Revisiting Algorithmic Lateral Inhibition and Accumulative Computation

Certainly, one of the prominent ideas of Professor Mira was that it is absolutely mandatory to specify the mechanisms and/or processes underlying each task and inference mentioned in an architecture in order to make operational that architecture. The conjecture of the last fifteen years of joint research of Professor Mira and our team at University of Castilla-La Mancha has been that any bottom-up organization may be made operational using two biologically inspired methods called "algorithmic lateral inhibition", a generalization of lateral inhibition anatomical circuits, and "accumulative computation", a working memory related to the temporal evolution of the membrane potential. This paper is dedicated to the computational formulations of both methods, which have led to quite efficient solutions of problems related to motion-based computer vision.

[1]  Antonio Fernández-Caballero,et al.  A Model of Neural Inspiration for Local Accumulative Computation , 2003, EUROCAST.

[2]  Antonio Fernández-Caballero,et al.  Length-speed ratio (LSR) as a characteristic for moving elements real-time classification , 2003, Real Time Imaging.

[3]  José Mira,et al.  ALGORITHMIC LATERAL INHIBITION AS A GENERIC METHOD FOR VISUAL INFORMATION PROCESSING WITH POTENTIAL APPLICATIONS IN ROBOTICS , 2002 .

[4]  Senén Barro,et al.  Local Accumulation of Persistent Activity at Synaptic Level: Application to Motion Analysis , 1995, IWANN.

[5]  Antonio Fernández-Caballero,et al.  Segmentation from motion of non-rigid objects by neuronal lateral interaction , 2001, Pattern Recognit. Lett..

[6]  E. Rolls,et al.  A Neurodynamical cortical model of visual attention and invariant object recognition , 2004, Vision Research.

[7]  Stefano Bistarelli,et al.  Representing Biological Systems with Multiset Rewriting , 2003 .

[8]  Francisco Sandoval,et al.  From Natural to Artificial Neural Computation , 1995 .

[9]  J. Mira Preface: Mechanisms and formal tools in neural modeling: analytic and synthetic approaches , 2007 .

[10]  Antonio Fernández-Caballero,et al.  Visual surveillance by dynamic visual attention method , 2006, Pattern Recognit..

[11]  María Teresa López Bonal Modelado computacional de los mecanismos de atención selectiva mediante redes de interacción lateral , 2004 .

[12]  Dietmar Heinke,et al.  Top-down guidance of visual search: A computational account , 2006 .

[13]  Moonis Ali,et al.  Innovations in Applied Artificial Intelligence , 2005 .

[14]  Miguel Angel Fernández Graciani Una arquitectura modular de inspiracion biológica con capacidad de aprendizaje para el análisis de movimiento en secuencias de imagen en tiempo real , 1996 .

[15]  Antonio Fernández-Caballero,et al.  Algorithmic lateral inhibition method in dynamic and selective visual attention task: Application to moving objects detection and labelling , 2006, Expert Syst. Appl..

[16]  Refractor Vision , 2000, The Lancet.

[17]  Antonio Fernández-Caballero,et al.  Dynamic visual attention model in image sequences , 2007, Image Vis. Comput..

[18]  Antonio Fernández-Caballero,et al.  On motion detection through a multi-layer neural network architecture , 2003, Neural Networks.

[19]  Roberto Moreno-Díaz,et al.  A Neurocybernetic Model of Modal Co‐operative Decisions in the Kilmer‐McCulloch Space , 1989 .

[20]  Allen Newell,et al.  The Knowledge Level , 1989, Artif. Intell..

[21]  José Mira Mira,et al.  What Can We Compute with Lateral Inhibition Circuits? , 2001, IWANN.

[22]  Antonio Fernández-Caballero,et al.  Lateral interaction in accumulative computation: a model for motion detection , 2003, Neurocomputing.

[23]  José Mira,et al.  Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence , 2001, Lecture Notes in Computer Science.

[24]  Antonio Fernández Caballero Modelos de interacción lateral en computación acumulativa para la obtención de siluetas , 2001 .

[25]  Antonio Fernández Caballero,et al.  Stereovision depth analysis by two-dimensional motion charge memories , 2007 .

[26]  G. Humphreys,et al.  Computational models of visual selective attention: A review , 2005 .

[27]  David Marr,et al.  VISION A Computational Investigation into the Human Representation and Processing of Visual Information , 2009 .

[28]  E. Rolls,et al.  Attention, short-term memory, and action selection: A unifying theory , 2005, Progress in Neurobiology.

[29]  Antonio Fernández-Caballero,et al.  Motion features to enhance scene segmentation in active visual attention , 2006, Pattern Recognit. Lett..

[30]  José Mira Mira,et al.  Symbols versus connections: 50 years of artificial intelligence , 2008, Neurocomputing.

[31]  Antonio Fernández-Caballero,et al.  Knowledge modelling for the motion detection task: the algorithmic lateral inhibition method , 2004, Expert Syst. Appl..

[32]  José R. Álvarez,et al.  Bio-Inspired Modeling of Cognitive Tasks , 2008 .

[33]  Antonio Fernández-Caballero,et al.  Motion-Based Stereovision Method with Potential Utility in Robot Navigation , 2005, IEA/AIE.

[34]  José Mira Mira,et al.  On how the computational paradigm can help us to model and interpret the neural function , 2007, Natural Computing.

[35]  Antonio Fernández-Caballero,et al.  Spatio-temporal shape building from image sequences using lateral interaction in accumulative computation , 2003, Pattern Recognit..