@article {Johnston331504, author = {Jamie Johnston and Sofie-Helene Seibel and L{\'e}a Simone Adele Darnet and Sabine Renninger and Michael Orger and Leon Lagnado}, title = {A retinal circuit generating a dynamic predictive code for orientated features}, elocation-id = {331504}, year = {2018}, doi = {10.1101/331504}, publisher = {Cold Spring Harbor Laboratory}, abstract = {Sensory systems must reduce the transmission of redundant information to function efficiently. One strategy is to continuously adjust the sensitivity of neurons to suppress responses to common features of the input while enhancing responses to new ones. Here we image both the excitatory synaptic inputs and outputs of retinal ganglion cells to understand how such dynamic predictive coding is implemented in the analysis of spatial patterns. Synapses of bipolar cells become tuned to orientation through presynaptic inhibition generating lateral antagonism in the orientation domain. Individual ganglion cells receive excitatory synapses tuned to different orientations but feedforward inhibition generates a high-pass filter that only transmits the initial activation of these inputs, thereby removing redundancy. These results demonstrate how a dynamic predictive code can be implemented by circuit motifs common to many parts of the brain.}, URL = {https://www.biorxiv.org/content/early/2018/06/05/331504}, eprint = {https://www.biorxiv.org/content/early/2018/06/05/331504.full.pdf}, journal = {bioRxiv} }