@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 {146,
	title = {Wiring Up Vision: Minimizing Supervised Synaptic Updates Needed to Produce a Primate Ventral Stream},
	booktitle = {International Conference on Learning Representations 2022 Spotlight},
	year = {2022},
	month = {April 25, 2022},
	type = {preprint},
	abstract = {<p>After training on large datasets, certain deep neural networks are surprisingly good models of the neural mechanisms of adult primate visual object recognition. Nevertheless, these models are considered poor models of the development of the visual system because they posit millions of sequential, precisely coordinated synaptic updates, each based on a labeled image. While ongoing research is pursuing the use of unsupervised proxies for labels, we here explore a complementary strategy of reducing the required number of supervised synaptic updates to produce an adult-like ventral visual stream (as judged by the match to V1, V2, V4, IT, and behavior). Such models might require less precise machinery and energy expenditure to coordinate these updates and would thus move us closer to viable neuroscientific hypotheses about how the visual system wires itself up. Relative to standard model training on labeled images in ImageNet, we here demonstrate that the total number of supervised weight updates can be substantially reduced using three complementary strategies: First, we find that only 2\% of supervised updates (epochs and images) are needed to achieve \&sim;80\% of a fully trained model\&rsquo;s match to adult ventral stream. Specifically, training benefits predictions of higher visual cortex the most whereas predictions of earlier areas improve only marginally over the course of training. Second, by improving the random distribution of synaptic connectivity, we find that 54\% of the brain match can already be achieved \&ldquo;at birth\&rdquo; (i.e. no training at all). Third, we find that, by training only \&sim;5\% of model synapses, we can still achieve nearly 80\% of the match to the ventral stream. This approach further improves on ImageNet performance over previous attempts in computer vision of minimizing trained components without substantially increasing the number of trained parameters. These results reflect first steps in modeling not just primate adult visual processing during inference, but also how the ventral visual stream might be \&ldquo;wired up\&rdquo; by evolution (a model\&rsquo;s \&ldquo;birth\&rdquo; state) and by developmental learning (a model\&rsquo;s updates based on visual experience).</p>
},
	keywords = {biologically plausible learning, computational neuroscience, convolutional neural networks, primate visual ventral stream},
	doi = {10.1101/2020.06.08.140111},
	url = {https://openreview.net/pdf?id=g1SzIRLQXMM},
	author = {Geiger, Franziska and Schrimpf, Martin and Marques, Tiago and DiCarlo, James J}
}
