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Abstract:
Although it is widely accepted that neurons in cortex encode information about the
external world at the population level, little is still known on the structure of neuronal
population codes under natural stimulation conditions. In particular, it is not known
whether correlations between neurons are important for encoding information. To
characterize the cortical population code underlying visual function, we recorded
extracellularly the simultaneous activity of neuronal populations in primary visual cortex
(V1) of an anaesthetized macaque, while the animal was viewing a video of monkeys
behaving in their cages. Neuronal activity was recorded with an electrode array with
approx. 1.5 mm spacing. We could record action potentials from a small cluster of
neurons at each of 6 different electrodes. We computed, from 30 repeated presentations
of the same video, the probability of each neuron firing in response to each part of the
video. From this probability distribution of spike patterns at different times of the video,
we computed the amount of Shannon’s Mutual Information I that each neuronal cluster
conveyed about the sequence of visual stimuli (methods similar to that of de Ruyter et al,
Science 1997). We found that each individual cluster conveyed on average 6 bits/sec of
information. In addition, to understand how information from different cells is combined
together, we computed the information about the video that can be extracted by
observing the simultaneous activity over a small population of neural clusters. The size
of the population was varied from 2 to 4, and we took the average of the information
conveyed by each subpopulation with a given size. We found that the average
information increased linearly with the population size. This suggests that information is
conveyed at the population level, and that each neuronal cluster carries fully
independent information about the visual stimuli. Then, we addressed whether
correlations are an important part of the neural population code. If this was the case, we
would expect that a downstream neural system decoding the V1 population activity
would lose a large amount of information when not paying attention to cross-correlations
between neurons. The importance of correlations in decoding can be formalized in
information theoretic terms by computing ΔI, the amount of information that is lost by
ignoring information when decoding the population activity (Latham and Nirenberg, J.
Neurosci. 2005). We found that ΔI was very small, of the order of 1 for population size
in the range 2-4. Thus cross-correlations between cortical neurons were not important
for transmitting information about natural stimuli.
In conclusion, the above results suggest that V1 represents natural visual stimuli through
a distributed population code that combines independent information coming from
different neurons without relying on correlations.
