Algorithm for identifying neuron receptive fields through estimation of neuronal spike generation
Traditional systems for identifying neuron receptive fields require rate-based assumptions, meaning they require the input and output of experiments to be in the continuous domain. Additionally, neurons upstream of sensory periphery do not have continuous input signals, instead having input in the form of spatiotemporal spikes that occur in the advent of the appropriate stimuli. This technology presents a theoretical approach for identifying receptive fields in neural circuit models by algorithmic estimation of neuron spike times. As this technology identifies receptive fields directly from neuronal spike times themselves, it accounts for spike generation in a nonlinear nature. This enables incoming continuous sensory signals, as well as complex spike trains, to be interpreted into a multiple current-based spiking model of a neuron. This technology can be implemented in any field where receptive field identification is critical, most pertinently in theoretical and experimental neuroscience.
Identifies receptive fields directly from neuronal spiking frequencyReduces the number of experiments required to calculate rate of responseAble to consider neural receptive fields with distinct in temporal, spectrotemporal, and spatiotemporal patternsCan take input from continuous sensory stimuli and multidimensional spike generation, as wellPatent Information:Patent Pending (WO/2014/011907) Tech Ventures Reference: IR CU13023
Bio or wet computingAdvanced bio-replicative neural networkingBrain computer interface technology Restorative 'smart' prostheticsRestorative hardware for damaged or incomplete sensory peripheryEnhancing sensory periphery for military applications
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