Unit 1. Neural Circuits of Intelligence

Unit Overview

Diagram of primate brain with colored patches highlighting regions associated with different facial orientations. Work by Winrich Freiwald and colleagues reveals a network of “face patches” in the primate brain containing neurons whose activity represents increasingly complex stages of face recognition. (Image courtesy of Winrich Friewald, used with permission.)

How does intelligence emerge from the activity of neural circuits in the brain? In this unit you will learn about empirical methods used to probe neural activity and what these methods reveal about the neural processes underlying important tasks such as visual recognition in humans and primates, and spatial navigation in lower mammals.

From Nancy Kanwisher, you will learn that the brain contains areas of functional specialization for the processing of faces, language, speech, and the Theory of Mind.

Neural signals flow from the sensory systems of vision, hearing, and touch, to higher cortical areas of cognitive function, and there is an even greater flow of information from higher to lower levels of the hierarchy of cortical areas. From Gabriel Kreiman, you will learn how the feedback of information from higher to lower levels of the visual system enables us to perform challenging tasks of object recognition and search.

Humans can recognize object categories in a brief glance of only 100 milliseconds. James DiCarlo takes an in-depth look at the neural circuits underlying rapid object recognition, examining how neurons encode properties of the visual scene and how these neural signals can be decoded into the object categories they represent.

From Winrich Freiwald, you will first learn about the important connection between sociality and intelligence, and the key role of face analysis in social behavior. You will then explore the function of a specialized network of face processing regions in the brain.

The hippocampus plays a central role in the formation of memories that connect locations and events in space and time. From Matt Wilson, you will learn how temporal sequences are encoded in neural signals, and the role of sleep in memory consolidation.

A fly may not seem very intelligent, but the simplicity of its neural circuitry provides the opportunity for a complete understanding of a biological function, from neurons to behavior. The guest lecture by Larry Abbott explores the computations performed by neural circuits of the fly’s olfactory system and how the fly learns to distinguish scents.

Unit Activities

Useful Background

  • Introduction to neuroscience, including the structure and function of neurons, functional organization of the brain, and common empirical methods such as single cell recording and fMRI. View the video tutorial on neuroscience by Leyla Isik.
  • Introduction to machine learning, including simple linear classification methods. View Part 1 of the video tutorial on machine learning by Lorenzo Rosasco.
  • Nancy’s Brain Talks includes short talks on fMRI and other brain imaging methods, and how these methods are used to study problems such as face perception.

Videos and Slides

Further Study

Additional information about the speakers’ research and publications can be found at their websites:

Aso, Y., D. Hattori, et al. “The Neuronal Architecture of the Mushroom Body Provides a Logic for Associative Learning.” (PDF - 7.7MB) eLife 3, no. e04577 (2014): 1–47.

Bendor, D., and M. A. Wilson. “Biasing the Content of Hippocampal Replay During Sleep.” (PDF) Nature Neuroscience 15 (2012): 1439–44.

Davidson, T. J., F. Kloosterman, et al. “Hippocampal Replay of Extended Experience.” Neuron 63, no. 4 (2009): 497–507.

Deen, B., K. Koldewyn, et al. “Functional Organization of Social Perception and Cognition in the Superior Temporal Sulcus.” Cerebral Cortex 25, no. 11 (2015): 4596–609.

DiCarlo, J. J., and D. D. Cox. “Untangling Invariant Object Recognition.” (PDF - 1.5MB) Trends in Cognitive Sciences 11, no. 8 (2007): 333–41.

DiCarlo, J. J., D. Zoccolan, et al. “How Does the Brain Solve Visual Object Recognition?Neuron 73, no. 3 (2012): 415–34.

Fedorenko, E., M. K. Behr, et al. “Functional Specificity for High-Level Linguistic Processing in the Human Brain.” (PDF - 1.1MB) Proceedings of National Academy of Sciences 108, no. 39 (2011): 16428–33.

Freiwald, W. A., and D. Y. Tsao. “Functional Compartmentalization and Viewpoint Generalization within the Macaque Face-Processing System.” Science 330, no. 6005 (2010): 845–51.

Freiwald, W. A., D. Y. Tsao, et al. “A Face Feature Space in the Macaque Temporal Lobe.” (PDF) Nature Neuroscience 12 (2009): 1187–96.

Hung, C. P., G. Kreiman, et al. “Fast Readout of Object Identity from Macaque Inferior Temporal Cortex.” (PDF) Science 310 (2005): 863–66.

Buy at MIT Press Kanwisher, N., and D. Dilks, D. [“The Functional Organization of the Ventral Visual Pathway in Humans.” (PDF)](http://web.mit.edu/bcs/nklab/media/pdfs/KanwisherDilks.in Chalupa_WernerTNVN.inpress.pdf) In The New Visual Neurosciences. Edited by L. Chalupa and J. Werner. MIT Press, 2013, pp. 733–48. ISBN: 9780262019163.

Kreiman, G. “Computational Models of Visual Object Recognition.” (PDF) In Principles of Neural Coding. Edited by R. Q. Quiroga and S. Panzeri. CRC Press, 2013, pp. 565–80. ISBN: 9781439853306. [Preview with Google Books]

Meyers, E. M., M. Borzello, et al. “Intelligent Information Loss: The Coding of Facial Identity, Head Pose, and Non-Face Information in the Macaque Face Patch System.” Journal of Neuroscience 35, no. 18 (2015): 7069–81.

Miconi, T., L. Groomes, et al. “There’s Waldo! A Normalization Model of Visual Search Predicts Single-Trial Human Fixations in an Object Search Task.” (PDF - 6.5MB) Cerebral Cortex 26, no. 7 (2015): 3064–82.

Nassi, J. J., C. Gomez-Laberge, et al. “Corticocortical Feedback Increases the Spatial Extent of Normalization.” (PDF - 2.9MB) Frontiers in Systems Neuroscience 8, no. 105 (2014): 1–13.

Tang, H., C. Buia, et al. “Spatiotemporal Dynamics Underlying Object Completion in Human Ventral Visual Cortex.” Neuron 83, no. 3 (2014): 736–48.

Yamins, D. L. K., H. Hong, et al. “Performance-optimized Hierarchical Models Predict Neural Responses in Higher Visual Cortex.” (PDF - 1.7MB) Proceedings of the National Academy of Sciences 111, no. 23 (2014): 8619–24.

Course Info

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