Standard inference methods, such as Generalized Linear Models (GLMs), typically regress for parameters on all neuronal activity at once. Such a global fitting approach can face identifiability difficulties—for example, where the statistical estimation of strong, opposing weights becomes ill-conditioned in excitatory-inhibitory balanced networks.
Standard inference methods, such as Generalized Linear Models (GLMs), typically regress for parameters on all neuronal activity at once.
Additionally, when combined with Maximum Caliber to construct a minimal dynamical model, the framework better captures temporal statistics—such as inter-spike intervals—than Maximum Entropy models.
Such a global fitting approach can face identifiability difficulties—for example, where the statistical estimation of strong, opposing weights becomes ill-conditioned in excitatory-inhibitory balanced networks.
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Standard inference methods, such as Generalized Linear Models (GLMs), typically regress for parameters on all neuronal activity at once. Such a global fitting approach can face identifiability difficulties—for example, where the statistical estimation of strong, opposing weights becomes ill-conditioned in excitatory-inhibitory balanced networks. Here, we introduce FLux-based Effective Coupling (FLEC), a framework that maps spike trains directly to probability fluxes on network state space.
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