Neurons in the brain communicate through action potentials (spikes) that are transmitted through chemical synapses. Throughout the last decades, the question how networks of spiking neurons represent and process information has remained an important challenge. Some progress has resulted from a recent family of supervised learning rules (tempotrons) for models of spiking neurons. However, these studies have viewed synaptic transmission as static and characterized synaptic efficacies as scalar quantities that change only on slow time scales of learning across trials but remain fixed on the fast time scales of information processing within a trial. By contrast, signal transduction at chemical synapses in the brain results from complex molecular interactions between multiple biochemical processes whose dynamics result in substantial short-term plasticity of most connections. Here we study the computational capabilities of spiking neurons whose synapses are dynamic and plastic, such that each individual synapse can learn its own dynamics. We derive tempotron learning rules for current-based leaky-integrate-and-fire neurons with different types of dynamic synapses. Introducing ordinal synapses whose efficacies depend only on the order of input spikes, we establish an upper capacity bound for spiking neurons with dynamic synapses. We compare this bound to independent synapses, static synapses and to the well established phenomenological Tsodyks-Markram model. We show that synaptic dynamics in principle allow the storage capacity of spiking neurons to scale with the number of input spikes and that this increase in capacity can be traded for greater robustness to input noise, such as spike time jitter. Our work highlights the feasibility of a novel computational paradigm for spiking neural circuits with plastic synaptic dynamics: Rather than being determined by the fixed number of afferents, the dimensionality of a neuron's decision space can be scaled flexibly through the number of input spikes emitted by its input layer.

Macromolecular protein complexes perform essential biological functions across life forms. A fundamental, though yet unsolved question in biology is how the function of such complexes is regulated by intracellular or extracellular signals. For instance, we have little understanding of how forces affect multi-protein machines whose function is often mechanical in nature. We address this question by studying the bacterial flagellar motor, a large complex that powers swimming motility in many bacteria. This rotary motor autonomously adapts to changes in mechanical load by adding or removing force-generating ‘stator’ units that power rotation. In the bacterium Escherichia coli, up to 11 units drive the motor at high load while all the units are released at low load. We manipulate motor load using electrorotation, a technique in which a rapidly rotating electric field applies an external torque on the motor. This allows us to change motor load at will and measure the resulting stator dynamics at single-unit resolution. We found that the force generated by the stator units controls their unbinding, forming a feedback loop that leads to autoregulation of the assembly. We complemented our experiments with theoretical models that provide insight into the underlying molecular interactions. Torque-dependent remodeling takes place within seconds, making it a highly responsive control mechanism, one that is mediated by the mechano-chemical tuning of protein interactions.

Intrinsically disordered proteins (IDPs), lack of a well-defined folded structure, have been recently shown to be critical to forming membrane-less organelles via liquid-liquid phase separation (LLPS). Due to the flexible conformations of IDPs, it could be challenging to investigate IDPs with solely experimental techniques. Computational models can therefore provide complementary views at several aspects, including the fundamental physics underlying LLPS and the sequence determinants contributing to LLPS. In this presentation, I will start with our coarse-grained computational framework that can help generate sequence dependent phase diagrams. The coarse-grained model further led to the development of a polymer model with empirical parameters to quickly predict LLPS of IDPs. At last, I will show our preliminary efforts on addressing molecular interactions within LLPS of IDPs using all-atom explicit-solvent simulations.

The Chiu laboratory focuses on neuro-immune interactions in pain, itch, and tissue inflammation. Dr. Chiu’s research has uncovered molecular interactions between the nervous system, the immune system and microbes that modulates host defense. He has found that sensory neurons can directly detect bacterial pathogens and their toxins to produce pain. Neurons in turn release neuropeptides that modulate immune cells in host defense. These interactions occur at major tissue barriers in the body including the gut, skin and lungs. In this talk, he will discuss these major neuro-immune interactions and how understanding them could lead to novel approaches to treat pain or inflammation.