Neurocomputing Lab

Department                           Neuroscience

Principal investigator          Mario Negrello

E-mail address             



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Unsupervised models of cerebellar plasticity (PF-PC plasticity in the cerebellar loop)

The olivocerebellar system consists of multiple feedback and feedforward loops. One of the main loops that we are interested in consists of the “Parallel Fiber (PF) – Purkinje cell (PC) – Deep Cerebellar Nuclei (DCN) – Inferior Olive (IO) – Purkinje cell” loop. Most  theories of cerebellar function are based on the coincidence of PF-PC signals with the activity arriving via the climbing fiber (CF) from the IO, as these signals modulate the PF-PC synapse. Hence, there is particular interest in investigating plasticity at the PF-PC synapse. We start by looking at plasticity with a simplified loop (no excitatory/inhibitory interneurons and Golgi and basket cells). This closed loop model of the cerebellar circuit will consist of a mixture of spiking neurons and conductance based neurons (Hodgkin-Huxley), with a model of synaptic plasticity to study how incoming signals change the cerebellar circuit.


  • Modelling neurons
  • Data analysis


Reconstruction and analysis of realistic olivocerebellar networks

Currently we have small (~1000 cell) biologically realistic networks of the inferior olive and cerebellum. We are currently expanding these networks to the scale of realistic mouse networks to recreate the mouse olivo-cerebellar loop of motor control  to study motor control and learning bottom-up, while at the same time increasing the level of morphological model detail. This will require efficient use of a large amount of computational resources. The neuronal clustering behaviour in these large-scale simulations will then be analyzed either by in-house developed statistical methods or something you come up with yourselves during the project.


  • Statistical methods
  • High-speed scientific computation


The influence of plasticity on cluster formation in the Inferior Olive

As said before, the neurons of the inferior olivary nucleus can be modelled as coupled nonlinear oscillators and the synchronization between neurons is thought to play an important role in motor control feedback. Apart from various parts of the brain that are able to specifically modulate this synchronization behaviour via glomerular synapses, the inferior olive itself is also able to change the connection strength of dendro-dendritic gap junctions over time via various plasticity methods. This conductance modulation will change how cells synchronize and is expected to have a key role in motor learning and motor feedback tuning. Your task will be to quantify the effect of various models of plasticity, including spike-timing dependent plasticity (STDP), on dynamical cluster formation in the inferior olive and then to connect this to motor control and learning.


  • Modelling of dynamical systems
  • Analysis of synchronisation


Homeostasis: How do neurons change to stay the same?

One of the fundamental differences between artificial neural networks and biological neural networks is that in the latter the neurons adjusts its own intrinsic properties to maintain functionality. Neurons maintain complex regulatory protein expression (i.e., ion channels) as a function of their activity. The molecular pathways involved in this regulation are only now beginning to be unveiled. This project studies the processes maintaining equilibrium either from a mathematical or a biological perspective. We model with a variety of neuronal types (Purkinje neurons, Inferior Olivary cells), and simulate them at various levels of detail.


  • Modelling neurons and networks
  • Data analysis