Project acronym FOUNDCOG
Project Curiosity and the Development of the Hidden Foundations of Cognition
Researcher (PI) Rhodri CUSACK
Host Institution (HI) THE PROVOST, FELLOWS, FOUNDATION SCHOLARS & THE OTHER MEMBERS OF BOARD OF THE COLLEGE OF THE HOLY & UNDIVIDED TRINITY OF QUEEN ELIZABETH NEAR DUBLIN
Country Ireland
Call Details Advanced Grant (AdG), SH4, ERC-2017-ADG
Summary How do human infants develop complex cognition? We propose that artificial intelligence (AI) provides crucial insight into human curiosity-driven learning and the development of infant cognition. Deep learning—a technology that has revolutionised AI—involves the acquisition of informative internal representations through pre-training, as a critical precursory step to learning any specific task. We propose that, similarly, curiosity guides human infants to develop ‘hidden’ mature mental representations through pre-training well before the manifestation of behaviour. To test this proposal, for the first time we will use neuroimaging to measure the hidden changes in representations during infancy and compare these to predictions from deep learning in machines. Research Question 1 will ask how infants guide pre-training through directed curiosity, by testing quantitative models of curiosity adapted from developmental robotics. We will also test the hypothesis from pilot data that the fronto-parietal brain network guides curiosity from the start. Research Question 2 will further test the parallel with deep learning by characterising the developing infant’s mental representations within the visual system using the powerful neuroimaging technique of representational similarity analysis. Research Question 3 will investigate how individual differences in curiosity affect later cognitive performance, and test the prediction from deep learning that the effects of early experience during pre-training grow rather than shrink with subsequent experience. Finally, Research Question 4 will test the novel prediction from deep learning that, following perinatal brain injury, pre-training creates resilience provided that curiosity is intact. The investigations will answer the overarching question of how pre-training learning lays the foundations for cognition and pioneer the new field of Computational Developmental Cognitive Neuroscience.
Summary
How do human infants develop complex cognition? We propose that artificial intelligence (AI) provides crucial insight into human curiosity-driven learning and the development of infant cognition. Deep learning—a technology that has revolutionised AI—involves the acquisition of informative internal representations through pre-training, as a critical precursory step to learning any specific task. We propose that, similarly, curiosity guides human infants to develop ‘hidden’ mature mental representations through pre-training well before the manifestation of behaviour. To test this proposal, for the first time we will use neuroimaging to measure the hidden changes in representations during infancy and compare these to predictions from deep learning in machines. Research Question 1 will ask how infants guide pre-training through directed curiosity, by testing quantitative models of curiosity adapted from developmental robotics. We will also test the hypothesis from pilot data that the fronto-parietal brain network guides curiosity from the start. Research Question 2 will further test the parallel with deep learning by characterising the developing infant’s mental representations within the visual system using the powerful neuroimaging technique of representational similarity analysis. Research Question 3 will investigate how individual differences in curiosity affect later cognitive performance, and test the prediction from deep learning that the effects of early experience during pre-training grow rather than shrink with subsequent experience. Finally, Research Question 4 will test the novel prediction from deep learning that, following perinatal brain injury, pre-training creates resilience provided that curiosity is intact. The investigations will answer the overarching question of how pre-training learning lays the foundations for cognition and pioneer the new field of Computational Developmental Cognitive Neuroscience.
Max ERC Funding
2 500 000 €
Duration
Start date: 2019-01-01, End date: 2023-12-31
Project acronym ReCaP
Project Regeneration of Articular Cartilage using Advanced Biomaterials and Printing Technology
Researcher (PI) Fergal O'BRIEN
Host Institution (HI) ROYAL COLLEGE OF SURGEONS IN IRELAND
Country Ireland
Call Details Advanced Grant (AdG), PE8, ERC-2017-ADG
Summary Adult articular cartilage has a limited capacity for repair and when damaged or injured, experiences a loss of function which leads to joint degeneration and ultimately osteoarthritis. Biomaterials-based treatments have had very limited success due to the complex zonal structure of the articular joint, problems with biomaterial retention at the joint surface and achieving integration with the host tissue while also maintaining load bearing capacity. Stem cell therapies have also failed to live up to significant hype for a number of reasons including the challenges with achieving formation of stable hyaline cartilage which does not undergo hypertrophy. Building on a wealth of experience in the area, we propose a solution. ReCaP will initially overcome the problems with traditional biomaterials approaches by utilising recent advances in the area of advanced manufacturing and 3D printing to develop a 3D printed multi-layered scaffold with pore architecture, mechanical properties and bioactive composition tailored to regenerate articular cartilage, intermediate calcified cartilage and subchondral bone. Following this, and building on internationally recognised pioneering research in the applicant’s lab on scaffold-mediated nanomedicine delivery, this system will be functionalised for the controlled non-viral delivery of nucleic acids (including plasmid DNA and microRNAs) to direct host stem cells to produce stable hyaline cartilage at the joint surface and encourage the rapid formation of vascularised bone in the subchondral region. A new paradigm-shifting surgical procedure will then be applied to allow this system to be anchored to the joint surface while directing host cell infiltration and tissue repair, thus promoting restoration of even large regions of the damaged joint through a joint surfacing approach. The proposed ReCaP platform is thus a paradigm shifting disruptive technology that will revolutionise the way joint injuries are treated.
Summary
Adult articular cartilage has a limited capacity for repair and when damaged or injured, experiences a loss of function which leads to joint degeneration and ultimately osteoarthritis. Biomaterials-based treatments have had very limited success due to the complex zonal structure of the articular joint, problems with biomaterial retention at the joint surface and achieving integration with the host tissue while also maintaining load bearing capacity. Stem cell therapies have also failed to live up to significant hype for a number of reasons including the challenges with achieving formation of stable hyaline cartilage which does not undergo hypertrophy. Building on a wealth of experience in the area, we propose a solution. ReCaP will initially overcome the problems with traditional biomaterials approaches by utilising recent advances in the area of advanced manufacturing and 3D printing to develop a 3D printed multi-layered scaffold with pore architecture, mechanical properties and bioactive composition tailored to regenerate articular cartilage, intermediate calcified cartilage and subchondral bone. Following this, and building on internationally recognised pioneering research in the applicant’s lab on scaffold-mediated nanomedicine delivery, this system will be functionalised for the controlled non-viral delivery of nucleic acids (including plasmid DNA and microRNAs) to direct host stem cells to produce stable hyaline cartilage at the joint surface and encourage the rapid formation of vascularised bone in the subchondral region. A new paradigm-shifting surgical procedure will then be applied to allow this system to be anchored to the joint surface while directing host cell infiltration and tissue repair, thus promoting restoration of even large regions of the damaged joint through a joint surfacing approach. The proposed ReCaP platform is thus a paradigm shifting disruptive technology that will revolutionise the way joint injuries are treated.
Max ERC Funding
2 999 410 €
Duration
Start date: 2018-08-01, End date: 2023-07-31
