Using Immersive Virtual Reality (VR) to Determine Causal Relationships in Animal Social Networks - Couzin
Objectives:
The application of Virtual Reality (VR) environments allows us to experimentally dissociate social input and responses, opening powerful avenues of inquiry into the dynamics of social influence and the physiological and neural mechanisms of collective behaviour. A key task for the nervous system is to make sense of complex streams of potentially-informative sensory input, allowing appropriate, relatively low-dimensional, motor actions to be taken, sometimes under conditions of considerable time constraint. ESR5 will employ fully immersive ‘holographic’ VR to investigate the behavioural mechanisms by which freely-swimming zebrafish obtain both social and non-social sensory information from their surroundings, and how they use this to inform movement decisions. Immersive VR allows extremely precise control over the appearance, body postural changes, and motion, allowing photorealistic virtual individuals to interact dynamically with unrestrained real animals. Similar to a method that has transformed neuroscience — the dynamic patch clamp paradigm in which inputs
to neurons can be based on fast closed-loop measurements of their present behaviour — VR creates the possibility for a ‘dynamic social patch clamp’ paradigm in which we can develop, and interrogate, decision-making models by integrating virtual organisms in
the same environment as real individuals. This tool will help us to infer the sensory basis of social influence, the causality of influence in (small) social networks, to provide highly repeatable stimuli (allowing us to evaluate inter-individual and within-individual
variation) and to interrogate the feedback loops inherent in social dynamics.
Collective Computation in Large Animal Groups - Couzin
Objectives:
Despite
the fact that social transmission of information is vital to many group-living animals, the organizing principles governing the networks of interaction that give rise to collective properties of animal groups, remain poorly understood. This project will employ
an integrated empirical and theoretical approach to investigate the relationship between individual computation (cognition at the level of the ‘nodes’ within the social network) and collective computation (computation arising from the structure of the social
network). The challenge for individuals in groups is to be both robust to noise, and yet sensitive to meaningful (often small) changes in the physical or social environment, such as when a predator is present. There exist two, non mutually- exclusive, hypotheses
for how individuals in groups could modulate the degree to which sensory input to the network is amplified; 1) it could be that individuals adjust internal state variable(s) (e.g. response threshold(s)), effectively adjusting the sensitivity of the “nodes”
within the network to sensory input and/or 2) it could be that individuals change their spatial relationships with neighbors (such as by modulating density) such that it is changes in the structure and strength of connections in the network that modulates
the information transfer capabilities, and thus collective responsiveness, of groups. Using schooling fish as a model system we will investigate these hypotheses under a range of highly controlled, ecologically-relevant scenarios that vary in terms of timescale
and type of response, including during predator avoidance as well as the search for, and exploitation of, resources. We will employ technologies such as Bayesian inference and unsupervised learning techniques developed in computational neuroscience and machine
learning to identify, reconstruct, and analyze the directed and time-varying sensory networks within groups,
and to relate these to the functional networks of social influence. As in neuroscience, we care about stimulus-dependent, history-dependent discrete stochastic events, including burstiness, refractoriness and habituation and throughout we will seek to isolate principles that extend beyond the specificities of our system.
