15-386/686 Neural Computation

Assignments

• There will be 5 or 6 assignments.
• Collaboration in team of two (for 15386 only) is allowed for the first two assignments. Students are encouraged to discuss the problem sets and help each other in Piazza, but not allowed to copy each other's homework. Each student should submit their codes and a write up to CANVAS as well as to Gradescope.

Late Policy

• You will have approximately 2 weeks to do each homework assignment. For the semester, you have a total of seven free-late-days (168 hours) allowance to submit homework late without penalty. However, for each homework, no more than four late days can be used. Each day beyond four days will entail a 5 percent discount on the homework grade you earned. No homework will be accepted one week after the due day.
• There will be no exception to the late policy unless an extension is granted to the whole class or you have a legitimate medical excuse with doctor's or academic adviser's letter. You may not submit a problem set once the solution set is released, typically one week after due day.

Examinations

• The midterm and final exam will cover materials covered in lectures and the problem sets. Problem set solutions are available to all students one week after due day.

Syllabus

Supplementary Reading List

Retinal Computation

• Gollisch and Meister (2010) Eye Smarter than Scientists Believed: Neural Computations in Circuits of the Retina Neuron 65: 151-164.
• Maheswaranathan et al. (2018) Deep learning models reveal internal structure and diverse computation in the retina under natural scenes. bioRxiv preprint.
• L. McIntosh, N. Maheswaranathan, A. Nayebi, S. Ganguli, S. A. Baccus (2016) Deep learning models of the retinal resposnes to natural scenes NIPS 2016.
• N. Maheswaranathan, S. A. Baccus, S. Ganguli (2018): Inferring hidden structure in multilayered neural circuits PLOS Computational Biology.

Sparse Coding

• Chalk, Marre and Tkacik (2018) Toward a unified theory of efficient, predictive and sparse coding PNAS 115(1):186-191.
• Beyeler, Rounds, Carlson, Dutt, Krichmar (2019) PLOS Computational Biology June 27.

Logical computation in Neurons

• Frank Rosenblatt (1958) The Perceptron: A Probabilistic model for information storage and organization in the brain. Psychological Review 65:6. 386-408.
• Gidon et al. (2020) Dendritic action potentials and computation in human layer 2/3 cortical neurons Science, Vol. 367, Issue 6473, pp. 83-87. Jan 2020.
• McCulloch and Pitts (1943) A Logical Calculus of Ideas Immanent in Nervous Activity Bulletin of Mathematical Biophysics 5: 115-133.

Biological Neural Circuits

• Larkum M. (2013) A cellular mechanism for cortical associations: an organizing principle for the cerebral cortex Trends in Neuroscience. 36 (3): 141-151.
• Manita et al. (2015) A Top-Down Cortical Circuit for Accurate Sensory Perception Neuron, 86(5) 1304-1316.
• Gollisch and Meister (2010) Eye Smarter than Scientists Believed: Neural Computations in Circuits of the Retina Neuron 65: 151-164.
• Maheswaranathan ... Ganguli (2018) Inferring hidden structure in multilayered neural circuits PLOS Computational Biology https://doi.org/10.1371/journal.pcbi.1006291

Neural Network models of Neural Circuits

• L. McIntosh, N. Maheswaranathan, D.B. Kastner, J. Melander, L. Brezovec, A. Nayebi, J. Wang, S. Ganguli, S. Baccus, (2020) Deep learning models reveal internal structure and diverse computations in the retina under natural scenes. Under review
• N. Maheswaranathan, S. A. Baccus, S. Ganguli, (2018) Inferring hidden structure in multilayered neural circuits PLoS Comput Biol 14(8): e1006291
• L. McIntosh*, N. Maheswaranathan*, A. Nayebi, S. Ganguli, S. A. Baccus, (2016) Deep learning models of the retinal responses to natural scenes Neural Information Processing Systems (NIPS) 2016.
• Burak, Y, Ronnie, U. Meister, M. Sompolinsky H. 2010. Bayesian model of dynamic image stabilization in the visual systems. PNAS. November 9, 2010 107 (45) 19525-19530
• Burak et al. PNAS. Supplementary Information PNAS.

Sparse Coding on computation and memory

• Babadi and Sompolinsky (2014) Sparseness and Expansion in Sensory Representations. Neuron.

Reinforcement Learning

• Radulescu, Niv and Ballard (2019) Holistic Reinforcement Learning: The Role of Structure and Attention. Trends in Cognitive NSciences
• Botvinick, Niv and Baro (2008) Hierarchically organized behavior and its neural foundations: A reinforcement learning perspective. Cognition. ...
• Niv and Langdon (2016) Current Opinion in Behavioral Sciences 2016, 11:67–73
• Yael Niv (2009) Reinforcement learning in the brain. Journal of Mathematical Psychology , 53 (3), 139–154.

Biological Plausible Deep Learning Algorithms

• LeCun, Bengio and Hinton (2015)
• Akrout et al. Deep Learning without weight transport. NeurIPS 2019
• Guerguiev et al. (2017) Towards deep learning with segregated dendrites. eLIFE, 2017.
• Bartunov et al. (2018) Assessing the scalability of bioloigically-motivated deep learning algorithms and architectures. arXiv.1807.04587.

Casual inference

• Kording, Beierholm, Ma, Quartz, Tenenbaum and Shams (2007) Causal inference in multi-sensory perception. PLOS ONE.
• Sabyasachi Shivkumar, DeAngelis and Haefner. (2019) A causal inference model for the perception of complex motion in the presence of self-motion
• Orban, Berkes, Fiser and Lengyel (2016). Neural Variability and Sampling-Based Probabilistic Representations in the Visual Cortex. Neuron.

Inverse Rational Control

• Wu, Kwon, Daptarda, Schrater, Pitkow (2020) Rational Thoughts in Neural Codes. BioRXiv (with codes)
• Daptardar S, Schrater P, Pitkow X (2019). Inverse Rational Control with Partially Observable Continuous Nonlinear Dynamics. arXiv 1908.04696. [

Spiking Bayesian Circuit

• Rao (2004) Bayesian Computaiton in Recurrent Neural Circuits. Neural Computation 16 (1)
• Deneve (2008) Bayesian spiking neurons I: Inference. Neural Computation 20(1).
• Deneve (2008) Bayesian spiking neurons II: Learning. Neural Computation 20(1).

Curiosity and Imagination

• Gruber, Gelman and Ranganath (2014) States of curiosity modulates hippocampus-dependent learning via the dopaminergic circuit. Neuron 84(2). 486-496.
• Kidd and Hayden (2015). The Psychology and Neuroscience of Curiosity. Neuron 88(3). 449-460.
• Burda et al. .. Efros (2018) Large-Scale Study of Curiosity-Driven Learning ICLR 2018
• Pathak, Agrawal, Efros and Darrell (2017) Curiosity-driven Exploration by Self-supervised Prediction. CVPR 2017

Reinforcement Learning and Song Birds

• Gadagkar et al. (2016) Dopamine neurons encode performance error in singing birds Science 354: 1278-1282.
• Gadagkar et al. (2019) Dopamine neurons change their tuning according to courtship context in singing birds. bioRxiv https://doi.org/10.1101/822817
• Fee and Goldberg (2011) A hypothesis for basal ganglia-dependent reinforcement learning in the songbird. Neuroscience 198: 152-170.
• Mackevicius and Fee (2018) Building a state space for song learning Current Opinion in Neurobiology 49: 59-68.

Emotion

• Haixu Yu, Pei Yang (2019) An Emotion-Based Approach to Reinforcement Learning Reward Design IEEE conference in networking, sensing and control
• Moerland, Broekens and Jonker (2018) Emotion in reinforcement learning agents and robots: a survey. Machine Learning volume 107, pages443–480(2018)
• Sequerira, Melo and Paiva (2011) Emotion-based Intrinsic Motivation for Reinforcement Learning Agents ACII 2011 NCS 6974, pp. 326–336.
• Picard, Vyzas and Healey (2001) Toward machine emotional intelligence: analysis of affective physiological state. IEEE PAMI Vol 23 (10). 1175-1191

Consciousness

• Bengio (2019) The Consciouness Prior. arXiv:1709:08568v2
• van Hateren (2019) A theory of consciouness: computation, algorithm and neurbioligcal realization.
• Mumford (2019) Can an artificial intelligence machine be conscious?
• Blum (2018). Can machine be conscious? Toward a Conscious AI -- the Consciouness Turing Machine. Simon Institute Lecture (youtube)
• Crick and Koch (2003). A framework for consciouness Nature Neuroscience 6, 119-126.
• Koch (2018). What is consciouness? Nature 557 (S9): May 2018.
• Quantum approach to consciouness Also check https://en.wikipedia.org/wiki/Quantum_mind

Glia and their functions

• Fields et al. (2014) Glia Biology in Learning and Cognition. The Neuroscientist. Vol 20(5) 426-431
• Mu et al. (2019) Glia Accumulate Evidence that Actions Are Futile and Suppress Unsuccessful Behavior. Cell 178, 27–43
• Koob (2009) The root of thought: what do glial cells do? Scientific American Oct 2019.
• Fink (2016) Beyond the neuron: Emerging roles of glial cells in neuroscience. A PLOS student BLoGS that contains nice references to ideas about glia cells.
• Sajedinia and Helie (2018) A New Computational Model for Astrocytes and Their Role in Biologically Realistic Neural Networks.
• Manninen et al. (2018) Computational Models for Calcium-Mediated Astrocyte Functions. Front. Comput. Neurosci., 04 April 2018 | https://doi.org/10.3389/fncom.2018.00014.