Memories are not stored in a stable manner in the brain. Instead they undergo an almost constant process of transformation and reorganization from the very moment of their formation. This phenomenon, going under the name of memory consolidation is also at the core of fundamental changes in the nature of the information carried by the neural representations. In fact, the switch from hippocampal-based short-term memories to cortex-based long-term memories also coincides with the transitions from episodic to semantic memories. In this proposal, we aim at investigating the role played in memory consolidation by stochasticity and criticality of neural dynamics. We will develop a quantitative approach to this question by combining statistical analysis of newly recorded data, with mathematical modelling of the cortical-hippocampal interaction. Our hypothesis takes the emergence of long-range correlation as the central mechanism characterizing semantic memory formation. We propose that cortical neural circuits poised at criticality can exploit activity avalanches associated with this state to induce the collective activation of large portions of the brain necessary to reconstruct the complete input statistics. Using a hierarchical neural network trained with an input of random samplings of the episodic representations stored in the hippocampus, we will explore the interaction between the performance of the network and its closeness to a critical state. We will complement this work with an analysis of the presence of criticality in the cortical cell assemblies formed after learning a complex behavioural task. We expect these cell assemblies to show maximal contribution to the propagation of neural avalanches and to present consistent coordination with the hippocampal activity. We intend to integrate these results in a coherent theory of long-term memory formation, explaining the ability to generalize starting from event-based experiences.

Falling raindrops will likely move downwards; red brake lights up ahead will predict a car slowing down. While perception of the world around us feels rich in detail, our brains lack the capacity to analyze every ‘pixel’ at every time-point. Instead, the brain relies on expectations to compress the total amount of sensory information from the environment. A failure to rely on expectations about visual inputs would lead to catastrophic consequences. For example, an air traffic controller without the ability to predict the likely trajectories of two planes coming in for landing would spell certain disaster. Remarkably, despite the obvious role in adaptively guiding behavior, the cognitive and neural mechanisms supporting perceptual expectations remain poorly understood. I will concurrently measure human behavior and neural responses to address two fundamental, yet outstanding questions: How are expectations about visual input acquired, and what is the impact of behavioral relevance? I anticipate mechanisms of expectation to change with learning and behavioral relevance. For example, driving in the rain, expectations about break lights could amplify this immediately relevant information. Expectations about falling rain, on the other hand, could support the equally important task of suppressing this irrelevant information so you may return home safely.

Current clinically approved drug delivery systems, such as liposome, PEGylated liposome, and polymeric micelle, predominantly rely on passive accumulation within tumor tissues by diffusion through the defective tumor vessels during circulation. The targeting efficacy toward cancer cells is very limited due to their inadequate interaction with cancer cells. The attachment of targeting ligands to nanocarriers has demonstrated its effectiveness in enhancing binding affinity and, consequently, facilitating cellular uptake via receptor-mediated endocytosis. However, the conventional methods employed for ligand attachment suffer from harsh conditions, low efficiency, and limited control over ligand orientation. These drawbacks compromise the targeting performance and are believed to result in the current absence of a targeted drug delivery system on the market. In this project, we propose a simple, efficient, and mild attachment method to spontaneously activate on demand nanocarriers. This innovative approach has the potential to have a multi- level effect, first to revolutionize various fields, including drug delivery, diagnostics, and nanotechnology, by providing advanced tools for targeted therapies and diagnostics. Secondly, by developing novel methodologies that can be applied to existing technologies to enhance uptake, localization, and efficacy while minimizing systemic toxicity thus potentially shifting the health- economic balance for some treatments which were previously inaccessible.