Computational Neuroscience and problems at the intersection of Machine Learning and Neuroscience. I am particularly interested in understanding how visual inputs are processed in both the brain and artificial neural networks, and how visual information is integrated with information from other sensory modalities and memory. I am also interested in collaborating on projects motivated by experimental results. In my research I use mathematical techniques from a wide range of disciplines, including physics, applied mathematics and machine learning.

• Obeid, D. and Konkle, T., 2021. Wiring minimization of deep neural networks reveal conditions in which multiple visuotopic areas emerge. Journal of Vision, 21(9), pp.2135-2135. (Abstract)- In preperation.

• Obeid, D. and Miller, K.D., 2021. Stabilized supralinear network: Model of layer 2/3 of the primary visual cortex. bioRxiv, pp.2020-12. (In submission)

• Obeid, D., Zavatone-Veth, J.A. and Pehlevan, C., 2020. Statistical structure of the trial-to-trial timing variability in synfire chains. Physical Review E, 102(5),p.052406.

• D. Obeid, H. Ramambason and C. Pehlevan, 2019. Structured and Deep Similarity Matching via Structured and Deep Hebbian Networks, Advances in Neural Information Processing Systems (NeurIPS), 32.

• Obeid, D., Kosterlitz, J.M. and Sandstede, B., 2010. State selection in the noisy stabilized Kuramoto-Sivashinsky equation. Physical Review E, 81(6), p.066205.

Conference talks

• D. Obeid and T. Konkle, Wiring minimization of deep neural networks reveal conditions in which multiple visuotopic areas emerge. Talk, Vision Sciences Society (VSS), 2021.

• D. Obeid, H. Ramambason and C. Pehlevan, Structured and Deep Similarity Matching via Structured and Deep Hebbian Networks. Talk, Computational and SystemsNeuroscience (COSYNE), 2020.

Poster presentations

• D. Obeid and T. Konkle, Topographic Organization in Deep Neural Networks: From Network Topology to Visuotopic Organization. Poster, Computational and Systems Neuroscience (COSYNE), 2021.

• D. Obeid and K. D. Miller, The stabilized supralinear network (SSN) model explains feature-specifc surround suppression and cortical activity decay times in V1. Poster, Computational and Systems Neuroscience (COSYNE), 2016.

• D. Obeid and K. D. Miller, The stabilized supralinear network (SSN) model explains feature-specifc surround suppression in V1. Poster, Society For Neuroscience (SFN), 2015.

• D. Obeid and K. D. Miller, A spatially extended rate model of the primary visual cortex. Poster, Computational and Systems Neuroscience (COSYNE), 2015.

• D. Obeid and K. D. Miller, A spiking network V1 model: Surround Suppression, Normalization and related phenomena. Poster, Society For Neuroscience (SFN), 2013.

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