On the Limits of Perceptual Quality Measures For Enhanced Underwater Images

The response of a biological neuron depends on the precise timing of afferent spikes. This temporal aspect of the neuronal code is essential in understanding information processing in neurobiology and applies particularly well to the output of neuromorphic hardware such as event-based cameras. Though, most artificial neuronal models do not take advantage of this minute temporal dimension and here, we develop a model for the efficient detection of temporal spiking motifs based on a layer of neurons with hetero-synaptic delays. indeed, the variety of synaptic delays on the dendritic tree allows to synchronize synaptic inputs as they reach the basal dendritic tree. We show this can be formalized as time-invariant logistic regression which can be trained using labelled data. We apply this model for solving the specific problem of motion detection and demonstrate its use by applying it on synthetic naturalistic videos transformed to event-based streams similar to the output of a silicon retina. in particular, we show how its accuracy may evolve as a function of the total computational load. This end-to-end event-based computational brick could help improve the performance of future Spiking Neural Network solutions currently used in neuromorphic chips.