COMPUTATIONAL NEUROENGINEERING

COMPUTATIONAL NEUROENGINEERING

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iten
Code
90533
ACADEMIC YEAR
2019/2020
CREDITS
6 credits during the 1st year of 10852 COMPUTER SCIENCE (LM-18) GENOVA
SCIENTIFIC DISCIPLINARY SECTOR
ING-INF/06
LANGUAGE
English
TEACHING LOCATION
GENOVA (COMPUTER SCIENCE )
semester
2° Semester
Teaching materials

AIMS AND CONTENT

LEARNING OUTCOMES

Learning computational techniques for the modeling of biological neural networks and understanding the brain and its function through a variety of theoretical constructs and computer science analogies.

AIMS AND LEARNING OUTCOMES

The emphasis is on neural information processing at “network level” in developing quantitative models, as well as in formalizing new paradigms of computation and data representation.

Teaching methods

Lectures and case-study discussion.

SYLLABUS/CONTENT

  • Neuron models: i) Biophysical model of neurons: passive and Hodgkin and Huxley models; ii) Reduced neuron models: Integrate-and-fire (IF) and Izhikevich models
  • Synaptic transmission and plasticity: i) Phenomenological models; ii) Dynamical models; iii) Spike Timing Dependent Plasticity (STDP).
  • Network models: i) overview of different strategies (firing vs spiking) to model large-scale neuronal dynamics; ii) Meta-networks; iii) Balanced networks and syn-fire chains; iv) Role of the connectivity in the emerging dynamics; v) overview of the graph theory and metrics for characterizing a network; vi) different kind of connectivity; functional vs structural connectivity; vii) interplay between connectivity and dynamics.
  • Computational paradigms: i) Coding and decoding information; ii) Feed-forward and recurrent networks, lateral inhibition.
  • Multidimensional data processing and representation: i) The case study of early sensory systems: receptive fields, tuning curves, population activity, read-out mechanisms; ii) Efficient coding and reduction of dimensionality; iii) Optimal decoding methods.
  • Computational synthesis of brain information processing: models of “perceptual engines”, potentialities and design examples.

RECOMMENDED READING/BIBLIOGRAPHY

Slides and other distributed material (available through Aulaweb).

Recommended texts:

  • Koch and Segev.  Methods in Neuronal Modeling. MIT press, 1999.
  • Gerstner and Kistler.  Spiking Neuron Models. Cambridge press, 2002.
  • Izhikevich. Dynamical systems in neuroscience. MIT press, 2007.
  • Dayan and Abbott. Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. The MIT Press, 2001.

TEACHERS AND EXAM BOARD

Ricevimento: Appointment by e-mail

Ricevimento: Monday               11am-13pm Thursday             16:00pm-17:00pm Office: pad. E, Via Opera Pia 13 (3rd floor) Lab: “Bioengineering - SyNaPSI”, pad. E, Via Opera Pia 13 (1st floor)

Exam Board

SILVIO PAOLO SABATINI (President)

PAOLO MASSOBRIO (President)

LESSONS

Teaching methods

Lectures and case-study discussion.

EXAMS

Exam description

Oral examination and evaluation of the presentation of a scientific paper selected by the student.

Assessment methods

After completing this course, the student will be able to:

  • Develop computational models of large-scale neuronal networks.
  • Analyze and synthetize neuromorphic processing paradigms at cellular, network, and system level.

Exam schedule

Date Time Location Type Notes
24/07/2020 09:00 GENOVA Esame su appuntamento
18/09/2020 09:00 GENOVA Esame su appuntamento
12/02/2021 09:00 GENOVA Esame su appuntamento