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

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.

One of the goals in neuroscience is to understand how the brain operates as a complex system and uncovering the neuronal mechanisms underlying normal perception and cognition. A major challenge is discovering what kinds of dynamic, systems level neuronal mechanisms exist within this complex distributed system, and how they represent rich integrated percepts that guide behaviour. Our aim is to advance our understanding of the complex spatio-temporal neuronal interactions, within and between higher visual processing areas in the temporal lobe and prefrontal choice-behaviour areas underlying object and face perception and discrimination. We are particularly interested in how fine structural and configurational details of multiple objects and faces present in a scene, are encoded simultaneously. Recent advances in neuronal recording methods facilitate the investigation of neuronal dynamics both within and between areas and cortical layers. We will use multi-electrode and intralaminar arrays to record simultaneously from hundreds of neurons across a variety of temporal and frontal areas involved in fine visual perception and discrimination of objects and faces in the primate brain. We will create large-scale neural datasets, with large numbers of stimuli presentations. We will apply state of the art computational and well-suited statistical tools to identify cell assemblies and extract complex multi-neuron spiking sequences within and across areas and cortical layers. This will allow us to characterise the spatiotemporal dynamics and encoding forms underlying the representation of objects and faces within and between cell assemblies across multiple areas. Our empirical recordings will be complemented by GPU optimised computer simulations using spiking neural network models to assess our findings and generate novel predictions. We will utilise network science for quantitative analysis of network structure and dynamics while maintaining information about all neurons.

Our work will have immediate and profound impact upon researchers working in related fields of neuroscience. The likelihood of this impact being substantial is attested to by our history of high profile publications and their high citation rate. Given the current state of relative ignorance of how brain regions causally interact with each other to mediate cognition, this research will have profound implications for increased scientific understanding of how the primate brain operates. In the UK our work will pioneer the combination of different experimental techniques, through interventions and reversible deactivations combined with multi-electrode recordings in NHP, with state-of-the art computational methods including GPU optimised simulations of spiking neural networks, and network science analyses, to both assess our empirical findings and generate further testable predictions. Oxford is amongst few institutions worldwide with the facilities and expertise to combine the needed experimental techniques as well as world class expertise in computation and a cutting-edge hardware to run our research. Hence, we expect our work to have an impact competitive with that from the best international institutions.

A significant class of beneficiary of our work is 'UK plc' as several specialized industries (e.g robotic control systems, brain-machine interfaces and neural prosthetics, neural network and artificial intelligence applications) are interested in practical application of our basic science research.. Buckley and Stringer are associated with the Oxford Foundation for Theoretical Neuroscience and Artificial Intelligence a registered charity which has established wholly-owned subsidiary companies, including Oxford Machine Intelligence (OxMI), a commercial software company specialised in artificial intelligence and advanced mathematical applications. Our aim to understand how brain systems interact may be relevant for the future generation of smart devices. Our work may also be relevant for establishing general principles for artificial neural networks displaying flexible behaviour for industrial and scientific applications.
Other major beneficiaries are research scientists working in non-directly related fields who are interested in how brain regions interact. These include engineers and computer scientists, artificial intelligence researchers, and researchers working on brain-machine interfaces.

Our research presentations at large international conferences and our links to the electronic industry will reach these aforementioned audiences.

Education impact: the applicants' work is cited in textbooks and review articles and features in several University courses. The applicants have written articles for compiled volumes and encyclopaedias accessible to students. All applicants give symposiums and lectures at a wide range of UK and International universities that are well attended by graduate students. We give talks to students contemplating psychology or neuroscience related degrees or careers.

Media impact: invasive animal research is not often reported directly to the lay public by individual researchers for fear of reprisals or intimidation by animal rights activists. However, our Oxford facility was featured by the BBC in 2014 for public awareness and recently won an Openness award on animal research in 2018 for Public engagement activity. The PI has written articles for lay audiences and we give talks explaining the importance of our research to non-specialist audiences. The technological component of the project will be key. A real time working model of our research will help with public engagement communication, providing an interactive tool to showcase our research.