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Place cells are neurons in the hippocampus that are sensitive to location within an environment. Simulations of a large-scale, computational model of the rat dentate gyrus using grid cell input have been performed resulting in granule cells that express multiple place fields. The typical method of detecting place fields using a global threshold on this data is unreliable as the characteristics of...
In order to accurately model the pattern of activation due to electrical stimulation of the hippocampus, a multi-scale computational approach is necessary. At the system level, the Admittance Method (ADM) is used to calculate the extracellular voltages created by a stimulating electrode. At the network and cellular levels, a large-scale multi-compartmental neuron network is used to calculate cellular...
Owing to the dramatic rise in treatment of neurological disorders with electrical micro-stimulation it has become apparent that the major technological limitation in deploying effective devices lies in the process of designing efficient, safe, and outcome specific electrode arrays. The time-consuming and low-fidelity nature of gathering test data using experimental means and the immense control and...
Postsynaptic calcium dynamics play a critical role in synaptic plasticity, but are often difficult to measure in experimental protocols due to their relatively fast rise and decay times, and the small spine dimensions. To circumvent these limitations, we propose to develop a computational model of calcium dynamics in the postsynaptic spine. This model integrates the main elements that participate...
It is well known that information is represented and transmitted among neuronal units by a series of all-or-none “neural codes”. In neuroinformatics study, this coding process, also termed as “spiking activity”, is not straightforward for prediction. It is owing to the high nonlinearity and dynamic property involved in generation of the neuronal spikes. In this paper, a novel generalized Volterra...
The topography, or the anatomical connectivity, of the excitatory entorhinal-dentate-CA3 circuit of the rat hippocampus has been implemented for a large-scale, biologically realistic, computational model of the rat hippocampus. The implementation thus far covers only the excitatory synapses for the principal neurons in the hippocampal subregions. Starting from layer II of the entorhinal cortex, the...
To perform large-scale simulations of the brain or build biologically-inspired cognitive architectures, it is essential to have a succinct and flexible model of spiking neurons. The model should be able to capture the nonlinear dynamical properties of various types of neurons and the nonstationary properties such as the spike-timing-dependent plasticity (STDP). In this paper, we propose a generalized...
A field-programmable gate array (FPGA)-based hardware architecture is proposed and utilized for prediction of neuronal population firing activity. The hardware system adopts the multi-input multi-output (MIMO) generalized Laguerre–Volterra model (GLVM) structure to describe the nonlinear dynamic neural process of mammalian brain and can switch between the two important functions: estimation of GLVM...
The brain is a perfect example of an integrated multi-scale system, as the complex interactions taking place at the molecular level regulate neuronal activity that further modifies the function of millions of neurons connected by trillions of synapses, ultimately giving rise to complex function and behavior at the system level. Likewise, the spatial complexity is accompanied by a complex temporal...
Neurons receive pre-synaptic spike trains and transform them into post-synaptic spike trains. This spike train to spike train temporal transformation underlies all cognitive functions performed by neurons, e.g., learning and memory. The transformation is a highly nonlinear dynamical process that involves both pre- and post-synaptic mechanisms. The ability to separate and quantify the nonlinear dynamics...
To develop hippocampal prosthetic devices that can restore the memory-dependent cognitive functions lost in diseases or injuries, it is essential to build a computational model that sufficiently captures the transformations of multiple memories performed by hippocampal sub-regions. A universal model with a single set of coefficients for all memories is desirable, since it can transform the memories...
Sparse Volterra model (sVM) is defined as a Volterra model (VM) that contains only a subset of its all possible model coefficients corresponding to its significant inputs and the existing terms of those inputs. Compared with ordinary VM, sVM is more efficient and interpretable in representing sparsely connected multiple-input systems, e.g., neuronal networks. In this paper, we formulate a rigorous...
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