arXiv subjects

Hamidreza Ramezanpour

Publications and source records attributed to Hamidreza Ramezanpour.

6 recordsLinked to original sources

arXiv2026

The macaque IT cortex but not current artificial vision networks encode object position in perceptually aligned coordinates

Efficient interaction with the visual world requires not only accurate object identification but also precise localization of objects in space. While spatial ("where") processing has traditionally been attributed to dorsal stream pathways, recent work has shown that object position can also be decoded from responses in ventral stream areas such as the inferior temporal (IT) cortex. However, because object position in these paradigms is tightly coupled to pixel-based location, it remains unclear whether ventral stream position signals reflect perceptually meaningful spatial representations or simply inherited retinotopic structure. To address this question, we used the motion aftereffect, a classic visual illusion that shifts perceived object position without changing retinal input. Combining large-scale intracortical recordings in macaque IT with matched human psychophysics, we found that motion adaptation induces systematic direction-opponent biases in IT population codes for object position that mirror human perceptual reports, despite identical pixel-level stimuli. These effects are accompanied by adaptation-driven changes in the geometry of IT population representations. We further tested whether artificial vision systems exhibit similar dynamics. Standard feedforward, recurrent, and state-of-the-art video-based neural networks accurately encode object position but fail to produce adaptation-induced position shifts. However, applying empirically derived transformations based on IT adaptation dynamics to model feature spaces is sufficient to generate similar biases. Together, these results indicate that IT represents object position in perceptually aligned coordinates and also highlight a gap between biological and artificial vision systems in capturing history-dependent spatial coding.

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arXiv2026

Modeling Dynamic Computations in the Primate Ventral Visual Stream

A major goal of computational neuroscience has been to explain how the primate ventral visual stream (VVS) transforms visual input into temporally evolving neural representations that support robust visual perception. Historically, most modeling efforts have assumed static conditions: monkeys fixate a dot, images are briefly flashed, and neural responses are analyzed through time-averaged metrics. Feedforward deep networks trained on static object recognition tasks outperform prior work in approximating these static snapshot-driven VVS responses. However, mounting neurophysiological evidence demonstrates that VVS responses are rich dynamical signals shaped not only by the retinal input but also by intrinsic circuit dynamics, recurrent interactions, and widespread top-down modulation. Moreover, real-world vision is inherently dynamic: objects move, the observer moves, and the eyes actively sample the environment. Here, we review recent progress in modeling dynamic responses in the macaque ventral stream across three domains: (1) intrinsic dynamics elicited by static images, (2) dynamics evoked by dynamic visual stimuli, and (3) dynamics generated by active sensing during eye movements. We argue that accurately modeling VVS dynamics will require representational, circuit-level, and behavioral perspectives, including multi-area recurrence, structured E/I interactions, and temporal objectives that better reflect natural behavior. We outline some key missing ingredients and propose a roadmap toward dynamic, multi-timescale models of the primate VVS.

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arXiv2024

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

Non-human primates (NHPs), especially rhesus macaques, have played a significant role in our current understanding of the neural computations underlying human vision. Apart from the established homologies in the visual brain areas between these two species, and our extended abilities to probe detailed neural mechanisms in monkeys at multiple scales, one major factor that makes NHPs an extremely appealing animal model of human-vision is their ability to perform human-like visual behavior. Traditionally, such behavioral studies have been conducted in controlled laboratory settings. Such in-lab studies offer the experimenter a tight control over many experimental variables like overall luminance, eye movements (via eye tracking), auditory interference etc. However, there are several constraints related to such experiments. 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. To overcome these limitations, many laboratories are now adapting home-cage behavioral training and testing of NHPs. Home-cage behavioral testing enables the administering of many vision-based behavioral tasks simultaneously across multiple monkeys with much reduced human personnel requirements, no NHP head restraint, and provide NHPs access to the experiments without specific time constraints. To enable more open-source development of this technology, here we provide the details of operating and building a portable, easy-to-use kiosk for conducting home-cage vision-based behavioral tasks in NHPs.

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arXiv2021

The role of temporal cortex in the control of attention

Attention is an indispensable component of active vision. Contrary to the widely accepted notion that temporal cortex processing primarily focusses on passive object recognition, a series of very recent studies emphasize the role of temporal cortex structures, specifically the superior temporal sulcus (STS) and inferotemporal (IT) cortex, in guiding attention and implementing cognitive programs relevant for behavioral tasks. The goal of this theoretical paper is to advance the hypothesis that the temporal cortex attention network (TAN) entails necessary components to actively participate in attentional control in a flexible task-dependent manner. First, we will briefly discuss the general architecture of the temporal cortex with a focus on the STS and IT cortex of monkeys and their modulation with attention. Then we will review evidence from behavioral and neurophysiological studies that support their guidance of attention in the presence of cognitive control signals. Next, we propose a mechanistic framework for executive control of attention in the temporal cortex. Finally, we summarize the role of temporal cortex in implementing cognitive programs and discuss how they contribute to the dynamic nature of visual attention to ensure flexible behavior.

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arXiv2014

Optimal Control of Nonlinear Systems Using the Homotopy Perturbation Method

This paper presents a new method for solving a class of nonlinear optimal control problems with a quadratic performance index. In this method, first the original optimal control problem is transformed into a nonlinear two-point boundary value problem (TPBVP) via the Pontryagin's maximum principle. Then, using the Homotopy Perturbation Method (HPM) and introducing a convex homotopy in topologic space, the nonlinear TPBVP is transformed into a sequence of linear time-invariant TPBVP's. By solving the presented linear TPBVP sequence in a recursive manner, the optimal control law and the optimal trajectory are determined in the form of infinite series. Finally, in order to obtain an accurate enough sub-optimal control law, an iterative algorithm with low computational complexity is introduced. An illustrative example demonstrates the simplicity and efficiency of proposed method.

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arXiv2012

An Iterative Procedure for Optimal Control of Bilinear Systems

This paper presents a new and straightforward procedure for solving bilinear quadratic optimal control problem. In this method, first the original optimal control problem is transformed into a nonlinear twopoint boundary value problem (TPBVP) via the Pontryagin's maximum principle. Then, the nonlinear TPBVP is transformed into a sequence of linear time-invariant TPBVPs using the homotopy perturbation method (HPM) and introducing a convex homotopy in topologic space. Solving the latter problems through an iterative process yields the optimal control law and optimal trajectory in the form of infinite series. Finally, sufficient condition for convergence of these series is proved by a theorem. Simplicity and efficiency of the proposed method is shown through an illustrative example.

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