# NEURODPT/NTP 640: Computational Neuroscience: From Single Cells to Whole Brain Models | UW–Madison

[View on UW Courses](https://uwcourses.com/courses/NEURODPT_NTP_640)

## Dataset

### revision

e243353dcb7d79b7247ced91d69443ef4c2a6349

### repository

twangodev/uwcourses

### schema version

6

### importer version

7

### projection id

52a78527ff09011d88cb04c7d43867d9fbfec2930bf1dd2ec359ebd2844e0b07

### observed at

2026-09-07T15:55:43.033547+00:00

### built at

2026-09-10T19:50:42.743001+00:00

### courses

8951

### current instructors

5754

### limited

false

### terms

* 1272
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### term

1272

### departments

| subject   | count |
| --------- | ----- |
| AAE       | 90    |
| ABT       | 20    |
| ACCTIS    | 36    |
| ACTSCI    | 14    |
| AFAERO    | 10    |
| AFRICAN   | 87    |
| AFROAMER  | 71    |
| AGROECOL  | 21    |
| AMERIND   | 53    |
| ANAT\&PHY | 7     |
| ANATOMY   | 2     |
| ANESTHES  | 8     |
| ANSCI     | 57    |
| ANTHRO    | 95    |
| ART       | 125   |
| ARTED     | 10    |
| ARTHIST   | 121   |
| ASIALANG  | 129   |
| ASIAN     | 109   |
| ASIANAM   | 28    |
| ASTRON    | 38    |
| ATMOCN    | 69    |
| BIOCHEM   | 51    |
| BIOCORE   | 10    |
| BIOLOGY   | 14    |
| BIOMDSCI  | 18    |
| BME       | 66    |
| BMI       | 41    |
| BMOLCHEM  | 10    |
| BOTANY    | 69    |
| BSE       | 44    |
| C\&ESOC   | 73    |
| CBE       | 48    |
| CHEM      | 101   |
| CHICLA    | 52    |
| CIVENGR   | 133   |
| CLASSICS  | 47    |
| CNP       | 10    |
| CNSRSCI   | 50    |
| COMARTS   | 137   |
| COMPBIO   | 15    |
| COMPLIT   | 11    |
| COMPSCI   | 138   |
| COUNPSY   | 78    |
| CRB       | 19    |
| CS\&D     | 71    |
| CSCS      | 40    |
| CURRIC    | 193   |
| DANCE     | 93    |
| DERM      | 8     |
| DS        | 88    |
| DYSCI     | 35    |
| ECE       | 155   |
| ECON      | 145   |
| EDPOL     | 114   |
| EDPSYCH   | 97    |
| ELPA      | 70    |
| EMA       | 53    |
| EMERMED   | 17    |
| ENGL      | 206   |
| ENTOM     | 40    |
| ENVIRST   | 148   |
| EP        | 15    |
| EPD       | 67    |
| ESL       | 16    |
| F\&WECOL  | 60    |
| FAMMED    | 22    |
| FINANCE   | 50    |
| FOLKLORE  | 40    |
| FOODSCI   | 43    |
| FRENCH    | 63    |
| GEN\&WS   | 145   |
| GENBUS    | 72    |
| GENECSLR  | 17    |
| GENETICS  | 60    |
| GEOG      | 113   |
| GEOSCI    | 83    |
| GERMAN    | 82    |
| GLE       | 48    |
| GNS       | 38    |
| GREEK     | 27    |
| HDFS      | 41    |
| HEBR-BIB  | 13    |
| HEBR-MOD  | 10    |
| HISTORY   | 233   |
| HISTSCI   | 56    |
| HONCOL    | 13    |
| ILS       | 43    |
| INFOSYS   | 9     |
| INTEGART  | 7     |
| INTEGSCI  | 22    |
| INTER-AG  | 18    |
| INTER-HE  | 11    |
| INTER-LS  | 22    |
| INTEREGR  | 16    |
| INTLBUS   | 21    |
| INTLST    | 50    |
| ISYE      | 83    |
| ITALIAN   | 53    |
| JEWISH    | 56    |
| JOURN     | 88    |
| KINES     | 128   |
| LACIS     | 26    |
| LANDARC   | 61    |
| LATIN     | 24    |
| LAW       | 120   |
| LEGALST   | 48    |
| LINGUIS   | 35    |
| LIS       | 89    |
| LITTRANS  | 78    |
| LSC       | 52    |
| M\&ENVTOX | 7     |
| MARKETNG  | 62    |
| MATH      | 153   |
| MDGENET   | 8     |
| ME        | 130   |
| MEDHIST   | 43    |
| MEDICINE  | 60    |
| MEDIEVAL  | 32    |
| MEDPHYS   | 39    |
| MEDSC-M   | 29    |
| MEDSC-V   | 41    |
| MHR       | 68    |
| MICROBIO  | 45    |
| MILSCI    | 16    |
| MM\&I     | 22    |
| MOLBIOL   | 6     |
| MS\&E     | 59    |
| MUSIC     | 165   |
| MUSPERF   | 126   |
| NAVSCI    | 20    |
| NE        | 44    |
| NEURODPT  | 11    |
| NEUROL    | 7     |
| NEURSURG  | 4     |
| NTP       | 11    |
| NURSING   | 111   |
| NUTRSCI   | 65    |
| OBS\&GYN  | 18    |
| OCCTHER   | 39    |
| ONCOLOGY  | 14    |
| OPHTHALM  | 5     |
| OTM       | 43    |
| PATH      | 34    |
| PATH-BIO  | 29    |
| PEDIAT    | 26    |
| PHARMACY  | 31    |
| PHILOS    | 77    |
| PHMCOL-M  | 11    |
| PHMPRAC   | 45    |
| PHMSCI    | 60    |
| PHYASST   | 37    |
| PHYSICS   | 88    |
| PHYSIOL   | 3     |
| PHYTHER   | 37    |
| PLANTSCI  | 52    |
| PLPATH    | 35    |
| POLISCI   | 192   |
| POPHLTH   | 58    |
| PORTUG    | 33    |
| PSYCH     | 101   |
| PSYCHIAT  | 22    |
| PUBAFFR   | 54    |
| PUBLHLTH  | 23    |
| RADIOL    | 11    |
| REALEST   | 41    |
| RELIGST   | 90    |
| RHABMED   | 9     |
| RMI       | 24    |
| RP\&SE    | 102   |
| S\&APHM   | 17    |
| SCANDST   | 73    |
| SLAVIC    | 89    |
| SOC       | 149   |
| SOCWORK   | 87    |
| SOILSCI   | 46    |
| SPANISH   | 83    |
| SRMED     | 21    |
| STAT      | 94    |
| STDYABRD  | 52    |
| STS       | 8     |
| SURGERY   | 25    |
| SURGSCI   | 33    |
| THEATRE   | 91    |
| URBRPL    | 60    |
| UROLOGY   | 5     |
| ZOOLOGY   | 89    |

## course

### run id

20260907T155543-ce3781c4

### semester

1272

### observed at

2026-09-07 15:55:43.033547+00:00

### record version id

b87f8f87198514b4966100d97f1431686eccd5547e05cf3ab4c4232014999a05

### course id

NEURODPT/NTP 640

### course uid

course\_30ba3691f2acc1d8f1bbfb13

### catalog version id

e102dc82fa3cdc634dae4a6d67443fcab40a0409223ac48438cfd9bbc223658f

### course number

640

### subjects

* NEURODPT
* NTP

### title

COMPUTATIONAL NEUROSCIENCE: FROM SINGLE CELLS TO WHOLE BRAIN MODELS

### description

Theory and application of methods in computational neuroscience across various levels of organization from single cells to global brain dynamics and cognition. Computational neuroscience is an approach to understanding the development and function of nervous systems in mechanistic terms at many different structural scales. Topics include biophysical properties of neurons and synapses, neural plasticity, sensory systems, neural circuits, whole brain analysis and modeling, and different views on brain function. Includes primers on relevant computational techniques (ICA, information theoretical approaches, dynamical systems) and a computational problem set. Starts with an introduction to MATLAB (used for problem sets).

### requirements text

PSYCH/​ZOOLOGY  523,PSYCH 454,MATH 221, and (PHYSICS 104,202,208, or248); or graduate/professional standing andNEURODPT/​NTP  610andPSYCH/​NEURODPT/​NTP  611

### credit offering ids

None recorded.

### llm job id

enrich-8b774950c2b6adfdc46d1b82

### llm output id

45f8c6453d1e4d92dc625d20968156dffa354e708cb20c51be9e76e6dc23fdc1

### llm model

nvidia/Qwen3.6-35B-A3B-NVFP4

### llm model revision

1355db6a052410cfd62085d94b58866fd0f2c3c5

### llm task version

14

### llm search status

valid

### llm summary

Computational neuroscience course covering methods from single cells to whole brain models using MATLAB.

### llm topics

* Biophysical properties, neural plasticity, sensory systems, neural circuits
* ICA, information theory, dynamical systems
* MATLAB introduction

### llm skills

* Computational neuroscience methods
* MATLAB programming and computational problem solving
* Neural system modeling and analysis

### llm assumed background

* Biological basis of behavior and neural mechanisms
* Calculus and analytic geometry
* Calculus-based physics (electricity, magnetism, optics, modern physics)

### llm search phrases

* computational neuroscience MATLAB
* neural modeling ICA dynamical systems
* biophysical neuron modeling
* whole brain analysis computational

### llm requirements status

valid

### llm student summary status

valid

### llm experience status

insufficient\_evidence

### catalog variants

None recorded.

### student summary

#### context hash

6d239396152fda23d59227d86a254910d937ed9ff049eb4cba01a526106f7b29

#### course id

NEURODPT/NTP 640

#### current instructors

None recorded.

#### difficulty workload

None recorded.

#### errors

None recorded.

#### historical context

None recorded.

#### message

No course-specific reviews available

#### offered

false

#### profile hash

5cb4dabf887cdbcd8c00d5a1312e10828b95c63f30bc3ea76aea199565390d02

#### quick take

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).

```json
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```

#### student experience

None recorded.

#### task hash

74fb0997943e888960bbc9e47db8c4fd12e4292d55c20509d8d89db6a9910f68

#### teaching history

None recorded.

#### term id

1272

#### term name

2026 Fall

#### version

2

### requirements

#### nodes

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    "id": "r14",
    "kind": "course"
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]
```

#### notes

None recorded.

#### root

r1

#### status

parsed

### instructors

None recorded.

### offerings

None recorded.

### sections

None recorded.

### grade instructors

```json
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        0,
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```

### grades

```json
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      "Meyer Jackson"
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    "semester": "1272",
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```

### statistics

#### gpa

3.903

#### graded

67

#### counts

* 58
* 5
* 4
* 0
* 0
* 0
* 0

### grade conflicts

None recorded.

### evidence

#### history

* [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/history/course\_30ba3691f2acc1d8f1bbfb13-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/history/course_30ba3691f2acc1d8f1bbfb13-0.json)

#### traces

* [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/traces/course\_30ba3691f2acc1d8f1bbfb13-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/traces/course_30ba3691f2acc1d8f1bbfb13-0.json)

#### results

* [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/results/course\_30ba3691f2acc1d8f1bbfb13-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/results/course_30ba3691f2acc1d8f1bbfb13-0.json)

### revision

e243353dcb7d79b7247ced91d69443ef4c2a6349

## context

### all

#### term



#### gpa

3.9029850746268657

#### count

67

#### university

##### size

2411

##### gpa Percentile

75

##### count Percentile

35

##### median Count

90

##### histogram

| range   | count | current |
| ------- | ----- | ------- |
| 0.0–0.4 | 0     | false   |
| 0.4–0.8 | 0     | false   |
| 0.8–1.2 | 0     | false   |
| 1.2–1.6 | 0     | false   |
| 1.6–2.0 | 0     | false   |
| 2.0–2.4 | 0     | false   |
| 2.4–2.8 | 17    | false   |
| 2.8–3.2 | 163   | false   |
| 3.2–3.6 | 604   | false   |
| 3.6–4.0 | 1627  | true    |

#### departments

| subject  | comparison |
| -------- | ---------- |
| NEURODPT |            |
| NTP      |            |

### terms

#### 1244

##### term

1244

##### gpa

3.9375

##### count

8

##### departments

| subject  | comparison |
| -------- | ---------- |
| NEURODPT |            |
| NTP      |            |

#### 1254

##### term

1254

##### gpa

3.909090909090909

##### count

33

##### university

###### size

1289

###### gpa Percentile

82

###### count Percentile

6

###### median Count

66

###### histogram

| range   | count | current |
| ------- | ----- | ------- |
| 0.0–0.4 | 0     | false   |
| 0.4–0.8 | 0     | false   |
| 0.8–1.2 | 0     | false   |
| 1.2–1.6 | 0     | false   |
| 1.6–2.0 | 0     | false   |
| 2.0–2.4 | 0     | false   |
| 2.4–2.8 | 17    | false   |
| 2.8–3.2 | 128   | false   |
| 3.2–3.6 | 379   | false   |
| 3.6–4.0 | 765   | true    |

##### departments

| subject  | comparison |
| -------- | ---------- |
| NEURODPT |            |
| NTP      |            |

#### 1264

##### term

1264

##### gpa

3.8846153846153846

##### count

26

##### departments

| subject  | comparison |
| -------- | ---------- |
| NEURODPT |            |
| NTP      |            |

### benchmarks

#### all

##### school

###### size

2411

###### gpa

3.6770271983084943

###### top Share

83.11222705251026

###### count

90

#### terms

##### 1244

###### school

###### size

1241

###### gpa

3.5975573126929192

###### top Share

78.29036874847135

###### count

65

##### 1254

###### school

###### size

1289

###### gpa

3.6126423469389106

###### top Share

79.48050408754871

###### count

66

##### 1264

###### school

###### size

1283

###### gpa

3.6229729166451925

###### top Share

80.13669149396227

###### count

66

## instructor Trends

```json
[
  {
    "uid": "instructor_86c07b4e85430743f381f03c",
    "name": "LARISSA ALBANTAKIS",
    "count": 67,
    "terms": [
      {
        "term": "1244",
        "count": 8,
        "sections": 1,
        "gpa": 3.9375
      },
      {
        "term": "1254",
        "count": 33,
        "sections": 1,
        "gpa": 3.909090909090909
      },
      {
        "term": "1264",
        "count": 26,
        "sections": 1,
        "gpa": 3.8846153846153846
      }
    ]
  },
  {
    "uid": "instructor_9f5230f2e1a9c4b929d5f05b",
    "name": "MEYER JACKSON",
    "count": 67,
    "terms": [
      {
        "term": "1244",
        "count": 8,
        "sections": 1,
        "gpa": 3.9375
      },
      {
        "term": "1254",
        "count": 33,
        "sections": 1,
        "gpa": 3.909090909090909
      },
      {
        "term": "1264",
        "count": 26,
        "sections": 1,
        "gpa": 3.8846153846153846
      }
    ]
  },
  {
    "uid": "instructor_e431b3a114b4b7f680906be4",
    "name": "MATHEW JONES",
    "count": 41,
    "terms": [
      {
        "term": "1244",
        "count": 8,
        "sections": 1,
        "gpa": 3.9375
      },
      {
        "term": "1254",
        "count": 33,
        "sections": 1,
        "gpa": 3.909090909090909
      }
    ]
  }
]
```

## following

None recorded.
