Neuroscience is the scientific study of the nervous system and is an interdisciplinary biological science that extends across multiple fields including chemistry, computer science, engineering, linguistics, mathematics, medicine, philosophy, physics, and psychology. Neuroscience involves various approaches to the study of the molecular, cellular, computational, systems, and cognitive aspects of the nervous system, using techniques from molecular and cellular studies of individual nerve cells to neuroimaging of complex human behaviors.

This course explores the social relevance of neuroscience, considering how emerging areas of brain research at once reflect and reshape social attitudes and agendas. Topics include brain imaging and popular media; neuroscience of empathy, trust, and moral reasoning; new fields of neuroeconomics and neuromarketing; ethical implications of neurotechnologies such as cognitive enhancement pharmaceuticals; neuroscience in the courtroom; and neuroscientific recasting of social problems such as addiction and violence. Guest lectures by neuroscientists, class discussion, and weekly readings in neuroscience, popular media, and science studies.

Philosophy and the Science of Human Nature pairs central texts from Western philosophical tradition (including works by Plato, Aristotle, Epictetus, Hobbes, Kant, Mill, Rawls, and Nozick) with recent findings in cognitive science and related fields. The course is structured around three intertwined sets of topics: Happiness and Flourishing; Morality and Justice; and Political Legitimacy and Social Structures.

Probability theory captures a number of essential characteristics of human cognition, including aspects of perception, reasoning, belief revision, and learning. Expressions of degree of belief were used in language long before people began codifying the laws of probability theory. This course explores the history and debates over codifying the laws of probability, how probability theory applies to specific cognitive processes, how it relates to the human understanding of causality, and how new computational approaches to causal modeling provide a framework for understanding human probabilistic reasoning.
This class is suitable for advanced undergraduates or graduate students specializing in cognitive science, artificial intelligence, and related fields.

Provides students with the basic tools for analyzing experimental data, properly interpreting statistical reports in the literature, and reasoning under uncertain situations. Topics organized around three key theories: Probability, statistical, and the linear model. Probability theory covers axioms of probability, discrete and continuous probability models, law of large numbers, and the Central Limit Theorem. Statistical theory covers estimation, likelihood theory, Bayesian methods, bootstrap and other Monte Carlo methods, as well as hypothesis testing, confidence intervals, elementary design of experiments principles and goodness-of-fit. The linear model theory covers the simple regression model and the analysis of variance. Places equal emphasis on theory, data analyses, and simulation studies.

Our brains are remarkably adaptable throughout our lives. Individual brain cells called neurons form synapses, sites of physical connection and communication between neurons, and then repeatedly rewire those connections in response to new experiences or to neuronal cell death caused by injury, disease, or aging. In this course, we will explore how neurons establish their synapses in the healthy brain during childhood and later in life, and how this process goes awry in disease states. More specifically, we will discuss how the brain forms its synapses early in life, stabilizes a subset of those synapses for long-term maintenance, and continues to add and remove synapses throughout life. We will then explore synapse dysfunction in diseases such as autism and Alzheimer’s disease, which involve abnormal increases or losses of synaptic connections, respectively. We will also consider synapse remodeling, a process of adding and removing synaptic connections to optimize our brain network, in the context of neuroinflammation, recovery from traumatic brain injury, and psychological trauma following prolonged stress.
This course is one of many Advanced Undergraduate Seminars offered by the Biology Department at MIT. These seminars are tailored for students with an interest in using primary research literature to discuss and learn about current biological research in a highly interactive setting. Many instructors of the Advanced Undergraduate Seminars are postdoctoral scientists with a strong interest in teaching.

This course offers a comprehensive exploration of cutting-edge tools and techniques designed to revitalize and enhance scientific practices in the cognitive and neuro-sciences. Students will identify obstacles to conducting robust scientific research, practice using tools meant to overcome these obstacles, and critically evaluate these tools’ potential and limitations.

This book contains 14 lectures on the topic of Global Hermeneutics. It can be used in philosophy classes to discuss the concept of the self, as it showcases different theoretical avenues of how the self is conceived. The synopsis reads as follows:

Why do human beings interpret their overall experience in terms of selfhood? How was the notion and sense of self shaped at different times and in different cultures? What sort of problems or paradoxes did these constructions face? These lectures address these and related questions by sketching a roadmap of possible theoretical avenues for conceiving of the self, bringing to the foreground its soteriological implications, while also testing this theoretical outlook against insights offered by various disciplines. Exploring the crosscultural spectrum of possible ways of conceiving of the self invites the more existential question of whether any of these possibilities might offer resources for dealing with the tragedies of today’s world, or maybe even saving it from some of them.

This activity is about how you form mental images of your body's position in space, independent of vision. Can you take a sip of water from a cup with your eyes closed? If so, how are you able to navigate this maneuver without seeing the cup? Find out here!

This video lecture series begins with an introduction to the basics of anatomical and functional MRI and the time course of the fMRI signal, then delves into several methods for analyzing fMRI data. The series emphasizes how to think about fMRI data and the steps of analysis rather than the technical execution of each step.
Lecture Topics:

fMRI Bootcamp Part 1 - Basics of fMRI
fMRI Bootcamp Part 2 - fMRI timecourse
fMRI Bootcamp Part 3 - Univariate analysis
fMRI Bootcamp Part 4 - Multivariate analysis
fMRI Bootcamp Part 5 - Multivoxel pattern analysis (MVPA)
fMRI Bootcamp Part 6 - Classification 
fMRI Bootcamp Part 7 - Representational similarity
fMRI Bootcamp Part 8 - fMRI & multiple comparisons
fMRI Bootcamp Part 9 - Hyperalignment

Additional Resources:

Rebecca Saxe’s Lab website
Analyzing fMRI Data page in Modeling and Data Analysis Tools and Datasets
Poldrack R. A., Mumford J. A., Nichols T. E. (2011) Handbook of Functional MRI Data Analysis, Cambridge University Press. ISBN: 9780521517669 [hardcover, eBook, Google Books preview]