Topic: CNN models

Natural visual environments are full of complex and diverse patterns. Recent neurophysiological experiments based on artificial patterns have suggested that macaque V1 neurons’ receptive fields are more complex and diverse in feature selectivity (Tang et al 2017) rather than just oriented Gabor filters. However, that finding was based on extensive parametric artificial stimuli used and might be biased. Hence, the nature of the neural codes of macaque V1 neurons remains controversial. To resolve this question, we performed 2-photon calcium imaging on three macaque monkeys to obtain 1689 V1 neur

Visual processing in the retina has been studied in great detail at all levels such that a comprehensive picture of the retina's cell types and the many neural circuits they form is emerging. However, the currently best performing models of retinal function are black-box CNN models which are agnostic to such biological knowledge. Here, I present two of our recent attempts to develop computational models of processing in the inner retina, which both respect biophysical and anatomical constraints yet provide accurate predictions of retinal activity