Project full title: Realistic Real-time Networks: computation dynamics in the cerebellum.

Information and Communication Technologies (ICT)

RESPONSABILE SCIENTIFICO: prof. Egidio D’Angelo
Participants
Coordinator: FONDAZIONE ISTITUTO NEUROLOGICO NAZIONALE CASIMIRO MONDINO, ITALY

 

 Organisations
 UNIVERSIDAD DE GRANADA SPAIN
 BAR ILAN UNIVERSITY ISRAEL
 THE UNIVERSITY OF SHEFFIELD UNITED KINGDOM
 TECHNISCHE UNIVERSITAT BERLIN GERMANY
 UNIVERSITA’ DEGLI STUDI DI PADOVA ITALY
 THE HEBREW UNIVERSITY OF JERUSALEM ISRAEL
 POLITECNICO DI MILANO ITALY

 

Project website
EC link
Abstract
In front of the richness of dynamical properties in neurons and central brain circuits, traditional computational architectures of artificial neuronal networks are merely based on connectivity rules.
Moreover while brain circuits elaborate spike sequences, theoretical analysis usually deals with continuous signals. To understand circuit computations a different approach is needed: to elaborate realistic spiking networks and use them, together with experimental recordings of network activity, to investigate the theoretical basis of central network computation. As a benchmark we will use the cerebellar circuit. The cerebellum is supposed to work as a general purpose comparator endowed with memories and to implement forward control loops regulating movement and cognition.
Experimental evidence has revealed that cerebellar circuits can dynamically regulate their activity on the millisecond scale and exploit complex spatio-temporal transformation of signals through non-linear neuronal responses and complex circuit loops. Moreover, distributed forms of plasticity can fine-tune circuit synaptic connections. In this project, we will develop specific chips and imaging techniques to perform neurophysiological recordings from multiple neurons in the cerebellar network and monitor its spatio-temporal dynamics. Based on the data, we will develop the first realistic real-time model of the cerebellum and connect it to robotic systems to evaluate circuit functioning under closed-loop conditions. The data deriving from recordings, large-scale simulations and robots will be used to explain the implicit dynamics of the circuit through the adaptable spatio-temporal filter theory. REALNET, through its network architecture based on realistic neurons, will provide a radically new view on dynamic computations in central brain circuits laying the basis for new technological applications in sensori-motor control and cognitive systems.

Project Objectives
The brain circuits of the central nervous system are formed by neurons and synapses endowed with complex dynamical properties. However, the traditional architectures of computational systems, like artificial neuronal networks, are based on connectivity rules while making use of very simplified neurons.
Moreover while brain circuits operate through discontinuous signal called spikes organized in complex sequences, theoretical analysis usually deals with continuous signals. To understand circuit computations a different approach is needed: to elaborate realistic spiking networks and use them, together with experimental recordings of network activity, to investigate the theoretical basis of central network computation.
As a benchmark we will use the cerebellar circuit. The cerebellum is supposed to compare expected and actual activity patterns and to reveal their congruence with respect to stored memories. By these means, the cerebellum takes part to control loops regulating movement and cognition. Experimental evidence has revealed that cerebellar circuits can dynamically regulate their activity on the millisecond time scale and operate complex spatio-temporal transformation of signals through nonlinear neuronal responses.
Moreover, synaptic connections can be fine-tuned by distributed forms of synaptic plasticity, the correlate of memory in neural circuits. In this project, we will develop specific chips and imaging techniques to perform neurophysiological recordings from multiple neurons in the cerebellar network. Based on the data, we will develop the first realistic real-time model of the cerebellum and connect it to robotic systems to evaluate circuit functioning under closed-loop conditions. The data deriving from recordings, large-scale simulations and robots will be used to explain circuit functioning through the adaptable filter theory.
REALNET will thus provide a radically new view on computation in central brain circuits laying the basis for new technological applications in sensori-motor control and cognitive systems.