Instructor: Michelle R. Greene, Ph.D
Email: mgreene2@bates.edu
Office hours: F 9:30-10:30; Hathorn 106
OR Book me via calendar if this time does not work
Logistics: MWF 8:25am - 9:20 (Carnegie 113)
Prerequisites: NS/PY 160 or 200
Although a central tenet of neuroscience is that information about the world in encoded in the patterns of neural firing, it is increasingly acknowledged that our assumptions about these patterns make qualitatively different predictions about neural function. This course examines major hypotheses related to information coding by individual neurons and populations of neurons. Specific themes include rate coding versus time-based codes, sparse versus dense codes, and the relationship between brain responses and the statistics of their inputs. Students examine biological data and artificial models to assess how various encoding schemes might produce skillful behavioral responses.
What is a “neural code”? This fundamental concept refers to the rules that transform action potentials into perceptions, concepts, memories, emotions, and actions. The neural code is the software of the brain.
Solving the neural code is the deepest, most foundational problem in neuroscience. Cracking this code will give us unlimited control over our own brains, allowing us to “fix” brains that have been broken through stroke or other injury. It will allow us to truly read minds, let us share our thoughts with others remotely, or even upload our consciousness when we die. More broadly, understanding the code will help us solve long-standing philosophical mysteries such as the mind-body problem and the existence of free will.
Just as solving the genetic code over half a century ago led to an understanding of human variability and disease and has paved the way for editing this code using techniques such as CRISPR-CAS9, solving the neural code will be just as transformative in the next century. In fact, Francis Crick, who co-discovered of the genetic code, spent the second half of his career working on the neural code.
We are still in relatively early days of this endeavor. Currently, we do not have a single neural code, but many. There are rate codes, temporal codes, population codes and grandmother-cell codes, quantum and chaotic and information codes, and codes based on oscillations and synchronies. In this course, we will explore many of these codes with an eye to how specific codes would enable the brain to efficiently solve hard problems.
By the end of this course, you will be able to:
Explain the concept of information as a quantifiable entity and be able to compute it mathematically from a probability distribution.
Explain the concept of a representation and be able to distinguish between a representation’s content and its function.
Identify the constraints that place bounds on the types of neural codes that can exist. Be able to use Fermi estimation to evaluate neural codes in light of these constraints.
Compare and contrast competing theories of neural coding, evaluating their comparative strengths and tradeoffs.
If you think you need accommodation for a disability or learning difference, please let me know as soon as possible. Some aspects of this course may be modified to facilitate your participation and progress. You will need to create documentation with the Office of the Dean of Students. I will treat any information about your disability with the utmost discretion.
Everyone has the right to be treated with dignity and respect in our classroom, and I am committed to making that happen. But this not only means your instructor treating you with respect, but also you treating each other that way. Disrespect or discrimination on any basis will not be tolerated. Whether inside or outside the classroom, if you encounter sexual harassment, sexual violence, or discrimination based on race, color, religion, age, national origin, ancestry, sex, sexual orientation, gender identity/expression, or disability, you are encouraged to report it to Gwen Lexow, Director of Title IX and Civil Rights Compliance at Bates at glexow@bates.edu or 207-786-6445. Additionally, please remember that Bates faculty are concerned about your well-being and development, and we are available to discuss any concerns you have. Students should be aware that faculty are legally obligated to share disclosures of sexual violence, sexual harassment, relationship violence, and stalking with the college’s Title IX Officer to help ensure that your safety and welfare are being addressed.
Cheating is bad, I think we can all agree to that. The less acknowledged truth is that it’s not even worth it. Cheating cheapens the value of your work and everyone else’s, and a single violation can literally ruin your entire academic and professional career. Please remind yourself of the Bates College policy on academic integrity and its definitions of plagiarism, use/misuse of sources, and cheating. Students’ work will be closely scrutinized for plagiarism and violations of the College policy will not be tolerated. If you are concerned that your collaboration might put you at risk of an academic integrity violation, please come see me during office hours as soon as possible. In my experience, violations of academic integrity are acts of desperation. If you are ever feeling desperate enough that a few extra points in this course seem to be worth risking so much, please consider talking to someone first — that could be me, a friend, or even someone at CAPS. I want you to succeed, and am happy to talk to you if you are feeling undue pressure from this course or anything else.
If I must cancel class due to weather or an emergency, I will inform you via the class email list. Please consider your Bates email to be the default place to look for class-related information and get into the habit of checking it daily.
In our classroom, we are mindful of how we use technology, both for our own learning, and for those around us. You may use a tablet or laptop for taking notes, but I strongly encourage you to avoid having your laptop out for other reasons: laptop use is correlated with lower learning outcomes for you and those around you, and the act of taking notes on the laptop is less effective than hand-written notes.
If I must cancel class due to weather or an emergency, I will inform you via the class email list. Please consider your Bates email to be the default place to look for class-related information and get into the habit of checking it daily.
The ongoing Covid-19 pandemic necessitates flexibility from everyone for this course to take place in a safe and supportive environment. Below are some key considerations:
To ensure the safety of other students and in line with Bates College and CDC health policies, everyone must wear a mask in the classroom at all times. Please ensure that your mask covers both your nose and mouth.
Some students may need to attend class remotely on a temporary basis due to 1) testing positive for Covid-19, 2) being in close contact with someone who tested positive for Covid-19, or 3) experiencing symptoms of Covid-19 or are otherwise not feeling well. I encourage you to err on the side of caution and attend remotely. If you need to attend class remotely, please watch the live class session after it is posted on Lyceum and email me to schedule an office hours appointment to address any questions you may have about class content.
As your instructor, I am working to ensure your safety by being fully vaccinated and boosted. However, if I test positive for Covid-19, have symptoms of Covid-19, or need to quarantine after spending time with someone who tests positive for Covid-19, the class will temporarily be moved to remote format via Zoom.
I recognize that this may all seem very unfair. It is. You did not ask for a global crisis, and you may personally have been doing everything possible to do your part. You may be experiencing stress in other ways, such as if someone close to you becomes ill. If you find yourself struggling, please come talk to me. I will help support you and point you towards additional resources.
Email is a good servant, but a bad master. As such, I try to limit my use of it to spend more time on the parts of my job that matter: giving you the best class experience possible, and being a great scientist. Statistically speaking, you probably don’t like email much either. Therefore, I process all of my email in one batch once a day. While I will always respond to you in the same day, this means that you may go 23 hours without a response. Need a faster response? Stop by my office - I’m there most of the time! Need to meet with me? Feel free to send me a calendar invite. Here are step-by-step instructions for this.
Homework: 40% of total
Starting on the second class period, there will be a set of 1-4 questions about the reading to guide your preparation for class. Please upload your responses to these questions (1-2 sentences each is sufficient) the night before class by midnight on Lyceum. These will be graded on a binary scale (0: no response or fundamentally incorrect response; 1: fundamentally correct response). 50% of assignments will be selected randomly for more feedback, and you are encouraged to meet with me during office hours if you would like more feedback or have questions about a question that were not covered in class.
Exams: 40% of total
The two exams will be given in class and will include a mix of multiple-choice, fill-in, and short answer questions. I will return the graded exam to you within one week. If you are not happy with your grade, you will have the opportunity to make corrections on the exam to earn back half of the lost points. For example, if you earned a 70 on an exam, you will be able to earn up to an 85 by doing corrections.
Exam 1: February 7 Will cover foundational materials (codes, information, and representations).
Exam 2: March 11 Will cover codes for single neurons and neuronal populations.
Take-home final: 20% of total
The final exam for this course is cumulative and open resource (open note, open reading, open collaboration). It will be released after the last class meeting on April 15, and will be due on Lyceum at the end of the scheduled final exam time: April 19 3:15pm.
Your final percentage score will be assigned a letter grade on the following scale:
Grade | Percentage | Grade | Percentage | |
---|---|---|---|---|
A+ | >95% | B- | 71-74% | |
A | 87-94% | C+ | 67-70% | |
A- | 83-86% | C | 63-66% | |
B+ | 79-82% | D | 50-62% | |
B | 75-78% | F | <50 |
All required papers will be available on this website and Lyceum, and should be done before class.
Goals: Get acquainted with the course; understand the problem of the neural code; practice Fermi estimation.
Reading: No reading expected ahead of time. Review the syllabus after class.
Study questions: None
Goals: Be able to articulate why neural codes may not be obvious; practice Fermi estimation.
Reading: Jerome Lettvin et al “What the Frog’s Eye Tells the Frog’s Brain”.
Study questions:
Goals: Attend the excellent programming throughout the college.
Reading: None
Study questions: None
Goals: Refine your understanding of the neural code by enumerating what information in the environment is there to be perceived.
Reading: J.J. Gibson “The Environment as a Source of Stimulation” from The Senses Considered as Perceptual Systems
Study questions:
Goals: Be able to define infomation as a quantitative concept. Explain how entropy in information theory related to entropy from physics.
Reading: Read: Pierce “The Origins of Information Theory” from An Introduction to Information Theory: Symbols, Signals, and Noise
Note: Pay attention to the notes in the margins. Some pages are optional.
Study questions:
Goals: Become comfortable calculating entropy from some common probability distributions.
Reading: Read: Stone “Information Theory” from Principles of Neural Information Theory: Computational Neuroscience and Metabolic Efficiency
Study questions:
Goals: Apply the concepts from information theory to neurons.
Reading: Read: Stone Selections from Chapter 3 from Principles of Neural Information Theory: Computational Neuroscience and Metabolic Efficiency
Study questions:
Goals: Develop strategies for efficiently coding information with neurons.
Reading: Read: Huffman Coding from Introduction to Algorithms by Cormen, Leiserson & Rivest.
Study questions:
Goals: Begin to create a working definition of ‘mental representation’ and articulate why this is not a straightforward task. Reading: Palmer, S. (1978) Fundamental Aspects of Cognitive Representation from Cognition and Categorization.
Study questions:
Goals: Refine our working definition of representations to distinguish between a representation’s content and its function.
Reading: Read: Ballard “On the Function of Visual Representation”
* Recommended reading: Marr (1982) “The Philosophy of the Approach” Especially section 1.2.
Study questions:
Goals: Explain the problem of the homunculus and question what is representation is for.
Reading:
Study questions:
No class will be held to give you time on the exam
Goals: Explain the concept of a ‘grandmother cell’ and its history
Reading: Read: Gross (2002) Geneology of the “grandmother cell”
Study questions:
Goals: Determine whether we have evidence for grandmother cell-like coding
Reading: Read: Quiroga et al (2005) Invariant visual representation by single neurons in the human brain.
Study questions:
Goals: Assess the plausibility of grandmother cell coding
Reading: Read: Excerpts from Anderson An Introduction to Neural Networks, chapter 10.
Study questions:
Goals: Compare and contrast a local grandmother cell code with an alternative: distributed codes
Reading: Read: Thorpe (1989) Local versus distributed coding Intellectica
Study questions:
Goals: Assess the possible advantages of a distributed coding scheme
Reading: Read: Hinton, McClelland, and Rumelhart Chapter 1, Parallel Distributed Processing
Study questions:
Goals: Dissect an example of a population code.
Reading: Read: Georgeopoulos (1986) Neural Coding of Movement Direction Science
Study questions:
Goals: Understand how time provides challenges and opportunities in the study of neural codes.
Reading: Read: Gerster “How can brains be so fast?” from 23 Problems in Systems Neuroscience
Study questions:
Goals: Be able to explain one temporal code: the time to first spike code.
Reading: Read: Van Rullen et al (2005) Spike times make sense. Trends in Neurosciences
Study questions:
Goals: Understand how information may be coded in population oscillations.
Reading: Read: Lisman & Jensen (2013) The Theta-Gamma Neural Code. Neuron
Study questions:
Goals: Assess the potential reliability of time-based codes.
Reading: Read: Stein et al (2005) Neuronal Variability: Noise or part of the signal? Nature Reviews Neuroscience Study questions:
No class. Exam will be released on Lyceum at 8:25.
Goal: Develop a theory of what problems a brain is trying to solve.
Reading: Read: Sterling & Laughlin “Why an Animal Needs a Brain” from Principles of Neural Design
Study questions:
Goal: Begin to build an energy budget for the brain.
Reading: Read: Attwell & Laughlin (2001) An Energy Budget for Signaling in the Grey Matter of the Brain Journal of Cerebral Blood Flow and Metabolism (The calculations on page 1134 are an optional quantitative adventure)
Study questions:
Goal: Be able to use the metabolic demands of the brain as a constaint on the types of codes the brain might use.
Reading: Read: Lennie (2003) The Cost of Cortical Computation Current Biology
Study questions:
Goal: Articulate how we might begin to measure the amount of information in our environments.
Reading: * Read: Attneave (1954) Some Informational Aspects of Visual Perception Psychological Review
Study questions:
Goals: Conduct a case study on one method for measuring the information in an environment.
Reading: Read: Kersten (1987) Predictability and redundancy of natural images
Study questions:
Goal: Be able to articulate how we can leverage the redundancy of the environment to create efficient neural codes.
Reading: * Read: Barlow (1961) Possible Principles Underlying the Transformations of Sensory Messages
Study questions:
Goal: Make an informed assessment about whether the goal of the neural code is to be maximally efficient.
Reading: Read: Barlow (2001) Redundancy reduction revisited. Network: Computational Neural Systems
Study questions:
Goal: Assess the extent to which we fully know sensory areas
Reading: Olshausen & Field “What’s the other 85% of V1 doing?” From 23 Problems in Systems Neuroscience
Study questions:
Goal: Define the notion of a sparse code and compare it to compact codes.
Reading: * Read: Field (1994) What is the goal of sensory coding?. Sections 1, 2, 5, and 6 required. Others optional, but recommended.
Study questions:
Goal: Develop a working definition of a sparse codes and explain its benefits.
Reading: Olshausen & Field (2004) Sparse Coding of Sensory Inputs Current Opinion in Neurobiology
Study questions:
Goal: Consider the extent to which the neural code is the same across sensory modality and species.
Reading: McAlpine and Palmer How general are neural codes in sensory systems? from 23 Problems in Systems Neuroscience
Study questions:
Goal: Gaze out on the frontier of knowledge and consider what we still don’t understand.
Reading: Yong (2021) Neuroscientists have discovered a phenomenon they can’t explain The Atlantic
Study questions:
Study question 1: Briefly define the concept of representational drift and provide an example of it from the reading.
Study question 2: In what way(s) is representational drift a problem for the way that we think about neural codes? In what way(s) might it be beneficial?
* (Optional) What other questions do you have about this reading?
Goal: Wrap up, reflect, review for final exam.
Reading: None
Study guide: Look at the practice final that is posted on Lyceum and come with your questions.