📖 Reading material

Why neuroscience?¶

• Interested in doing a PhD?
• AI and neuroscience: the virtuous circle from Deep Mind podcast

History of neuroscience and machine learning¶

• McCulloch and Pitts (1943) “A logical calculus of the ideas immanent in nervous activity”
• Von Neumann (1945) “First draft of a report on the EDVAC”
• Rosenblatt (1958) “The perceptron: A probabilistic model for information storage and organization in the brain”
• Goodman et al. (2013) “Decoding neural responses to temporal cues for sound localization”
• Minsky and Papert (1969) “Perceptrons: An Introduction to Computational Geometry”
• Larsen et al. (2022) “Towards a more general understanding of the algorithmic utility of recurrent connections”
• Rumelhart et al. (1986) “Learning representations by back-propagating errors”
• Introduction to neural networks and backpropagation by 3Blue1Brown (excellent, easy to follow YouTube series)
• Lillicrap et al. (2016) “Random synaptic feedback weights support error backpropagation for deep learning”
• Lillicrap et al. (2020) “Backpropagation and the brain”
• Hubel and Wiesel (1959) “Receptive fields of single neurones in the cat’s striate cortex”
• Hubel and Wiesel (1962) “Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex”
• Fukushima (1980) “Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position”
• LeCun et al. (1989) “Backpropagation Applied to Handwritten Zip Code Recognition”
• LeCun et al. (1998) “Gradient-based learning applied to document recognition”
• Yamins et al. (2014) “Performance-optimized hierarchical models predict neural responses in higher visual cortex”
• Kell et al. (2018) “A Task-Optimized Neural Network Replicates Human Auditory Behavior, Predicts Brain Responses, and Reveals a Cortical Processing Hierarchy”
• Schrimpf et al. (2020) “Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like?”
• Brain-Score website
• Jacob et al. (2021) “Qualitative similarities and differences in visual object representations between brains and deep networks”
• Weerts et al. (2022) “The Psychometrics of Automatic Speech Recognition”
• Adolfi et al. (2023) “Successes and critical failures of neural networks in capturing human-like speech recognition”
• Thorndike (1898) “Animal intelligence: An experimental study of the associative processes in animals”
• Sutton and Barto (2018) “Reinforcement Learning: An Introduction”
• Turing (1948) “Intelligent Machinery”
• Hassabis et al. (2017) " Neuroscience-Inspired Artificial Intelligence"
• Zador et al. (2023) “Catalyzing next-generation Artificial Intelligence through NeuroAI”
• Doerig et al. (2023) “The neuroconnectionist research programme”
• Patrick Mineault “NeuroAI archive” and website

Challenges for ML and neuroscience¶

• AlphaGo The Movie
• Silver et al. (2017) “Mastering the game of Go without human knowledge”
• Vicarious 2016 on Schema networks (broken videos) (and preprint)
• Tsividis et al. (2017) “Human learning in Atari”
• Goodfellow et al. (2014) “Explaining and Harnessing Adversarial Examples”
• Geirhos et al. (2019) “ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness”
• OpenAI: Multimodal neurons in artificial neural networks
• Cramer et al. (2022) “The Heidelberg Spiking Data Sets for the Systematic Evaluation of Spiking Neural Networks”