Opera Medica et Physiologica

Detection of Spontaneous Action Potentials in Extracellular Recordings of Visual Cortex Neurons Using EEG Predictors for a Machine Learning- Based Approach

Abstract: 

Spontaneous activity is known to be a characteristic feature of the vast majority of the neocortical principal cells including neurons of the primary sensory areas. The question of how spontaneous activity interacts with perception and encoding of sensory information remains open. In the present study, pyramidal neurons of the mouse primary visual cortex were recorded extracellularly under urethane anesthesia and simultaneous single-channel EEG recording was performed. To evaluate orientation and direction selectivity of the recorded neurons, mice were presented with visual stimuli consisting of moving sinusoidal gratings of different orientations displayed on a monitor. We noted quite regular bursts of generalized brain activity that were manifested in the recorded neuron as bundles of action potentials accompanied with a distinctive EEG pattern. Clearly, whenever such spontaneous activity shows up during visual stimulation, it is considered as noise, which significantly compromises the characteristics of the neuron’s measured visual response. To eliminate this effect, we developed a machine learning-based algorithm that enables to identify EEG predictors of generalized spontaneous activity and then to exclude spontaneous (i.e. not evoked by visual stimulation) action potentials from the recording. Our algorithm was shown to reliably detect action potentials that have been caused by generalized brain activity. Removal of action potentials of this origin from extracellular recordings obtained during visual stimulation allows for a more adequate estimation of parameters of neuronal receptive fields, in particular their orientation selectivity.