A Neural Basis Computational Model of Emotional Brain for Online Visual Object Recognition

In this study, we propose a novel visual object recognizer inspired by the human brain’s emotional learning. In the proposed computational model, the visual information is transferred through the ventral visual pathway to the amygdala, which is responsible for emotional visual stimuli. In the model, the orbitofrontal cortex (OFC) evaluates the amygdala response and tries to prevent inappropriate answers. The proposed visual recognizer is based on threshold logic units defined on the neural models of the amygdala and the OFC. According to the experimental results, the presented model learns the visual patterns quickly and shows higher performance than the brain emotional learning-based pattern recognizer (BRLPR) and multilayer perceptron (MLP) with Levenberg–Marquardt backpropagation (BPG) learning algorithm, in which the adaptive neurofuzzy inference system (ANFIS) cannot be trained because of the curse of dimensionality. The main features of the proposed model are the lower time and spatial complexity. Hence, it can be utilized in real-time visual object recognition.

[1]  L. Pessoa,et al.  Emotion processing and the amygdala: from a 'low road' to 'many roads' of evaluating biological significance , 2010, Nature Reviews Neuroscience.

[2]  Adnan Khashman,et al.  Modeling cognitive and emotional processes: A novel neural network architecture , 2010, Neural Networks.

[3]  Caro Lucas,et al.  Real-time embedded emotional controller , 2010, Neural Computing and Applications.

[4]  Thomas Serre,et al.  Object recognition with features inspired by visual cortex , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[5]  Ehsan Lotfi,et al.  Supervised brain emotional learning , 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

[6]  K. Micheva.  Excitatory and Inhibitory Synapses , 2010 .

[7]  Rick Richardson,et al.  Phosphorylation of mitogen-activated protein kinase in the medial prefrontal cortex and the amygdala following memory retrieval or forgetting in developing rats , 2012, Neurobiology of Learning and Memory.

[8]  Min Zhuo,et al.  Interplay of Amygdala and Cingulate Plasticity in Emotional Fear , 2011, Neural plasticity.

[9]  L. Nadel,et al.  Decay happens: the role of active forgetting in memory , 2013, Trends in Cognitive Sciences.

[10]  Joseph E LeDoux.  Emotion circuits in the brain. , 2009, Annual review of neuroscience.

[11]  Xi Liu,et al.  A feature binding computational model for multi-class object categorization and recognition , 2011, Neural Computing and Applications.

[12]  D. Goleman.  Emotional Intelligence: Why It Can Matter More Than IQ , 1995 .

[13]  Yixin Zhong,et al.  An Improved SalBayes Model with GMM , 2011, CAIP.

[14]  Gregory P. Lee,et al.  Different Contributions of the Human Amygdala and Ventromedial Prefrontal Cortex to Decision-Making , 1999, The Journal of Neuroscience.

[15]  J L McGaugh,et al.  Amygdala activity at encoding correlated with long-term, free recall of emotional information. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[16]  Ali Azadeh,et al.  An emotional learning-neuro-fuzzy inference approach for optimum training and forecasting of gas consumption estimation models with cognitive data , 2015 .

[17]  J. Eccles.  The emotional brain. , 1980, Bulletin et memoires de l'Academie royale de medecine de Belgique.

[18]  E. Rolls.  The Neurophysiology and Computational Mechanisms of Object Representation , 2009 .

[19]  N. Logothetis,et al.  Cortical mechanisms of sensory learning and object recognition , 2008, Philosophical Transactions of the Royal Society B: Biological Sciences.

[20]  Ehsan Lotfi,et al.  Adaptive brain emotional decayed learning for online prediction of geomagnetic activity indices , 2014, Neurocomputing.

[21]  Babak Nadjar Araabi,et al.  Brain emotional learning based intelligent controller applied to neurofuzzy model of micro-heat exchanger , 2007, Expert Syst. Appl..

[22]  Mark A. Richardson,et al.  An improved cortex-like neuromorphic system for target recognitions , 2010, Security + Defence.

[23]  Christian Balkenius,et al.  EMOTIONAL LEARNING: A COMPUTATIONAL MODEL OF THE AMYGDALA , 2001, Cybern. Syst..

[24]  Neslihan Serap Sengör,et al.  From Occasional Choices to Inevitable Musts: A Computational Model of Nicotine Addiction , 2012, Comput. Intell. Neurosci..

[25]  PoggioTomaso,et al.  Robust Object Recognition with Cortex-Like Mechanisms , 2007 .

[26]  Martin Lotze,et al.  The functional connectivity between amygdala and extrastriate visual cortex activity during emotional picture processing depends on stimulus novelty , 2011, Biological Psychology.

[27]  Caro Lucas,et al.  Reinforcement _recurrent fuzzy rule based system based on brain emotional learning structure to predict the complexity dynamic system , 2008, 2008 Third International Conference on Digital Information Management.

[28]  C. Lucas,et al.  Emotional controller (BELBIC) for electric drives — A review , 2010, IECON 2010 - 36th Annual Conference on IEEE Industrial Electronics Society.

[29]  Ehsan Lotfi,et al.  BRAIN EMOTIONAL LEARNING-BASED PATTERN RECOGNIZER , 2013, Cybern. Syst..

[30]  Caro Lucas.  BELBIC and Its Industrial Applications: Towards Embedded Neuroemotional Control Codesign , 2011 .

[31]  A. Damasio,et al.  Emotion, decision making and the orbitofrontal cortex. , 2000, Cerebral cortex.

[32]  Josemir W. Sander,et al.  The Limbic System Conception and Its Historical Evolution , 2011, TheScientificWorldJournal.

[33]  J. Michael.  Textbook of Medical Physiology , 2005 .

[34]  Garrison W. Cottrell,et al.  Color Constancy Algorithms for Object and Face Recognition , 2010, ISVC.

[35]  Baher Abdulhai,et al.  Forecasting of short-term traffic-flow based on improved neurofuzzy models via emotional temporal difference learning algorithm , 2012, Eng. Appl. Artif. Intell..

[36]  Caro Lucas,et al.  Learning based brain emotional intelligence as a new aspect for development of an alarm system , 2008, Soft Comput..

[37]  Jean-Luc Buessler,et al.  Image Receptive Fields Neural Networks for Object Recognition , 2011, ICANN.

[38]  Siti Zaiton Mohd Hashim,et al.  A review of emotional learning and it's utilization in control engineering , 2010, SOCO 2010.

[39]  M. Mishkin,et al.  Object Recognition and Location Memory in Monkeys with Excitotoxic Lesions of the Amygdala and Hippocampus , 1998, The Journal of Neuroscience.

[40]  Gang Zheng,et al.  Analysis and Design on Key Updating Policies for Satellite Networks , 2008, Int. J. Comput. Commun. Control.

[41]  Ehsan Lotfi,et al.  Emotional Brain-Inspired Adaptive Fuzzy Decayed Learning for online prediction problems , 2013, 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[42]  Gabriel Kreiman,et al.  How cortical neurons help us see: visual recognition in the human brain. , 2010, The Journal of clinical investigation.

[43]  Kan Hong,et al.  Enhanced Object Recognition in Cortex-Like Machine Vision , 2011, EANN/AIAI.

[44]  Arnold W. M. Smeulders,et al.  The Amsterdam Library of Object Images , 2004, International Journal of Computer Vision.

[45]  Caro Lucas,et al.  Introducing Belbic: Brain Emotional Learning Based Intelligent Controller , 2004, Intell. Autom. Soft Comput..

[46]  Babak Nadjar Araabi,et al.  INTELLIGENT MODELING AND CONTROL OF WASHING MACHINE USING LOCALLY LINEAR NEURO-FUZZY (LLNF) MODELING AND MODIFIED BRAIN EMOTIONAL LEARNING BASED INTELLIGENT CONTROLLER (BELBIC) , 2008 .

[47]  Edmund T. Rolls,et al.  Neurophysiology and functions of the primate amygdala. , 1992 .

[48]  Tomaso Poggio,et al.  Models of object recognition , 2000, Nature Neuroscience.

[49]  Baher Abdulhai,et al.  Short-term traffic flow forecasting : parametric and nonparametric approaches via emotional temporal difference learning , 2013 .

[50]  Ali Abedi,et al.  Position Control of Hybrid Stepper Motor Using Brain Emotional Controller , 2012 .

[51]  Adnan Khashman,et al.  Application of an emotional neural network to facial recognition , 2009, Neural Computing and Applications.

[52]  Ehsan Lotfi,et al.  Practical emotional neural networks , 2014, Neural Networks.

[53]  E. Murray.  The amygdala, reward and emotion , 2007, Trends in Cognitive Sciences.

[54]  Bibiana Scelfo,et al.  Learning-related long-term potentiation of inhibitory synapses in the cerebellar cortex , 2008, Proceedings of the National Academy of Sciences.

[55]  Adnan Khashman,et al.  A Modified Backpropagation Learning Algorithm With Added Emotional Coefficients , 2008, IEEE Transactions on Neural Networks.

[56]  T. Sejnowski,et al.  Fast Odor Learning Improves Reliability of Odor Responses in the Locust Antennal Lobe , 2005, Neuron.

[57]  M. Fedurco.  Long-Term Memory Search across the Visual Brain , 2012, Neural plasticity.

[58]  J. Morén,et al.  A Computational Model of Emotional Conditioning in the Brain , 1998 .

[59]  James J. DiCarlo,et al.  How Does the Brain Solve Visual Object Recognition? , 2012, Neuron.

[60]  M. Maroun,et al.  The Role of the Medial Prefrontal Cortex-Amygdala Circuit in Stress Effects on the Extinction of Fear , 2007, Neural plasticity.