Spatial and temporal jitter distort estimated functional properties of visual sensory neurons

Abstract

The functional properties of neural sensory cells or small neural ensembles are often characterized by analyzing response-conditioned stimulus ensembles. Many widely used analytical methods, like receptive fields (RF), Wiener kernels or spatio-temporal receptive fields (STRF), rely on simple statistics of those ensembles. They also tend to rely on simple noise models for the residuals of the conditional ensembles. However, in many cases the response-conditioned stimulus set has more complex structure. If not taken explicitly into account, it can bias the estimates of many simple statistics, and lead to erroneous conclusions about the functionality of a neural sensory system. In this article, we consider sensory noise in the visual system generated by small stimulus shifts in two dimensions (2 spatial or 1-space 1-time jitter). We model this noise as the action of a set of translations onto the stimulus that leave the response invariant. The analysis demonstrates that the spike-triggered average is a biased estimator of the model mean, and provides a de-biasing method. We apply this approach to observations from the stimulus/response characteristics of cells in the cat visual cortex and provide improved estimates of the structure of visual receptive fields. In several cases the new estimates differ substantially from the classic receptive fields, to a degree that may require re-evaluation of the functional description of the associated cells.

How Invariant Feature Selectivity Is Achieved in Cortex

“…Previous analyses showed that for neurons tuned to a specific frequency, the temporal profiles of the two most relevant features form a quadrature pair in a sense that they are described by a pair of integration/differentiation features (Sharpee et al, 2011a). This type of selectivity could be consistent with shifts in temporal offsets or temporal jitter (Aldworth et al, 2005; Dimitrov and Gedeon, 2006; Gollisch, 2006; Dimitrov et al, 2009) as well as with changes in cadence or time dilation. However, detailed statistical analysis ruled out temporal jitter as the cause underlying integration/differentiation pair of features for that dataset (Sharpee et al, 2011a).…”

“…In particular, mid- and high-level sensory neurons exhibit stronger responses when exposed to natural stimuli as compared to randomized inputs (Sen et al, 2001). Historically, randomized stimuli have primarily been used to characterize neural feature selectivity because they allow for computationally simpler estimation procedures (Bialek and de Ruyter van Steveninck, 2005; Gollisch, 2006; Schwartz et al, 2006; Dimitrov et al, 2009; Samengo and Gollisch, 2012). However, the increased availability of computing resources now makes estimation procedures tenable with natural stimuli.…”

Estimating linear–nonlinear models using Rényi divergences

“…The spike-history effects can be taken into account by expanding the linear-nonlinear model so that spike trains can influence future spike probabilities in either the same neuron (Pillow et al, 2005) or interconnected pairs (Pillow et al, 2008). With respect to spike-time jitter, recent studies (Aldworth et al, 2005; Dimitrov and Gedeon, 2006; Dimitrov et al, 2009; Gollisch, 2006) demonstrate that the estimation of the relevant stimulus dimensions can be improved significantly in the case of Gaussian stimuli by taking spike-time jitter into account. The dimensionality reduction techniques discussed here in the case of natural stimuli can presumably be also improved by incorporating the spike-timing jitter into account.…”

Dynamic Alignment Models for Neural Coding

“…When latencies are strongly fluctuating or spike time-jitter is large, many modeling techniques that assume a fixed stimulus-response relationship , such as spike triggered averaging, yield suboptimal results [15], [16]: The estimated receptive fields are blurred and the accuracy of predicted responses to novel stimuli is low (Fig. 1B).…”