Chapter 3: Multineuron representations of visual attention
Jasper Poort, Arezoo Pooresmaeili and Pieter Roelfsema
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