HNN U24 DISSEMINATION PROJECT SUMMARY The Human Neocortical Neurosolver (HNN) neural modeling tool was developed with BRAIN Initiative funding (R01EB022889: 09/2016–06/2020) to meet the Initiative’s goal to “develop innovative technologies to understand brain circuits and ensembles of circuits that infor
Project Summary. We propose a new coil array that will enable next-generation causal brain mapping with unprecedented flexibility, resolution and precision. The proposed ARES2 hybrid array generates E-fields in the cortex for transcranial mag- netic stimulation (TMS) as well as B-fields for spatial and diffusion encoding applications including high-resolu- tion, high b-value diffusion MRI and fMRI.
PROJECT SUMMARY Within every brain region, neurons can be classified into dozens or hundreds of different cell types, each with unique functional roles and unique impacts on disease states. Traditionally, in vivo electrophysiological recordings—which have made invaluable contributions to our understanding of the neural basis of behavior— have not been able to distinguish the activity of genetically defined cell types.
Abstract The human brain is estimated to contain over 100 billion neurons that are wired together by more than 100 trillion synapses. These synaptic connections increase the computational capabilities of neural circuits and are essential for sensation, perception, learning and memory, and the selection and expression of distinct behavioral states. While many tools now exist to activate, inhibit, or modulate specific cell types in the brain, none are currently capable of manipulating activity between user-defined pre- and postsynaptic cell types.
PROJECT SUMMARY/ABSTRACT Because neurons integrate and process information via modulation of their membrane potential, the ability to monitor voltage is critical to understanding how single and groups of neurons compute. Genetically encoded voltage indicators (GEVIs) —fluorescent proteins that report voltage dynamics as changes in brightness— are emerging as a preferred recording method because they can track voltage transients with high spatiotemporal resolution and cell type specificity.
In natural vision, recognizing objects based on the retinal image is challenging and is often an ill-posed problem because a single image is compatible with multiple interpretations. Nevertheless, the primate brain has a remarkable ability to understand ambiguous scenes and solve difficult object recognition problems. Converging evidence suggests that this process, especially in challenging contexts—e.g., occlusion or low-visibility environments—is based on the integration of sensory information with prior knowledge built from experience.
PROJECT SUMMARY/ABSTRACT An important longstanding goal in neuroscience research is to understand how large-scale spatiotemporal patterns of neural activity emerge in the brain and whether they have a direct role in shaping the brain’s computational processes and thereby mammalian behavior.
SUMMARY High-throughput connectomics is needed to generate the TB-, PB- and EB-scale wiring diagrams of mammalian brains, but is limited to the few research institutes (e.g., Janelia, Allen, Max Planck) with sufficient infrastructure. As resource-rich as these institutes are, none are able to do a whole brain at nanometer scale on their own. The failure to broaden participation to a larger community is an obstacle to scaling connectomics. We propose a new and more affordable imaging strategy that will allow many more teams to engage in connectomics.
Project Summary Mapping the brain-wide connections of neurons provides a foundation for understanding the structure and functions of a brain. Neuroanatomical techniques based on light-microscopy or electron microscopy have advanced tremendously in throughput and cost in recent years, but it remains challenging to scale them up to systematically interrogate large non-human primate (NHP) brains.
SUMMARY This ambitious proposal will establish an integrated experimental-computational platform to create the first comprehensive brain-wide mesoscale connectivity map in a non-human primate, the common marmoset (Callithrix jacchus),. It will do so by tracing axonal projections of RNA barcode-identified neurons brain-wide in the marmoset, utilizing a sequencing-based imaging method that also permits simultaneous transcriptomic cell typing of the identified neurons.
