Research Project
10360723
ABSTRACT (PROJECT SUMMARY) The classification of neurons in the mammalian brain has long been a focus of intensive investigation in neuroscience. Neurons are widely recognized as the fundamental computational elements of the nervous system, and the broad diversity of their morphological, physiological, and molecular properties may provide crucial insights into their function and involvement in disease. Long-range axonal projections, in particular, are the quintessential determinants of network connectivity, providing a key nexus between cellular organization and circuit architecture. Converging technological breakthroughs in microscopic imaging, genetic labeling, and algorithmic development have only recently enabled the high-throughput collection of large-scale, whole-brain axonal reconstructions under the BRAIN initiative. Using a principled statistical strategy, the proposed project leverages such information-rich resources to rigorously identify, from each brain region, all ?projection neuron types? with objectively distinct patterns of anatomical targeting. This application will thus directly test the seminal hypothesis that the axonal trajectories of individual neurons follow specific coordination plans as opposed to aiming randomly within the constraints of regional connections. While this data-driven classification necessarily depends on the existing digital tracings, we will deploy our full analysis workflow as an automated pipeline on public cloud servers, allowing not only free community access, but also continuous refinement of the resulting knowledge as more datasets become available. Moreover, our approach allows the quantitative estimation of the population size of every separate neuron class as well as its unique distribution of path distances from the soma to each target, defining the basic temporal dynamics of information transmission. We will also determine if different projection neuron types vary in their dendritic morphology, providing an important clue as to whether input processing is specifically tuned for the intended outputs. Furthermore, we will extend this innovative methodology by applying it to complementary datasets obtained by stochastic nucleic acid barcoding, laser capture microdissection, and sequencing, yielding far greater sample sizes in exchange for lower anatomical resolution. Last but not least, we will model the discovered axonal projection patterns into a novel artificial neural network design (?projectron nets?) to systematically explore their possible selective advantages in learning and memory robustness and performance. Achieving these goals will thus quantify, for the first time, the relevant single-neuron motifs to outline the functional blueprint of the mammalian central nervous system and related impairments for the long-lasting benefit of public health.
