Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: To incorporate variation of neuron shape in neural models,  developed a method of generating a population of realistically shaped neurons. Parameters that characterize a neuron include soma diameters, distances to branch points, fiber diameters, and overall dendritic tree shape and size. Experimentally measured distributions provide a means of treating these morphological parameters as stochastic variables in an algorithm for production of neurons. Stochastically generated neurons shapes re used in a model of hippocampal dentate gyrus granule cells. A large part of the variation of whole neuron input resistance RN is due to variation in shape. Membrane resistivity Rm computed from RN varies accordingly. Statistics of responses to synaptic activation re computed for different dendritic shapes. Magnitude of response variation depended on synapse location, measurement site, and attribute of response.
['James L. Winslow', 'Stephan F. Jou', 'Sabrina Wang', 'J. Martin Wojtowicz']
I am writing a research paper on a idea Signals in Stochastically Generated Neurons
[{"from": "human", "value": "I am writing a research paper on a idea Signals in Stochastically Generated Neurons"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: To incorporate variation of neuron shape in neural models,  developed a method of generating a population of realistically shaped neurons. Parameters that characterize a neuron include soma diameters, distances to branch points, fiber diameters, and overall dendritic tree shape and size. Experimentally measured distributions provide a means of treating these morphological parameters as stochastic variables in an algorithm for production of neurons. Stochastically generated neurons shapes re used in a model of hippocampal dentate gyrus granule cells. A large part of the variation of whole neuron input resistance RN is due to variation in shape. Membrane resistivity Rm computed from RN varies accordingly. Statistics of responses to synaptic activation re computed for different dendritic shapes. Magnitude of response variation depended on synapse location, measurement site, and attribute of response."}]