Spatiotemporal neuronal system dynamics underlying hierarchical visual representations of objects and faces for primate perception and discrimination

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The human and (non-human primate) brain is one of the most complex biological systems. A major challenge in neuroscience is to understand how the brain operates as a dynamic complex system and what neuronal mechanisms underlie normal perception and cognition. Visual representations of objects and faces are simultaneously encoded across multiple reciprocally connected regions with highly parallelized and hierarchically organized processing stages. Our objective is to advance scientific understanding of systems level neuronal interactions by discovering the spatio-temporal processes operating within and between higher visual areas in the temporal lobe and choice-related prefrontal regions, that together underlie object/face perception and discrimination. We can only learn how brain areas causally interact at the neuronal level by combining multi-electrode, multi-area recordings with interventions. To do this we must record the fundamental functional units in the brain, neurons, (not neuroimaging 'voxels' containing hundreds of thousands of neurons) and we must use animal models because such recording is invasive. Recent technological advances facilitate multi-area multi-electrode recordings and investigation of neuronal dynamics both within and between many areas and cortical layers. Moreover, we can only learn how brain areas causally interact at the neuronal level by combining neuronal recordings with interventions. Ever since Hebb's principles of synaptic plasticity, spatiotemporal dynamics of interconnected neuronal activities have been implicated as key principle underlying learning. Spatiotemporal firing patterns exist in many areas across different behavioral tasks. In the case of vision, the binding of distributed visual representations may exploit such principles but the extraction of complex neuronal firing ('spike') sequences, at multiple temporal scales and time-lags, is computationally complex. Now, new eEfficient algorithms have been developed for identifying larger assemblies of neurons with consistent spike delays at varying temporal scales without making assumptions of the underling encoding method. We will apply new computational and statistical tools to identify cell assemblies and extract consistent multi-neuron spiking sequences within and across areas. Our empirical recordings will be complemented by running GPU optimised simulations of spiking neural networks models to assess our findings and generate novel predictions. Recording from more neurons simultaneously raises the 'curse of dimensionality' and Network science, offers advanced analytical and scalable tools for systems neuroscience, complementing dimension-reduction approaches, and allowing for quantitative analyses of network structure and probabilistic descriptions of population-wide activity. These approaches allow us to better understand computations, quantify and track dynamics of key concepts such as 'cell assemblies', and track changes elicited by behaviour or brain interventions.Our objective is divided into 3 sub-goals: The first two are to understand the dynamic spatiotemporal representations and mechanisms of neuronal interaction operating within and between multiple higher visual areas, and cortical layers, that underlie normal perception of objects and faces respectively. In the case of faces we will study such dynamics and interactions across different temporal and frontal lobe face patches. Our third sub-goal is to understand how these mechanisms and interactions differ in the context of memory, categorization, and choice behaviour with respect to objects and faces with a special emphasis on frontal lobe - temporal lobe interactions.