Neural Coding of Natural Stimuli: Information at Sub-Millisecond Resolution

Abstract

Sensory information about the outside world is encoded by neurons in sequences of discrete, identical pulses termed action potentials or spikes. There is persistent controversy about the extent to which the precise timing of these spikes is relevant to the function of the brain. We revisit this issue, using the motion-sensitive neurons of the fly visual system as a test case. Our experimental methods allow us to deliver more nearly natural visual stimuli, comparable to those which flies encounter in free, acrobatic flight. New mathematical methods allow us to draw more reliable conclusions about the information content of neural responses even when the set of possible responses is very large. We find that significant amounts of visual information are represented by details of the spike train at millisecond and sub-millisecond precision, even though the sensory input has a correlation time of ∼55 ms; different patterns of spike timing represent distinct motion trajectories, and the absolute timing of spikes points to particular features of these trajectories with high precision. Finally, the efficiency of our entropy estimator makes it possible to uncover features of neural coding relevant for natural visual stimuli: first, the system's information transmission rate varies with natural fluctuations in light intensity, resulting from varying cloud cover, such that marginal increases in information rate thus occur even when the individual photoreceptors are counting on the order of one million photons per second. Secondly, we see that the system exploits the relatively slow dynamics of the stimulus to remove coding redundancy and so generate a more efficient neural code.

Role of myelin plasticity in oscillations and synchrony of neuronal activity

“…Using information-theoretic measures it was found that the temporal precision of the auditory information coding is coarser than 1 ms, but finer than 5 ms (Kayser et al, 2010). Similar findings hold for visual (Victor and Purpura, 1996; Butts et al, 2007) and other temporal tasks (Nemenman et al, 2008), with the consensus that the required precision for spike arrival timing was on the order of few or several milliseconds. In some specialized circuits, at the system level, the timing constraints can be at the sub-millisecond range.…”

A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula

“…MI has been used to compare different neural response codes [Ince et al, 2013; Kayser et al, 2009; Reich et al, 2001], characterize different neurons [Sharpee, 2014] as well as quantify the effect of correlations between neurons [Ince et al, 2009, 2010; Moreno‐Bote et al, 2014] and the importance of spike timing [Kayser et al, 2010; Nemenman et al, 2008; Panzeri et al, 2001]. Recent studies have begun to explore its application to neuroimaging [Afshin‐Pour et al, 2011; Caballero‐Gaudes et al, 2013; Gross et al, 2013; Guggenmos et al, 2015; Ostwald and Bagshaw, 2011; Panzeri et al, 2008; Salvador et al, 2007; Saproo and Serences, 2010; Schyns et al, 2011; Serences et al, 2009].…”

Depth information in natural environments derived from optic flow by insect motion detection system: a model analysis

“…How motion information is encoded by fly motion sensitive wide-field neurons has already been analyzed under outdoor conditions. For methodological reasons, these studies could only address the neural responses to rotational displacements of the animal (Egelhaaf et al, 2001; Lewen et al, 2001; Nemenman et al, 2008). Therefore, they had to focus on how reliably self-rotations are represented by the visual motion pathway, but could not address how spatial information is represented.…”