Distributed Fading Memory for Stimulus Properties in the Primary Visual Cortex
It is currently not known how efficiently distributed neuronal responses in early visual areas carry stimulus-related information. We made multi-electrode recordings from cat primary visual cortex and applied methods from machine learning (e.g., support vector machines) in order to analyze the temporal evolution of stimulus-related information in the spiking activity of large ensembles of around 100 neurons. We used sequences of up to three different visual stimuli (letters) presented for 100 ms and with intervals of 100 ms. Most of the information about visual stimuli extractable by advanced methods of machine learning, was also extractable by simple linear classification such as can be achieved by perceptrons or individual neurons. Surprisingly, new stimuli did not erase information about previous stimuli. The responses to the most recent stimulus contained about equal amounts of information about both this and the preceding stimulus. This information was encoded both in the discharge rates (response amplitudes) of the ensemble of neurons and in the precise timing of individual spikes, and persisted for several 100 ms beyond the offset of stimuli. The results indicate that the network from which we recorded is endowed with fading memory and performs online computations by combining information about temporally sequential stimuli. This result challenges models assuming frame-by-frame analyses of sequential inputs.