Search Results for author: Kohitij Kar

Found 8 papers, 4 papers with code

Low-cost, portable, easy-to-use kiosks to facilitate home-cage testing of non-human primates during vision-based behavioral tasks

1 code implementation8 Jan 2024 Hamidreza Ramezanpour, Christopher Giverin, Kohitij Kar

These include, 1) limited total experimental time, 2) requirement of dedicated human experimenters for the NHPs, 3) requirement of additional lab-space for the experiments, 4) NHPs often need to undergo invasive surgeries for a head-post implant, 5) additional time and training required for chairing and head restraints of monkeys.

The Quest for an Integrated Set of Neural Mechanisms Underlying Object Recognition in Primates

no code implementations10 Dec 2023 Kohitij Kar, James J DiCarlo

However, over the last decade, this scientific mystery has been illuminated by the discovery and development of brain-inspired, image-computable, artificial neural network (ANN) systems that rival primates in this behavioral feat.

Object Object Recognition

Interpretability of artificial neural network models in artificial Intelligence vs. neuroscience

no code implementations7 Jun 2022 Kohitij Kar, Simon Kornblith, Evelina Fedorenko

Given the widespread calls to improve the interpretability of AI systems, we here highlight these different notions of interpretability and argue that the neuroscientific interpretability of ANNs can be pursued in parallel with, but independently from, the ongoing efforts in AI.

Decision Making

Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like?

1 code implementation2 Jan 2020 Martin Schrimpf, Jonas Kubilius, Ha Hong, Najib J. Majaj, Rishi Rajalingham, Elias B. Issa, Kohitij Kar, Pouya Bashivan, Jonathan Prescott-Roy, Franziska Geiger, Kailyn Schmidt, Daniel L. K. Yamins, James J. DiCarlo

We therefore developed Brain-Score – a composite of multiple neural and behavioral benchmarks that score any ANN on how similar it is to the brain’s mechanisms for core object recognition – and we deployed it to evaluate a wide range of state-of-the-art deep ANNs.

Object Recognition

Aligning Artificial Neural Networks to the Brain yields Shallow Recurrent Architectures

no code implementations ICLR 2019 Jonas Kubilius, Martin Schrimpf, Ha Hong, Najib J. Majaj, Rishi Rajalingham, Elias B. Issa, Kohitij Kar, Pouya Bashivan, Jonathan Prescott-Roy, Kailyn Schmidt, Aran Nayebi, Daniel Bear, Daniel L. K. Yamins, James J. DiCarlo

Deep artificial neural networks with spatially repeated processing (a. k. a., deep convolutional ANNs) have been established as the best class of candidate models of visual processing in the primate ventral visual processing stream.

Anatomy Object Categorization

Task-Driven Convolutional Recurrent Models of the Visual System

1 code implementation NeurIPS 2018 Aran Nayebi, Daniel Bear, Jonas Kubilius, Kohitij Kar, Surya Ganguli, David Sussillo, James J. DiCarlo, Daniel L. K. Yamins

Feed-forward convolutional neural networks (CNNs) are currently state-of-the-art for object classification tasks such as ImageNet.

General Classification Object Recognition

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