Computational Neuroscience

Computational Neuroscience

Thesis

Signal encoding in a morphologically detailed cerebellar spiking model for simulating an adaptive reaching task.

SupervisorProf. Alessandra Pedrocchi (alessandra.pedrocchi@polimi.it)
Co-Supervisors

Benedetta Gambosi
(benedetta.gambosi@polimi.it)
Alessandra Trapani
(alessandramaria.trapani@polimi.it)

Collaborations 
 
 Description

Aim:
The bottom-up strategy is one possible approach to studying the brain. It attempts to construct biologically realistic neuronal network models based on the detailed knowledge of the constituting components. The assumption is that with sufficient accuracy in structure and dynamics, the correct functions will emerge. Toward this aim, we want to simulate a control system integrated with a detailed model of the cerebellum able to perform an adaptive reaching task.
The candidate will first investigate how to encode motion-related signals as cerebellar inputs; then, they will analyse how these signals propagate into the cerebellar circuit and turn into the physiological cerebellar output signals. The model’s overall performance will be evaluated by embedding it in a comprehensive control system to drive a closed-loop reaching task simulation.

Project phases:

  • Literature review
  • Definition and analysis of somatotopic input for the cerebellar circuit
  • Tuning and Refinement of the model
  • Testing in an adaptive reaching task protocol

Requirements:

  • Knowledge of Python
  • Knowledge of C++ (not mandatory, but preferable)
  • Willingness to work in a collaborative environment
Thesis

Modelling neural plasticity with realistic biological models

Supervisor

Prof. Alberto Antonietti (alberto.antonietti@polimi.it)
Prof. Alessandra Pedrocchi (alessandra.pedrocchi@polimi.it)

Co-Supervisors

Benedetta Gambosi
(benedetta.gambosi@polimi.it)

Collaborations

University of Pavia
Blue Brain Project, EPFL

 
 Description

Aim:
Neural plasticity is a complex and fascinating topic in neuroscience and many experimental data were collected through the years. However, a coherent understanding of how plasticity work is yet to come. Computational models of neural plasticity can be therefore developed to imitate learning behavior in neural systems. The goal is develop and employ biologically detailed models of long-plasticity to study how learning and memory can happen, with a specific focus on cerebellar and hippocampal plasticity.

Project phases:

  • Literature Review of Biological models of long-term plasticity
  • Formulation of the proposed model
  • Data mining to optimize model parameters
  • Validation of the model on other experiments not used to constrain the model

Requirements:

  • Basic knowledge of neuroscience (Physiology course, Neuroengineering course)
  • Willingness to deepen the neurophysiological aspects of plasticity
  • Python programming skills (to be acquired before starting the project)