@conference {182,
	title = {Probing Biological and Artificial Neural Networks with Task-dependent Neural Manifolds},
	booktitle = {Conference on Parsimony and Learning (Proceedings Track)},
	year = {2023},
	address = {Hong Kong, China},
	abstract = {<p>Recently, growth in our understanding of the computations performed in both biological and artificial neural networks has largely been driven by either low-level mechanistic studies or global normative approaches. However, concrete methodologies for bridging the gap between these levels of abstraction remain elusive. In this work, we investigate the internal mechanisms of neural networks through the lens of neural population geometry, aiming to provide understanding at an intermediate level of abstraction, as a way to bridge that gap. Utilizing manifold capacity theory (MCT) from statistical physics and manifold alignment analysis (MAA) from high-dimensional statistics, we probe the underlying organization of task-dependent manifolds in deep neural networks and macaque neural recordings. Specifically, we quantitatively characterize how different learning objectives lead to differences in the organizational strategies of these models and demonstrate how these geometric analyses are connected to the decodability of task-relevant information. These analyses present a strong direction for bridging mechanistic and normative theories in neural networks through neural population geometry, potentially opening up many future research avenues in both machine learning and neuroscience.</p>
},
	keywords = {Biologically inspired vision models, computational neuroscience, Neural Geometry, Neural Manifolds, Neuro-AI, Representational Geometry},
	url = {https://openreview.net/forum?id=MxBS6aw5Gd},
	author = {Michael Kuoch and Chi-Ning Chou and Nikhil Parthasarathy and Joel Dapello and James J. DiCarlo and Haim Sompolinsky and Sueyeon Chung}
}
@conference {169,
	title = {Neural Population Geometry Reveals the Role of Stochasticity in Robust Perception},
	booktitle = {Neural Information Processing Systems (NeurIPS)},
	year = {2021},
	address = {Lisbon, Portugal},
	abstract = {<p>Adversarial examples are often cited by neuroscientists and machine learning researchers as an example of how computational models diverge from biological sensory systems. Recent work has proposed adding biologically-inspired components to visual neural networks as a way to improve their adversarial robustness. One surprisingly effective component for reducing adversarial vulnerability is response stochasticity, like that exhibited by biological neurons. Here, using recently developed geometrical techniques from computational neuroscience, we investigate how adversarial perturbations influence the internal representations of standard, adversarially trained, and biologically-inspired stochastic networks. We find distinct geometric signatures for each type of network, revealing different mechanisms for achieving robust representations. Next, we generalize these results to the auditory domain, showing that neural stochasticity also makes auditory models more robust to adversarial perturbations. Geometric analysis of the stochastic networks reveals overlap between representations of clean and adversarially perturbed stimuli, and quantitatively demonstrate that competing geometric effects of stochasticity mediate a tradeoff between adversarial and clean performance. Our results shed light on the strategies of robust perception utilized by adversarially trained and stochastic networks, and help explain how stochasticity may be beneficial to machine and biological computation.</p>
},
	url = {https://proceedings.neurips.cc/paper/2021/file/8383f931b0cefcc631f070480ef340e1-Paper.pdf},
	author = {Joel Dapello and Jenelle Feather and Tiago Marques and David Cox and Josh McDermott and James J DiCarlo and Sueyeon Chung}
}
