Mechanisms and Functions of Neuronal Population Dynamics in C. elegans

Populations of neurons in the brains of many different animals, ranging from invertebrates to primates, typically coordinate their activities to generate low dimensional and transient activity dynamics, an operational principle serving many neuronal functions like sensory coding, decision making and motor control. However, the mechanism that bind individual neurons to global population states are not yet known. Are population dynamics driven by a smaller number of pacemaker neurons or are they an emergent property of neuronal networks? What are the features in global network architecture that support coordinated network wide dynamics? In order to address these problems, we study neuronal population dynamics in C. elegans. We recently developed a calcium imaging approach to record the activity of nearly all neuron in the worm brain in real time and at single cell resolution. We show that brain activity of C. elegans is dominated by brain wide coordinated population dynamics involving a large fraction of interneurons and motor neurons. The activity patterns of these neuronal ensembles recur in an orderly and cyclical fashion. In subsequent experiments, we characterized these brain dynamics functionally and found that they represent action commands and their assembly into a typical action sequence of these animals: forward crawling – backward crawling – turning. Deciphering the mechanisms underlying neuronal population dynamics is key to understanding the principal computations performed by neuronal networks in the brains of animals, and perhaps will inspire the design of novel machine learning algorithms for robotic control. In this talk, I will discuss three of our approaches to uncover these mechanisms: First, using graph theory, we aim to identify the key features of neuronal network architecture that support functional dynamics. We found that rich club neurons, i.e. highly interconnected network hubs contribute most to brain dynamics. However, simple measures of synaptic connectivity (e.g. connection strength) failed to predict functional interactions between these neurons; unlike higher order network statistics that measure the similarity in synaptic input patterns. We next performed systematic perturbations by interrogation of rich club neurons via transgenic neuronal inhibition tools. Using whole brain imaging in combination with computational analysis methods we found that upon inhibition of critical hubs, leading to a disintegration of the network, most other individual neurons remain vigorously active, however the global coordination across neurons was abolished. Based on these results we hypothesize that neuronal population dynamics are an emergent property of neuronal networks. Finally, we aim to recapitulate C. elegans brain dynamics in silico. Here, we generate neuronal network simulations based on deterministic and stochastic biophysical models of neurons and synapses, at multiscale levels of abstraction. We then adopt a genetic algorithm for neuronal circuit parameter optimization, to find the best matches between simulations and measured calcium dynamics. This approach enables us to test our hypotheses, and to predict unknown properties of neural circuits important for brain dynamics.