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Welcome to Abbasi Lab Webpage

The Abbasi Lab develops interpretable models and algorithms based on machine learning principles to understand functions of the brain and related disorders. To discover and model the functions of neurons, the lab studies applications of machine learning in the analysis of large-scale multi-modal datasets including electrophysiology, calcium imaging, electron microscopy, and spatial gene expression profiles. In the next step and to reconcile our understanding of the brain with related disorders, the Abbasi Lab builds computational tools for scientific discovery in large-scale neurological clinical datasets.

Our research programs revolve around the development of transparent supervised and unsupervised machine learning tools to integrate multi-modal data collected from brain (and body) in both microscopic and macroscopic resolutions.

We are hiring!

We have multiple openings for postdoctoral fellows to work with us at UCSF on exciting applied problems at the intersection of machine learning and computational/clinical neuroscience. Please see Openings for more information or directly contact Reza via Reza.AbbasiAsl@ucsf.edu to learn more!

News and Events

Feb 2022: Reza will present as the plenary speaker at the ICRTB 2022.

Feb 2022: Our paper on the robust registration of MRIs is now published in Algorithms.

Jan 2022: We have been awarded a new multi-institution grant from the Weill Neurohub to develop ML-based computational tools for the integrated analysis of 7T and 3T MRI.

Jan 2022: We have received a second New Frontiers Research Award from the Sandler Program for Breakthrough Biomedical Research.

Dec 2021: Reza is presenting as an invited speaker at the CMStatistics 2021.

Oct 2021: Roozbeh Farhoodi joins the lab as a new postdoctoral scholar. Welcome, Roozbeh!

August 2021: Congrats to Austin Jang and Shiladitya Dutta, two undergraduate students in the lab, for winning the UC Berkeley URAP fellowship for their research projects!

August 2021: New paper published in Frontiers in Big Data: Structural Compression of Convolutional Neural Networks with Applications in Interpretability.

June 2021: New paper on arXiv: Multi-Modal Prototype Learning for Interpretable Multivariable Time Series Classification.

June 2021: Congrats to Austin Jang and Shiladitya Dutta, for winning the URAP summer fellowship!

May 2021: We got awarded the New Frontiers Research Award from the Sandler Program for Breakthrough Biomedical Research.

May 2021: We are hiring a fully-funded postdoc fellow to lead efforts for our new Neuroscape-based project on computational models of multi-modal biosensor data! Reach out if you are interested!

May 2021: Gavin Cui joins the lab as a bioengineering PhD student. Welcome Gavin!

April 2021: Our NIH grant in collaboration with Neuroscape PIs (Ted Zanto and David Ziegler) on “Neural markers of impending task performance” just got funded!

Feb 2021: One abstract accepted at VSS 2021 on our large-scale standardized survey of neural receptive fields in an entire column in mouse V1.

Feb 2021: We are hiring two new postdoc fellows to join us at UCSF and lead our efforts on developing a computational pipeline to analyze spatial gene expression in mice.

Feb 2021: Reza will be giving a talk at the faculty seminar at the Kavli Institute.

Jan 2021: Reza becomes a Weill Neurohub Investigator.

Jan 2021: We got awarded the Weill Neurohub Next Great Ideas Fund! Our proposed project is focused on spatial gene expression analysis in mice.

Nov 2020: Reza will give an invited talk at the 2020 Basic and Clinical Neuroscience.

Oct 2020: One abstract accepted at CSHL NAISys meeting on interpretable models of neurons in visual cortex.

August 2020: One abstract accepted at JSM 2020 on Revealing Spatial Gene Patterns and Interactions in Mouse Brain via Stability-Driven NMF.

May 2020: One abstract accepted at CNS 2020 on compressed models of neurons in V4!

May 2020: Our paper featured on the cover of Neuron!

March 2020: Reza is now a joint faculty at the Department of Bioengineering and Therapeutic Sciences at UCSF.

March 2020: We are now officially affiliated with Neuroscape!

March 2020: The dataset from our recent eNeuro paper is now available on the CRCNS data repository.

March 2020: Here is a nice read from Allen Institute on our recent Neuron paper.

March 2020: Due to the recent Corona Virus outbreak, the lab has moved to fully remote mode! Hope everyone is staying safe and healthy!

March 2020: We are happy to welcome to the lab Oluwaseun Adegbite as rotation PhD student, Luke Chen as an RA, and five UC Berkeley undergraduates through the URAP program: Kevin Lin, Rohan Divate, Austin Jang, Anmol Parande, Felix Ching!

Feb 2020: Reza will give an invited talk at ITA 2020 on interpretation of neural networks with applications in comp neuroscience.

Jan 2020: We will be present at Cosyne 2020 with one invited talk and one accepted abstract on compressed interpretable models of neurons. Reza will give the invited talk at the workshop on “Scrutinizing models of brain function: from in-silico stimulus synthesis to direct brain perturbation”.

Jan 2020: Our paper titled “Systematic Integration of Structural and Functional Data into Multi-Scale Models of Mouse Primary Visual Cortex” has been accepted at Neuron.

Jan 2020: Our paper titled “Superficial bound of the depth limit of 2-photon imaging in mouse brain” appeared on eNeuro.

Jan 2020: We are now affiliated with Bakar Computational Health Sciences Institute at UCSF! Reza will be a faculty affiliate at the institute.

Dec 2019: Welcome to the lab Neel!

Nov 2019: Welcome to the lab Ahyeon and Franklin!

Nov 2019: Reza joined the UC Berkeley-UCSF graduate program in bioengineering. We have multiple openings for rotation students!

Oct 2019: Our paper titled “Definitions, methods, and applications in interpretable machine learning” appeared on PNAS.

Oct 2019: Reza is presenting at SfN on functional imaging of excitatory neurons in an entire column in mouse visual cortex. Here is the abstract.

Sep 2019: Abbasi Lab officially started!

Research

Our lab’s vision is to better understand brain functions and its related disorders by designing specialized interpretable tools from machine learning and statistics. More specifically, our research programs revolve around the development of transparent supervised and unsupervised machine learning tools to integrate multi-modal data collected from brain (and body) in both microscopic and macroscopic resolutions. Some of the specific projects in the lab include:

Functional modeling in brain

We develop models and algorithms based on principals in statistics and machine learning to understand functions in the visual cortex of brain. To achieve this, we study large-scale datasets with a variety of modalities such as electrophysiology, calcium imaging, and electron microscopy, and gene expression.

Specific projects:

Defining the function of any organ including the brain, depends on defining and describing its building blocks: tissues, tissue domains, and cell types. Genome-wide characterization of gene expression, and machine learning approaches have transformed the understanding of cell types that build the nervous system. However, the precise arrangement of cells types and their differences across the brain areas will require interpretation of spatial gene expression data. In collaboration with Hongkui Zeng and Bosiljka Tasic at the Allen Institute and Bin Yu at UC Berkeleythis project aims to integrate spatial gene expression and neural connectivity data to reveal building blocks of spatial gene expression profiles in the mouse and human brain. These building blocks will partition the brain into completely data-driven functional 3D brain areas and establish local gene networks. The project involves designing and validating unsupervised and interpretable machine learning frameworks and statistical tools. 

Characterizing the relationship between neural function and connectivity is an eminent question of visual sensory processing. In order to explore this relationship and in collaboration the Allen Institute, we are analyzing recorded visual responses from pan-excitatory neurons within an 800X800 um region of primary visual cortex, spanning all visual layers from pia to white matter. This includes 750 2-photon and 35 3-photon calcium imaging planes spaced by ~16 um. Our goal is to examine the single-cell and population activity in primary visual cortex, and along with Electron Microscopic reconstruction from the same tissue, will serve as a valuable resource to study the functional connectome in mouse cortex.

Models based on deep neural network are effective in predicting single neuron responses in primate visual cortex. Despite their high predictive accuracy, these models are generally difficult to interpret. This limits their applicability in characterizing neuron function. We investigate methods and algorithms to elicit interpretation and visualization of models of neurons based on deep neural networks. To achieve this, we develop frameworks based on stability, compression, and sparsity.

Computational clinical neuroscience

As a part of the UCSF Neuroscape Center and in order to bridge our understanding of the brain to patient data, we build tools to study and visualize large-scale neurological clinical datasets. Our goals are to characterize the relationship between multi-modal patient data and the disease processes to enable efficient biomarker identification.  This involves analyzing and visualizing longitudinal medical images, genomic data, biosensor data, and other relevant datasets collected from patients.

Specific projects:

Multiple Sclerosis (MS) is the leading cause of nontraumatic neurological disability in young adults, and it is estimated that 3 in 1000 US adults, or nearly 1 million individuals, are affected. Development of effective therapeutics for MS represents one of the great success stories of modern molecular medicine, with near-complete suppression of clinical attacks and focal brain inflammation now possible for most patients.  However, the more disabling neurodegenerative component of the disease – progressive MS – remains poorly treated, and the development of potent therapeutics for progressive MS has been limited by an inability to efficiently extract clinically meaningful MRI data corresponding to changes in white matter lesions, global and regional atrophy, and neurodegeneration. Various data modalities are used to assess the progression of MS including brain MRIs, behavioral measures, and genomic data. The multi-modal nature of the large-scale data collected from these patients demand for automated computational tools processing methods. In collaboration with the UCSF Multiple Sclerosis (MS) Center, this project aims to systematically integrate and analyze longitudinal MRI and genomics data from hundreds of patients with MS to better understand the disease process and its biomarkers. More specifically, this project leverages one-of-a-kind UCSF EPIC and ORIGINS MS datasets to build state-of-the-art interpretable machine learning pipelines to characterize dynamic changes over time and their relationship to MS progression. Additionally, the project involves the development of state-of-the-art and transparent machine learning tools to build computational pipelines for 3T and 7T MRI.

A deeper understanding of an individual’s state (e.g, stress, mood, attention, arousal, awareness) requires recording continuous data across multiple modalities and integrating these signals to generate meaningful and predictive composite measures. In collaboration with the UCSF Neuroscape Center, this project aims to systematically integrate hundreds of different biosensor data collected from the brain and body to predict the emotional state in humans. The data consists of high-density EEG, EMG, ECG, EOG, Continuous blood pressure, Trans Radial Electrical Impedance Velocimetry, respiration, EDA/GSR, and accelerometers readings. This project involves the development of interpretable machine learning tools to integrate and analyze large-scale time-series data. 

Advanced Parkinson’s disease (PD) is characterized by motor and non-motor symptoms which are highly disabling and significantly impair quality of life. Optimizing dopaminergic medication, Deep Brain Stimulation (DBS), and lifestyle interventions to minimize symptoms is central to the management of PD. Achieving this goal requires accurate measures of symptom severity and fluctuations. At-home objective symptom quantification introduces a potential solution, but due to technical limitations, wearable tracking devices have not yet been readily adopted into clinical practice for PD. In collaboration with the UCSF Movement Disorders and Neuromodulation Center and Google Research, this project aims to develop the next generation of at-home video-assisted technology to track and diagnose PD. This solution is enabled with advanced machine learning tools and has the potential to significantly enhance diagnostic accuracy and optimize personalized medical therapy in PD.

Interpretable Machine Learning

Guided by scientific questions from neuroscience and biomedicine, we are interested in the general problem of interpreting machine learning models. In the past decade, research in machine learning has been principally focused on the development of algorithms and models with high predictive capabilities. However, interpreting these models remains a challenge, primarily because of the large number of parameters involved. We investigate methods based on statistical principles to build more interpretable machine learning models. 

People

Reza Abbasi-Asl

Principal Investigator

Reza is an Assistant Professor in the Department of Neurology and the Department of Bioengineering and Therapeutic Sciences at UCSF. He is a core faculty member at the UCSF Neuroscape Center, a Weill Neurohub Investigator, and the Director of Data Analytics and Visualization at the UCSF Weill Institute for Neuroscience.

Before joining UCSF, Reza was a scientist at the Allen Institute for Brain Science in Seattle. He completed his PhD and MSc in Electrical Engineering and Computer Sciences at UC Berkeley in 2018, where he worked with Bin Yu and Jack Gallant to develop interpretable machine learning tools with applications in computational neuroscience. Reza received his MSc in Biomedical Engineering from Sharif University of Technology in 2013 and BSc in Electrical Engineering from Amirkabir University of Technology (Tehran Polytechnic) in 2010. He is the recipient of the New Frontiers Research Award from the Sandler Program for Breakthrough Biomedical Research (PBBR) in 2021, and the Eli Jury Award from UC Berkeley, Department of Electrical Engineering and Computer Sciences in 2018. He received the May J. Koshland Fund in Memory of H.A. Jastro Award from UC Berkeley Graduate Division in 2016, the Excellence Award in Biomedical Engineering from Sharif University of Technology in 2013, and the Excellence Award in Electrical Engineering from Tehran Polytechnic in 2010. 

Roozbeh Farhoodi

Postdoctoral scholar

Roozbeh is a postdoctoral scholar at the Abbasi Lab working at the intersection of computational neuroscience and machine learning. His project is focused on understanding the relationships between neural structure and neural activities in the primary visual cortex. Before joining UCSF, he was a postdoc at the University of Pennsylvania under the supervision of Konrad Kording where he studied the theory of deep learning and how the morphologies of neurons contribute to the computation in the brain. Roozbeh received his Ph.D. in Mathematics from Sharif University in 2017 where he worked on the emergent patterns in neural networks.

Zhinoos Razavi

Postdoctoral scholar

Zhinoos is a Postdoctoral scholar at the Abbasi Lab working on brain mapping project.
Before joining UCSF, she was a Research fellow at Department of Medicine in
the University of Melbourne, Melbourne Australia. She has received her PhD from
Data analytic labs, School of Computer Science at Royal Melbourne Institute of
Technology (RMIT), Australia. Prior to joining RMIT, Zhinoos was a researcher at the data analytic group at IBM, Melbourne, Australia; a research scientist at University of Aveiro,
Portugal; and a research scientist at the Georgia Institute of Technology, Atlanta,
USA. She contributed to many machine learning projects and co-supervised master and PhD projects in the multidisciplinary field of seizure detection/prediction and machine learning. Her research areas include data science, machine learning, signal processing, and computational neuroscience

Gavin Cui

PhD Student, Bioengineering

Gavin is a PhD student in the UC Berkeley-UCSF joint Bioengineering program. Before coming to the program, Gavin was an undergraduate researcher at McGill University, where he generated MRI-based simulations of transcranial Direct Current Stimulation. Gavin’s background in Neuroscience and Medical Imaging prompts him to explore projects that apply machine learning methods for predicting neurological disorder prognosis. Currently, Gavin’s focus in the lab is to build models that can combine longitudinal MRI data and genomics data to improve Multiple sclerosis prognosis.

Maria Olaru

PhD Candidate, Neuroscience

Maria is a PhD candidate in the UCSF Neuroscience program working with time-series data from neural implants, iEEG, and wearable sensors. Currently, she is developing neuro-electrophysiological predictive algorithms for continuous symptom measures in patients with Parkinson’s Disease that are physiologically interpretable. She is also developing closed-loop deep brain stimulation algorithms using direct EEG signal feedback that update patient stimulation parameters to alleviate symptoms and adverse stimulation-related effects. Prior to starting her PhD, she worked in the Neuroradiology Department at UCSF under the supervision of Dr. Leo Sugrue. During this time, she designed a neuroimaging framework that allows for real-time quantification of language hemisphere localization for surgical patients to guide resection procedures and developed lab-wide statistical tools for biological and behavioral data. In her spare time, she enjoys running long distances, climbing outdoors, and reading comprehensive documentation.

Meera Mehta

Research Assistant

Meera is an undergraduate student at UC Berkeley studying Chemical Biology and Computer Science. At the Abbasi Lab, she is working on using machine learning methods to associate multi-omics and GWAS data to allow the interpretation of MS variants. Previously she has worked as a research apprentice at the Plant Gene Expression Center on a project utilizing CRISPR to study the role of miRNA’s on plant immunity.

Zeyu Yun

Research Assistant

Zeyu is an undergraduate student majoring in Computer Science and Math at UC Berkeley. At Abbasi Lab, he is working on designing a method to estimate the stability of patterns extracted using deep autoencoders. His project involves leveraging these stability-driven analyses to identify low-dimensional representations in spatial gene expression datasets.

Chirag Sharma

Research Assistant

Chirag is an undergraduate at UC Berkeley majoring in Computer Science and Physics. Chirag is currently working on a collaboration project with the UCSF Weill Institute for Neurosciences and Google Health to analyze time-series motion data of Parkinson’s patients from video data. This project seeks to develop few-shot machine-learned models that can predict symptom severity and extract meaningful clinical features for identifying the incidence of motion-related neurological disorders.

Gaurav Ghosal 

Research Assistant

Gaurav is an undergraduate Electrical Engineering and Computer Science major at UC Berkeley. Gaurav is currently working on designing an interpretable machine learning framework for multi-variable time-series classification. In the future, Gaurav will apply his framework to multi-modal bio-sensing datasets, as well as continue to develop interpretable machine learning methods. Prior to joining the lab, he worked on research in applying reinforcement learning for network scheduling.

Austin Jang

Research Assistant

Austin is an undergraduate student studying Electrical Engineering and Computer Science at UC Berkeley. At Abbasi Lab, he is interested in developing tools to interpret and train deep learning models to understand neurological responses to image stimuli. His project involves assessing biophysical models of the brain through state-of-the-art machine learning models.

Shiladitya Dutta

Research Assistant

Former members

Neelroop Parikshak, Neurology Resident

Rohan Divate, Undergraduate researcher

Arbaaz Muslim, Undergraduate researcher

Kevin Chen, Undergraduate researcher

Ahyeon Hwang, Research Assistant

Franklin Heng, Research Assistant

Anmol Parande, Undergraduate researcher

Join us!

We have several openings for post-docs, graduate and undergraduate students and software developers. Contact Reza directly at Reza.AbbasiAsl@ucsf.edu to learn more!

Publications

Pre-prints

Gaurav Ghosal, R. Abbasi-Asl. Multi-Modal Prototype Learning for Interpretable Multivariable Time Series Classification, 2021.

R. Abbasi-Asl∗, Y. Chen∗, A. Bloniarz, M. Oliver, Ben Willmore, J. L. Gallant and B. Yu. The DeepTune framework for modeling and characterizing neurons in visual cortex area V4, bioRxiv preprint, 2019. (WEB)

Peer-Reviewed Publications

R. Abbasi-Asl, A. Ghaffari and E. Fatemizadeh. Robust registration of medical images in the presence of spatially-varying noise, Algorithms (in press), 2022. (arXiv version)

R. Abbasi-Asl and B. Yu. Structural Compression of Convolutional Neural Networks with Applications in Interpretability, Frontiers in Big Data, 2021. (WEB)

Y.N. Billeh, B. Cai, S.L. Gratiy, K. Dai, R. Iyer, N.W. Gouwens, R. Abbasi-Asl, X. Jia, J.H. Siegle, S.R. Olsen, C. Koch, S. Mihalas, and A. Arkhipov. Systematic Integration of Structural and Functional Data into Multi-Scale Models of Mouse Primary Visual Cortex, Neuron, 2020. (Featured cover Image, WEB, bioRxiv version, DATA, Allen Institute Coverage)

K. Takasaki, R. Abbasi-Asl, J. Waters. Superficial bound of the depth limit of 2-photon imaging in mouse brain, eNeuro, 2020. (WEB, bioRxiv version , DATA)

J. Murdoch∗, C. Singh∗, K. Kumbier†, R. Abbasi-Asl†, and B. Yu. Interpretable machine learning: definitions, methods, and applications, PNAS, 2019. (WEB, NEWS)

R. Abbasi-Asl, M. Keshavarzi, D. Y. Chan. Brain-Computer Interface in Virtual Reality, 9th International IEEE EMBS Conference on Neural Engineering (NER), 2019.

K. Takasaki, J. Larkin, R. Abbasi-Asl, D. Denman, D. Millman, S. de Vries, M. Takeno, N. M da Costa, R C. Reid, J. Waters. 3-Photon Calcium Imaging of Deep Cortical Layers for Functional Connectomics, Optics and the Brain, 2019.

A. Arkhipov, N. W. Gouwens, Y. N. Billeh, S. Gratiy, R. Iyer, Z. Wei, Z. Xu, R. Abbasi-Asl, J. Berg, M. Buice, N. Cain, N. da Costa, S. de Vries, D. Denman, S. Durand, D. Feng, T. Jarsky, J. Lecoq, B. Lee, L. Li, S. Mihalas, G. K. Ocker, S. R. Olsen, R. C. Reid, G. Soler-Llavina, S. A. Sorensen, Q. Wang, J. Waters, M. Scanziani, C. Koch. Visual physiology of the Layer 4 cortical circuit in silico. PLOS Comp. Bio., 2018. (WEB, DATA)

R. Abbasi-Asl and B. Yu. Interpreting Convolutional Neural Networks Through Compression, Neural Information Processing Conference 2017 Symposium on Interpretable Machine Learning (NIPS IML Symp.), Long Beach, CA, 2017. (WEB)

R. Abbasi-Asl, C. Pehlevan, B. Yu and D. B. Chklovskii. Do retinal ganglion cells project natural scenes to their principal subspace and whiten them? IEEE Proceedings of 50th Asilomar Conference on Signals, Systems and Computers (ACSSC), Pacific Grove, CA, 2016.

A. Ahadi, H. Hayati, J. Mitra, R. Abbasi-Asl, K. Awodele. A new method for estimating the longevity and degradation of photovoltaic systems considering weather states. Frontiers in Energy. 2016; 10 (3) :277 – 285.

R. Abbasi-Asl, R. Khorsandi, B. Vosooghi Vahdat. Hammerstein-Wiener Model: A New Ap- proach to the Estimation of Formal Neural Information. Basic and Clinical Neuroscience. 2012; 3 (4) :45-51. (WEB) (A conference paper version published in IEEE Proceedings of The 16th CSI Symposium on Artificial Intelligence and Signal Processing (AISP))

A. Ghanbari, R. Abbasi-Asl, A. Ghaffari, E. Fatemizadeh. Automatic B-spline Image Registra- tion Using Histogram-based Landmark Extraction. In: IEEE-EMBS Conference on Biomedical Engineering & Sciences (IECBES), 2012. (WEB)

R. Abbasi-Asl, R. Khorsandi, Sh. Farzampour, E. Zahedi. Estimation of Muscle Force with EMG Signals Using Hammerstein-Wiener Model. In: IFMBE Proceedings 5th Kuala lumpur International Conference on Biomedical Engineering (BIOMED), Malaysia, Vol. 35, pp. 157- 160, 2011. (WEB)

R. Abbasi-Asl, E. Fatemizadeh. MMRO: A Feature Selection Criterion for MR Images Based on Alpha Stable Filter Responses. In: IEEE Proceedings of 7th Iranian Conference on Machine Vision and Image Processing (MVIP), Iran, 2011.

S. Abadpour, R. Abbasi-Asl, Gh. Moradi. Analysis of Push-Push Oscillators and Designing a Push-Push Oscillator in S Band. In: IEEE Proceedings of 10th Mediterranean Microwave Symposium (MMS), Turkey, 2010.


Selected Conference Abstracts

R. Abbasi-Asl, Arbaaz Muslim, J. Larkin, K. Takasaki, D. Millman, D. Denman, J. Lecoq, A.Arkhipov, N. W. Gouwens, J. Waters, R. C. Reid, S. E. J. DE Vries. A large-scale standardized survey of neural receptive fields in an entire column in mouse V1. VSS, 2021

R. Abbasi-Asl and B. Yu. Interpretable modeling of neurons in cortical area V4 via compressed convolutional neural networks. CNS, 2020.

R. Abbasi-Asl and B. Yu. Compressed interpretable models of neurons in cortical area V4, Cosyne, 2020.

R. Abbasi-Asl, J. Larkin, K. Takasaki, D. Millman, D. Denman, J. Lecoq, A. Arkhipov, N. W. Gouwens, J. Waters, R. C. Reid, S. E. J. DE Vries. Functional imaging of excitatory neurons in an entire column in mouse visual cortex, SfN, 2019.

G. K. Ocker, D. Millman, P. Ledochowitsch, M. D. Oliver, R. Abbasi-Asl, M. A. Buice, S. E. J. DE Vries. Measuring the effects of locomotion on visual activity in the mouse cortex in the Allen Brain Observatory, SfN, 2019.

Y.N. Billeh, B. Cai, S.L. Gratiy, K. Dai, R. Iyer, R. Abbasi-Asl, X. Jia, J.H. Siegle, S.R. Olsen, C. Koch, S. Mihalas, and A. Arkhipov. Systematic integration of structural and functional data into a multi-scale model of mouse primary visual cortex, SfN, 2019.

Y.N. Billeh, B. Cai, S.L. Gratiy, K. Dai, R. Iyer, N.W. Gouwens, R. Abbasi-Asl, X. Jia, J.H. Siegle, S.R. Olsen, C. Koch, S. Mihalas, and A. Arkhipov. Systematic Integration of Experimental Data in Biologically Realistic Models of Mouse Primary Visual Cortex: Insights and Predictions, CNS, 2019.

R. Abbasi-Asl, M. Keeshavarzi, D. Y. Chan. Brain-Computer Interface in Virtual Reality, 7th International BCI Meeting, Pacific Grove, CA, 2018. (Accepted abstract and poster presentation)

R. Abbasi-Asl, Y. Chen, A. Bloniarz, M. Oliver, J. L. Gallant and B. Yu. Deep Nets Meet Real Neurons: Pattern Selectivity in V4, 8th International Workshop on Statistical Analysis of Neuronal Data (SAND8), Pittsburgh, PA, 2017. (Accepted abstract and poster presentation)

R. Abbasi-Asl, Y. Chen, A. Bloniarz, J. L. Gallant and B. Yu. Artificial Neurons Meet Real Neurons: Pattern Selectivity In V4, INFORMS Annual Meeting, Nashville, TN, 2016. (Invited oral presentation)

R. Abbasi-Asl, Y. Chen, A. Bloniarz, J. Mairal, J. L. Gallant and B. Yu. Explaining V4 Neurons Pattern Selectivity via Convolutional Neural Network, 7th International Workshop on Statistical Analysis of Neuronal Data (SAND7), Pittsburgh, PA, 2015. (Accepted abstract and poster presentation)

R. Abbasi-Asl, R. Khorsandi, N. Mozaffari, Sh. Farzampour. A Novel Model for Estimation of the Forearm Motor Forces Using EMG Signals. In: Proceedings of The 1st International Congress of Neuromuscular and Electrodiagnostic Medicine, Iran, 2011. (Accepted abstract and poster presentation)