Abstract
Conventional methods used to characterize multidimensional neural feature selectivity, such as spike-triggered covariance (STC) or maximally informative dimensions (MID), are limited to Gaussian stimuli or are only able to identify a small number of features due to the curse of dimensionality. To overcome these issues, we propose two new dimensionality reduction methods that use minimum and maximum information models. These methods are information theoretic extensions of STC that can be used with non-Gaussian stimulus distributions to find relevant linear subspaces of arbitrary dimensionality. We compare these new methods to the conventional methods in two ways: with biologically-inspired simulated neurons responding to natural images and with recordings from macaque retinal and thalamic cells responding to naturalistic time-varying stimuli. With non-Gaussian stimuli, the minimum and maximum information methods significantly outperform STC in all cases, whereas MID performs best in the regime of low dimensional feature spaces.
Highlights
In recent years it has become apparent that many types of sensory neurons simultaneously encode information about more than one stimulus feature in their spiking activity
Examples can be found across a wide variety of modalities, including the visual [1,2,3,4,5,6,7,8,9,10,11,12], auditory [13], olfactory [14], somatosensory [15] and mechanosensory [16] systems. This discovery was facilitated by the development of dimensionality reduction techniques like spiketriggered covariance (STC) [17,18,19,20,21,22] and maximally informative dimensions (MID) [23]
STC can identify many relevant features for stimuli whose parameters are distributed in a Gaussian manner but can fail when natural stimuli are used, whereas MID works well for arbitrary stimuli but requires exponentially larger data sets to find more than a few features
Summary
In recent years it has become apparent that many types of sensory neurons simultaneously encode information about more than one stimulus feature in their spiking activity. Examples can be found across a wide variety of modalities, including the visual [1,2,3,4,5,6,7,8,9,10,11,12], auditory [13], olfactory [14], somatosensory [15] and mechanosensory [16] systems This discovery was facilitated by the development of dimensionality reduction techniques like spiketriggered covariance (STC) [17,18,19,20,21,22] and maximally informative dimensions (MID) [23]. We propose two novel techniques based on minimum and maximum mutual information; these new approaches can be seen as an extension of STC to arbitrary stimuli
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