@article {5616, title = {What if Eye...? Computationally Recreating Vision Evolution}, journal = {arXiv}, year = {2025}, month = {01/2025}, abstract = {

Vision systems in nature show remarkable diversity, from simple light-sensitive patches to complex camera eyes with lenses. [1, 2] While natural selection has produced these eyes through countless mutations over millions of years, they represent just one set of realized evolutionary paths. [3, 4] Testing hypotheses about how environmental pressures shaped eye evolution remains challenging since we cannot experimentally isolate individual factors. [5] Computational evolution offers a way to systematically explore alternative trajectories. [6{\textendash}10] Here we show how environmental demands drive three fundamental aspects of visual evolution through an artificial evolution framework that co-evolves both physical eye structure and neural processing in embodied agents. First, we demonstrate computational evidence that task specific selection drives bifurcation in eye evolution - orientation tasks like navigation in a maze leads to distributed compound-type eyes while an object discrimination task leads to the emergence of high-acuity camera-type eyes. Second, we reveal how optical innovations like lenses naturally emerge to resolve fundamental tradeoffs between light collection and spatial precision. Third, we uncover systematic scaling laws between visual acuity and neural processing, showing how task complexity drives coordinated evolution of sensory and computational capabilities. Our work introduces a novel paradigm that illuminates evolutionary principles shaping vision by creating targeted single-player games where embodied agents must simultaneously evolve visual systems and learn complex behaviors. Through our unified genetic encoding framework, these embodied agents serve as next-generation hypothesis testing machines while providing a foundation for designing manufacturable bio-inspired vision systems.

}, url = {https://arxiv.org/abs/2501.15001}, author = {Kushagra Tiwary and Aaron Young and Zaid Tasneem and Tzofi Klinghoffer and Akshat Dave and Tomaso Poggio and Dan-Eric Nilsson and Brian Cheung and Ramesh Raskar} } @article {5609, title = {Self-Assembly of a Biologically Plausible Learning Circuit}, number = {152}, year = {2024}, month = {12/2024}, abstract = {

Over the last four decades, the amazing success of deep learning has been driven by the use of Stochastic Gradient Descent (SGD) as the main optimization technique. The default implementation for the computation of the gradient for SGD is backpropagation, which, with its variations, is used to this day in almost all computer implementations. From the perspective of neuroscientists, however, the consensus is that backpropagation is unlikely to be used by the brain. Though several alternatives have been discussed, none is so far supported by experimental evidence. Here we propose a circuit for updating the weights in a network that is biologically plausible, works as well as backpropagation, and leads to verifiable predictions about the anatomy and the physiology of a characteristic motif of four plastic synapses between ascending and descending cortical streams. A key prediction of our proposal is a surprising property of self-assembly of the basic circuit, emerging from initial random connectivity and heterosynaptic plasticity rules.

}, author = {Qianli Liao and Liu Ziyin and Yulu Gan and Brian Cheung and Mark Harnett and Tomaso Poggio} } @article {5520, title = {How to Guess a Gradient}, journal = {arXiv}, year = {2023}, month = {12/2023}, abstract = {

How much can you say about the gradient of a neural network without computing a loss or knowing the label? This may sound like a strange question: surely the answer is {\textquotedblleft}very little.{\textquotedblright} However, in this paper, we show that gradients are more structured than previously thought. Gradients lie in a predictable low-dimensional subspace which depends on the network architecture and incoming features. Exploiting this structure can significantly improve gradient-free optimization schemes based on directional derivatives, which have struggled to scale beyond small networks trained on toy datasets. We study how to narrow the gap in optimization performance between methods that calculate exact gradients and those that use directional derivatives. Furthermore, we highlight new challenges in overcoming the large gap between optimizing with exact gradients and guessing the gradients.

}, url = {https://arxiv.org/abs/2312.04709}, author = {Utkarsh Singhal and Brian Cheung and Kartik Chandra and Jonathan Ragan-Kelley and Joshua B. Tenenbaum and Tomaso Poggio and Stella X. Yu} } @proceedings {5399, title = {System Identification of Neural Systems: If We Got It Right, Would We Know?}, volume = {202}, year = {2023}, month = {07/2023}, pages = {12430-12444}, abstract = {

Artificial neural networks are being proposed as models of parts of the brain. The networks are compared to recordings of biological neurons, and good performance in reproducing neural responses is considered to support the model{\textquoteright}s validity. A key question is how much this system identification approach tells us about brain computation. Does it validate one model architecture over another? We evaluate the most commonly used comparison techniques, such as a linear encoding model and centered kernel alignment, to correctly identify a model by replacing brain recordings with known ground truth models. System identification performance is quite variable; it also depends significantly on factors independent of the ground truth architecture, such as stimuli images. In addition, we show the limitations of using functional similarity scores in identifying higher-level architectural motifs.

}, url = {https://proceedings.mlr.press/v202/han23d.html}, author = {Yena Han and Tomaso Poggio and Brian Cheung} } @article {5133, title = {System identification of neural systems: If we got it right, would we know?}, year = {2022}, month = {07/2022}, abstract = {

Various artificial neural networks developed by engineers have been evaluated as models of the brain, such as the ventral stream in the primate visual cortex. After being trained on large datasets, the network outputs are compared to recordings of biological neurons. Good performance in reproducing neural responses is taken as validation for the model. This system identification approach is different from the traditional ways to test theories and associated models in the natural sciences. Furthermore, it lacks a clear foundation in terms of theory and empirical validation. Here we begin characterizing some of these emerging approaches: what do they tell us? To address this question, we benchmark their ability to correctly identify a model by replacing the brain recordings with recordings from a known ground truth model. We evaluate commonly used identification techniques such as neural regression (linear regression on a population of model units) and centered kernel alignment (CKA). Even in the setting where the correct model is among the candidates, we find that the performance of these approaches at system identification is quite variable; it also depends significantly on factors independent of the ground truth architecture, such as scoring function and dataset.

}, author = {Yena Han and Tomaso Poggio and Brian Cheung} }