About

Members

Publications

Projects

Welcome to the
Laboratory for computational neurodynamics and cognition

canal-20131101

About the Lab

Although it is something most of us do every day without effort, memorizing is in fact an incredibly complex task. For instance, the simple act of storing and retrieving a given perceptual pattern is something a computer cannot do with anything approaching human efficiency and robustness.

The CONEC lab aims to better understand how human cognitive system accomplishes the complex task of create (and enhance) representation from patterns as well as recognize, identify, categorize and classify them. In particular, research focuses on a nonlinear dynamics system perspective where time and change are the key variables.

To understand how the human cognitive system works, we need to develop formal models. CONEC lab uses recurrent artificial neural networks that are massively parallel and where the information is distributed among the units. Therefore the main objective is the development of a general bidirectional associative memory that can take into account supervised, unsupervised and reinforcement learning while being constrained by neuroscience data. From models development, it is hope that we will have a better understanding on how the brains work.

Members

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Director

Chartier, Sylvain

Sylvain

Director of the Laboratory for computational neurodynamics and cognition and Associate Professor, School of Psychology

Ph.D. (2004) – Psychology – Université du Québec à Montréal
B.Sc. (Honours) (1998) – Psychology – Université du Québec à Montréal
B.A. (1996) – Psychology – University of Ottawa

 

Room: VNR 3022

Work E-mail: Sylvain.Chartier@uOttawa.ca

Dr. Sylvain Chartier received the B.A. degree from the University of Ottawa, in 1993 and the B.Sc. and Ph.D. degrees from the Université du Québec à Montréal, in 1996 and 2004, respectively, all in psychology. His doctoral thesis was on the development of an artificial neural network for autonomous categorization. From 2004 to 2007, Dr. Chartier was post-doctoral fellow at the Centre de recherche de l’Institut Philippe-Pinel de Montréal where he conducted research on eye-movement analysis and classification. Since 2007, He is a Professor at the University of Ottawa.

Keywords

  • Recurrent Associative Memories
  • Nonlinear dynamic systems
  • Quantitative methods

Contact

Email: Sylvain.Chartier@uOttawa.ca

Room: VNR 3022

Affiliated Researcher

Cyr, André

Andre

Dr André Cyr graduated in Medicine at the University of Montreal and has completed a PhD in Informatics and Cognition at the University of Quebec in Montreal. Its first field of interests is the general understanding of the intelligence phenomenon with possible links in the artificial intelligence domain. Learning and memory are the main topics in his researches, studied from a bio-inspired computational perspective. The methodology used for the experiments consists in developing artificial spiking neural networks acting as brain controller for complete cognitive virtual and physical robotic agents. The subjects of investigation are various but they commonly share the simulation of characteristics or behaviors of natural low-level intelligence, as in the invertebrates. The different research hypotheses are first explored in virtual scenarios, then the SNN are embodied in physical robots for tests in real world constraints. The data produced are studied from the level of the synapses up to the cognitive agent behaviors. Two examples of current projects are the simulation of the visual attention phenomenon and learning abstract concept of sameness / difference.

Keywords

  • General artificial intelligence
  • Cognition
  • Adaptative behavior
  • Learning and memory
  • Bio-inspired-robotics
  • Computational neuroscience
Graduate Students

Berberian, Nareg

Nareg

B.Sc. Psychology (2015)

Keywords

  • Analysis of electrophysiological data (Multi-electrode Utah Array; Single cell recordings; Calcium imaging; EEG)
  • Plasticity in networks of spiking neurons
  • Bio-inspired vision and learning in robotics
  • Associative memory
  • Decision-making

Email

nberb062@uOttawa.ca

Church, Kinsey

Kinsey Church
BSc. Psychology (2019)

Research Interests: Artificial neural networks, cognition, learning, behaviour, and artificial intelligence. My current project focuses on the Exploration-Exploitation trade off in cognition.

Email: kchur026@uOttawa.ca

Rolon-Mérette, Damiem

Damiem

Degrees: B.Sc. spécialisé en Psychologie et B.Sc. spécialisé approfondie Majeur Biochimie avec Majeur en Psychologie (Année)

Research interest: I am currently focusing on the mechanisms behind learning and memory in the human brain, more specifically associative learning. To do so, artificial neural networks are used to model the phenomenon in other to draw parallel conclusion to the human brain. Key concepts that I am currently interested are the one of one-to-many associations, role of context in associative learning and how this can lead to general purpose artificial intelligence.

Email: drolo083@uOttawa.ca

Rolon-Mérette, Thaddé

Thadde

Since 2017, Ph.D. student in experimental psychology at the University of Ottawa.

Received a B.Sc. in biomedical sciences at the University of Ottawa in 2015.

Received a B.Sc. in psychology at the University of Ottawa in 2016.

His research interest spans the areas of cognition and artificial neural networks, with an emphasis on:

  • Associative memories
  • Learning
  • Contexts
  • Growing architecture
  • Feature extraction
  • Deep learning

Ross, Matthew

Matthew
Matt completed his Honours BSc. with spec. in psychology at the university of Ottawa and is currently doing is PhD in Experimental Psychology under the supervision of Dr. Chartier. His research interests lay in the development and testing of artificial neural networks (ANNs), for modeling emergent properties of cortical circuitry, with a focus on learning. Recently, Matt has been more focused on biologically inspired models of vision, and the pursuit of real world testing using robotic agents. 
Honours Students

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Alumni

Publications

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Applications

B

  • Berberian, N., MacPherson, A., Giraud, E., Richardson, L., & Thivierge, J.P. (2017). Neuronal Pattern Separation of Motion-Relevant Input in LIP activity. Journal of Neurophysiology, Vol. 117, No. 2, pp. 738-755.

C

  • Calderini, M., Zhang S., Berberian N., Thivierge J.P., (2018). Optimal Readout of Correlated Neural Activity in a Decision Making Circuit. Neural Computation, Vol. 30, No. 6, pp. 1573-1611.

  • Chartier, S. & Johnson, M. (2015, July). Learning Valid Categorical Syllogisms using an Associative Memory. Proceedings of the International Joint Conference on Neural Networks, Killarney, Ireland, 6 pages.

  • Chartier, S. & Lepage, R. (2002, August). Learning and Extracting Edges from Images by a Modified Hopfield Neural Network. Proceeding of the 16th International Conference on Pattern Recognition, Vol.3, Quebec City, Canada, pp. 431-434.

  • Chartier, S. & Renaud, P. (2006, May). Eye-tracker Data Filtering Using Pulse Coupled Neural Network. Proceeding of the 17th IASTED International Conference on Modelling and Simulation, Montréal, Canada, pp. 91-96.

  • Chartier, S. & Renaud, P. (2008, March). An Online Noise Filter for Eye-Tracker Data Recorded in a Virtual Environment. Proceedings of Eye Tracking Research & Applications Symposium, Savannah, USA, pp. 153-156.

  • Chartier, S., Cousineau, D., & Charbonneau, D. (2004, July). A Connexionist Model of the Attentional Blink Effect During a Rapid Serial Visual Presentation Task. In M. Lovett, C. Schunn, C. Lebiere, P. Munro (Eds.) Proceedings of the Sixth International Conference on Cognitive Modelling, (pp. 64-69). Mahwah, NJ: Lawrence Erlbaum Associates Publishers. Pittsburgh, USA.

  • Chartier, S., Leth-Steensen, C. & Hébert, M.-F.  (2012). Performing Complex Associations Using a Generalized Bidirectional Associative Memory. Journal of Experimental & Theoretical Artificial Intelligence, Vol. 24, No. 1, pp. 23-42.

  • Chartier, S., Renaud, P. & Caron, S. (2007, June). Autonomous Classification of Sexual Offenders from Eye Pattern Behavior, In G. Bourgon, R.K. Hanson, J.D. Pozzulo, K.E. Morton Bourgon, & C.L. Tanasichuk (Eds.), The Proceedings of the 2007 North American Correctional & Criminal Justice Psychology Conference (pp. 158-161). Ottawa: Public Safety Canada.

  • Chartier, S., Renaud, P., Bouchard, S., Proulx, J., Rouleau, J. L., Fedoroff P., & Bradford, J., (2006). Sexual Preference Classification from Gaze Behavior Data using a Multilayer Perceptron, Annual Review of CyberTherapy and Telemedicine, vol. 4, pp. 149-157.

  • Cyr, A., & Thériault, F. (2015). Action Selection and Operant Conditioning: A Neurorobotic Implementation. Journal of Robotics. Vol. 2015, Article ID 643869, 10 pages.

H

  • Hébert, M.-F., Chartier, S., & Tremblay, C. (2014, July). Solving Valid Syllogistic Problems using a Bidirectional Heteroassociative Memory. Proceedings of the 36th Annual Meeting of the Cognitive Science Society, Quebec City, Canada, pp. 2345-2350.

  • Hélie, S., Chartier, S. & Proulx, R. (2006). Are Unsupervised Neural Networks Ignorant? Sizing the Effect of the Environmental Distribution on Unsupervised Learning. Cognitive Systems Research, vol. 7, no. 4, pp. 357-371.

L

  • Lahmiri, S., Boukadoum, M. & Chartier, S. (2010, May). Daily Stock Market Forecasting using Volume: A comparison of ANFIS and Time Delay Recurrent Neural Networks. Sixth International Conference on Intelligent Systems: Theory and Applications. Rabat, Morocco, 10 pages.

  • Lahmiri, S. Boukadoum, M. & Chartier, S. (2013, May) Information Fusion and S&P500 Trend Prediction. Proceedings of the International Conference on Computer Systems and Applications, FES, Morocco. 6 pages.

  • Lahmiri S., Boukadoum, M. & Chartier, S. (2013). A supervised classification system of financial data based on wavelet packet and neural networks. International Journal of Strategic Decision Sciences, Vol. 4, No. 4, pp. 72-84.

  • Lahmiri S., Boukadoum, M. & Chartier, S. (2014). Exploring Information Categories and Artificial Neural Networks Numerical Algorithms in S&P500 Trend Prediction: A Comparative Study. International Journal of Strategic Decision Sciences, vol. 5, no. 1, pages 76-94.

  • Leth-Steensen, C., Chartier, S., Langlois, D. & Hébert, M. (2010, May). Performing Complex Associations using a Feature-Extracting Bidirectional Associative Memory. In H. W. Guesgen & R. C. Murray (Eds.), Florida Artificial Intelligence Research Society Conference (pp. 367-372). Florida, USA: AAAI Press.

M

  • Morissette, L. & Chartier, S. (2015, July). Saliency model of auditory attention based on frequency, amplitude and spatial location. Proceedings of the International Joint Conference on Neural Networks, Killarney, Ireland, 6 pages.

  • Morissette, L., Chartier, S., Vandermeulen, R. & Watier, N. (2012). Depth of Treatment Sensitive Noise Resistant Dynamic Artificial Neural Networks Model of Recall in People with Prosopagnosia. Neural Networks, Vol. 32, pp.46-56.

N

  • Nejadgholi, I., Seyyedsalehi, S. A. & Chartier, S. (2012). Chaotic Feature Extracting BAM and Its Application in Implementing Memory Search. Neural Processing Letters, Vol. 36, pp. 69-99.

O

  • Orbán, L. & Chartier, S. (2013, April). Unsupervised non-linear neural networks capture aspects of floral choice behavior. Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium, pp. 149-154.

  • Orbán, L. L., Plowright CMS, Chartier, S., Thompson, E., & Xu, V. (2015) Visual Choice Behaviour by Bumblebees (Bombus impatiens) Confirms Unsupervised Neural Network’s Prediction. Journal of Comparative Psychology. Vol.129, No. 3, 229-236.

R

  • Ross, M., Payeur, P. & Chartier, S. (2019, June). Task Allocation for Heterogeneous Robots Using a Self-Organizing Contextual Map. IEEE International Symposium on RObotic and Sensors Environments (ROSE), Ottawa, Canada. 6 pages.

T

  • Tabari, K., Boukadoum, M., Chartier, S. & Lounis. H. (2006, December) Reconnaissance d’expressions faciales à l’aide d’une mémoire associative bidirectionnelle à fonction de sortie chaotique. Proceedings of Maghrebian Conference on Software Engineering and Artificial Intelligence, Agadir, Morocco, pp. 422-426.

  • Tremblay, C., Harding, B., Chartier, S., & Cousineau, D. (2014). System Factorial Technology applied to Artificial Neural Network Information Processing. In B. Goertzel, L. Orseau, J. Snaider (Eds.) Artificial General Intelligence: Lecture Notes in Computer Science, (pp. 258-261). Springer International Publishing.

V

  • Vandermeulen, R., Morrissette, L. & Chartier, S. (2011, July). Modeling Prosopagnosia Using Dynamic Artificial Neural Networks. Proceedings of the International Joint Conference on Neural Networks, San Jose, USA. pp. 2074-2079.

W

  • Wu, W., Payeur, P., Al-Buraiki, O., & Ross, M. (2019, August). Vision-Based Target Objects Recognition and Segmentation for Unmanned Systems Task Allocation. Proceedings of the International Conference on Image Analysis and Recognition, Waterloo, Canada, pp. 252-263.

Artificial neural networks

A

  • Amiri, M., Davande, H., Sadeghian, A. & Chartier, S. (2010). Feedback Associative Memory Based on a New Hybrid Model of Generalized Regression and Self Feedback Neural Networks. Neural Networks, Vol. 23, No. 7, 892-904.

  • Amiri, M., Sadeghian, A, & Chartier, S. (2010, July). One-shot Training Algorithm for Self-Feedback Neural Networks. North American Fuzzy Information Processing Society, Toronto, Canada. pp. 104-109.

B

  • Berberian, N., Aamir, Z., Hélie, S. & Chartier, S. (2016, July). Encoding Sparse Features in a Bidirectional Associative Memory. IEEE World Congress on Computational Intelligence, Vancouver, Canada, pp. 5119-5126.

C

  • Chartier, S. & Boukadoum, M. (2006). A New Bidirectional Heteroassociative Memory for Binary and Grey-Level Patterns. IEEE Transactions on Neural Networks, vol. 17, no. 2, pp. 385 - 396.

  • Chartier, S. & Boukadoum, M. (2006). A Sequential Dynamic Heteroassociative Memory for MultiStep Pattern Recognition and One-to-Many Association. IEEE Transactions on Neural Networks, vol. 17, no. 1, pp. 59-68.

  • Chartier, S. & Boukadoum, M. (2006, December) A Chaotic Bidirectional Associative Memory. Proceedings of Maghrebian Conference on Software Engineering and Artificial Intelligence, Agadir, Morocco, pp. 498-501.

  • Chartier, S. & Boukadoum, M.  (2011). Encoding Static and Temporal Patterns with a Bidirectional Heteroassociative Memory. Journal of Applied Mathematics. Article ID 301204, 31 pages.

  • Chartier, S. & Giguère, G. (2008, July). Autonomous Perceptual Feature Extraction in a Topology-Constrained Architecture. In B. C. Love, K. McRae, & V. M. Sloutsky (Eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society (pp. 1868-1873). Austin, TX: Cognitive Science Society.

  • Chartier, S. & Johnson, M. (2015, July). Learning Valid Categorical Syllogisms using an Associative Memory. Proceedings of the International Joint Conference on Neural Networks, Killarney, Ireland, 6 pages.

  • Chartier, S. & Lepage, R. (2002, August). Learning and Extracting Edges from Images by a Modified Hopfield Neural Network. Proceeding of the 16th International Conference on Pattern Recognition, Vol.3, Quebec City, Canada, pp. 431-434.

  • Chartier, S. & Proulx, R. (1999, July). A Self-Scaling Procedure in Unsupervised Correlational Neural Networks, Proceedings of the International Joint Conference on Neural Networks, Vol. 2, Washington D.C., USA, pp. 1092-1096.

  • Chartier, S. & Proulx, R. (2001, July). A New Online Unsupervised Learning Rule for the BSB Model, Proceedings of the International Joint Conference on Neural Networks, Vol. 1, Washington D.C., USA, pp. 448-453.

  • Chartier, S. & Proulx, R. (2005). NDRAM: A Nonlinear Dynamic Recurrent Associative Memory for Learning Bipolar and Nonbipolar Correlated Patterns. IEEE Transactions on Neural Networks, vol. 16, no. 6, pp. 1393-1400.

  • Chartier, S. & Renaud, P. (2008, March). An Online Noise Filter for Eye-Tracker Data Recorded in a Virtual Environment. Proceedings of Eye Tracking Research & Applications Symposium, Savannah, USA, pp. 153-156.

  • Chartier, S., Boukadoum, M. and Amiri, M. (2009), BAM Learning of Nonlinearly Separable Tasks by Using an Asymmetrical Output Function and Reinforcement Learning, IEEE Transactions on Neural Networks, vol. 20, no. 8, pp. 1281-1292.

  • Chartier, S., Cousineau, D., & Charbonneau, D. (2004, July). A Connexionist Model of the Attentional Blink Effect During a Rapid Serial Visual Presentation Task. In M. Lovett, C. Schunn, C. Lebiere, P. Munro (Eds.) Proceedings of the Sixth International Conference on Cognitive Modelling, (pp. 64-69). Mahwah, NJ: Lawrence Erlbaum Associates Publishers. Pittsburgh, USA.

  • Chartier, S., Giguère, G., & Langlois, D. (2009). A new bidirectional heteroassociative memory encompassing correlational, competitive and topological properties. Neural Networks, Vol. 22, No. 5-6, pp. 568-578.

  • Chartier, S., Giguère, G., Langlois, D & Sioufi, R.  (2009, June)  Bidirectional Associative Memories, Self-Organizing Maps and k-Winners-Take-All: Uniting Feature Extraction and Topological Principles, Proceedings of the International Joint Conference on Neural Networks, Atlanta, USA, pp. 503-510.

  • Chartier, S., Giguère, G., Renaud, P., Lina, J.-M., & Proulx, R. (2007, August). FEBAM: A Feature-Extracting Bidirectional Associative Memory, Proceedings of the International Joint Conference on Neural Networks. Orlando, USA, pp. 1679-1684.

  • Chartier, S., Hélie, S., Boukadoum, M. & Proulx, R. (2005, August). SCRAM: Statistically Converging Recurrent Associative Memory. Proceedings of the International Joint Conference on Neural Networks, 2, Montréal, Canada, pp. 723-728.

  • Chartier, S., Leth-Steensen, C. & Hébert, M.-F.  (2012). Performing Complex Associations Using a Generalized Bidirectional Associative Memory. Journal of Experimental & Theoretical Artificial Intelligence, Vol. 24, No. 1, pp. 23-42.

  • Chartier, S., Renaud, P. & Caron, S. (2007, June). Autonomous Classification of Sexual Offenders from Eye Pattern Behavior, In G. Bourgon, R.K. Hanson, J.D. Pozzulo, K.E. Morton Bourgon, & C.L. Tanasichuk (Eds.), The Proceedings of the 2007 North American Correctional & Criminal Justice Psychology Conference (pp. 158-161). Ottawa: Public Safety Canada.

  • Chartier, S., Renaud, P., Bouchard, S., Proulx, J., Rouleau, J. L., Fedoroff P., & Bradford, J., (2006). Sexual Preference Classification from Gaze Behavior Data using a Multilayer Perceptron, Annual Review of CyberTherapy and Telemedicine, vol. 4, pp. 149-157.

  • Chartier S., Renaud, P & Boukadoum, M. (2008). A Nonlinear Dynamic Artificial Neural Network Model of Memory, New Ideas in Psychology, vol. 26, no 2, pp. 252-277.

  • Church, K. A., Ross, M. & Chartier, S. (2020, July). Using a Bidirectional Associative Memory and Feature Extraction to model Nonlinear Exploitation Problems. Proceedings of the International Conference on Cognitive Modeling, Held online, 7 pages.

D

  • Davande, H., Amiri, M., Sadeghian, A. & Chartier, S., (2008, June) Auto-associative memory based on a new hybrid model of SFNN and GRNN: performance comparison with NDRAM, ART2 and MLP. Proceedings of the International Joint Conference on Neural Networks, Hong Kong, China, pp. 1698-1703.

G

  • Giguère, G., Chartier, S., Proulx, R., & Lina, J.-M. (2007, July). Creating Perceptual Features Using a BAM Architecture, In D. S. McNamara & J. G. Trafton (Eds.), Proceedings of the 29th Annual Cognitive Science Society (pp. 1025-1030). Austin, TX: Cognitive Science Society.

  • Giguère, G., Chartier, S., Proulx, R., Lina, J.-M. (2007, July). Category development and reorganization using a bidirectional associative memory-inspired architecture. In R.L. Lewis, T.A. Polk, & J.E. Laird (Eds.), Proceedings of the 8th International Conference on Cognitive Modeling, (pp. 97-102). Ann Arbor, Mi: University of Michigan.

H

  • Harding, B., Goulet, M.-A., Cousineau, D. & Chartier, S. (2017, May). Are Recurrent Associative Memories Good Models of Decision Making? Modelling discrimination decisions from different perspectives Bradley Harding,  Proceedings of the International Joint Conference on Neural Networks, Anchorage, USA, pages 2621-2628.

  • Hébert, M.-F., Chartier, S., & Tremblay, C. (2014, July). Solving Valid Syllogistic Problems using a Bidirectional Heteroassociative Memory. Proceedings of the 36th Annual Meeting of the Cognitive Science Society, Quebec City, Canada, pp. 2345-2350.

  • Hélie, S., Chartier, S. & Proulx, R. (2006). Are Unsupervised Neural Networks Ignorant? Sizing the Effect of the Environmental Distribution on Unsupervised Learning. Cognitive Systems Research, vol. 7, no. 4, pp. 357-371.

J

  • Johnson, M. & Chartier, S. (2018, July). Is There a Purpose to Network Redundancy? Proceedings of the International Joint Conference on Neural Networks, Rio de Janeiro, Brasil. 8 pages.

  • Johnson, M., & Chartier, S. (2014). Increasing Accuracy in a Bidirectional Associative Memory through Expended Databases. In B. Goertzel, L. Orseau, J. Snaider (Eds.) Artificial General Intelligence: Lecture Notes in Computer Science, (pp. 53-62). Springer International Publishing.

  • Johnson, M., & Chartier, S. (2017, September). Model Derived Spike Time Dependent Plasticity. In Lintas A., Rovetta S., Verschure P., Villa A. (eds). Artificial Neural Networks and Machine Learning – ICANN 2017, Alghero, Italy, pages 345-353.

L

  • Lahmiri, S., Boukadoum, M. & Chartier, S. (2010, May). Daily Stock Market Forecasting using Volume: A comparison of ANFIS and Time Delay Recurrent Neural Networks. Sixth International Conference on Intelligent Systems: Theory and Applications. Rabat, Morocco, 10 pages.

  • Lahmiri, S. Boukadoum, M. & Chartier, S. (2013, May) Information Fusion and S&P500 Trend Prediction. Proceedings of the International Conference on Computer Systems and Applications, FES, Morocco. 6 pages.

  • Lahmiri S., Boukadoum, M. & Chartier, S. (2013). A supervised classification system of financial data based on wavelet packet and neural networks. International Journal of Strategic Decision Sciences, Vol. 4, No. 4, pp. 72-84.

  • Lahmiri S., Boukadoum, M. & Chartier, S. (2014). Exploring Information Categories and Artificial Neural Networks Numerical Algorithms in S&P500 Trend Prediction: A Comparative Study. International Journal of Strategic Decision Sciences, vol. 5, no. 1, pages 76-94.

  • Leth-Steensen, C., Chartier, S., Langlois, D. & Hébert, M. (2010, May). Performing Complex Associations using a Feature-Extracting Bidirectional Associative Memory. In H. W. Guesgen & R. C. Murray (Eds.), Florida Artificial Intelligence Research Society Conference (pp. 367-372). Florida, USA: AAAI Press.

M

  • Morissette, L., Chartier, S., Vandermeulen, R. & Watier, N. (2012). Depth of Treatment Sensitive Noise Resistant Dynamic Artificial Neural Networks Model of Recall in People with Prosopagnosia. Neural Networks, Vol. 32, pp.46-56.

  • Morrissette, L. & Chartier, S. (2013, August). FEBAMSOM-BAM*: Neural Network Model of Human Categorization of the N-Bits Parity Problem. Proceedings of the International Joint Conference on Neural Networks, Dallas, USA, pp. 1897-1901.

N

  • Nejadgholi, I., Chartier, S. & Seyyedsalehi, S. A. (2013) Controlling Deterministic Output Variability in a Feature Extracting Chaotic BAM. Neurocomputing, Vol. 120, pp. 298-309.

  • Nejadgholi, I., SeyyedSalehi, S. A. & Chartier, S. (2017). A Brain-Inspired Method of Facial Expression Generation Using Chaotic Feature Extracting Bidirectional Associative Memory. Neural Processing Letters. Vol. 46, No. 3, pp 943–960.

  • Nejadgholi, I., Seyyedsalehi, S. A. & Chartier, S. (2012). Chaotic Feature Extracting BAM and Its Application in Implementing Memory Search. Neural Processing Letters, Vol. 36, pp. 69-99.

O

  • Orbán, L. & Chartier, S. (2013, April). Unsupervised non-linear neural networks capture aspects of floral choice behavior. Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium, pp. 149-154.

  • Orbán, L.L., & Chartier, S. (2015). Unsupervised neural network quantifies the cost of visual information processing. PLoS One. Vol.10, No. 7, e0132218, 14 pages.

R

  • Rolon-Mérette, D., Rolon-Mérette, T. & Chartier, S. (2018, July). Distinguishing Highly Correlated Patterns using a Context Based Approach in Bidirectional Associative Memory. Proceedings of the International Joint Conference on Neural Networks, Rio de Janeiro, Brasil. 8 pages

  • Rolon-Mérette, D., Rolon-Mérette, T. & Chartier, S. (2019, July). Learning and Recalling Arbitrary Lists of Overlapping Exemplars in a Recurrent Artificial Neural Network. Proceedings of the International Conference on Cognitive Modeling, Montréal, Canada. 6 pages.

  • Rolon-Merette, T., Rolon-Merette, D. & Chartier, S. (2018). Generating Cognitive Context with Feature-Extracting Bidirectional Associative Memory. Procedia Computer Science, vol. 145, pp.428-436.

  • Rolon-Mérette, T., Rolon-Mérette, D. & Chartier, S. (2019, July). Different Brain, Same Prototype? Cognitive Variability within a Recurrent Associative Memory. Proceedings of the International Conference on Cognitive Modeling, Montréal, Canada. 6 pages.

  • Ross, M., Berberian, N. & Chartier, S. (2020, July) Should I Stay or Should I Grow? A Dynamic Self-Governed Growth for Determining Hidden Layer Size in a Multilayer Perceptron. Proceedings of the International Joint Conference on Neural Networks. Held Online, 8 pages.

  • Ross, M., Chartier, S., & Hélie, S. (2017). The neurodynamics of categorization: Critical challenges and proposed solutions. In Claire Lefebvre & Henri Cohen (Eds.), Handbook of Categorization in Cognitive Science, (1053-1076). Elsevier.

  • Ross, M., Payeur, P. & Chartier, S. (2019, June). Task Allocation for Heterogeneous Robots Using a Self-Organizing Contextual Map. IEEE International Symposium on RObotic and Sensors Environments (ROSE), Ottawa, Canada. 6 pages.

S

  • Salmon R., Sadeghian A. & Chartier, S. (2010, July). Reinforcement Learning using Associative Memory Networks. Proceedings of the International Joint Conference on Neural Networks, Barcelona, Spain, 7 pages.

T

  • Tabari, K., Boukadoum, M., Chartier, S. & Lounis. H. (2006, December) Reconnaissance d’expressions faciales à l’aide d’une mémoire associative bidirectionnelle à fonction de sortie chaotique. Proceedings of Maghrebian Conference on Software Engineering and Artificial Intelligence, Agadir, Morocco, pp. 422-426.

  • Tremblay, C. & Chartier, S. (2014, May). BAM Learning in High Level of Connection Sparseness. Proceedings of the Twenty-Seventh International Florida Artificial Intelligence Research Society Conference, Pensacola Beach, USA, pp. 97-100.

  • Tremblay, C., Berberian, N., & Chartier, S., (2014, July). A new Bidirectional Associative Memory for Short-term Memory Learning. Proceedings of the 36th Annual Meeting of the Cognitive Science Society, Quebec City, Canada, pp. 1917-1922.

  • Tremblay, C., Dorville, M., Myers, K. & Chartier, S. (2013, August). Spreading Activation and Sparseness in a Bidirectional Associative Memory. Proceedings of the International Joint Conference on Neural Networks, Dallas, USA, pp. 1917-1923.

  • Tremblay, C., Harding, B., Chartier, S., & Cousineau, D. (2014). System Factorial Technology applied to Artificial Neural Network Information Processing. In B. Goertzel, L. Orseau, J. Snaider (Eds.) Artificial General Intelligence: Lecture Notes in Computer Science, (pp. 258-261). Springer International Publishing.

  • Tremblay, C., Myers-Stewart, K., Morissette, L., & Chartier, S. (2013, July). Bidirectional Associative Memory and Learning of Nonlinearly Separable Tasks. In R. West & T. Stewart (Eds.), Proceedings of the 12th International Conference on Cognitive Modeling, Ottawa, Canada, pp. 420-425.

V

  • Vandermeulen, R., Morrissette, L. & Chartier, S. (2011, July). Modeling Prosopagnosia Using Dynamic Artificial Neural Networks. Proceedings of the International Joint Conference on Neural Networks, San Jose, USA. pp. 2074-2079.

W

  • Wu, W., Payeur, P., Al-Buraiki, O., & Ross, M. (2019, August). Vision-Based Target Objects Recognition and Segmentation for Unmanned Systems Task Allocation. Proceedings of the International Conference on Image Analysis and Recognition, Waterloo, Canada, pp. 252-263.

Z

  • Zamani, M., Sadeghian A. & Chartier, S. (2010, July). A Bidirectional Associative Memory Based on Cortical Spiking Neurons Using Temporal Coding. Proceedings of the International Joint Conference on Neural Networks, Barcelona, Spain, 7 pages.

Features extraction

C

  • Chartier, S. & Giguère, G. (2008, July). Autonomous Perceptual Feature Extraction in a Topology-Constrained Architecture. In B. C. Love, K. McRae, & V. M. Sloutsky (Eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society (pp. 1868-1873). Austin, TX: Cognitive Science Society.

  • Chartier, S., Giguère, G., & Langlois, D. (2009). A new bidirectional heteroassociative memory encompassing correlational, competitive and topological properties. Neural Networks, Vol. 22, No. 5-6, pp. 568-578.

  • Chartier, S., Giguère, G., Langlois, D & Sioufi, R.  (2009, June)  Bidirectional Associative Memories, Self-Organizing Maps and k-Winners-Take-All: Uniting Feature Extraction and Topological Principles, Proceedings of the International Joint Conference on Neural Networks, Atlanta, USA, pp. 503-510.

  • Chartier, S., Giguère, G., Renaud, P., Lina, J.-M., & Proulx, R. (2007, August). FEBAM: A Feature-Extracting Bidirectional Associative Memory, Proceedings of the International Joint Conference on Neural Networks. Orlando, USA, pp. 1679-1684.

  • Chartier, S., Leth-Steensen, C. & Hébert, M.-F.  (2012). Performing Complex Associations Using a Generalized Bidirectional Associative Memory. Journal of Experimental & Theoretical Artificial Intelligence, Vol. 24, No. 1, pp. 23-42.

G

  • Giguère, G., Chartier, S., Proulx, R., & Lina, J.-M. (2007, July). Creating Perceptual Features Using a BAM Architecture, In D. S. McNamara & J. G. Trafton (Eds.), Proceedings of the 29th Annual Cognitive Science Society (pp. 1025-1030). Austin, TX: Cognitive Science Society.

  • Giguère, G., Chartier, S., Proulx, R., Lina, J.-M. (2007, July). Category development and reorganization using a bidirectional associative memory-inspired architecture. In R.L. Lewis, T.A. Polk, & J.E. Laird (Eds.), Proceedings of the 8th International Conference on Cognitive Modeling, (pp. 97-102). Ann Arbor, Mi: University of Michigan.

L

  • Leth-Steensen, C., Chartier, S., Langlois, D. & Hébert, M. (2010, May). Performing Complex Associations using a Feature-Extracting Bidirectional Associative Memory. In H. W. Guesgen & R. C. Murray (Eds.), Florida Artificial Intelligence Research Society Conference (pp. 367-372). Florida, USA: AAAI Press.

M

  • Morissette, L., Chartier, S., Vandermeulen, R. & Watier, N. (2012). Depth of Treatment Sensitive Noise Resistant Dynamic Artificial Neural Networks Model of Recall in People with Prosopagnosia. Neural Networks, Vol. 32, pp.46-56.

  • Morrissette, L. & Chartier, S. (2013, August). FEBAMSOM-BAM*: Neural Network Model of Human Categorization of the N-Bits Parity Problem. Proceedings of the International Joint Conference on Neural Networks, Dallas, USA, pp. 1897-1901.

N

  • Nejadgholi, I., Chartier, S. & Seyyedsalehi, S. A. (2013) Controlling Deterministic Output Variability in a Feature Extracting Chaotic BAM. Neurocomputing, Vol. 120, pp. 298-309.

  • Nejadgholi, I., SeyyedSalehi, S. A. & Chartier, S. (2017). A Brain-Inspired Method of Facial Expression Generation Using Chaotic Feature Extracting Bidirectional Associative Memory. Neural Processing Letters. Vol. 46, No. 3, pp 943–960.

  • Nejadgholi, I., Seyyedsalehi, S. A. & Chartier, S. (2012). Chaotic Feature Extracting BAM and Its Application in Implementing Memory Search. Neural Processing Letters, Vol. 36, pp. 69-99.

O

  • Orbán, L. & Chartier, S. (2013, April). Unsupervised non-linear neural networks capture aspects of floral choice behavior. Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium, pp. 149-154.

  • Orbán, L.L., & Chartier, S. (2015). Unsupervised neural network quantifies the cost of visual information processing. PLoS One. Vol.10, No. 7, e0132218, 14 pages.

R

  • Rolon-Merette, T., Rolon-Merette, D. & Chartier, S. (2018). Generating Cognitive Context with Feature-Extracting Bidirectional Associative Memory. Procedia Computer Science, vol. 145, pp.428-436.

  • Rolon-Mérette, T., Rolon-Mérette, D. & Chartier, S. (2019, July). Different Brain, Same Prototype? Cognitive Variability within a Recurrent Associative Memory. Proceedings of the International Conference on Cognitive Modeling, Montréal, Canada. 6 pages.

  • Ross, M., Chartier, S., & Hélie, S. (2017). The neurodynamics of categorization: Critical challenges and proposed solutions. In Claire Lefebvre & Henri Cohen (Eds.), Handbook of Categorization in Cognitive Science, (1053-1076). Elsevier.

T

  • Tremblay, C., Myers-Stewart, K., Morissette, L., & Chartier, S. (2013, July). Bidirectional Associative Memory and Learning of Nonlinearly Separable Tasks. In R. West & T. Stewart (Eds.), Proceedings of the 12th International Conference on Cognitive Modeling, Ottawa, Canada, pp. 420-425.

V

  • Vandermeulen, R., Morrissette, L. & Chartier, S. (2011, July). Modeling Prosopagnosia Using Dynamic Artificial Neural Networks. Proceedings of the International Joint Conference on Neural Networks, San Jose, USA. pp. 2074-2079.

Memory

B

  • Berberian, N., Aamir, Z., Hélie, S. & Chartier, S. (2016, July). Encoding Sparse Features in a Bidirectional Associative Memory. IEEE World Congress on Computational Intelligence, Vancouver, Canada, pp. 5119-5126.

C

  • Chartier, S. & Boukadoum, M. (2006). A New Bidirectional Heteroassociative Memory for Binary and Grey-Level Patterns. IEEE Transactions on Neural Networks, vol. 17, no. 2, pp. 385 - 396.

  • Chartier, S. & Boukadoum, M. (2006). A Sequential Dynamic Heteroassociative Memory for MultiStep Pattern Recognition and One-to-Many Association. IEEE Transactions on Neural Networks, vol. 17, no. 1, pp. 59-68.

  • Chartier, S. & Boukadoum, M. (2006, December) A Chaotic Bidirectional Associative Memory. Proceedings of Maghrebian Conference on Software Engineering and Artificial Intelligence, Agadir, Morocco, pp. 498-501.

  • Chartier, S. & Boukadoum, M.  (2011). Encoding Static and Temporal Patterns with a Bidirectional Heteroassociative Memory. Journal of Applied Mathematics. Article ID 301204, 31 pages.

  • Chartier, S. & Giguère, G. (2008, July). Autonomous Perceptual Feature Extraction in a Topology-Constrained Architecture. In B. C. Love, K. McRae, & V. M. Sloutsky (Eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society (pp. 1868-1873). Austin, TX: Cognitive Science Society.

  • Chartier, S. & Lepage, R. (2002, August). Learning and Extracting Edges from Images by a Modified Hopfield Neural Network. Proceeding of the 16th International Conference on Pattern Recognition, Vol.3, Quebec City, Canada, pp. 431-434.

  • Chartier, S. & Proulx, R. (1999, July). A Self-Scaling Procedure in Unsupervised Correlational Neural Networks, Proceedings of the International Joint Conference on Neural Networks, Vol. 2, Washington D.C., USA, pp. 1092-1096.

  • Chartier, S. & Proulx, R. (2001, July). A New Online Unsupervised Learning Rule for the BSB Model, Proceedings of the International Joint Conference on Neural Networks, Vol. 1, Washington D.C., USA, pp. 448-453.

  • Chartier, S. & Proulx, R. (2005). NDRAM: A Nonlinear Dynamic Recurrent Associative Memory for Learning Bipolar and Nonbipolar Correlated Patterns. IEEE Transactions on Neural Networks, vol. 16, no. 6, pp. 1393-1400.

  • Chartier, S., Giguère, G., & Langlois, D. (2009). A new bidirectional heteroassociative memory encompassing correlational, competitive and topological properties. Neural Networks, Vol. 22, No. 5-6, pp. 568-578.

  • Chartier, S., Giguère, G., Langlois, D & Sioufi, R.  (2009, June)  Bidirectional Associative Memories, Self-Organizing Maps and k-Winners-Take-All: Uniting Feature Extraction and Topological Principles, Proceedings of the International Joint Conference on Neural Networks, Atlanta, USA, pp. 503-510.

  • Chartier, S., Giguère, G., Renaud, P., Lina, J.-M., & Proulx, R. (2007, August). FEBAM: A Feature-Extracting Bidirectional Associative Memory, Proceedings of the International Joint Conference on Neural Networks. Orlando, USA, pp. 1679-1684.

  • Chartier, S., Hélie, S., Boukadoum, M. & Proulx, R. (2005, August). SCRAM: Statistically Converging Recurrent Associative Memory. Proceedings of the International Joint Conference on Neural Networks, 2, Montréal, Canada, pp. 723-728.

  • Chartier S., Renaud, P & Boukadoum, M. (2008). A Nonlinear Dynamic Artificial Neural Network Model of Memory, New Ideas in Psychology, vol. 26, no 2, pp. 252-277.

  • Cyr A., Thériault F., Ross M., Chartier S. (2018, August) Associative Memory: An Spiking Neural Network Robotic Implementation. In Iklé M., Franz A., Rzepka R., Goertzel B. (eds) Artificial General Intelligence 2018. Lecture Notes in Computer Science, (31-41). Springer

G

  • Giguère, G., Chartier, S., Proulx, R., & Lina, J.-M. (2007, July). Creating Perceptual Features Using a BAM Architecture, In D. S. McNamara & J. G. Trafton (Eds.), Proceedings of the 29th Annual Cognitive Science Society (pp. 1025-1030). Austin, TX: Cognitive Science Society.

  • Giguère, G., Chartier, S., Proulx, R., Lina, J.-M. (2007, July). Category development and reorganization using a bidirectional associative memory-inspired architecture. In R.L. Lewis, T.A. Polk, & J.E. Laird (Eds.), Proceedings of the 8th International Conference on Cognitive Modeling, (pp. 97-102). Ann Arbor, Mi: University of Michigan.

H

  • Harding, B., Goulet, M.-A., Cousineau, D. & Chartier, S. (2017, May). Are Recurrent Associative Memories Good Models of Decision Making? Modelling discrimination decisions from different perspectives Bradley Harding,  Proceedings of the International Joint Conference on Neural Networks, Anchorage, USA, pages 2621-2628.

J

  • Jeanson, F., & Chartier, S. (2013, July). Memory Control in a FitzHugh-Nagumo Network via STDP. In R. West & T. Stewart (Eds.). Proceedings of the 12th International Conference on Cognitive Modeling, Ottawa, Canada, pp. 137-142.

  • Johnson, M. & Chartier, S. (2018, July). Is There a Purpose to Network Redundancy? Proceedings of the International Joint Conference on Neural Networks, Rio de Janeiro, Brasil. 8 pages.

  • Johnson, M., & Chartier, S. (2014). Increasing Accuracy in a Bidirectional Associative Memory through Expended Databases. In B. Goertzel, L. Orseau, J. Snaider (Eds.) Artificial General Intelligence: Lecture Notes in Computer Science, (pp. 53-62). Springer International Publishing.

  • Johnson, M., & Chartier, S. (2017, September). Model Derived Spike Time Dependent Plasticity. In Lintas A., Rovetta S., Verschure P., Villa A. (eds). Artificial Neural Networks and Machine Learning – ICANN 2017, Alghero, Italy, pages 345-353.

M

  • Morissette, L., Chartier, S., Vandermeulen, R. & Watier, N. (2012). Depth of Treatment Sensitive Noise Resistant Dynamic Artificial Neural Networks Model of Recall in People with Prosopagnosia. Neural Networks, Vol. 32, pp.46-56.

N

  • Nejadgholi, I., SeyyedSalehi, S. A. & Chartier, S. (2017). A Brain-Inspired Method of Facial Expression Generation Using Chaotic Feature Extracting Bidirectional Associative Memory. Neural Processing Letters. Vol. 46, No. 3, pp 943–960.

R

  • Rolon-Mérette, D., Rolon-Mérette, T. & Chartier, S. (2018, July). Distinguishing Highly Correlated Patterns using a Context Based Approach in Bidirectional Associative Memory. Proceedings of the International Joint Conference on Neural Networks, Rio de Janeiro, Brasil. 8 pages

  • Rolon-Mérette, D., Rolon-Mérette, T. & Chartier, S. (2019, July). Learning and Recalling Arbitrary Lists of Overlapping Exemplars in a Recurrent Artificial Neural Network. Proceedings of the International Conference on Cognitive Modeling, Montréal, Canada. 6 pages.

  • Rolon-Merette, T., Rolon-Merette, D. & Chartier, S. (2018). Generating Cognitive Context with Feature-Extracting Bidirectional Associative Memory. Procedia Computer Science, vol. 145, pp.428-436.

  • Rolon-Mérette, T., Rolon-Mérette, D. & Chartier, S. (2019, July). Different Brain, Same Prototype? Cognitive Variability within a Recurrent Associative Memory. Proceedings of the International Conference on Cognitive Modeling, Montréal, Canada. 6 pages.

  • Ross, M., Chartier, S., & Hélie, S. (2017). The neurodynamics of categorization: Critical challenges and proposed solutions. In Claire Lefebvre & Henri Cohen (Eds.), Handbook of Categorization in Cognitive Science, (1053-1076). Elsevier.

S

  • Salmon R., Sadeghian A. & Chartier, S. (2010, July). Reinforcement Learning using Associative Memory Networks. Proceedings of the International Joint Conference on Neural Networks, Barcelona, Spain, 7 pages.

T

  • Tremblay, C. & Chartier, S. (2014, May). BAM Learning in High Level of Connection Sparseness. Proceedings of the Twenty-Seventh International Florida Artificial Intelligence Research Society Conference, Pensacola Beach, USA, pp. 97-100.

  • Tremblay, C., Berberian, N., & Chartier, S., (2014, July). A new Bidirectional Associative Memory for Short-term Memory Learning. Proceedings of the 36th Annual Meeting of the Cognitive Science Society, Quebec City, Canada, pp. 1917-1922.

Z

  • Zamani, M., Sadeghian A. & Chartier, S. (2010, July). A Bidirectional Associative Memory Based on Cortical Spiking Neurons Using Temporal Coding. Proceedings of the International Joint Conference on Neural Networks, Barcelona, Spain, 7 pages.

Robotics

B

  • Berberian, N., Ross, M. & Chartier, S. (2019). Discrimination of Motion Direction in a Robot Using a Phenomenological Model of Synaptic Plasticity. Computational Intelligence and Neuroscience. Article ID 6989128, 14 pages.

C

  • Cyr, A., & Boukadoum, M. (2012). Classical conditioning in different temporal constraints: an STDP learning rule for robots controlled by spiking neural networks. Adaptive Behavior, Vol. 20, No 4, pp. 257-272.

  • Cyr, A., & Boukadoum, M. (2013). Habituation: a non-associative learning rule design for spiking neurons and an autonomous mobile robots implementation. Bioinspiration & Biomimetics, Vol. 8, No.1, 016007.

  • Cyr, A., & Thériault, F. (2015). Action Selection and Operant Conditioning: A Neurorobotic Implementation. Journal of Robotics. Vol. 2015, Article ID 643869, 10 pages.

  • Cyr, A., & Thériault, F. (2019). Spatial Concept Learning: A Spiking Neural Network Implementation in Virtual and Physical Robots. Computational Intelligence and Neuroscience, Article ID 8361369, 8 pages.

  • Cyr, A., Avarguès-Weber, A. & Thériault,F. (2017). Sameness/difference spiking neural circuit as a relational concept precursor model: A bio-inspired robotic implementation,
    Biologically Inspired Cognitive Architectures,Vol. 21, pp. 59-66.

  • Cyr, A., Boukadoum, M., & Poirier, P. (2009). AI-SIMCOG: a simulator for spiking neurons and multiple animats’ behaviours. Neural Computing and Applications, Vol. 18, No. 5, 431-446.

  • Cyr, A., Boukadoum, M., & Thériault, F. (2012). NeuroSim: A Virtual 3D-World to Investigate the Intelligence Phenomenon within the Perspective of Bio-inspired Robotic Agents. In Virtual worlds: Artificial ecosystems and digital art exploration. Bornhofen, S., et al. eds., Science eBooks, Chapter 12, pp. 167-185.

  • Cyr, A., Boukadoum, M., & Thériault, F. (2014). Operant conditioning: a minimal components requirement in artificial spiking neurons designed for bio-inspired robot's controller. Frontiers in neurorobotics, 8.

  • Cyr, A., Morand-Ferron, J., & Thériault, F. (2020). Dual exploration strategies using artificial spiking neural networks in a robotic learning task. Adaptive Behavior, 1059712320924744.

  • Cyr, A., Theriault, F. (2019): Bio-inspired visual attention process using spiking neural networks controlling a camera. In press. International Journal of Computational Vision and Robotics, vol. 9, no. 1, pp. 39-55.

  • Cyr, A., Thériault, F., & Chartier, S. (2019). Revisiting the XOR problem: a neurorobotic implementation. Neural Computing and Applications, 1-9. DOI:10.1007/s00521-019-04522-0

  • Cyr, A., Thériault, F., Ross, M., Berberian, N. & Chartier, S. (2018). Spiking Neurons Integrating Visual Stimuli Orientation and Direction Selectivity in a Robotic Context. Frontiers in Neurorobotics vol. 12, Article 75, 10 pages.

R

  • Ross, M., Berberian, N., Cyr, A., Thériault, F., Chartier, S. (2017, September). Learning Distance-Behavioural Preferences Using a Single Sensor in a Spiking Neural Network. In Lintas A., Rovetta S., Verschure P., Villa A. (eds). Artificial Neural Networks and Machine Learning – ICANN 2017, Alghero, Italy, pp.110-118.

  • Ross, M., Payeur, P. & Chartier, S. (2019, June). Task Allocation for Heterogeneous Robots Using a Self-Organizing Contextual Map. IEEE International Symposium on RObotic and Sensors Environments (ROSE), Ottawa, Canada. 6 pages.

W

  • Wu, W., Payeur, P., Al-Buraiki, O., & Ross, M. (2019, August). Vision-Based Target Objects Recognition and Segmentation for Unmanned Systems Task Allocation. Proceedings of the International Conference on Image Analysis and Recognition, Waterloo, Canada, pp. 252-263.

Spiking neural networks

B

  • Berberian, N., MacPherson, A., Giraud, E., Richardson, L., & Thivierge, J.P. (2017). Neuronal Pattern Separation of Motion-Relevant Input in LIP activity. Journal of Neurophysiology, Vol. 117, No. 2, pp. 738-755.

  • Berberian, N., Ross, M. & Chartier, S. (2019). Discrimination of Motion Direction in a Robot Using a Phenomenological Model of Synaptic Plasticity. Computational Intelligence and Neuroscience. Article ID 6989128, 14 pages.

  • Berberian, N., Ross, M., Chartier, S., Thievierge, J.-P. (2017, December). Synergy Between Short-Term and Long-Term Plasticity Explains Direction-Selectivity in Visual Cortex. IEEE Symposium Series on Computational Intelligence (SSCI), Honolulu, USA. 8 pages.

C

  • Calderini, M., Zhang S., Berberian N., Thivierge J.P., (2018). Optimal Readout of Correlated Neural Activity in a Decision Making Circuit. Neural Computation, Vol. 30, No. 6, pp. 1573-1611.

  • Chartier, S. & Renaud, P. (2006, May). Eye-tracker Data Filtering Using Pulse Coupled Neural Network. Proceeding of the 17th IASTED International Conference on Modelling and Simulation, Montréal, Canada, pp. 91-96.

  • Cyr, A., & Boukadoum, M. (2012). Classical conditioning in different temporal constraints: an STDP learning rule for robots controlled by spiking neural networks. Adaptive Behavior, Vol. 20, No 4, pp. 257-272.

  • Cyr, A., & Boukadoum, M. (2013). Habituation: a non-associative learning rule design for spiking neurons and an autonomous mobile robots implementation. Bioinspiration & Biomimetics, Vol. 8, No.1, 016007.

  • Cyr, A., & Thériault, F. (2015). Action Selection and Operant Conditioning: A Neurorobotic Implementation. Journal of Robotics. Vol. 2015, Article ID 643869, 10 pages.

  • Cyr, A., & Thériault, F. (2019). Spatial Concept Learning: A Spiking Neural Network Implementation in Virtual and Physical Robots. Computational Intelligence and Neuroscience, Article ID 8361369, 8 pages.

  • Cyr, A., Avarguès-Weber, A. & Thériault,F. (2017). Sameness/difference spiking neural circuit as a relational concept precursor model: A bio-inspired robotic implementation,
    Biologically Inspired Cognitive Architectures,Vol. 21, pp. 59-66.

  • Cyr, A., Boukadoum, M., & Poirier, P. (2009). AI-SIMCOG: a simulator for spiking neurons and multiple animats’ behaviours. Neural Computing and Applications, Vol. 18, No. 5, 431-446.

  • Cyr, A., Boukadoum, M., & Thériault, F. (2012). NeuroSim: A Virtual 3D-World to Investigate the Intelligence Phenomenon within the Perspective of Bio-inspired Robotic Agents. In Virtual worlds: Artificial ecosystems and digital art exploration. Bornhofen, S., et al. eds., Science eBooks, Chapter 12, pp. 167-185.

  • Cyr, A., Boukadoum, M., & Thériault, F. (2014). Operant conditioning: a minimal components requirement in artificial spiking neurons designed for bio-inspired robot's controller. Frontiers in neurorobotics, 8.

  • Cyr, A., Morand-Ferron, J., & Thériault, F. (2020). Dual exploration strategies using artificial spiking neural networks in a robotic learning task. Adaptive Behavior, 1059712320924744.

  • Cyr, A., Theriault, F. (2019): Bio-inspired visual attention process using spiking neural networks controlling a camera. In press. International Journal of Computational Vision and Robotics, vol. 9, no. 1, pp. 39-55.

  • Cyr, A., Thériault, F., & Chartier, S. (2019). Revisiting the XOR problem: a neurorobotic implementation. Neural Computing and Applications, 1-9. DOI:10.1007/s00521-019-04522-0

  • Cyr, A., Thériault, F., Ross, M., Berberian, N. & Chartier, S. (2018). Spiking Neurons Integrating Visual Stimuli Orientation and Direction Selectivity in a Robotic Context. Frontiers in Neurorobotics vol. 12, Article 75, 10 pages.

  • Cyr A., Thériault F., Ross M., Chartier S. (2018, August) Associative Memory: An Spiking Neural Network Robotic Implementation. In Iklé M., Franz A., Rzepka R., Goertzel B. (eds) Artificial General Intelligence 2018. Lecture Notes in Computer Science, (31-41). Springer

J

  • Jeanson, F., & Chartier, S. (2013, July). Memory Control in a FitzHugh-Nagumo Network via STDP. In R. West & T. Stewart (Eds.). Proceedings of the 12th International Conference on Cognitive Modeling, Ottawa, Canada, pp. 137-142.

  • Johnson, M. & Chartier, S. (2018). Spike Neural Models Part II: Abstract Neural Models. Tutorials in Quantitative Methods for Psychology, Vol. 14, No. 1, 2018, pp. 1-16.

  • Johnson, M., & Chartier, S. (2017, September). Model Derived Spike Time Dependent Plasticity. In Lintas A., Rovetta S., Verschure P., Villa A. (eds). Artificial Neural Networks and Machine Learning – ICANN 2017, Alghero, Italy, pages 345-353.

  • Johnson., M. & Chartier, S. (2017). Spike neural models Part I: The Hodgkin-Huxley model, The Quantitative Methods for Psychology, Vol. 13, no. 2, pp. 105-119.

M

  • Morissette, L. & Chartier, S. (2015, July). Saliency model of auditory attention based on frequency, amplitude and spatial location. Proceedings of the International Joint Conference on Neural Networks, Killarney, Ireland, 6 pages.

R

  • Ross, M., Berberian, N., Cyr, A., Thériault, F., Chartier, S. (2017, September). Learning Distance-Behavioural Preferences Using a Single Sensor in a Spiking Neural Network. In Lintas A., Rovetta S., Verschure P., Villa A. (eds). Artificial Neural Networks and Machine Learning – ICANN 2017, Alghero, Italy, pp.110-118.

Z

  • Zamani, M., Sadeghian A. & Chartier, S. (2010, July). A Bidirectional Associative Memory Based on Cortical Spiking Neurons Using Temporal Coding. Proceedings of the International Joint Conference on Neural Networks, Barcelona, Spain, 7 pages.

Tutorials

C

  • Chartier, S. & Allaire, J.-F. (2007). Power Estimation in MANOVA. The Quantitative Methods for Psychology, vol. 3, no. 2, pp. 70-78.

  • Chartier, S. & Cousineau, D. (2011). Computing Mixed-Design (split-plot) ANOVA. The Mathematica Journal, Vol. 13, 22 pages.

  • Chartier, S. & Faulkner, A. (2008). General Linear Models: An Integrated Approaches to Statistics. The Quantitative Methods for Psychology, vol. 4, no. 2, pp. 65-78.

  • Cousineau, D. & Chartier, S. (2010) Outliers detection and treatment: A review. International Journal of Psychological Research, Vol. 3, No. 1, 58-67.

J

  • Johnson, M. & Chartier, S. (2018). Spike Neural Models Part II: Abstract Neural Models. Tutorials in Quantitative Methods for Psychology, Vol. 14, No. 1, 2018, pp. 1-16.

  • Johnson., M. & Chartier, S. (2017). Spike neural models Part I: The Hodgkin-Huxley model, The Quantitative Methods for Psychology, Vol. 13, no. 2, pp. 105-119.

L

  • Langlois, D. Chartier, S. & Gosselin, D. (2010). Independent component analysis. The Quantitative Methods for Psychology, Vol. 6, No. 1, 31-38.

M

  • Morissette, L. & Chartier, S. (2013). The k-means clustering technique: general considerations and implementation in Mathematica. The Quantitative Methods for Psychology, Vol. 9, No. 1, pp. 15-24.

V

  • Van Roon, P., Zakizadeh, J., & Chartier, S. (2014). Partial Least Squares tutorial for analyzing neuroimaging data. The Quantitative Methods for Psychology, Vol. 10, No. 2, pages 200-215.

W

  • Watier, N., Lamontagne, C. & Chartier, S. (2011). What does the mean mean? Journal of Statistics Education, Vol. 19, No. 2, 20 pages.

  • Watier, N., Lamontagne, C. & Chartier, S. (2014) Descriptive Statistics. Probability and Statistics. In José I. Barragués, Adolfo Morais & Jenaro Guisasola (Eds.), Probability and Statistics: A Didactic Introduction, (pp. 1-37). CRC Press.

Vision

B

  • Berberian, N., Ross, M. & Chartier, S. (2019). Discrimination of Motion Direction in a Robot Using a Phenomenological Model of Synaptic Plasticity. Computational Intelligence and Neuroscience. Article ID 6989128, 14 pages.

C

  • Cyr, A., Thériault, F., Ross, M., Berberian, N. & Chartier, S. (2018). Spiking Neurons Integrating Visual Stimuli Orientation and Direction Selectivity in a Robotic Context. Frontiers in Neurorobotics vol. 12, Article 75, 10 pages.

O

  • Orbán, L. L., Plowright CMS, Chartier, S., Thompson, E., & Xu, V. (2015) Visual Choice Behaviour by Bumblebees (Bombus impatiens) Confirms Unsupervised Neural Network’s Prediction. Journal of Comparative Psychology. Vol.129, No. 3, 229-236.

Projects

istock_project_resized
Architecture Development and Nonlinearly Separable Tasks

Usually, bidirectional associative memories can only discriminate between two classes; if a straight line can separate the data on a plane. Such supervised learning is encountered in many real-life situations. For example, associating a name with a phone number is a classic linear association. In logic, it is similar of accomplishing the OR gate. This type of classification is robust to noise and can generalize to new data. However, there are many examples in real life where linear separation does not happen. A well-known example is the fulcrum problem in which humans integrate information across two dimensions (in this case, weight and distance). In logic this is called the XOR gate. Many multi-layer networks can accomplish nonlinearly separable tasks. However, most of them lack biological plausibility. Therefore, if we want to increase the BAM model’s explanatory power, it should also take into account nonlinear separable tasks (1). This way, both unsupervised and complex supervised learning could be incorporated within the same model. A solution would be to consider an iterative architecture development scheme (e.g. cascade correlation; (2)). It has been shown that such a technique enables networks to accomplish the task (3). Hence, another hypothesis is that a “growing” network could tackle this class of problems. To test this hypothesis, we will need to add a free parameter to decide when to “expand” the model (e.g., when a new unit needs to be recruited). In addition, we will need to study how each unit is connected and trained in regards to the initial BAM.

  1. Chartier, S., Leth-Steensen, C. & Hébert, M.-F. (2012). Performing Complex Associations Using a Generalized Bidirectional Associative Memory. Journal of Experimental & Theoretical Artificial Intelligence, vol. 24, no. 1, pages 23-42
  2. Fahlman, S. E., & Lebiere, C. (1990). The cascade-correlation learning architecture. Advances in Neural Information Processing Systems II, 524-532.
  3. Tremblay, C., Myers-Stewart, K., Morissette, L., & Chartier, S. (2013, July). Bidirectional Associative Memory and Learning of Nonlinearly Separable Tasks. In R. West & T. Stewart (Eds.), Proceedings of the 12th International Conference on Cognitive Modeling, Ottawa, Canada, pp. 420-425.
Reinforcement Learning for Associative Memory

Both unsupervised and supervised learning are passive. In contrast, reinforcement learning implies that the network must be active; it must generate a possible action (output). The environment provides only success or failure of a given solution. Therefore, in the case of failure, the network must try a new potential solution based on its encoded knowledge (exploitation). In some situations, the network could have exhausted all potential solutions and therefore it must generate a novel solution (exploration) (1). Because of the passive aspect of BAM, few attempts have been made to implement reinforcement learning. First a simple reinforcement learning technique was used (2). Then a better implementation, using Q-learning, was used in a recurrent associative memory (3). In both cases, results showed the possibility of implementing reinforcement learning. 

  1. Sutton, R. S. & Barto, A. G. (1998). Reinforcement Learning: An Introduction. Cambridge: MIT Press.
  2. Chartier, S. Boukadoum, M. & Amiri, M. (2009). BAM learning of nonlinearly seperable tasks by using an asymmetrical output function and reinforcement learning, IEEE Transactions on neural networks, vol. 20, pp. 1281-1292.
  3. Salmon, R., Sadeghian, A. & Chartier, S. (2010). Reinforcement learning using associative memory networks, Proceedings of the International Joint Conference on Neural Networks, Barcelona, Spain, 7 pages.
Plasticity in Developing Neural Circuits

Activity-dependent changes in synaptic transmission arise from a large number of mechanisms known as synaptic plasticity. Synaptic plasticity can be divided into three broad categories: (1) long-term plasticity, where changes are prolonged for hours or longer that result in learning and memory (2) homeostatic plasticity, where synapses and neurons maintain excitability and connectivity despite abrupt changes resulting from experience-dependent plasticity; (3) short-term plasticity, where changes in synaptic strength occur over milliseconds to minutes. In neural circuits, neurons coding for loaded items exhibit patterns of activity that are quite distinguishable from others that stay at baseline level of activity. Within this scheme, it remains unknown how the collective contribution of short-term, long-term and homeostatic plasticity results in the gain, maintenance, or loss of information in neural circuits. To tackle this problem, it becomes important to examine the synergistic interaction between these distinct, yet ubiquitous mechanisms of plasticity in neural circuits.

  1. Berberian N., Ross M., Chartier S., Thivierge J.P. (2017). Synergy Between Short-Term and Long-Term Plasticity Explains Direction Selectivity in Visual Cortex. IEEE Symposium Series on Computational Intelligence (IEEE SSCI), 1-8.
  2. Costa R P., Froemke R. C., Sjöström P. J., & van Rossum M. C. W. (2015). Unified pre- and postsynaptic long-term plasticity enables reliable and flexible learning, Elife, vol. 4, pp. 1–16.
  3. Mongillo G., Barak O., & Tsodyks M. (2008). Synaptic Theory of Working Memory. Science, vol. 319, no. 5869, pp. 1543–1546.
Learning Relational Concepts

Relational concepts learning represents the process of abstracting rules between stimuli, without any precise references of their physical features. Several animal species already show this capacity (1). As an example, Above/Below is one of the possible spatial relational concepts that may be learned from natural agents, be they small as invertebrates (2). Moreover, more than one relation could be learned at the same time (3). Even if empirical data are available from small neural organisms, a precise circuit involving a relational concept learning process remains to be found. This cognitive phenomenon may also be investigated from different computational tools, as with artificial spiking neurons controlling a robot (4). One objective of our lab is to challenge our models, inserting relevant facts to this cognitive process from an artificial intelligence perspective.

  1. Zentall, T. R., Wasserman, E. A., & Urcuioli, P. J. (2014). Associative concept learning in animals. Journal of the experimental analysis of behavior, 101(1), 130-151.
  2. Avarguès-Weber, A., Dyer, A. G., & Giurfa, M. (2010). Conceptualization of above and below relationships by an insect. Proceedings of the Royal Society of London B: Biological Sciences, rspb20101891.
  3. Avarguès-Weber, A., Dyer, A. G., Combe, M., & Giurfa, M. (2012). Simultaneous mastering of two abstract concepts by the miniature brain of bees. Proceedings of the National Academy of Sciences, 201202576.
  4. Cyr, A., Avarguès-Weber, A., & Theriault, F. (2017). Sameness/difference spiking neural circuit as a relational concept precursor model: A bio-inspired robotic implementation. Biologically Inspired Cognitive Architectures, 21, 59-66.

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Laboratory for computational neurodynamics and cognition

School of Psychology
Faculty of Social Sciences
University of Ottawa
136 Jean-Jacques Lussier
Vanier Hall, Room 3022
Ottawa, Ontario, Canada K1N 6N5
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Tel.: 613-562-5800 ext. 4419

Sylvain.Chartier@uOttawa.ca