@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}
}
@article {151,
	title = {Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations},
	journal = {Neural Information Processing Systems (NeurIPS; spotlight)},
	year = {2020},
	month = {June 17, 2020},
	type = {preprint},
	abstract = {<p>Current state-of-the-art object recognition models are largely based on convolutional neural network (CNN) architectures, which are loosely inspired by the primate visual system. However, these CNNs can be fooled by imperceptibly small, explicitly crafted perturbations, and struggle to recognize objects in corrupted images that are easily recognized by humans. Here, by making comparisons with primate neural data, we first observed that CNN models with a neural hidden layer that better matches primate primary visual cortex (V1) are also more robust to adversarial attacks. Inspired by this observation, we developed VOneNets, a new class of hybrid CNN vision models. Each VOneNet contains a fixed weight neural network front-end that simulates primate V1, called the VOneBlock, followed by a neural network back-end adapted from current CNN vision models. The VOneBlock is based on a classical neuroscientific model of V1: the linear-nonlinear-Poisson model, consisting of a biologically-constrained Gabor filter bank, simple and complex cell nonlinearities, and a V1 neuronal stochasticity generator. After training, VOneNets retain high ImageNet performance, but each is substantially more robust, outperforming the base CNNs and state-of-the-art methods by 18\% and 3\%, respectively, on a conglomerate benchmark of perturbations comprised of white box adversarial attacks and common image corruptions. Finally, we show that all components of the VOneBlock work in synergy to improve robustness. While current CNN architectures are arguably brain-inspired, the results presented here demonstrate that more precisely mimicking just one stage of the primate visual system leads to new gains in ImageNet-level computer vision applications.</p>
},
	doi = {10.1101/2020.06.16.154542},
	url = {https://www.biorxiv.org/content/10.1101/2020.06.16.154542v27},
	author = {Dapello, Joel and Marques, Tiago and Schrimpf, Martin and Geiger, Franziska and Cox, David D and DiCarlo, James J}
}
