Dear all,
Below might be of interest. I am planning for my PhD proposal and comments would greatly be appreciated. Best, Olivier Noise as a Computational Resource in Brains and Neuromorphic Hardware (NOICOR) Its working hypothesis is, that brains employ their inherent stochasticity, apparent in all brain systems in the form of trial-to-trail variability, as a computational resource. NOICOR can analyze computational capabilities and learning capabilities of stochastic dynamic systems in general and of highly structured brain circuits in particular. It can explore through theoretical analysis and computer simulations the hypothesis that the knowledge encoded in the brain to some extent in the form of “embodied” probability distributions over network states and trajectories of network states, from which networks of neurons sample through their stochastic dynamics. Previous computational brain models had focused instead on the elimination of noise in order to support deterministic computing paradigms. Objectives of NOICOR: 1. to answer several fundamental theoretical questions as follows, -- Does a data-based (rather than constructed) model for a stochastic network of spiking neurons in the brain also have a stationary distribution p, for which it can therefore carry out probabilistic inference? -- Does this hold in particular also for detailed (and diverse) data-based models for neurons and synapses, as they are for example reported in the experimental literature? --How do biological details of neurons, synapses, and connectivity patterns affect the convergence time of these stochastic systems? -- How can the overall network activity support a fast generation and readout of network states y with high probability? -- How do changes in network connectivity, synaptic weights, and other network parameters affect the stationary distribution p that is embodied by the network? 2. to provide a systematic understand which constraint satisfaction problems (CSPs) can be solved efficiently by stochastic networks of spiking neurons, and which dynamical and connectivity properties optimize their convergence speed. 3. to provide a theoretical framework for understanding temporal aspects of computations in stochastic networks of spiking neurons. This can provide a theoretical framework for understanding temporal aspects of computations in stochastic networks of spiking neurons. 4. to analyze network learning from the perspective of stochastic computations. 5. to develop on the basis of theoretical insight gained from work on the preceding objectives new methods for analyzing simultaneous recording from many neurons in the cortex, and to develop in collaboration with experimental neuroscientists- methods for testing predictions of competing models for the organization of computations in cortical networks of neurons. This can provide new methods for analyzing the high dimensional data streams which result from fast 2-photon imaging of dynamics of Ca in hundreds of neurons, or in dendritic branches of pyramidal cells, especially in combination with ontogenetic controls. 6. to develop principled methods for using noise as a computational resource in neuromorphic and other novel computing hardware, and to significantly advance the state of the art with regard to complexity of computational problems that can be solved by neuromorphic computers. In fact, NOICOR develops principle methods for using noise as a computational resource in future computing hardware. This research can provide a first theoretical foundation for computations in neuromorphic hardware that uses noise as a computational resource and can design a new paradigm for solving NP-complete problem through stochastic hardware approximations, based on insight from work on the second objective. The sixth objective can mark a drastic departure from the direction of previous research on the design of future computing hardware. _______________________________________________ Neur-sci mailing list [hidden email] http://www.bio.net/biomail/listinfo/neur-sci |
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