Collective dynamics

…bonding and quantum effects with collective dynamics, interfaces, biological function, extreme conditions and atmospheric water. Participants study first-principles simulations, machine-learned potentials and multiscale modelling. The first week provides foundational training; the second features current research, discussions and posters. Applicants should have grounding in statistical mechanics, quantum mechanics and molecular simulation or related computation. Graduate st

…activity and the emergence of diverse collective dynamics in the cortex from neuronal interactions is still a key challenge in neuroscience. This research elucidates how static structural connections may give rise to the emergence of a large repertoire of dynamic patterns in spontaneous brain activity on a large scale.We employ a modular neural model. Each module, consisting of an ensemble of leaky integrate and fire (LIF) neurons, represents a cortical region, to mimic neuronal cortical dyn

…or examining distributed collective dynamics. Whether and how these two aspects interact to shape computations is currently unclear. Using a novel approach to extract computational mechanisms from recurrent networks trained on neuroscience tasks, we show that the collective dynamics and sub-population structure play fundamentally complementary roles. Although various tasks can be implemented in networks with fully random population structure, we found that flexible i

…or examining the low-dimensional dynamics of collective activity. Whether and how these two aspects interact to shape computations is currently unclear. Using a novel approach to extract computational mechanisms from networks trained on neuroscience tasks, here we show that the dimensionality of the dynamics and subpopulation structure play fundamentally com- plementary roles. Although various tasks can be implemented by increasing the dimensionality in networks with ful

…the biological brain, revealing the collective dynamics of vast numbers of neurons. In principle, these collective dynamics can be of almost arbitrarily high dimension, with many independent degrees of freedom — and this may reflect powerful capacities for general computing or information. In practice, neural datasets reveal a range of outcomes, including collective dynamics of much lower dimension — and this may reflect other desiderata for neural codes. For what networks does each case occur? We begin by exploring bottom-up

…analytically how homeostasis changes collective dynamics and consequently information flow - depending on the input to the network. We then studied how input and homeostasis on a recurrent network of LIF neurons impacts information flow and task performance. We showed how we can tune the working point of the network, and found that, contrary to previous assumptions, there is not one optimal working point for a family of tasks, but each task may requir

…behavior in the nucleation dynamics of promiscuously interacting molecules with multiple stable crystalline phases. Using a combination of theory and experiments, we show how the physics of the system dictates relationships between the difficulty of the