Neuronal selectivity, population sparseness, and ergodicity in the inferior temporal visual cortex
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The sparseness of the encoding of stimuli by single neurons and by populations of neurons is fundamental to understanding the efficiency and capacity of representations in the brain, and was addressed as follows. The selectivity and sparseness of firing to visual stimuli of single neurons in the primate inferior temporal visual cortex were measured to a set of 20 visual stimuli including objects and faces in macaques performing a visual fixation task. Neurons were analysed with significantly different responses to the stimuli. The firing rate distribution of 36% of the neurons was exponential. Twenty-nine percent of the neurons had too few low rates to be fitted by an exponential distribution, and were fitted by a gamma distribution. Interestingly, the raw firing rate distribution taken across all neurons fitted an exponential distribution very closely. The sparseness as or selectivity of the representation of the set of 20 stimuli provided by each of these neurons (which takes a maximal value of 1.0) had an average across all neurons of 0.77, indicating a rather distributed representation. The sparseness of the representation of a given stimulus by the whole population of neurons, the population sparseness ap, also had an average value of 0.77. The similarity of the average single neuron selectivity as and population sparseness for any one stimulus taken at any one time ap shows that the representation is weakly ergodic. For this to occur, the different neurons must have uncorrelated tuning profiles to the set of stimuli.
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- Atick JJ (1992) Could information theory provide an ecological theory of sensory processing?. Nature 3:213–251Google Scholar
- Barlow HB (1961) Possible principles underlying the transformation of sensory messages. In: Rosenblith W (ed) MIT Press, Sensory Communication. CambridgeGoogle Scholar
- Feigenbaum JD, Rolls ET (1991) Allocentric and egocentric spatial information processing in the hippocampal formation of the behaving primate. Psychobiology 19:21–40Google Scholar
- Foldiak P (2003) Sparse coding in the primate cortex. In: Arbib MA (ed) The handbook of brain theory and neural networks. MIT Press, Cambridge, pp 1064–1068Google Scholar
- Rolls ET (1984) Neurons in the cortex of the temporal lobe and in the amygdala of the monkey with responses selective for faces. Human Neurobiol 3:209–222Google Scholar
- Rolls ET (2008) Memory, attention, and decision-making: a unifying computational neuroscience approach. Oxford University Press, OxfordGoogle Scholar
- Rolls ET, Deco G (2002) Computational neuroscience of vision. Oxford University Press, OxfordGoogle Scholar
- Rolls ET, Treves A (1998) Neural networks and brain function. Oxford University Press, OxfordGoogle Scholar