Moses - Models of Neural Systems

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Diese Modulbeschreibung gilt nicht im aktuellen Semester.

Die Modulbeschreibung gilt nur für den Zeitraum SoSe 2020 - SoSe 2021. Die Version für das aktuelle Semester (SoSe 2024) finden Sie hier: Modulbeschreibung des SoSe 2024

Module / Version:
#40042 / #4

Validity:
SoSe 2020 - SoSe 2021

Default display language:
English

Module title:
Models of Neural Systems

Credits:
12

Responsible person:
Obermayer, Klaus

Grading:
benotet

Type of exam:
Mündliche Prüfung

Zugehörigkeit

Faculty:
Fakultät IV

Institute:
Institut für Softwaretechnik und Theoretische Informatik

Area of expertise:
34351300 FG Neuronale Informationsverarbeitung

Prüfungsausschuss:
No information

Kontakt

Office:
MAR 5-6

Contact person:
Velenosi, Lisa Alexandria

E-mail address:
graduateprograms@bccn-berlin.de

Website:

https://www.bccn-berlin.de/courses-and-modules.html

Learning Outcomes

After this module, students will know: - the basic concepts of computational neuroscience, their theoretical foundation, and the most common models used - the relevant basic neurobiological knowledge and the relevant theoretical approaches as well as the findings resulting form these approaches so far - strengths and limitations of the different models - how to appropriately choose the theoretical methods for modeling neural systems - how to apply these methods while taking into account the neurobiological findings - how to critically evaluate results obtained. - how to adapt models to new problems as well as to develop new models of neural systems.

Content

This module provides basic knowledge about the constituents of neural systems and their modeling, which includes basic neurobiological concepts and models concerning information processing within neurons and neural circuitry. Specific topics addressed are: - Electrical properties of neurons (Nernst equation, Goldman equation, Goldman-Hodgkin-Katz current equation, membrane equation) - Hodgkin-Huxley model (voltage-dependent conductances, gating variables, transient and persistent conductances, action-potential generation) - Channel models (state diagram, stochastic dynamics) - Synapse models (chemical and electrical synapses) - Single-compartment neuron models (integrate-and-fire, conductance-based) - Models of dendrites and axons (cable theory, Rall model, multi-compartment models, action-potential propagation) - Models of synaptic plasticity and learning (release probability, short-term depression and facilitation, long-term plasticity, Hebbian rule, timing-based plasticity rules, supervised/unsupervised and reinforcement learning) - Network models (feedforward and recurrent, excitatory-inhibitory, firing-rate and stochastic, associative memory) - Phase-space analysis of neuron and network models (linear stability analysis, phase portraits, bifurcation theory

Module Components

Pflichtgruppe:

All Courses are mandatory.

Course Name	Type	Cycle	Language	SWS
Models of Neural Systems – Theoretical Lecture	VL	WiSe	No information	2
Models of Neural Systems – Tutorial	UE	WiSe	No information	2
Models of Neural Systems – Computer Lab	UE	WiSe	No information	2
Models of Neural Systems – Experimental Lecture	VL	WiSe	No information	2

Workload and Credit Points

Models of Neural Systems – Theoretical Lecture (VL):

Workload description	Multiplier	Hours	Total
Attendance	15.0	2.0h	30.0h
Lecture rehearsals/ individual studies	15.0	2.0h	30.0h
			60.0h(~2 LP)

Models of Neural Systems – Tutorial (UE):

Workload description	Multiplier	Hours	Total
Attendance	15.0	2.0h	30.0h
Homework assignments	15.0	6.0h	90.0h
			120.0h(~4 LP)

Models of Neural Systems – Computer Lab (UE):

Workload description	Multiplier	Hours	Total
Attendance	15.0	2.0h	30.0h
Homework assignments	15.0	6.0h	90.0h
			120.0h(~4 LP)

Models of Neural Systems – Experimental Lecture (VL):

Workload description	Multiplier	Hours	Total
Attendance	15.0	2.0h	30.0h
Lecture rehearsals/ individual studies	15.0	2.0h	30.0h
			60.0h(~2 LP)

The Workload of the module sums up to 360.0 Hours. Therefore the module contains 12 Credits.

Description of Teaching and Learning Methods

The lecture part consists of teaching in front of the class. Participants are expected to rehearse topics after class, using their class notes as well as recommended book chapters, in preparation for the exercises and tutorials. Homework assignments are given on a regular basis, and must be usually solved within one or two weeks. These assignments cover analytical & mathematical exercises as well as numerical simulations & programming exercises. Working in small groups of two to three students is encouraged. Homework assignments and their solutions are discussed during the tutorial. In addition, selected topics presented during the lecture are rehearsed by the tutor as needed. Tutorials also cover brief mathematics primer, and recommendations are provided for students for the module “individual studies”, if deficits in their mathematical knowledge become obvious.

Requirements for participation and examination

Desirable prerequisites for participation in the courses:

- Mathematical knowledge: Analysis, linear algebra, probability calculus and statistics, on a level comparable to mathematics courses for engineers (worth 24 credit points) - Basic programming skills - Good command of the English language

Mandatory requirements for the module test application:

1. Requirement

[CNS] Successful participation in the MNS tutorial

2. Requirement

[CNS] Successful participation in the MNS programming lab

Module completion

Grading

graded

Type of exam

Oral exam

Language

English

Duration/Extent

35 Min.

Duration of the Module

The following number of semesters is estimated for taking and completing the module:
1 Semester.

This module may be commenced in the following semesters:
Wintersemester.

Maximum Number of Participants

The maximum capacity of students is 20.

Registration Procedures

Enrollment to the module is handled in the first class of each module component (cf. 3). Students must be present in person. Registration has to be done with the examination office (Prüfungsamt) of TU Berlin at least three working days prior to the examination date. sekr@ni.tu-berlin.de

Recommended literature
01. Dayan, Abbott, Theoretical Neuroscience, MIT Press, 2001. (recommended)
02. Izhikevich, Dynamical Systems in Neuroscience, MIT Press, 2007. (recommended)
03. Johnston, Wu, Foundations of Cellular Neurophysiology, MIT Press,1995. (recommended)
04. Hertz, Krogh, Palmer, Introduction to the Theory of Neural Computation, Addison-Wesley, 1991. (additional)
05. Hille, Ion Channels of Excitable Membranes, Sinauer, 2001. (additional)
06. Koch, Biophysics of Computation, Oxford University Press, 1999. (additional)
07. Koch, Segev, Methods in Neuronal Modelling, MIT Press, 1998. (additional)

Assigned Degree Programs

Semester:

Zeige auch ausgelaufene Studiengänge und StuPOs

This module is used in the following Degree Programs (new System):

	Studiengang / StuPO	StuPOs	Verwendungen	Erste Verwendung	Letzte Verwendung
This module is not used in any degree program.

Miscellaneous

Responsible for this module are: Prof. Dr. Richard Kempter, HU Berlin (r.kempter@biologie.hu-berlin.de) Prof. Dr. Benjamin Lindner, HU Berlin (benjamin.lindner@physik.hu-berlin.de)

Models of Neural Systems