Biology subjects

Muller, S. Z.

Publications and source records attributed to Muller, S. Z..

5 recordsLinked to original sources

bioRxiv2026

A mechanism for attenuating responses to anticipated sounds in the dorsal cochlear nucleus

The dorsal cochlear nucleus (DCN) is a cerebellum-like structure in the mammalian auditory brainstem that combines auditory nerve input with diverse auditory and non-auditory signals conveyed by granule cells. Granule cells form excitatory synapses onto inhibitory interneurons known as cartwheel cells, and in vitro studies have demonstrated an anti-Hebbian form of plasticity at these synapses. However, the function of cartwheel cells and their plastic granule cell input has remained unknown. Using in vivo electrophysiological recordings, optogenetics, and computational modeling, we provide evidence that intrinsic electrophysiological properties of cartwheel cells invert the expected effects of anti-Hebbian plasticity, generating a positive feedback loop that enhances cartwheel cell inhibition of DCN output neurons at the onset of anticipated sounds. This combined cellular and synaptic mechanism may implement a novel form of predictive processing that is robust to variability in the timing of anticipated sensory input.

neuroscience↗

bioRxiv2026

Critical period plasticity enables credit assignment

Synaptic plasticity is often guided by instructive inputs to neural circuits, but learning only succeeds when these instructions reach neurons that mediate relevant outputs. This creates the credit assignment problem: how does a neuron receive instructions suited to its own behavioral function? Here we report a developmental mechanism that enables credit assignment by using instructive inputs to organize the downstream architecture through which learning is expressed. In the olivocerebellar learning system, the inferior olive provides instructive inputs that guide plasticity within the cerebellum. During circuit assembly in zebrafish, we find these same inputs regulate long-range cerebellar projections during a highly plastic, two-day critical period. Developmental experience caused specific maturation of projections to targets that were coactivated with olivary inputs. Mathematical theory and computational modeling show how the resulting architecture constrains later learning, such that a single input can sculpt downstream connectivity and then leverage that circuit to assign credit. Simulated learning became paradoxically more robust if we reduced plasticity after a developmental critical period, protecting key architecture from corruption during learning. Thus, instructive inputs can first build the circuits they later teach, coordinating development and learning to enable effective credit assignment.

neuroscience↗

bioRxiv2025

Connectome analysis of a cerebellum-like circuit for sensory prediction

Stable and accurate perception involves comparing incoming sensory input with internally- generated predictions 1-3. A mechanistic understanding of this process has been elusive due to the size and complexity of the relevant brain regions in mammals. Here we leverage connectomics to comprehensively map the cell types and synaptic connections underlying a well-characterized and ecologically relevant form of predictive sensory processing in the cerebellum-like electrosensory lobe (ELL) of weakly electric fish 4,5. Connectome analysis reveals highly-structured feedforward and recurrent synaptic connectivity mediating the cancellation of predictable electrosensory input. A computational model constrained by prior electrophysiological recordings shows how this connectivity supports the formation of predictions at multiple sites within the network and how the ELL solves a continual learning problem by maintaining fast and accurate predictions despite noise and changes in environmental context. Overall, these findings provide a blueprint for using connectomics to elucidate learning in vertebrate nervous systems.

neuroscience↗

bioRxiv2023

Complex spikes perturb movements, revealing the sensorimotor map of Purkinje cells

The cerebellar cortex performs computations that are critical for control of our actions, and then transmits that information via simple spikes of Purkinje cells (P-cells) to downstream structures. However, because P-cells are many synapses away from muscles, we do not know how their output affects behavior. Furthermore, we do not know the level of abstraction, i.e., the coordinate system of the P-cells output. Here, we recorded spiking activities of hundreds of P-cells in the oculomotor vermis of marmosets during saccadic eye movements and found that following the presentation of a visual stimulus, the olivary input to a P-cell encoded a probabilistic signal that coarsely described both the direction and the amplitude of that stimulus. When this input was present, the resulting complex spike briefly suppressed the P-cells simple spikes, disrupting the P-cells output during that saccade. Remarkably, this brief suppression altered the saccades trajectory by pulling the eyes toward the part of the visual space that was preferentially encoded by the olivary input to that P-cell. Thus, analysis of behavior in the milliseconds following a complex spike unmasked how the P-cells output influenced behavior: the preferred location in the coordinates of the visual system as conveyed probabilistically from the inferior olive to a P-cell defined the action in the coordinates of the motor system for which that P-cells simple spikes directed behavior. SignificanceWe are lacking general principles that can describe how changes in a P-cells simple spikes might alter behavior. Here, we show that a brief suppression of a P-cells simple spikes in the oculomotor vermis consistently pulls the eyes in a direction that corresponds to the preferred location of the sensory space as conveyed probabilistically to that P-cell from the inferior olive. Thus, the inferior olive defines the coordinate system regarding the information that a P-cell is providing to the rest of the brain.

neuroscience↗

bioRxiv2022

A Biophysical Basis for Learning and Transmitting Sensory Predictions

Homeostatic (anti-Hebbian) forms of synaptic are effective at eliminating "prediction errors" that signal the differences between predicted and actual sensory input. However, such mechanisms appear to preclude the possibility of transmitting the resulting predictions to downstream circuits, severely limiting their utility. Using modeling and recordings from the electrosensory lobe of mormyrid fish, we reveal interactions between axonal and dendritic spikes that support both the learning and transmission of predictions. We find that sensory input modulates the rate of dendritic spikes by adjusting the amplitude of backpropagating axonal action potentials. Homeostatic plasticity counteracts these effects through changes in the underlying membrane potential, allowing the dendritic spike rate to be restored to equilibrium while simultaneously transmitting predictions through modulation of the axonal spike rate. These results reveal how two types of spikes dramatically enhance the computational power of single neurons in support of an ethologically relevant multi-layer computation.

neuroscience↗