📖 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”