COMPUTATIONAL NEUROSCIENCE: FROM SINGLE CELLS TO WHOLE BRAIN MODELS

Prerequisites & course information

Requisites: PSYCH/ZOOLOGY 523 , PSYCH 454 , MATH 221 , and ( PHYSICS 104 , 202 , 208 , or 248 ); or graduate/professional standing and NEURODPT/NTP 610 and PSYCH/NEURODPT/NTP 611 Course Designation: Core GenEd - Natural Science & Wellness Breadth - Biological Science Level - Advanced L&S Credit - L&S Liberal Arts and Science Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Spring 2026 Learning Outcomes: 1. Explain the basic functioning of a neuron in biophysical terms (including how action potentials are generated, the role of dendrites, and postsynaptic integration). Audience: Both Grad & Undergrad 2. Summarize the computational properties of sensory neurons reacting to sensory stimuli (tuning curves, receptive fields, feature selectivity). Audience: Both Grad & Undergrad 3. Demonstrate technical familiarity in evaluating the statistical and information theoretical properties of neuronal activity (basics of signal detection theory, spike train statistics, firing rate models, PCA/ICA analysis). Audience: Both Grad & Undergrad 4. Name various types of neural circuit models and their areas of application. Audience: Both Grad & Undergrad 5. List the main differences between artificial neural networks as developed in computer science and computational models of biological neural networks. Audience: Both Grad & Undergrad 6. Demonstrate how to characterize the dynamical properties of neurons and neural networks. Audience: Both Grad & Undergrad 7. Summarize the hierarchical organization of the brain in computational terms (canonical microcircuit, mini-columns, functional brain networks). Audience: Both Grad & Undergrad 8. Distinguish between anatomical, functional, and effective cortical connectivity. Audience: Both Grad & Undergrad 9. Summarize main theoretical approaches to understanding brain functioning (graph theory, dynamical systems, information processing, decoding, whole-brain computational models). Audience: Both Grad & Undergrad 10. Run and adapt MATLAB scripts for building and simulating neural models. Audience: Both Grad & Undergrad 11. Evaluate and critique the assumptions and limitations of different computational models and approaches to address currently open questions in neuroscience. Audience: Graduate 12. Summarize key concepts, methods, and results from primary research articles. Integrate information from these articles to explain how authors reached their conclusions. Judge the strengths and limitations of the methods, the data, and the conclusions. Audience: Graduate 13. Run and evaluate a self-designed computational model of a biological neural circuit synthesizing concepts from different parts of the course based on computer labs. Audience: Graduate Learning Outcomes: 1. Explain the basic functioning of a neuron in biophysical terms (including how action potentials are generated, the role of dendrites, and postsynaptic integration). Audience: Both Grad & Undergrad 2. Summarize the computational properties of sensory neurons reacting to sensory stimuli (tuning curves, receptive fields, feature selectivity). Audience: Both Grad & Undergrad 3. Demonstrate technical familiarity in evaluating the statistical and information theoretical properties of neuronal activity (basics of signal detection theory, spike train statistics, firing rate models, PCA/ICA analysis). Audience: Both Grad & Undergrad 4. Name various types of neural circuit models and their areas of application. Audience: Both Grad & Undergrad 5. List the main differences between artificial neural networks as developed in computer science and computational models of biological neural networks. Audience: Both Grad & Undergrad 6. Demonstrate how to characterize the dynamical properties of neurons and neural networks. Audience: Both Grad & Undergrad 7. Summarize the hierarchical organization of the brain in computational terms (canonical microcircuit, mini-columns, functional brain networks). Audience: Both Grad & Undergrad 8. Distinguish between anatomical, functional, and effective cortical connectivity. Audience: Both Grad & Undergrad 9. Summarize main theoretical approaches to understanding brain functioning (graph theory, dynamical systems, information processing, decoding, whole-brain computational models). Audience: Both Grad & Undergrad 10. Run and adapt MATLAB scripts for building and simulating neural models. Audience: Both Grad & Undergrad 11. Evaluate and critique the assumptions and limitations of different computational models and approaches to address currently open questions in neuroscience. Audience: Graduate 12. Summarize key concepts, methods, and results from primary research articles. Integrate information from these articles to explain how authors reached their conclusions. Judge the strengths and limitations of the methods, the data, and the conclusions. Audience: Graduate 13. Run and evaluate a self-designed computational model of a biological neural circuit synthesizing concepts from different parts of the course based on computer labs. Audience: Graduate