Population coding in electric sensing: origin and function of noise correlations

In many cases, great knowledge regarding single neuron activity during the encoding of sensory signals or the generation of behavioral outputs has been achieved. On another scale, however, we still lack detailed information of the mechanisms of concerted neuronal activity, i.e. population coding. This is crucial to understand the neuronal code and remains a central problem in neuroscience. Extrapolating the knowledge of single unit activity to the scale of a neuronal population is often complicated by the fact that the activities of neurons are typically correlated rather than being independent. Such correlations, which can arise in terms of the average responses to different stimuli as well as in terms of trial to trial variability, were shown to substantially impact the efficacy of population codes. To investigate the sources and function of correlations we use the weakly electric fish Apteronotus leptorhynchus as a model system, due to the wealth of physiological and anatomical knowledge that is available with regard to electrosensory processing. These fish sense electric fields with an array of electroreceptors that project to three parallel segments of the medullary electrosensory lateral line lobe (ELL). Previous studies established, that the size and the organization of receptive fields differs substantially between pyramidal neurons in the different segments, which should consequently result in very different amounts of correlations in each segment. In contrast to this, our experimental recordings revealed very similar levels of correlation magnitudes. To explain this surprising result, we investigated the differential receptive field interactions using a modeling approach. Considering the antagonistic center-surround organisation of receptive fields, we were able to show that very different receptive field organization can give rise to very similar amounts of correlations. After establishing the presence of noise correlations in the ELL, we assessed their potential impact and function for the encoding of electrosensory stimuli. Our preliminary results suggest that ELL noise correlations encode stimuli independent of classical measures of neuronal activity (i.e. firing rate). The stimulus dependent changes in correlation levels are potentially modulated via the recurrent ELL connectivity which will be a focus of our future investigations to unravel the mechanisms mediating this independent additional channel of information transmission in the brain.Non UBCUnreviewedAuthor affiliation: McGill UniversityPostdoctora