Abstract
Lehky et al. (2011) provided a statistical analysis on the responses of the recorded 674 neurons to 806 image stimuli in anterior inferotemporalm (AIT) cortex of two monkeys. In terms of kurtosis and Pareto tail index, they observed that the population sparseness of both unnormalized and normalized responses is always larger than their single-neuron selectivity, hence concluded that the critical features for individual neurons in primate AIT cortex are not very complex, but there is an indefinitely large number of them. In this work, we explore an “inverse problem” by simulation, that is, by simulating each neuron indeed only responds to a very limited number of stimuli among a very large number of neurons and stimuli, to assess whether the population sparseness is always larger than the single-neuron selectivity. Our simulation results show that the population sparseness exceeds the single-neuron selectivity in most cases even if the number of neurons and stimuli are much larger than several hundreds, which confirms the observations in Lehky et al. (2011). In addition, we found that the variances of the computed kurtosis and Pareto tail index are quite large in some cases, which reveals some limitations of these two criteria when used for neuron response evaluation.
Highlights
Our goal is to investigate the following “inverse problem” on the conclusions in Lehky et al (2011) by simulation: Assuming each neuron only responds to a very limited number of stimuli among a large number of neurons and stimuli, whether the population sparseness is always larger than the single-neuron selectivity, especially when the number of neurons and stimuli increases
In this work, assuming the conclusions in Lehky et al (2011) always hold true regardless of the number of neurons and stimuli, that is, the critical features for individual neurons in primate anterior inferotemporalm (AIT) cortex are not very complex, but there are an indefinitely large number of such features, we simulate a large number of neuron responses subject to this assumption under various conditions by varying the neuron number, the stimulus number, the noise level, and use the same criteria, as did in Lehky et al (2011) for monkey AIT neurons, to assess whether the population sparseness for the synthetic responses is always larger than the single-neuron selectivity by both the kurtosis criterion and the Pareto tail index criterion
General Results in Lehky et al (2011) Lehky et al (2011) showed that:. For both the unnormalized and normalized neuron responses, the population sparseness is always greater than the singleneuron selectivity in terms of the mean kurtosis, median kurtosis, and mean Pareto tail index, as listed in Table 1
Summary
Many researchers have investigated statistics of neuron responses in different visual cortical areas, since statistical characteristics of neurons are important to theories of object representation and population decoding (Riesenhuber and Poggio, 1999; Hinton et al, 2006; Lehky et al, 2007; Baldassi et al, 2013; Cadieu et al, 2014; Yamins et al, 2014; Dong et al, 2016; Chang and Tsao , 2017).Single-neuron selectivity and population sparseness are two important characteristics of neuron responses, which have been extensively investigated in literatures (Lehky et al, 2005, 2011; Franco et al, 2007). Many researchers have investigated statistics of neuron responses in different visual cortical areas, since statistical characteristics of neurons are important to theories of object representation and population decoding (Riesenhuber and Poggio, 1999; Hinton et al, 2006; Lehky et al, 2007; Baldassi et al, 2013; Cadieu et al, 2014; Yamins et al, 2014; Dong et al, 2016; Chang and Tsao , 2017). In contrast to the results in Franco et al (2007), Lehky et al (2011) provided a statistical analysis on the responses of 674 monkey IT neurons to 806 stimulus images.
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