Biology subjects

Thier, P.

Publications and source records attributed to Thier, P..

23 records · Page 2Linked to original sources

bioRxiv2019

Individual variation of the dark-background-contingent upshift of gaze: effect of past habituation

We studied the shift of gaze direction induced by dark background in monkeys. Faced with large inter-individual variability, we asked how common the upshift is, and how the upshift size is distributed. Furthermore, we sought to reckon processes influencing the variability. Approaching these questions necessitates a large sample. Here we report data from 10 rhesus monkeys recorded in Tubingen, together with reported data from 4 cynomolgus monkeys studied in Rehovot. In all 14 monkeys, dark background induced upshift - but no systematic horizontal shift. The upshift might be thought of as a simple sensorimotor response; nevertheless, surprisingly, the monkeys previous experience appeared to have a decisive role in influencing the upshifts size. All the monkeys were previously trained in tasks that involved vision and eye movements; by their previous training, the monkeys were naturally divided into two groups. Monkeys of the first, bright-habituated group, previously trained in photopic, bright ambient-light conditions; monkeys of the second, dark-habituated group previously trained mostly with isolated dots of light appearing in dim ambient lighting or in full darkness. The dark-habituated monkeys had a larger upshift than the bright-habituated: the groups were separated by a border-value such that 6/7 of the dark-habituated monkeys had upshift larger than the border, and 5/7 of the bright-habituated monkeys had upshift smaller than the border. Thus, the size of the dark-background-induced upshift largely reflects the extent to which a monkey is habituated to work in the dark. Though the upshift is reflex-like sensorimotor behavior, its amplitude largely reflects cumulative experience.

neuroscience↗

bioRxiv2019

Using deep neural networks to detect complex spikes of cerebellar Purkinje Cells

One of the most powerful excitatory synapses in the entire brain is formed by cerebellar climbing fibers, originating from neurons in the inferior olive, that wrap around the proximal dendrites of cerebellar Purkinje cells. The activation of a single olivary neuron is capable of generating a large electrical event, called "complex spike", at the level of the postsynaptic Purkinje cell, comprising of a fast initial spike of large amplitude followed by a slow polyphasic tail of small amplitude spikelets. Several ideas discussing the role of the cerebellum in motor control are centered on these complex spike events. However, these events are extremely rare, only occurring 1-2 times per second. As a result, drawing conclusions about their functional role has been very challenging, as even few errors in their detection may change the result. Since standard spike sorting approaches cannot fully handle the polyphasic shape of complex spike waveforms, the only safe way to avoid omissions and false detections has been to rely on visual inspection of long traces of Purkinje cell recordings by experts. Here we present a supervised deep learning algorithm for rapidly and reliably detecting complex spikes as an alternative to tedious visual inspection. Our algorithm, utilizing both action potential and local field potential signals, not only detects complex spike events much faster than human experts, but it also excavates key features of complex spike morphology with a performance comparable to that of such experts. Significance statementClimbing fiber driven "complex spikes", fired at perplexingly low rates, are known to play a crucial role in cerebellum-based motor control. Careful interpretations of these spikes require researchers to manually detect them, since conventional online or offline spike sorting algorithms (optimized for analyzing the much more frequent "simple spikes") cannot be fully trusted. Here, we present a deep learning approach for identifying complex spikes, which is trained on local field and action potential recordings from cerebellar Purkinje cells. Our algorithm successfully identifies complex spikes, along with additional relevant neurophysiological features, with an accuracy level matching that of human experts, yet with very little time expenditure.

neuroscience↗

bioRxiv2019

A fronto-temporo-parietal network disambiguates potential objects of joint attention

We use the others gaze direction to identify her/his object of interest and to shift our attention to the same object, i.e. to establish joint attention. However, gaze direction may not be sufficient to unambiguously identify the object of interest as the others gaze may hit more than one object. In this case, the observer must use a priori information to disambiguate the object choice. Using fMRI, we suggest that the disambiguation is based on a 3-component network. A first component, the well-known gaze following patch in the posterior STS is activated by gaze following per se. BOLD activity here is determined exclusively by the usage of gaze direction and is independent of the need to disambiguate the relevant object. On the other hand, BOLD activity revealing a priori information for the disambiguation and starting early enough to this end is confined to a patch of cortex at the inferior frontal junction. Finally, BOLD activity reflecting the convergence of both, a priori information and gaze direction, needed to shift attention to a particular object location is confined to the posterior parietal cortex.

neuroscience↗

bioRxiv2018

V1 neurons encode the perceptual compensation of false torsion arising from Listings law

We try to deploy the retinal fovea to optimally scrutinize an object of interest by directing our eyes to it. Horizontal and vertical components of these fixation eye movements are determined by the objects location. However, fixation eye movements also involve a torsional component, which according to Listings law is fully determined by the 2D eye position acquired. According to Von Helmholtz knowledge of the torsion provided by this law alleviates the perceptual interpretation of the image tilt that changes with fixation, a view supported by psychophysical experiments he pioneered. We address the question where and how Listings law is implemented in the visual system and we show that neurons in monkey area V1 use knowledge of torsion to compensate the image tilt associated with specific eye positions as set by Listings law.

neuroscience↗

bioRxiv2018

Monkeys use the rod-dense retinal region rather than the fovea to visually fixate small targets in scotopic vision

Monkeys appear to visually fixate targets in scotopic conditions. The function fixations fulfill in photopic vision, keeping the targets image on the fovea, is nullified in scotopic vision, because the fovea, with its cones, is desensitized in dim light. Here we followed the hypothesis that a previously described retinal region, the locus of maximal rod density, functionally replaces the fovea; we found that with dark background, most of the fixations direct the fovea above the target, so that the targets image appears to fall on the line connecting the fovea with the locus of maximal rod density. There is considerable trial-by-trial variation in the fixation positions along this line. On the whole, the closer the visual conditions are to full scotopic, the higher is this gaze upshift, indicating the closer does the target fall to the locus of maximal rod density. Mesopic background induces low mean upshift. Full (45-min) dark adaptation was essential to achieving high upshift values. There is no analogous photopic effect - 45-min bright adaptation did not shift the locus of photopic fixation.

neuroscience↗