Biology subjects

Markanday, A.

Publications and source records attributed to Markanday, A..

3 recordsLinked to original sources

bioRxiv2026

Direction-Dependent Encoding of Forward and Inverse Models in the Same Purkinje Cells

Saccadic eye movements are too fast to be guided by sensory feedback and therefore depend on precisely calibrated internal models. The cerebellum is widely thought to compute both forward and inverse models, yet how these two computations coexist in the same circuit remains unclear. We hypothesized that the same Purkinje cells (PCs) can serve as a forward model for saccades in one direction and as an inverse model for saccades in the opposite direction, because target neurons on either side of the cerebellar nuclei contribute differently to leftward and rightward saccades. To test this hypothesis, we recorded from PCs in the oculomotor vermis of macaque monkeys during repetitive saccade paradigms that induce adaptation and fatigue. Using the preferred error direction of each PC conveyed by increased complex spike (CS) activity (CS-on direction) as a proxy of the side of the cerebellar nuclei it projects to, we compared population activity for saccades directed against the CS-on direction (CS-on opposed) and along it (CS-on aligned). CS-on opposed saccades, (toward the PC's projection side) were accompanied by a precisely timed, sharp burst of simple spike (SS) activity at saccade onset that scaled with peak velocity, and CS error sensitivity was highest after the saccade. SS activity also changed systematically after adaptation, reflecting the altered kinematics of the adapted saccade. These properties are consistent with a forward model that estimates ongoing movement and updates itself based on post-movement error. In contrast, the CS-on aligned saccades, (away from the PC's projection side) were accompanied by broader SS modulation around saccade offset, and the CS error sensitivity was highest before the saccade. CSs around saccade onset encoded target location and initial eye position, and their occurrence was associated with reduced saccade amplitude. This reduction likely adjusts the motor command to the starting eye position, as expected for an inverse model that pre-programs the motor command before movement begins. Together, these findings provide neural evidence that the same PC population can contribute to both forward and inverse model computations, depending on saccade direction.

neuroscience↗

bioRxiv2022

Multidimensional cerebellar computations for flexible kinematic control of movements

Both the environment and our body keep changing dynamically. Hence, ensuring movement precision requires adaptation to multiple demands occurring simultaneously. Here we show that the cerebellum performs the necessary multi-dimensional computations for the flexible control of different movement parameters depending on the prevailing context. This conclusion is based on the identification of a manifold-like activity in both mossy fibers (MF, network input) and Purkinje cells (PC, output), recorded from monkeys performing a saccade task. Unlike MFs, the properties of PC manifolds developed selective representations of individual movement parameters. Error feedback-driven climbing fiber input modulated the PC manifolds to predict specific, error type-dependent changes in subsequent actions. Furthermore, a feed-forward network model that simulated MF-to-PC transformations revealed that amplification and restructuring of the lesser variability in the MF activity is a pivotal circuit mechanism. Therefore, flexible control of movement by the cerebellum crucially depends on its capacity for multi-dimensional computations.

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↗