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About Me:

A Computational Neuroscientist by heart and a Deep Learner in practice. I aim to uncover the computational mysteries of mind to build General Artificial Intelligence.


I am working as a Postdoctoral Research Scientist on the intersection of AI and Neuroscience in Mathis Lab, Center for Intelligent Systems (CIS) | ELLIS Unit, Swiss Federal Institute of Technology .

Prior to this, I briefly worked as a Postdoctoral Researcher (and currently serve as a Visiting Scientist) at after finishing my PhD in Computational Neuroscience on the topic: 'Exploring brain-wide development through deep learning' at Institut für Hirnforschung (HiFo) and Zentrum für Neurowissenschaften Zürich (ZNZ), UZH|ETH Zürich. My PhD thesis was advised by Prof. Theofanis Karayannis along with Prof. Fritjof Helmchen and Prof. Mehmet Fatih Yanik as advisory committe members. The focus of my PhD project was to develop deep learning-based tools to analyse neuro-imaging datasets which includes detection of neurons and generating an automated atlas for developing mouse and human brain sections captured through various imaging modalities. Morover, I worked on explaining the functional and anatomical correlates of neural responses in the mouse somatosensory and visual cortex.

Before starting my PhD, I studied Electrical Engineering as an undergrad followed by a Masters in Neural Systems and Computation at the Institute of Neuroinformatics ( ), Department of IT and Electrical Engineering (D-ITET), ETH Zürich and worked on 'Enhancing scale-invariance in a convolutional neural network' as a thesis project by introducing a neuro-inspired layer in the deep neural network that mimics the functionality of complex cells in the visual cortex. Moreover, I developed a robust framework to detect very fast moving objects in real-time through frame-based running deep neural network with an interface to a spike-based running neuromorphic retina sensor. My Master thesis was advised by Prof. Shih-Chii Liu, Prof. Tobi Delbruck and Prof. Rodney Douglas.


Research Experience:

From Summer 2019 to Spring 2020, I worked as an Artificial Intelligence (AI) Resident on a NeuroMoonshot at - The Moonshot Factory, Mountain View, California.

In Summer of 2018, I attended Summer Workshop on the Dynamic Brain (SWDB) at and in Seattle, Washington and worked on developing a deep neural network-based decoder to predict neural responses in mouse visual cortex.

In Summer of 2016, I worked as a Research Intern with Brain-inspired Computing Team on classification of hand gestures through deep learning and its implementation on TrueNorth neuromorphic chip at - Almaden, San Jose, California.

From Spring 2015 to Spring 2016, I worked as a Visiting Researcher with Prof. Jim DiCarlo in Brain and Cognitive Sciences Department, at Cambridge, Massachusetts. I worked on predicting neural responses in primate visual cortex through performance optimized deep neural networks.

From 2013 to 2014, I worked as a Research Assistant with Prof. Jerry Chen and Prof. Fritjof Helmchen at Brain Research Institute, . I worked on developing algorithms to analyse whisker detection in mouse somatosensory tasks through LeapMotion device.


Research Interests:

My research interests are broadly categorized into the following:

  • Neuro-inspired Artificial Intelligence
  • Neuroimaging with Deep Learning
  • Computer Vision
  • Reinforcement Learning
  • Neuromorphic Engineering

In general, I have been working on exploring the computational principles of cortical neurons in the developing mouse brain. Some of my recent efforts in this direction has resulted into high-throughput deep learning-based tools to analyse large scale brain imaging datasets.


Publications (recent):

Rahel Kastli*, Rasmus Vighagen*, Alexander van der Bourg*, Ali Ozgur Argunsah*, Asim Iqbal, Fabian F. Voigt, Daniel Kirschenbaum, Adriano Aguzzi, Fritjof Helmchen, and Theofanis Karayannis. "Developmental divergence of sensory stimulus representation in cortical interneurons." Nature Communications 11, 5729 (2020).[pdf]

Payette, Kelly, Priscille de Dumast, Hamza Kebiri, Ivan Ezhov, Johannes C. Paetzold, Suprosanna Shit, Asim Iqbal et al. "A comparison of automatic multi-tissue segmentation methods of the human fetal brain using the FeTA Dataset." arXiv preprint arXiv:2010.15526 (2020).[pdf] In Review, Nature Scientific Data

Hassan, Mahmood, Asim Iqbal, and S. M. Shamsul Islam. "Exploring Intensity Invariance in Deep Neural Networks for Brain Image Registration." arXiv preprint arXiv:2009.10058 (2020).[pdf] Accepted in DICTA 2020, Melbourne, Australia

Asim Iqbal, Asfandyar Sheikh, and Theofanis Karayannis. "DeNeRD: high-throughput detection of neurons for brain-wide analysis with deep learning." Nature Scientific Reports 9, no. 1 (2019): 1-13.[pdf]

Asim Iqbal, Phil Dong, Christopher M. Kim, and Heeun Jang. "Decoding neural responses in mouse visual cortex through a deep neural network." In International Joint Conference on Neural Networks (IJCNN), pp. 1-7. IEEE, 2019. [pdf]

Asim Iqbal (featured) "A deeply learned brain atlas." Nature Methods (2019): 680-680.[pdf]

Asim Iqbal, Romesa Khan, and Theofanis Karayannis. "Developing a brain atlas through deep learning." Nature Machine Intelligence 1, no. 6 (2019): 277-287.[pdf]

Asim Iqbal, Asfandyar Sheikh, and Theofanis Karayannis. "Exploring brain-wide development of inhibition through deep learning. arXiv (2018).[pdf]


Research Projects:


[SeBRe]: Segmenting Brain Regions with deep learning

One of the main challenges faced by biologists in general and neuroscientists in partcular is to register mouse/human brain section images to a standard reference atlas. It requires an anatomical expert with a basic training to recognise and precisely annotate brain regions which is a very time consuming and exhausting process. Although, transformation algorithms have tackeled the automated brain image registration problem to a large extent but they still require fine tuning of parameters for any unseen brain section image. This fine tuning process easily takes hours as these algorithms are just trying to minimise a cost function to match the input image to a reference image. Furthermore, these algorithms are prone to errors and cannot simply work if any brain region is missing or distorted. We introduce a concept of registration through segmentation: we train a deep learning model for instance segmentation to classify and segment all the brain regions in a given image with high accuracy. This approach is independent of data modality and can also serve to help generate an atlas for animal brain ages on which no anatomical atlas is pre-existed. Hence, developing a brain atlas through deep learning. Our study is published in Nature Machine Intelligence and made it to the cover of the journal. [paper] [code]

Asim Iqbal, Romesa Khan, and Theofanis Karayannis. "Developing a brain atlas through deep learning." 
Nature Machine Intelligence 1.6 (2019): 277-287.


[DeNeRD]: Detecting Neurons in Regions of Development with deep learning

Neurons are captured with variability in shape, size, structure, intensity, etc. which becomes a challenge to detect them for high-throughput analysis of brain section images. We consider it as an object classification and detection problem and generated a ground-truth dataset of thousands of labelled neurons from mouse brain with different imaging modalities and genetic markers. We develop a deep neural network-based architecture to detect neurons in the entire 2D brain sections with high precision. Our study is published in Scientific Reports and made it to the Top 100 in Neuroscience. [paper] [code]

Asim Iqbal, Asfandyar Sheikh, and Theofanis Karayannis. "DeNeRD: high-throughput detection of neurons for brain-wide analysis with deep learning." 
Scientific Reports 9.1 (2019): 1-13.


Exploring the functional and anatomical motifs of developing mouse somatosensory cortex

Two inhibitory cell types involved in modulating barrel cortex activity and perception during active whisking in adult mice, are the VIP+ and SST+ interneurons. Here we identify a developmental transition point of structural and functional rearrangements onto these interneuron types around the start of active sensation at P14. Using in vivo two-photon Ca2+ imaging, we find that before P14, both interneuron types respond stronger to a multi-whisker stimulus, whereas after P14 their responses diverge, with VIP+ cells losing their multi-whisker preference and SST+ neurons enhancing theirs. Rabies virus tracings followed by tissue clearing, as well as photostimulation-coupled electrophysiology reveal that SST+ cells receive higher cross-barrel inputs compared to VIP+ at both time points. In addition, we also uncover that whereas prior to P14 both cell types receive direct input from the sensory thalamus, after P14 VIP+ cells show reduced inputs and SST+ cells largely shift to motor-related thalamic nuclei. The pre-print of our study is available in bioRxiv. [preprint]

Rahel Kastli, Rasmus Vighagen, Alexander van der Bourg, Ali Ozgur Argunsah, 
Asim Iqbal, Fabian F. Voigt, Daniel Kirschenbaum, Adriano Aguzzi, Fritjof Helmchen, and Theofanis Karayannis. 
"Developmental Divergence of Sensory Stimulus Representation in Cortical Interneurons." bioRxiv (2020).



1. Developing a brain atlas through deep learning

2. DeNeRD: high-throughput detection of neurons for brain-wide analysis with deep learning



  • Our paper Developmental divergence of sensory stimulus representation in cortical interneurons is featured by Nature Communications as a spotlight in From Brain to Behaviour!

  • Starting Summer 2020, I am joining Mathis Lab at Swiss Federal Institute of Technology (EPFL) as a Postdoctoral Fellow to work on Neuro-inspired AI systems!

  • LUMS featured my talk on Exploring the computational principles of brain-wide development through deep learning in SBASSE Newsletter 2020!

  • DeNeRD is featured by Scientific Reports in Top 100 in Neuroscience!

  • DeNeRD is featured by Medical Xpress in DeNeRD: an AI-based method to process whole images of the brain!

  • SeBRe is featured by Singularity Hub in Three Invaluable Ways AI and Neuroscience Are Driving Each Other Forward!

  • SeBRe is featured by Nature Methods in A deeply learned brain atlas!

  • SeBRe is featured by SciGlow in Developing brain maps through artificial intelligence!

  • SeBRe is featured by Swiss Cognitive in Thanks to deep learning, the tricky business of making brain atlases just got a lot easier!

  • SeBRe is featured by Singularity Hub in How Deep Learning Is Transforming Brain Mapping!

  • SeBRe is featured by Tech Xplore in Developing brain atlas using deep learning algorithms!



Courses Taught:

  • Neuroscience Block Course (Spring 2019), UZH/ETH Zurich
  • Programming in Biology (Fall 2018), UZH/ETH Zurich
  • Neuroscience Block Course (Spring 2017), UZH/ETH Zurich

Supervised Students:

  • Valentin Bruttin (M.Sc. Life Sciences & Engineering, EPFL) - Master Thesis
  • Ella McPherson (M.Sc. Health Sciences and Technology, ETH Zurich) - Internship
  • Ali Bukhari (B.Sc. Computer Science, LUMS) - Bachelor Thesis
  • Hamza Khalid (B.Sc. Computer Science, LUMS) - Internship
  • Namra Aamir (B.Sc. Biology, LUMS) - Internship
  • Romesa Khan (M.Sc. Neuroscience, ETH Zurich) - Master Thesis
  • Asfandyar Shiekh (B.Sc. Electrical Engineering, ETH Zurich) - Internship
  • Markus Suter (M.Sc. Neuroscience, UZH) Master Thesis Co-supervised
  • Laurens Bohlen (M.Sc. Neuroscience, UZH) Neuroscience Block Course
  • Victor Ibañez (M.Sc. Neuroscience, UZH) Neuroscience Block Course
  • Andrin Abegg (M.Sc. Neuroscience, ETH Zurich) Neuroscience Block Course
  • Michel Schmidlin (M.Sc. Neuroscience, ETH Zurich) Neuroscience Block Course

Interested Students:

Any undergrad or graduate student in STEM field interested in working with me on topics related to Neuro-inspired Artificial Intelligence then please feel free to reach out:





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