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A multi-input modeling approach is introduced to quantify hippocampal neural dynamics. It is based on the Volterra modeling approach extended to multiple inputs. The computed Volterra kernels allow quantitative description of hippocampal transformations and define a predictive model that can produce responses to arbitrary input patterns. Electrophysiological data from several CA3 and CA1 cells in...
Contemporary measurement techniques enable noninvasive observations of cardiovascular functions, both from the central and peripheral points of view. Cardiovascular dynamics is found to be characterised by several distinct frequency components, and these are present at each site of the system. The corresponding oscillatory processes are mutually dependent via couplings that lead to amplitude/frequency...
The depth of anesthesia estimation has been one of the most research interests in the field of EEG signal processing in recent decades. In this paper we present a new methodology to quantify the depth of anesthesia by quantifying the dynamic fluctuation of the EEG signal. Extraction of useful information about the nonlinear dynamic of the brain during anesthesia has been proposed with the optimum...
Nonlinear kernel models are developed and estimated for the spike train transformation from hippocampal CA3 region to CA1 region. The physiologically plausible model structure consists of nonlinear feedforward kernels that model synaptic transmission and dendritic integration, a linear feedback kernel that models spike-triggered after potential, a threshold, an adder, and a noise term that assesses...
Contemporary measurement techniques enable noninvasive observations of cardiovascular functions, both from the central and peripheral points of view. Cardiovascular dynamics is found to be characterised by several distinct frequency components, and these are present at each site of the system. The corresponding oscillatory processes are mutually dependent via couplings that lead to amplitude/frequency...
Dentate granule cells receive inputs from the entorhinal cortex as the "perforant path". There are two components of the perforant path: the lateral component (LPP) and the medial component (MPP). LPP and MPP convey different sensory modality information. It remains elusive as to how signals from different inputs interact and integrate at the granule cell level. We attempted to address this...
A multi-input modeling approach is introduced to quantify hippocampal neural dynamics. It is based on the Volterra modeling approach extended to multiple inputs. The computed Volterra kernels allow quantitative description of hippocampal transformations and define a predictive model that can produce responses to arbitrary input patterns. Electrophysiological data from several CA3 and CA1 cells in...
Nonlinear kernel models are developed and estimated for the spike train transformation from hippocampal CA3 region to CA1 region. The physiologically plausible model structure consists of nonlinear feedforward kernels that model synaptic transmission and dendritic integration, a linear feedback kernel that models spike-triggered after potential, a threshold, an adder, and a noise term that assesses...
The depth of anesthesia estimation has been one of the most research interests in the field of EEG signal processing in recent decades. In this paper we present a new methodology to quantify the depth of anesthesia by quantifying the dynamic fluctuation of the EEG signal. Extraction of useful information about the nonlinear dynamic of the brain during anesthesia has been proposed with the optimum...
Dentate granule cells receive inputs from the entorhinal cortex as the "perforant path". There are two components of the perforant path: the lateral component (LPP) and the medial component (MPP). LPP and MPP convey different sensory modality information. It remains elusive as to how signals from different inputs interact and integrate at the granule cell level. We attempted to address this...
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