Project phases:

• Literature review
• Calcium signal analysis
• Behavioural data analysis

Requirements:

• Good knowledge of biosignal processing
• Knowledge of Python programming language (suggested)

Background:

Since the discovery of Nitric Oxide (NO) acting as an intracellular messenger in the brain, there is growing evidence that NO is responsible for the coordination of synaptic activity, both excitatory and inhibitory. Cellular types that can produce NO molecules have been found in the cerebral cortex, hippocampus and in the cerebellum. In the granular layer of the cerebellum, NO notably acts as a retrograde messenger, being produced in the Granule Cells (GrCs) and regulating the neurotransmitter release probability of the mossy fiber (mf) terminals. As NO synthesized in response to an external stimulus diffuses freely across the cell membrane, spreading rapidly in the extracellular space, it is able to provide a type of neural communication that goes beyond the mere synaptic transmission . In the past few years, an increasingly number of studies suggested that certain stimulation patterns of a closely-packed group of neurons, containing neurnal NO synthase (nNOS) enzyme, may generate a diffuse cloud of NO, thus acting as a volume transmitter, with a relatively large area of influence.

Aim:

We have already developed a model able to simulate the production and diffusion of NO molecules and integrated it in a 3D realistic inspired cerebellar model. This model will be used as a starting point to integrate the diffusive properties of NO in a SpikeTime Dependent Plasticity model and build a NO-dependent diffusive plasticity reflecting the experimental evidence found in literature, regarding the role of NO in Long Term Potentiation mechanisms in the granular layer.

Project phases:

• Literature review
• Model implementation
• Simulation and data analysis
• Discussion of the results and comparison with the literature data

Requirements:

• Proficiency in C++ language 

Aim:

Pyramidal cells in the cerebral cortex perform pattern recognition and other computationally relevant functions by means of the synapses on their dendrites.
Point neurons models, widely used in neuroscientific simulations thanks to their reduced computational cost, cannot capture these mechanisms due to the absence of morphological details.

The aim of this work is to implement the dendritic computation mechanism in a spiking neural network and integrate it in neural simulator NEST.

Project phases:

• Literature review
• Implementation of a new model in NEST simulation environment
• Testing and validation of the network

Requirements:

• Proficiency in Python language,
• C++ language (optional but strongly suggested)

Aim:

In recent years, the cerebellum has been proposed to play a dual role in motor control and adaptation. From the one hand. it may work as an inverse-dynamics model of the muscolo-skeletal system by providing corrective feedback to the motor commands generated by the motor cortex. From the other hand, the cerebellum may be considered as a forward model, which predicts the sensory consequences of a motor command. 

The aim of the thesis is to further investigate the dual nature of the cerebellum, by replicating complex sensorimotor tasks in a virtual robotic environment.

Project phases:

• Literature review
• Transfer of the control system to the virtual environment
• Simulation and data analysis
• Discussion of the results and comparison with the literature

Requirements:

• Basic knowledge of Python 

Aim:

Neural decoding is a critical step in BCI technologies. Different machine learning algorithms have been used to guide neural prosthetic limbs but results are far to get close at the natural body performance. In the last years, the availability of multi-electrode array system equipped with more and more recording channels is requiring a big data approach necessary. Together with the rising in computing power, the artificial neural networks (ANN) are a promising tool to address neural decoding problem. We propose to take advantage of modern ANN implementations to decode motor intentions from neural data recorded from behaving monkeys. Exploring different ANN architectures, the aim is to increase decoder robustness and reliability to be effectively implemented in clinical neuroprosthesis.

Project phases:

• Literature research
• Implementation of the ANN architecture
• Data analysis
• Interpretation and discusion of the results

This thesis foresees a development part at Scuola Superiore Sant’Anna in Pisa.

Aim:

During tasks and actions of humans and animals, neuronal populations communicate using a complex interplay of transient oscillatory rhythms. Neural signals generated by the activity of neuronal populations also display this type of transient behavior. This makes non-stationary spectral analysis of neural signals using wavelet functions a useful tool to investigate neural activity.

Wavelet–based techniques are suitable for this type of signals since they possess one key property called multiresolution. Multiresolution allows to accurately determines low frequency components and simultaneously localize time rapidly transient events.

Here, we propose to analyze extracellular signals recorded from multi-electrode array implanted in the posterior parietal area of monkeys during reach-to-grasp and reach-to-target task. Specifically, we will consider the low frequency part (< 300 Hz) of the extracellular field, called Local Field Potential (LFP). LFPs will be analyzed using wavelet-based techniques, such as Continuous Wavelet Transform and Wavelet Coherence to determine which type of scales (frequencies) characterizes the different tasks both at single and multi-electrode levels. This is a key step in the decoding of neuronal population dynamics for brain-machine-interfaces and biomedical applications

Project phases:

– Literature research
– Data analysis
– Interpretation and discusion of the results

This thesis foresees a development part at Scuola Superiore Sant’Anna in Pisa.