Chapter 3: Multineuron representations of visual attention

Jasper Poort, Arezoo Pooresmaeili and Pieter Roelfsema
The Netherlands Institute for Neurosciences


Recently techniques have become available that allow simultaneous recording from multiple neurons in awake behaving higher primates. These recordings can be analyzed with multivariate statistical methods, such as Fisher linear discriminant analysis or support vector machines to determine how information is represented in the activity of a population of neurons. We have applied these techniques to recordings from groups of neurons in visual primary cortex (area V1). We find that neurons in area V1 do not only code basic stimulus features, but also whether image elements are attended or not. These attentional signals are weaker than the feature-selective responses, and it might be suspected that the reliability of attentional signals in area V1 is limited by the noisiness of neuronal responses as well as by the tuning of the neurons to low-level features. Our surprising finding is that the locus of attention can be decoded on a single trial from the activity of a small population of neurons in area V1. One critical factor that determines how well information from multiple neurons is combined is the correlation of the response variability, or noise correlation, across neurons. It has been suggested that correlations between the activities of neurons that are part of a population limit the information gain, but we find that the impact of these noise correlations depends on the relative position of the neurons’ receptive fields: the correlations reduce the benefit of pooling neuronal responses evoked by the same object, but actually enhance the advantage of pooling responses evoked by different objects. These opposing effects cancel each other at the population level, so that the net effect of the noise correlations is negligible and attention can be decoded reliably. We next investigated if it is possible to decode attention if we introduce large variations in luminance contrast, because luminance contrast has a strong effect on the activity of V1 neurons and therefore may disrupt the decoding of attention. However, we find that some neurons in area V1 are modulated strongly by attention and others only by luminance contrast so that attention and contrast are represented by largely separable codes. These results demonstrate the advantages of multi-neuron representations of visual attention.


Key words: vision, primary visual cortex, attention, luminance contrast, Fisher linear discriminant, support vector machines