Spring 2027

Computational Neuroscience: From Single Cells to Whole Brain Models

Computational Neuroscience: From Single Cells to Whole Brain Models covers theory and application of methods in computational neuroscience across various levels of organization from single cells to global brain dynamics and cognition.

offering recorded3 credits
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 3

Recent recorded grades — Spring 2024: 3.94 GPA, 100.0% A/AB (n=8 letter grades); Spring 2025: 3.91 GPA, 90.9% A/AB (n=33 letter grades); Spring 2026: 3.88 GPA, 96.2% A/AB (n=26 letter grades).

What you’ll be able to do

  • Explain the basic functioning of a neuron in biophysical terms (including how action potentials are generated, the role of dendrites, and postsynaptic integration).

  • Summarize the computational properties of sensory neurons reacting to sensory stimuli (tuning curves, receptive fields, feature selectivity).

  • 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).

  • Name various types of neural circuit models and their areas of application.

  • List the main differences between artificial neural networks as developed in computer science and computational models of biological neural networks.

  • Demonstrate how to characterize the dynamical properties of neurons and neural networks.

  • Summarize the hierarchical organization of the brain in computational terms (canonical microcircuit, mini-columns, functional brain networks).

  • Distinguish between anatomical, functional, and effective cortical connectivity.

  • Summarize main theoretical approaches to understanding brain functioning (graph theory, dynamical systems, information processing, decoding, whole-brain computational models).

  • Run and adapt MATLAB scripts for building and simulating neural models.

  • Evaluate and critique the assumptions and limitations of different computational models and approaches to address currently open questions in neuroscience.

  • 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.

  • 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.

Source: UW Guide 1 UW Guide 2

Grade history

↗
average GPA
letter grades
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AB
B
BC
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All recorded terms · compare terms & instructors

Prerequisites

Course map

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

NEURODPT/NTP 640

This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree

Professors

Spring 2027

No instructors recorded for this selection.

Historical instructors & teaching patterns

Recorded history may be incomplete and does not establish a future schedule.

Calendar & sections

Spring 2027

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LAB 301Classroom Instruction0 / 200
LEC 002Classroom Instruction0 / 200
LAB 301Classroom Instruction0 / 300
LEC 002Classroom Instruction0 / 300

Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.

Meeting source records
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Student experience

the class

Recent recorded grades — Spring 2024: 3.94 GPA, 100.0% A/AB (n=8 letter grades); Spring 2025: 3.91 GPA, 90.9% A/AB (n=33 letter grades); Spring 2026: 3.88 GPA, 96.2% A/AB (n=26 letter grades).

difficulty & workload

No workload feedback recorded.

Topics

  • Biophysical properties of neurons and synapses, neural plasticity, sensory systems, neural circuits, whole brain analysis and modeling
  • Computational techniques including ICA, information theoretical approaches, and dynamical systems

Skills

  • Running and adapting MATLAB scripts for neural model simulation
  • Designing and evaluating computational models of biological neural circuits
  • Characterizing dynamical properties of neurons and neural networks
  • Evaluating statistical and information theoretical properties of neuronal activity

Grades

Latest available · Spring 2026— not enough history to project Spring 2027.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

A
AB
B
BC
C
D
F

Grades over time

Through Spring 2026

More grade details Grade mix, volume & source data

Not enough comparable courses for Spring 2027 in UW–Madison.

Sources & history

UW–Madison

Catalog & offerings

Descriptions, prerequisites, and recorded course offerings.

Catalog observation history

Observations at scan time; dates do not imply when a catalog change took effect.

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Selected offering source records
NEURODPT/NTP 640 · Spring 2027

Computational Neuroscience: From Single Cells to Whole Brain Models

Recorded 2026-10-14
NEURODPT/NTP 640 · Spring 2027

Computational Neuroscience: From Single Cells to Whole Brain Models

Recorded 2026-10-14
Raw records 
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    "course_id": "NEURODPT/NTP 640",
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    "title": "Computational Neuroscience: From Single Cells to Whole Brain Models",
    "credits_min": 3,
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    "title": "Computational Neuroscience: From Single Cells to Whole Brain Models",
    "credits_min": 3,
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    "typically_offered": "Not Applicable"
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Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

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Dataset & model

Recorded 2026-10-14

View the dataset on Hugging Face
Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
Download part 1
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Download part 1
Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "9220630d2449fc58447b7b1c2f68dd6243d37de754620d87424718b015b4ea6c",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}