Superposition of information in large ensembles of neurons in primary visual cortex

We 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 in primary visual cortex of anesthetized cats. We present ed sequences of up to 3 different visual stimuli (letters) that lasted 100 ms and followed at intervals of 100 ms. We f ound that most of the information about visual stimuli extractable by advanced methods from machine learning (e.g., Sup port Vector Machines) could also be extracted by simple linear classifiers (perceptrons). Hence, in principle this info rmation can be extracted by a biological neuron. A surprising result was that new stimuli did not erase information abo ut previous stimuli. In fact, information about the nature of the preceding stimulus remained as high as the informatio n about the current stimulus. Separately trained linear readouts could retrieve information about both the current and the preceding stimulus from responses to the current 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 seve ral 100 ms beyond the offset of stimuli. This superposition of information about sequentially presented stimuli constrains computational models for visual proce ssing. It poses a conundrum for models that assume separate classification processes for each frame of visual input and supports models for cortical computation ([Buonomano, Merzenich, 1995], [Maass, Natschlaeger, Markram, 2002]) which arg ue that a frame-by frame processing is neither feasible within highly recurrent networks nor useful for classifying and predicting rapidly changing stimulus sequences. Specific predictions of these alternative computational models are tha i) information from different frames of visual input is superimposed in recurrent circuits and ii) nonlinear combinations of different information components are immediately provided in the spike output. Our results indicate that the network from which we recorded provided nonlinear combinations of information from sequen tial frames. Such nonlinear preprocessing increases the discrimination capability of any linear readout neurons receivi ng distributed input from the kind of cells we recorded from. These readout neurons could be implemented within V1 and/ or at subsequent processing levels.

Superposition of information in large ensembles of neurons in primary visual cortex

Abstract: