Mr. Aarush Mohit Mittal is currently a PhD student in the lab of Prof. Nitin Gupta in the Department of Biological Sciences & Bioengineering at the Indian Institute of Technology, Kanpur. He completed his B.Tech from IIT Kanpur. Mr. Mittal is the first author of the paper titled “Multiple network properties overcome random connectivity to enable stereotypic sensory responses”, published in Nature Communications (2020).
Author Interview
How would you explain your paper’s key results to the non-scientific community?
All organisms exhibit predictable behaviors in response to certain stimuli—salivating at food, approaching flowers, or fleeing from danger. These behaviors are controlled by the brain, which consists of millions of interconnected neurons, forming complex neural networks. Unlike electric circuits, where connections are fixed and known, neural connections vary between individuals. Yet, behavior remains consistent across individuals. How?
We explored this puzzle using the insect olfactory system as a model. Insects have two antennae with neurons that detect odors. These neurons send signals to the brain’s antennal lobe (layer 1), which then relays information to layer 2, and subsequently to layer 3. While the connections from layer 1 to layer 2 are random and differ between individuals, layer 3 responses are remarkably consistent.
We discovered that although individual layer 2 neurons respond differently, the collective response is consistent across individuals. This is due to convergence—each layer 3 neuron receives input from many layer 2 neurons. The more inputs it gets, the better it can differentiate odors.
We also found that learning, contrary to previous studies, actually reduces this consistency. Our model, which did not include learning, still achieved consistent responses—suggesting that randomness plus convergence is sufficient to create stereotypic behaviors.
What are the possible consequences of these findings for your research area?
The insect olfactory circuit resembles mammalian (including human) sensory circuits. Our findings help explain why people commonly like or dislike the same smells and provide insights into how consistent sensory responses emerge from random connections. This principle may extend to other neural systems, indicating that precise wiring isn’t necessary for consistent outputs—random connections with convergence can suffice.
What was the exciting moment (eureka moment) during your research?
The first breakthrough came when we realized that even though individual layer 2 neurons weren’t consistent, their total summed response was. This became a cornerstone of our research. Another moment was when theoretical models aligned perfectly with our analytical data, validating our findings beyond the insect system.
What do you hope to do next?
We aim to build a comprehensive map of the insect olfactory system, layer by layer. Since our lab works with mosquitoes, we hope to use this understanding to engineer strategies to combat mosquito-borne diseases like dengue and malaria. Personally, I’m interested in understanding how the brain integrates multiple sensory inputs—light, sound, odors—and processes them together.
Where do you seek scientific inspiration?
Nature is my greatest inspiration. Our brains are astonishing machines, and understanding how they work is still a huge mystery. Only by decoding our own natural intelligence can we hope to build truly intelligent artificial systems. The challenge and wonder of uncovering these mechanisms motivate me.
How do you intend to help Indian science improve?
This work was done in collaboration with a lab specializing in the fly model system. It showed me how vital collaborations are in science. I believe fostering more inter-institutional and interdisciplinary collaborations across India can dramatically improve scientific productivity and innovation. I hope to contribute toward building such collaborative ecosystems.
Reference
Mittal A M, Gupta D, Singh A, Lin A C, Gupta N. Multiple network properties overcome random connectivity to enable stereotypic sensory responses. Nature Communications (2020), 11:1023.
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