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  • Assistant Professor, Elect & Computer Engineering
  • Research Assistant Professor, Ophthalmology & Visual Science
  • Adjunct Assistant Professor, Biomedical Engineering
  • Affiliate faculty, Neuroscience Program

Publications

  • Niknam, K. & Akbarian Aghdam, A., Noudoost, B., Nategh, N (2019). A Novel Computational Model Capturing Dynamic Changes in the Perisaccadic Response of Visual Neurons. Computational and Systems Neuroscience. Accepted, 01/2019.
  • Akbarian Aghdam, A. & Niknam, K., Noudoost, B., Nategh, N. (2018). Inferring saccadic modulation sources and their computations using a model-based characterization of spiking responses in extrastriate visual cortex. Society for Neuroscience. Published, 11/2018.
  • Nategh, N. (2018). Adaptive Image Processing and Computational Vision. Computational Optical Sensing and Imaging. Published, 11/2018.
  • Niknam, K. & Akbarian Aghdam, A., Noudoost, B., Nategh, N. (2018). A state-based statistical model for characterizing the modulation of sensory cortical responses. Society for Neuroscience. Published, 11/2018.
  • Niknam, K. & Akbarian Aghdam, A., Noudoost, B., Nategh, N. (2018). Characterizing Unobserved Factors Driving Local Field Potential Dynamics Underlying a Time-varying Spike Generation. IEEE Global Conference on Signal and Information Processing. Published, 11/2018.
  • Niknam, K. & Akbarian Aghdam, A., Noudoost, B., Nategh, N. (2018). Characterizing Unobserved Factors Driving Local Field Potential Dynamics Underlying a Time-varying Spike Generation. IEEE Global Conference on Signal and Information Processing. Published, 09/2018.
  • Niknam, K., Akbarian Aghdam, A., Noudoost, B., Nategh, N. "A Computational Model for Characterizing MT Visual Information Using Both Spikes and Local Field Potentials". Proceedings of the 8th International IEEE EMBS Conference on Neural Engineering. Published, 08/15/2017.
  • Niknam, K., Akbarian Aghdam, A., Noudoost, B., Nategh, N. "Model-based Decoding of Time-varying Visual Information During Saccadic Eye Movements Using Single- and Multi-neuron MT Responses". Paper accepted for the 51st Annual Asilomar Conference on Signals, Systems and Computers. Accepted, 07/2017.
  • Niknam, K. & Akbarian Aghdam, A., Noudoost, B., Nategh, N. (2017). A Computational Model for Characterizing MT Visual Information Using Both Spikes and Local Field Potentials. International IEEE EMBS Conference on Neural Engineering. Published, 05/2017.
  • Noudoost, B., Nategh. N., Clark, K., Esteky, H. "Stimulus Context Alters Neural Representations of Faces in Inferotemporal Cortex". J Neurophysiol. Published, 01/01/2017.
  • Akbarian Aghdam, A. & Parsa, MB., Nategh, N., Noudoost, B. (2016). MT Neurons Preserve Visually Selective Signals Across Eye Movements. Society for Neuroscience. Published, 11/2016.
  • Niknam, K., Akbarian Aghdam, A., Noudoost, B., Nategh, N. "Modeling Perisaccadic Visual Representation in MT Neurons by Incorporating Population-level Information". (Abstract). Neuroscience 2016. Published, 11/2016.
  • Akbarian Aghdam, A., Parsa, MB., Noudoost, B., Nategh, N. "Spatiotemporal Dynamics of MT Receptive Fields During Eye Movements". (Abstract). Neuroscience 2015. Published, 10/2015.
  • Nategh, N., Manu, M., Baccus, S.A. "Understanding Modulatory Computations in Neural Pathways of the Retina". (Abstract). Neuroscience 2014. Published, 11/2014.
  • Nategh, N., Manu, M., Baccus, S.A. "Characterizing the Modulatory Contribution of a Sensory Interneuron in the Retina". (Abstract). Neuroscience 2012. Published, 10/2012.
  • Nategh, N., Manu, M., Baccus, S.A. "Contribution of Amacrine Transmission to Fast Adaptation of Retinal Ganglion Cells". (Abstract). Computational and Systems Neuroscience (Cosyne 2010). Published, 02/2010.
  • Nategh, N., Manu, M., Baccus, S.A. "Feature-specific Control of Retinal Ganglion Cell Gain and Kinetics by Amacrine Cells". (Abstract). Neuroscience 2009. Published, 10/2009.

Research Statement

The research in our lab exploits and extends computational and theoretical techniques from multiple disciplines, including statistical signal processing, statistical machine learning, dimensionality reduction, statistical inference, information theory, and dynamical systems. We also collaborate closely with experimental neuroscience labs at the University of Utah and at Stanford University to collect electrophysiological data from the brain and the retina.

Current research includes:

VISUAL CODING AND COMPUTATION

We exploit, extend, and develop statistical models and methods to study:

  • nonlinear, adaptive code of visual information processing in the retina
  • retinal object motion computations in the presence of eye movements
  • spatiotemporal receptive field estimation of cortical neurons during saccadic eye movements
  • encoding and decoding of visual information during eye movements
  • multilayer network models of visual motion representation in spiking neurons
  • feedback network architectures to capture more complex dynamical processes in visual coding

NEURO-INSPIRED COMPUTATIONAL VISION

By incorporating our knowledge about the computational properties of neural systems, we introduce novel approaches for computer vision solutions by enabling concurrent autonomy, adaptability, efficiency, and robustness in real world settings. The current focuses of our lab include the following developments:

  • computational algorithms for detection, segmentation and tracking of moving objects based on retinal and cortical visual motion processing
  • computational frameworks for visual simultaneous localization and mapping (SLAM) based on computational models of visual attention
  • deep network architectures incorporating phenomenological models of neurovisual computations, which are interpretable in the context of biophysical mechanisms
  • computational solutions for image stabilization and camera motion compensation based on the encoding and decoding models of eye movement signals

The projects have been funded by National Science Foundation (NSF) and National Aeronautics and Space Administration (NASA).

More information about our research can be found on our lab webpage at https://visionlab.ece.utah.edu/research/.

Presentations

  • Nategh, N. The 54th Annual Asilomar Conference on Signals, Systems and Computers (Asilomar 2020). Pacific Grove, CA, 2020. Invited Talk/Keynote, Accepted, 01/2020.
  • Characterizing Unobserved Factors Driving Local Field Potential Dynamics Underlying a Time-varying Spike Generation. Presented at the 6th IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2018. Authors: Niknam, K., Akbarian Aghdam, A., Noudoost, B., Nategh, N. Conference Paper, Refereed, Presented, 11/2018.
  • A state-based statistical model for characterizing the modulation of sensory cortical responses. Poster presented at Neuroscience 2018, Society for Neuroscience Meeting, San Diego, CA Authors: Niknam, K., Akbarian Aghdam, A., Noudoost, B., Nategh, N. Other, Presented, 11/2018.
  • Inferring saccadic modulation sources and their computations using a model-based characterization of spiking responses in extrastriate visual cortex. Poster presented at Neuroscience 2018, Society for Neuroscience Meeting, San Diego, CA Authors: Akbarian Aghdam, A., Niknam, K., Noudoost, B., Nategh, N. Other, Presented, 11/2018.
  • Understanding the brain computations underpinning our robust vision during eye movements Presented at Snowbird Neuroscience Symposium, Salt Lake City, UT. Invited Talk/Keynote, Presented, 10/2018.
  • Adaptive Image Processing and Computational Vision. Presented at Computational Optical Sensing and Imaging (COSI), The Optical Society, Orlando, FL. Invited Talk/Keynote, Presented, 06/2018.
  • Niknam, K., Akbarian Aghdam, A., Noudoost, B., Nategh, N. "Model-based Decoding of Time-varying Visual Information During Saccadic Eye Movements Using Single- and Multi-neuron MT Responses". The 51st Annual Asilomar Conference on Signals, Systems and Computers. Poster, Accepted, 07/2017.
  • Niknam, K., Akbarian Aghdam, A., Noudoost, B., Nategh, N. "A Computational Model for Characterizing MT Visual Information Using Both Spikes and Local Field Potentials". The 8th International IEEE EMBS Conference on Neural Engineering, Shanghai, China. Poster, Presented, 05/2017.
  • Nategh, N. "Multidimensional Nonlinear Code of the Retina". American Mathematical Society Spring Western Sectional Meeting, Pullman, WA. Invited Talk/Keynote, Presented, 04/2017.
  • Nategh, N. "Bio-inspired Image Processing: cracking the nonlinear code of the retina". Electrical and Computer Engineering Department, University of Utah, Salt Lake City, UT. Invited Talk/Keynote, Presented, 04/2017.
  • Nategh, N. "Modulatory computations in the neural pathways of the visual system". Ophthalmology and Visual Sciences Department, University of Utah, Salt Lake City, UT. Invited Talk/Keynote, Presented, 04/2017.
  • Niknam, K., Akbarian Aghdam, A., Noudoost, B., Nategh, N. "Modeling Perisaccadic Visual Representation in MT Neurons by Incorporating Population-level Information". Neuroscience 2016, Society for Neuroscience Meeting, San Diego, CA. Poster, Presented, 11/2016.
  • Akbarian Aghdam, A., Parsa, MB., Nategh, N., Noudoost, B. "MT Neurons Preserve Visually Selective Signals Across Eye Movements". Neuroscience 2016, Society for Neuroscience Meeting, San Diego, CA. Poster, Presented, 11/2016.
  • Nategh, N. "Neuroscience, Neuro-engineering and Neuro-innovation". College of Engineering Seminar, Montana State University, Bozeman, MT. Invited Talk/Keynote, Presented, 11/2015.
  • Akbarian Aghdam, A., Parsa, MB., Noudoost, B., Nategh, N. "Spatiotemporal Dynamics of MT Receptive Fields During Eye Movements". Neuroscience 2015, Society for Neuroscience Meeting, Chicago, IL. (Nanosymposium). Presentation, Presented, 10/2015.
  • Nategh, N., Manu, M., Baccus, S.A. "Understanding Modulatory Computations in Neural Pathways of the Retina." Neuroscience 2014, Society for Neuroscience Meeting, Washington, DC. (Nanosymposium). Presentation, Presented, 11/2014.
  • Nategh, N. "Adaptive Coding in Natural and Artificial Vision". ECE Department Seminar, Montana State University, Bozeman, MT. Invited Talk/Keynote, Presented, 01/2013.
  • Nategh, N., Manu, M., Baccus, S.A. "Characterizing the Modulatory Contribution of a Sensory Interneuron in the Retina". Neuroscience 2012, Society for Neuroscience Meeting, New Orleans, LA. Poster, Presented, 10/2012.
  • Nategh, N. "A Nature-inspired Algorithm to Detect, Track and Segment Multiple Moving Objects in the Presence of a Moving Background". Brain-CS Meeting, Computer Science Department, Stanford University, Stanford, CA. Presentation, Presented, 03/2010.
  • Nategh, N., Manu, M., Baccus, S.A. "Contribution of Amacrine Transmission to Fast Adaptation of Retinal Ganglion Cells". Computational and Systems Neuroscience (Cosyne), Salt Lake City, UT. Poster, Presented, 02/2010.
  • Nategh, N., Manu, M., Baccus, S.A. "Feature-specific Control of Retinal Ganglion Cell Gain and Kinetics by Amacrine Cells". Neuroscience 2009, Society for Neuroscience Meeting, Chicago, IL. Poster, Presented, 10/2009.
  • Baccus, S.A., Nategh, N. "Characterization and Identification of Image Operators in the Retinal Network". Stanford Neuro-Innovation and Translational Neurosciences, Stanford University, Stanford, CA. Presentation, Presented, 03/2009.

Research Groups

  • Yasin Zamani, Graduate Student. Electrical and Computer Engineering. 01/2019 - present.
  • Amir Akbarian, Graduate Student. Electrical and Computer Engineering. 09/2014 - present.
  • Kaiser Niknam, Graduate Student. Electrical and Computer Engineering. 01/2016 - present.
  • Yasin Zamani, Visiting Student. Computer Engineering, Sharif University of Technology. 04/2016 - 11/2016.
  • MB Parsa, Graduate Student. School of Computing, Montana State University. 09/2015 - 05/2017.

Patents

  • System and method for robust real-time 1D barcode detection (#8,608,073). Status: Issued. Inventors: Farhan Baqai, Vivek Athalye, Neda Nategh, Todd Sachs. File date 01/26/2012; Issue date 12/17/2013. Assignee: Apple Inc.