06 - WCCI'06),
Vancouver,
Canada,
pp.
9541-9546.
ISBN:
0-7803-9490-9. July 16-21
(2006). [pdf]
[B8] J.M. Jerez, I. Molina, L. Franco,
and
J.L. Subirats. Missing Data
Imputation in Breast Cancer Prognosis. Proceedings of the IASTED
International Multiconference in Biomedical Engineering,
Innsbruck, Austria, pp. 323-328. ISBN: 0-88986-576-0.
February 15-17 (2006). [pdf]
[B7] L. Franco, J.M. Jerez and E. Alba. Artificial neural networks and prognosis
in medicine. Survival analysis in breast cancer patients. Proceedings of the European
Symposium on Artificial Neural Networks (ESANN'05), Bruges,
Belgium, pp. 91-102. ISBN 2-930307-05-6. April 27-29 (2005). [pdf]
[B6] L. Franco and M. Anthony. On a Generalization complexity measure for
Boolean functions. Proceedings
of
the
IEEE
International
Joint
Conference
on
Neural
Networks
(IJCNN'04),
Budapest,
Hungary,
pp. 973-978. ISBN 0-7803-8359. July 25-28 (2004). [pdf]
[B5] L. Franco and J.M. Jerez. Modularity: a Natural Way of Improving
Generalization. Proceedings
of
the
3rd
WSES
International
Conference
on
Neural
Networks,
Interlaken, Switzerland. ISBN: 960-8052-48-3. February 11-15.
(2002). [abstract]
[B4] L. Franco and A. Treves. A Neural Network Face Expression
Recognition System using Unsupervised Local Processing. Proceedings of the Second
International Symposium on Image and Signal Processing and Analysis
(ISPA 01), Croatia, pp. 628-632. ISBN: 953-96769-4-0. June
19-21. (2001). [pdf]
[B3] L. Franco, A. Montagnini and A. Treves. A modular Neural Network for Facial
Expression Recognition. Proceedings of the International
Conference of Cognitive Modeling (ICCM). Halifax, VA. July 26–28
(2001).
[B2] L. Franco and S.A. Cannas. Improving Network Generalization through
Selection of Examples. Proceedings
of
IV
Argentinian
Meeting
on
Computer
Science
(CACIC
98).
Neuquén. Argentina, pp. 373-382. October 26-31 (1998). [pdf]
[B1] L. Franco and S. A. Cannas. Energy landscape and learning on a neural
network for the sum problem. Proceedings of the Second
International Workshop on (semi) numerical techniques in polynomial
equation solving TERA 97, Trabajos de Matemática serie B, FaMAF,
Universidad Nacional de Cordoba, Argentina, pp. 28-33. September 4-6
(1997). [pdf]
Grants supporting our research activities:
[G4]
P10-TIC-5770,
Junta
de
Andalucía:
Modelo
Neurocomputacional
de confort térmico en espacios públicos
urbanos. IP: Leonardo Franco.
[G3]
TIN2008-04985,
MICIIN
:
Nuevas
estrategias
en
el
diseño
de
sistemas
neurocomputacionales.
Aplicación
al
procesamiento
de
información en
oncología. 89.540€. IP: Leonardo Franco.
[G2]
A/6030/06 y A/12805/07, AECI: Inferencia Bayesiana mediante redes
neuronales y su aplicación a biomedicina. 42.500 €. IP: Leonardo Franco.
[G1]
Proyecto
Ramón
y
Cajal,
MICIIN,
Financiación
adicional:
Redes
Neuronales:
Estudio
de
la
complejidad
de
generalización
y aplicaciones
a la predicción de recidiva de cáncer de mama. 12.000€. IP: Leonardo
Franco.
[GE4]
TIN2010-16556, MICIIN, Sistemas inteligentes bioinspirados
aplicados a medicina personalizada, 92.000 €. IP: José M. Jerez
Aragonés. (10/01/2011
---31/12/2013)
[GE3]
P08-TIC-04026, Junta de Andalucía, Proyectos de Investigación de
Excelencia: Diseño de métodos constructivos en sistemas
neurocomputacionales y aplicación a minería de datos en
Oncología, 225.635,68 €. IP: José M. Jerez Aragonés. (12/01/2008 ---11/01/2012)
[GE2] TIN2005-02984 , MICIIN:
: Diseño de un sistema
neurocomputacional para predicción de recidiva en cáncer de mama
operable . Estudio de la relación complejidad-generalización en redes
neuronales artificiales. 87.000€. IP: José M. Jerez Aragonés
[GE1]
P06-TIC-01615, Junta de Andalucía: Sistemas de
teledetección Inteligentes. 190.536 €. IP: José Muñoz Perez
Thesis:
[T2] L. Franco. Learning
and Generalization Properties of Feed-Forward Neural Networks. Ph.D.
Thesis. Biblioteca FaMAF. Universidad Nacional de Córdoba. Argentina.
(2000). [pdf]
(In Spanish) ,
[T1] L. Franco. Study of
Learning Properties of Feed-Forward Neural Networks using Generalized
Simulated Annealing algorithms. Thesis submitted within the
requirements to obtain the degree of Licenciado in Physics. FaMAF
Library. Argentina. (1995). [abstract]
Some Recent & Future Presentations:
"Nuevas estrategias en el aprendizaje
de Redes Neuronales mediante el uso de algoritmos Constructivos", FACET
UNT, Tucumán, Argentina, 5th August 2011.
"Extension of the Generalization
Complexity Measure to Real Valued Input Data Sets", ISNN 2010,
Shanghai, China, 7th June 2010.
"Information encoding in the inferior
temporal code", Max Planck Institute for Cybernetics, Tubingën, January
23rd 2009.
"Active learning using a constructive
neural network algorithm", ICANN' 2008, Prague, Czech Republic,
September 6th, 2008.
"Codificación
de
la
información
en
la
corteza
visual: Experimentos
y
modelos
relacionados
con
el
reconocimiento
de
objetos " X
Neurotaller Argentino de Neurociencia, Huerta Grande, Córdoba,
Argentina, 11-13 Abril de 2008.
"Early breast
cancer
prognosis
prediction and rule extraction using a new constructive neural
network algorithm". IWANN 2007, San Sebastián, España, June 2007.
"Sensory processing",
Curso de Neurociencia computacional en la 2da Escuela Argentina de
Biología y Matemática, BIOMAT 2007, La Falda, Córdoba, Argentina,
29 Junio -7 Julio de 2007.
"Modelos de
Predicción" (Predictive models). Curso de Doctorado en Oncología,
Facultad de Medicina, Málaga, April 2007.
"Codificación Neuronal
y el reconocimiento de objetos en la corteza infero-temporal",
Jornadas de Ciencia y Sociedad a los 50 años de FaMAF, Córdoba,
Argentina, Diciembre de 2006.
"A new constructive
approach for
the set of linearly separable functions", World Conference on
Computational Intelligence (WCCI) , IJCNN'07, Vancouver, Canada,
July 2006.
"Synthesis of Boolean functions by
threshold circuits", Instituto Nacional de Microelectrónica de Sevilla,
Sevilla, July 2006.
"Generalization
Properties and Computational Capabilities of Feed-forward Neural
Networks", Neural Computing Research Group, Aston University, May 2006.
"The
Importance of function complexity on the neural network
architecture selection process ", Centre for Computational
Intelligence, DeMontfort
University, May
2006.
"Modelos de
Predicción" (Predictive models). Curso de Doctorado en Oncología,
Facultad de Medicina, Málaga, May 2006.
"Information transmission in the
inferior temporal visual cortex".
Cosyne'06. Salt Lake City, March 2006.
"Role of function
complexity and network size in the generalization ability of
feedforward networks". IWANN'05, Vilanova y la Geltru. June 2005.
"Information theory
and population coding" , MSc in Neuroscience Lecture. University
of Oxford, February 2004.
"The
generalization complexity of Boolean functions", Universidad de
Málaga, November 2003.
Master and Student Projects (PFC) supervised:
MSc3:
Aplicación de algoritmos de clustering y clasificación a datos de
pacientes operados de cáncer de mama. Sindy Rengifo, 2011.
MSc2:
Selección de variables relevantes en datos de microarrays deADN, Yasel
Couce Sardiñas. 2011.
MSc1: Análisis
del ``Memory-Prediction FrameWork'' para aplicaciones inteligentes,
Diego Rosado. Junio de 2008.
PFC6:
Desarrollo e implementación de un nuevo algoritmo para la
discretización de datos usando redes neuronales y su aplicación a
problemas de
clasificación, Juan Jesús Carneros Godoy, Diciembre de 2011.
PFC5:
"Implementación
de
un
algoritmo
para
el
tratamiento
de
datos
de
tipo
real,
ordinal
y
categórico
y su transformación en datos binarios. Aplicación al uso del
algoritmo constructivo DASG en problemas de clasificaición." Esther Ramos Retamero, Julio
de 2009.
PFC4:
"Método
para evitar el sobre-entrenamiento en el algoritmo DASG, su
implementación en el entorno WEKA y su aplicación a problemas de
predicción de cáncer de mama", Raúl Pérez Arrebola, Junio de 2009.
PFC3:
Sistema integral para la coordinación de procesos en SOM. Daniel
Urda
Muñoz,
2008.
PFC2: "Estudio
de la capacidad de generalización en redes neuronales", José Luis
Subirats Contreras, Marzo de 2006.
PFC1:
"Predicción
de
recidiva
de
cáncer
de
mama
usando
redes
neuronales
artificiales:
aplicaciones
a
la
base
de
datos del GEICAM", Eduardo Jerez,
Junio de 2005.
External Collaborators:
Prof. Emilio Alba, Hospital Universitario de
Málaga, Málaga, Spain.
Prof. Martin Anthony,
Centre
for
Discrete
Mathematics,
London
School
of
Economics
(LSE),
London, United Kingdom.
Prof.
Sergio
A.
Cannas. FaMAF, Córdoba, Argentina.
Dr. David Elizondo,
De
Montfort
University,
Leicester,
United
Kingdom.
Dr. Vassilis Hadjianasstassiou, St.
Thomas Hospital, London, United Kingdom.
Prof.
Paulo Lisboa, John Moores University, Liverpool, United Kingdom.
Dr. Juan P. Neirotti, Aston University, United
Kingdom.
Prof. Edmund T.
Rolls, University of Oxford, Oxford, United Kingdom.
Prof.
Alessandro Treves, SISSA, Trieste, Italy.
Ing. Gustavo Juárez, FACET UNT, Tucumán,
Argentina.
Local Collaborators (Members of the ICB research group) :
Dr. José M. Jerez Aragonés
Dr. Ignacio Molina.
Ing. Iván Gómez
Ing. José L. Subirats
Ing. Daniel Urda Muñoz
MSc Yasel Couce Sardiñas
Ing. Francisco Ortega
miscellaneous
Invited
speaker to Betcon 2012, La
Paz, Bolivia
IWANN'2011
will take place in Torremolinos, Málaga (I am part of the local
committee).
IEEE FOCI 2011
will be in Paris (I am part of the Programme committee).
Special
Session on
Constructive Neural Networks at ICANN'08
X Taller
Argentino de Neurociencias. Nota del
Diario La Voz del Interior
(Córdoba, Argentina)
2nd Argentine school on Mathematics and Biology, BIOMAT-07
An interview
(in spanish) for Málaga Hoy
about our
research programme on Breast Cancer (2005).
An Interview
(in spanish) for La Gaceta
about my research in Oxford (2002).
APARU
Association
of
Professionals
from
Argentina
working
in
the
UK.
