Biology subjects

Katti, H.

Publications and source records attributed to Katti, H..

4 recordsLinked to original sources

bioRxiv2026

Immersive display systems for simulating natural vision

Vision is traditionally studied using simplified stimuli presented briefly near the center of a flat display while subjects maintain visual fixation. This paradigm contrasts sharply with real-world vision, which is immersive, dynamic and strongly entrained to self-initiated actions. Traditional approaches have allowed researchers to systematically study the neural encoding of visual features and the influence of cognitive operations such as attention. However, other methods are needed to study more holistic and first-person perspectives on vision, such as those related to physical space, continuous time, and self-movement. To enable the study of these and other aspects of real-world vision, we developed hemispherical ("dome") display systems for macaque visual neuroscience. For functional MRI experiments, a compact rear-projection dome display fits within the bore of a clinical MRI scanner. For electrophysiological recordings, a larger front-projection dome display is illuminated from above using a spherical mirror. Both setups enable complete and dynamic stimulation of approximately 180{degrees} of the subjects field of view, thus facilitating studies requiring visual immersion. To ensure accurate angular geometry across the hemispherical display, we present unified rendering and calibration software that supports natural fisheye videos, conventional visual stimuli, and virtual 3D environments. The calibration procedure automatically compensates for projector, mirror, and dome distortions through geometric pre-warping, ensuring correct visual-angle representation across the display. Pilot fMRI experiments demonstrate robust activation of peripheral visual cortex during both conventional pattern stimulation and naturalistic self-movement. Together, these dome systems provide a flexible platform for investigating aspects of vision that are difficult to study with conventional displays, including peripheral processing, visual immersion, optic flow, self-motion, and holistic scene perception.

neuroscience↗

bioRxiv2020

Turning traditional neurophysiology on its head: A naturalistic environment to study natural behaviors and cognitive tasks in freely moving monkeys

Macaque monkeys are widely used to study the neural basis of cognition. In the traditional approach, the monkey is brought into a lab to perform tasks while it is restrained to obtain stable gaze tracking and neural recordings. This unnatural setting prevents studying brain activity during natural, social and complex behaviors. Here, we designed a naturalistic environment with an integrated behavioral workstation that enables complex task training with viable gaze tracking in freely moving monkeys. We used this facility to train monkeys on a challenging same-different task. Remarkably, this facility enabled a naive monkey to learn the task merely by observing trained monkeys. This social training was faster primarily because the naive monkey first learned the task structure and then the same-different rule. We propose that such hybrid environments can be used to study brain activity during natural behaviors as well as during controlled cognitive tasks.

animal behavior and cognition↗

bioRxiv2020

A separable neural code in monkey IT enables perfect CAPTCHA decoding

Reading distorted letters is easy for us but so challenging for machine vision that it is used on websites as CAPTCHAs (Completely Automated Public Turing test to tell Computers and Humans Apart). How does our brain solve this problem? One solution is to have neurons invariant to letter distortions but selective for letter combinations. Another is for neurons to separately encode letter distortions and combinations. Here, we provide evidence for the latter using neural recordings in the monkey inferior temporal (IT) cortex. Neurons encoded letter distortions as a product of letter and distortion tuning, and letter combinations as a sum of letters. These rules were sufficient for perfect CAPTCHA decoding and were also present in neural networks trained for word recognition. Taken together, our findings suggest that a separable neural code enables efficient letter recognition.

neuroscience↗

bioRxiv2019

Do deep neural networks see the way we do?

Deep neural networks have revolutionized computer vision, and their object representations match coarsely with the brain. As a result, it is widely believed that any fine scale differences between deep networks and brains can be fixed with increased training data or minor changes in architecture. But what if there are qualitative differences between brains and deep networks? Do deep networks even see the way we do? To answer this question, we chose a deep neural network optimized for object recognition and asked whether it exhibits well-known perceptual and neural phenomena despite not being explicitly trained to do so. To our surprise, many phenomena were present in the network, including the Thatcher effect, mirror confusion, Webers law, relative size, multiple object normalization and sparse coding along multiple dimensions. However, some perceptual phenomena were notably absent, including processing of 3D shape, patterns on surfaces, occlusion, natural parts and a global advantage. Our results elucidate the computational challenges of vision by showing that learning to recognize objects suffices to produce some perceptual phenomena but not others and reveal the perceptual properties that could be incorporated into deep networks to improve their performance.

neuroscience↗