Aarush Mohit Mittal

About Aarush Mohit Mittal

Aarush Mohit Mittal, With an exceptional h-index of 3 and a recent h-index of 3 (since 2020), a distinguished researcher at Indian Institute of Technology Kanpur, specializes in the field of Neuroscience.

His recent articles reflect a diverse array of research interests and contributions to the field:

Mosquito Olfactory Response Ensemble: a curated database of behavioral and electrophysiological responses enables pattern discovery

Mosquito Olfactory Response Ensemble enables pattern discovery by curating a behavioral and electrophysiological response database

Pairwise Relative Distance (PRED) is an intuitive and robust metric for assessing vector similarity and class separability

Multiple network properties overcome random connectivity to enable stereotypic sensory responses

Aarush Mohit Mittal Information

University

Indian Institute of Technology Kanpur

Position

___

Citations(all)

23

Citations(since 2020)

23

Cited By

8

hIndex(all)

3

hIndex(since 2020)

3

i10Index(all)

1

i10Index(since 2020)

1

Email

University Profile Page

Indian Institute of Technology Kanpur

Aarush Mohit Mittal Skills & Research Interests

Neuroscience

Top articles of Aarush Mohit Mittal

Mosquito Olfactory Response Ensemble: a curated database of behavioral and electrophysiological responses enables pattern discovery

Authors

Abhishek Gupta,Swikriti S Singh,Aarush M Mittal,Pranjul Singh,Shefali Goyal,Karthikeyan R Kannan,Arjit K Gupta,Nitin Gupta

Journal

bioRxiv

Published Date

2022/1/4

Many experimental studies have examined behavioral and electrophysiological responses of mosquitoes to odors. However, the differences across studies in data collection, processing, and reporting make it difficult to perform large-scale analyses combining data from multiple studies. Here we extract and standardize data for 12 mosquito species, along with Drosophila melanogaster for comparison, from over 170 studies and curate the Mosquito Olfactory Response Ensemble (MORE), publicly available at https://neuralsystems.github.io/MORE. We demonstrate the ability of MORE in generating biological insights by finding patterns across studies. Our analyses reveal that ORs are tuned to specific ranges of several physicochemical properties of odorants; the empty-neuron recording technique for measuring OR responses is more sensitive than the Xenopus oocyte technique; there are systematic differences in the behavioral preferences reported by different types of assays; and odorants tend to become less attractive or more aversive at higher concentrations.

Mosquito Olfactory Response Ensemble enables pattern discovery by curating a behavioral and electrophysiological response database

Authors

Abhishek Gupta,Swikriti S Singh,Aarush M Mittal,Pranjul Singh,Shefali Goyal,Karthikeyan R Kannan,Arjit K Gupta,Nitin Gupta

Journal

Iscience

Published Date

2022/3/18

Many experimental studies have examined behavioral and electrophysiological responses of mosquitoes to odors. However, the differences across studies in data collection, processing, and reporting make it difficult to perform large-scale analyses combining data from multiple studies. Here we extract and standardize data for 12 mosquito species, along with Drosophila melanogaster for comparison, from over 170 studies and curate the Mosquito Olfactory Response Ensemble (MORE), publicly available at https://neuralsystems.github.io/MORE. We demonstrate the ability of MORE in generating biological insights by finding patterns across studies. Our analyses reveal that ORs are tuned to specific ranges of several physicochemical properties of odorants; the empty-neuron recording technique for measuring OR responses is more sensitive than the Xenopus oocyte technique; there are systematic differences in the …

Pairwise Relative Distance (PRED) is an intuitive and robust metric for assessing vector similarity and class separability

Authors

Aarush Mohit Mittal,Andrew C Lin,Nitin Gupta

Journal

bioRxiv

Published Date

2021/8/15

Scientific studies often require assessment of similarity between ordered sets of values. Each set, containing one value for every dimension or class of data, can be conveniently represented as a vector. The commonly used metrics for vector similarity include angle-based metrics, such as cosine similarity or Pearson correlation, which compare the relative patterns of values, and distance-based metrics, such as the Euclidean distance, which compare the magnitudes of values. Here we evaluate a newly proposed metric, pairwise relative distance (PRED), which considers both relative patterns and magnitudes to provide a single measure of vector similarity. PRED essentially reveals whether the vectors are so similar that their values across the classes are separable. By comparing PRED to other common metrics in a variety of applications, we show that PRED provides a stable chance level irrespective of the number of classes, is invariant to global translation and scaling operations on data, has high dynamic range and low variability in handling noisy data, and can handle multi-dimensional data, as in the case of vectors containing temporal or population responses for each class. We also found that PRED can be adapted to function as a reliable metric of class separability even for datasets that lack the vector structure and simply contain multiple values for each class.

Multiple network properties overcome random connectivity to enable stereotypic sensory responses

Authors

Aarush Mohit Mittal,Diksha Gupta,Amrita Singh,Andrew C Lin,Nitin Gupta

Journal

Nature communications

Published Date

2020/2/24

Connections between neuronal populations may be genetically hardwired or random. In the insect olfactory system, projection neurons of the antennal lobe connect randomly to Kenyon cells of the mushroom body. Consequently, while the odor responses of the projection neurons are stereotyped across individuals, the responses of the Kenyon cells are variable. Surprisingly, downstream of Kenyon cells, mushroom body output neurons show stereotypy in their responses. We found that the stereotypy is enabled by the convergence of inputs from many Kenyon cells onto an output neuron, and does not require learning. The stereotypy emerges in the total response of the Kenyon cell population using multiple odor-specific features of the projection neuron responses, benefits from the nonlinearity in the transfer function, depends on the convergence:randomness ratio, and is constrained by sparseness. Together, our …

See List of Professors in Aarush Mohit Mittal University(Indian Institute of Technology Kanpur)

Aarush Mohit Mittal FAQs

What is Aarush Mohit Mittal's h-index at Indian Institute of Technology Kanpur?

The h-index of Aarush Mohit Mittal has been 3 since 2020 and 3 in total.

What are Aarush Mohit Mittal's top articles?

The articles with the titles of

Mosquito Olfactory Response Ensemble: a curated database of behavioral and electrophysiological responses enables pattern discovery

Mosquito Olfactory Response Ensemble enables pattern discovery by curating a behavioral and electrophysiological response database

Pairwise Relative Distance (PRED) is an intuitive and robust metric for assessing vector similarity and class separability

Multiple network properties overcome random connectivity to enable stereotypic sensory responses

are the top articles of Aarush Mohit Mittal at Indian Institute of Technology Kanpur.

What are Aarush Mohit Mittal's research interests?

The research interests of Aarush Mohit Mittal are: Neuroscience

What is Aarush Mohit Mittal's total number of citations?

Aarush Mohit Mittal has 23 citations in total.

What are the co-authors of Aarush Mohit Mittal?

The co-authors of Aarush Mohit Mittal are Nitin Gupta, Swikriti Saran Singh, Shashank Chepurwar.

    Co-Authors

    H-index: 23
    Nitin Gupta

    Nitin Gupta

    Indian Institute of Technology Kanpur

    H-index: 3
    Swikriti Saran Singh

    Swikriti Saran Singh

    Indian Institute of Technology Kanpur

    H-index: 3
    Shashank Chepurwar

    Shashank Chepurwar

    Georg-August-Universität Göttingen

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