Biophysics Journal Club On Neural Networks And Brain

12 Feb 2024

Hierarchical neural circuits motivate deep convolutional neural networks (CNNs) 2012

• Rumelhart DE, Hinton GE, Williams RJ (1986). Learning representations by back-propagating errors. Nature 323: 533–536. [paper][notes]
• LeCun Y, Boser B, Denker JS, Henderson D, Howard RE, Hubbard W, Jackel LD (1989). Backpropagation applied to handwritten zip code recognition. Neural Comput 1: 541–551. [paper][notes]
• Krizhevsky A, Sutskever I, Hinton GE (2012). ImageNet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25:1097–105. [paper][notes]

Task-optimized deep CNNs predict aspects of neural responses in brains 2014

• Yamins DLK, Hong H, Cadieu CF, Solomon EA, Seibert D, DiCarlo JJ (2014). Performance-optimized hierarchical models predict neural responses in higher visual cortex. Proc Natl Acad Sci USA 111: 8619–8624. [paper][notes]
• Khaligh-Razavi SM, Kriegeskorte N (2014). Deep supervised, but not unsupervised, models may explain IT cortical representation. PLoS Comput Biol 10: e1003915. [paper][notes]

Visualizing unit activity in deep CNNs and brains

• Zeiler MD, Fergus R (2014). Visualizing and Understanding Convolutional Networks. arxiv.org/abs/1311.2901. [paper][notes]
• Bashivan P, Kar K, DiCarlo JJ (2019). Neural population control via deep image synthesis. Science 364, eaav9436. [paper][notes]

Inferring mechanisms of neural circuit computation from deep CNNs

• McIntosh L, Maheswaranathan N, Nayebi A, Ganguli S, Baccus S (2016). Deep Learning Models of the Retinal Response to Natural Scenes. Advances in Neural Inf Processing Systems 29:1369–1377. [paper][notes]
• Lindsey J, Ocko SA, Ganguli S, Deny S (2019). A Unified Theory Of Early Visual Representations From Retina To Cortex Through Anatomically Constrained Deep CNNs. https://doi.org/10.1101/511535. [paper][notes]

Recurrent networks are dynamic

• Kar K, Kubilius J, Schmidt K, Issa EB, DiCarlo JJ (2019). Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior. Nature neuroscience, 22(6), 974. [paper][notes]
• Kietzmann TC, Spoerer CJ, Sörensen LKA, Cichy RM, Hauk O, Kriegeskorte N (2019). Recurrence is required to capture the representational dynamics of the human visual system. PNAS 116: 21854-21863. [paper][notes]

Reinforcement learning explores 2016

• Cross L, Cockburn J, Yue Y, O’Doherty JP (2021). Using deep reinforcement learning to reveal how the brain encodes abstract state-space representations in high-dimensional environments. Neuron 109: 724-738. [paper][notes]
• Wang JX, Kurth-Nelson Z, Kumaran D, Tirumala D, Soyer H, Leibo JZ, et al (2018). Prefrontal cortex as a meta-reinforcement learning system. Nat. Neurosci. 21: 860–868. [paper][notes]

Unsupervised learning is biologically plausible

• Lotter W, Kreiman G, Cox D (2017). Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning. https://arxiv.org/abs/1605.08104. [paper][notes]
• Zhuang C, Yan S, Nayebi A, Schrimpf M, Frank MC, DiCarlo JJ, Yamins DLK (2021). Unsupervised neural network models of the ventral visual stream. Proc of the National Academy of Sciences 118: e2014196118. [paper][notes]