Embedded Ensemble Encoding

摘要

Abstract We are developing a novel embedded-ensemble encoding (EEE) theory for mammalian neocortex to unify data from cell and network experiments, and to infer general principles of how information is processed in the brain. Our combination of investigators includes a theorist/modeler, an experimentalist/modeler and a modeler/ neuroinformatician. Our theory is based on the observation that cortical pyramidal neurons produce synaptically-induced dendritic plateau potentials that place an individual neuron into an activated state. This brings that neuron near to threshold, and also reduces membrane time constant, so that the activated cell PNact can readily and rapidly follow synaptic inputs. We hypothesize that ensembles of these activated cells provide the activated ensemble Eact, embedded in the overall cells of the column. There is then a second embedding of an ensemble based on synchronized spiking among the cells of Eact. This twice-embedded ensemble is denoted as Esync, with Esync Eact. Synchronized spike coding within area then provides the substrate for a broad distributed ensemble across areas that would allow the binding of multimodal features into coherent object perception (based on binding-by-synchrony theory). EEE theory has direct implications for interpretation of both binding-by-synchrony theory, and for theories of Bayesian predictive coding. Developed tools will be used to facilitate other projects through our end-users: 1. developing further reduced models for more detailed analysis (Mihalas); 2. develop models for place cell theory (Kubie); 3. develop new data analysis and stimulation protocols in macaque for use in brain-machine interface development (Francis). We propose to work primarily in a multiscale model both to develop further details of EEE theory, and to make specific predictions. In neuroscience, unlike in physics, detailed predictions for measures in the brain must be obtained by instantiating the theory in simulation, which allows the experimentalist to identify a particular scale and aspect of the theory that is accessible through their experimental measures. Our Specific Aims are: 1. Develop a set of single cell models of Layer 5 pyramidal cells based on available experimental data and morphologies, and test input/output activity patterns for inputs on basilar and apical oblique dendrites. Generate model predictions that can be tested in in vitro or in vivo experiments with dendritic imaging. 2. Build networks and test with firing variability, coding density, information-theoretic signal flow-through, graph-theoretic measures. Verification will be performed across multiple model instantiations. Specific experimental predictions will be made for future model validation. 3. Disseminate theory, models and experimental predictions through model sharing, workshops, tutorials, and courses. Tools to be developed and shared include genetic algorithms for model parameter fitting, background-driving and activation-input data-suites, and specific cell and network models.