D. Nikolic, S. Haeusler, W. Singer, and W. Maass
It is currently not known how 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 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 or larger. Most of the information about
visual stimuli extractable by sophisticated methods of machine learning, i.e.
support vector machines with non-linear kernel functions, was also
extractable by simple linear classification such as can be achieved by
individual neurons. 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, when using short time-constants for integration
(e.g., 20 ms), in the precise timing of individual spikes (<=
ms), 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 is capable of performing online computations utilizing information
about temporally sequential stimuli. This result challenges models assuming
frame-by-frame analyses of sequential inputs.