Biology subjects

Lisberger, S. G.

Publications and source records attributed to Lisberger, S. G..

9 recordsLinked to original sources

bioRxiv2025

Neural circuit mechanisms to transform cerebellar population dynamics for motor control in monkeys

We identify the input-output transformations performed by the cerebellar circuit and isolate the circuit properties that implement them. Extracellular recordings exploited new technology for neuron-type identification in part of the cerebellar cortex that is causal for an oculomotor behavior whose neural substrate is well-understood in monkeys. Data demonstrate how multiple classes of interneurons in the cerebellar circuit cooperate to transform incoming mossy fiber responses into Purkinje cell outputs. Computer models suggest that temporal decomposition in the granule cell layer enables flexible reconstruction of transformed output dynamics, regulated by cerebellar plasticity, to convert eye position-like inputs into predominantly velocity-driven Purkinje cell outputs. Convergence of mossy fiber inputs with diverse direction preferences would increase input layer dimensionality and enable learning of highly flexible direction tuning. The neural circuit mechanisms suggested by our data outline a cerebellar operating principle that enables flexible input-output transformations and could generalize across cerebellar and other brain areas.

neuroscience↗

bioRxiv2025

Strategies to decipher neuron identity from extracellular recordings in the cerebellum of behaving non-human primates

Identification of neuron type is critical to understand computation in neural circuits through extracellular recordings in awake, behaving animal subjects. Yet, modern recording probes have limited power to resolve neuron type. Here, we leverage the well-characterized architecture of the cerebellar circuit to perform expert identification of neuron type from extracellular recordings in behaving non-human primates. Using deep-learning classifiers we evaluate the information contained in readily accessible extracellular features for neuron identification. Waveform, discharge statistics, anatomical layer, and functional interactions each can inform neuron labels for a sizable fraction of cerebellar units. Together, as inputs to a deep-learning classifier, the features perform even better. Our tools and methodologies, validated during smooth pursuit eye movements in the cerebellar floccular complex of awake behaving monkeys, can guide expert identification of neuron type during cerebellar-dependent tasks in behaving animals across species. They lay the groundwork for characterization of information processing in the cerebellar cortex. Impact statementTo understand how the brain performs computations in the service of behavior, we develop methods to link neuron type to functional activity within well-characterized neural circuits. Here, we show how features derived from extracellular recordings provide complementary information to disambiguate neuron identity in the cerebellar cortex.

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bioRxiv2024

A deep-learning strategy to identify cell types across species from high-density extracellular recordings

High-density probes allow electrophysiological recordings from many neurons simultaneously across entire brain circuits but dont reveal cell type. Here, we develop a strategy to identify cell types from extracellular recordings in awake animals, revealing the computational roles of neurons with distinct functional, molecular, and anatomical properties. We combine optogenetic activation and pharmacology using the cerebellum as a testbed to generate a curated ground-truth library of electrophysiological properties for Purkinje cells, molecular layer interneurons, Golgi cells, and mossy fibers. We train a semi-supervised deep-learning classifier that predicts cell types with greater than 95% accuracy based on waveform, discharge statistics, and layer of the recorded neuron. The classifiers predictions agree with expert classification on recordings using different probes, in different laboratories, from functionally distinct cerebellar regions, and across animal species. Our classifier extends the power of modern dynamical systems analyses by revealing the unique contributions of simultaneously-recorded cell types during behavior.

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bioRxiv2023

Rate versus synchrony codes for cerebellar control of motor behavior

Control of movement requires the coordination of multiple brain areas, each containing populations of neurons that receive inputs, process these inputs via recurrent dynamics, and then relay the processed information to downstream populations. Information transmission between neural populations could occur through either coordinated changes in firing rates or the precise transmission of spike timing. We investigate the nature of the code for transmission of signals to downstream areas from a part of the cerebellar cortex that is crucial for the accurate execution of a quantifiable motor behavior. Simultaneous recordings from Purkinje cell pairs in the cerebellar flocculus of rhesus macaques revealed how these cells coordinate their activity to drive smooth pursuit eye movements. Purkinje cells show millisecond-scale coordination of spikes (synchrony), but the level of synchrony is small and likely insufficient to impact the firing of downstream neurons in the vestibular nucleus. Further, analysis of previous metrics for assaying Purkinje cell synchrony demonstrates that these metrics conflate changes in firing rate and neuron-neuron covariance. We conclude that the output of the cerebellar cortex uses primarily a rate code rather than synchrony code to drive activity of downstream neurons and thus control motor behavior. Impact statementInformation transmission in the brain can occur via changes in firing rate or via the precise timing of spikes. Simultaneous recordings from pairs of Purkinje cells in the floccular complex reveals that information transmission out of the cerebellar cortex relies almost exclusively on changes in firing rates rather than millisecond-scale coordination of spike timing across the Purkinje cell population.

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bioRxiv2022

Sensory-motor computations in a cortico-pontine pathway

Subcortical circuits decode the output from multiple cortical areas to create commands for movement. By recording during monkeys smooth pursuit eye movements, we reveal how the dorsolateral pontine nucleus (DLPN) and nucleus reticularis tegmenti pontis (NRTP) decode cortical output. Preparatory activity is inherited from the smooth eye movement region of the frontal eye fields (FEFSEM). Consistent with its effects on eye movement responses to visual motion, preparatory activity modulates the gain of pontine responses to visual motion input from visual area MT. Furthermore, the "place" code for target speed in area MT becomes the motor commands "rate" code in the pons, through linear weighting of MTs output. The division into decoding that happens in the pons and residual computations that occur downstream reveals that the pons is neither a relay nor the source of a final motor command. It is part of a complex, distributed cortical decoder for motor control.

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bioRxiv2021

Evaluation and resolution of many challenges of neural spike-sorting: a new sorter

We evaluate existing spike sorters and present a new one that resolves many sorting challenges. The new sorter, called "full binary pursuit" or FBP, comprises multiple steps. First, it thresholds and clusters to identify the waveforms of all unique neurons in the recording. Second, it uses greedy binary pursuit to optimally recognize the spike events in the original voltages. Third, it resolves spike events that are described more accurately as the superposition of spikes from two other neurons. Fourth, it resolves situations where the recorded neurons drift in amplitude or across electrode contacts during a long recording session. Comparison with other sorters on real and simulated ground-truth datasets reveals many of the failure modes of spike sorters. We suggest a set of post-sorting analyses that can improve the veracity of neural recordings by minimizing the intrusion of those failure modes into analysis and interpretation of neural data.

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bioRxiv2020

Neural structure of a sensory decoder for motor control

We seek to understand the neural mechanisms that perform sensory decoding for motor behavior, advancing the field by designing decoders based on neural circuits. A simple experiment produced a surprising result that shapes our approach. Changing the size of a target for smooth pursuit eye movements changes the relationship between the variance and mean of the evoked behavior in a way that contradicts the regime of "signal-dependent noise" and defies traditional decoding approaches. A theoretical analysis leads us to conclude that sensory decoding circuits for pursuit include multiple parallel pathways and multiple sources of variation. Behavioral and neural responses with biomimetic statistics emerge from a biologically-motivated circuit model with noise in the pathway that is dedicated to flexibly adjusting the strength of visual-motor transmission. Flexible adjustment of transmission strength applies much more broadly to issues in sensory-motor control such as Bayesian integration and control strategies to optimize motor behavior.

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bioRxiv2019

Mechanisms that allow cortical preparatory activity without inappropriate movement

We reveal a novel mechanism that explains how preparatory activity can evolve in motor-related cortical areas without prematurely inducing movement. The smooth eye movement region of the frontal eye fields (FEFSEM) is a critical node in the neural circuit controlling smooth pursuit eye movement. Preparatory activity evolves in FEFSEM during fixation in parallel with an objective measure of visual-motor gain. We propose that the use of FEFSEM output as a gain signal allows for preparation to progress in the pursuit system without causing movement. We also show that preparatory modulation of firing rate in FEFSEM progresses in a way that predicts movement, providing evidence against the \"movement-null\" space hypothesis of how preparatory activity can progress without movement. Finally, there is partial reorganization of FEFSEM population activity between preparation and movement. We propose that this reorganization allows for a directionally non-specific component of preparatory visual-motor gain enhancement in the pursuit system.

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bioRxiv2019

The neural basis for response latency in a sensory-motor behavior

We seek a neural circuit explanation for sensory-motor reaction times. We have found evidence that two of three possible mechanisms could contribute to reaction times in smooth pursuit eye movements. In the smooth eye movement region of the frontal eye fields (FEFSEM), an area that causally affects the initiation of smooth pursuit eye movement, neural and behavioral latencies have significant trial-by-trial correlations that can account for 40% to 100% of the variation in behavioral latency. The amplitude of preparatory activity, which represents the motor systems expectations for target motion, shows negative trial-by-trial correlations with behavioral latency and could contribute to the neural computation of reaction time. In contrast, the traditional \"ramp-to-threshold\" model is contradicted by the responses of many, but not all FEFSEM neurons. As evidence of neural processing that determines reaction time, the local field potential in FEFSEM includes a brief wave in the 5-15 Hz frequency range that precedes pursuit initiation and whose phase is correlated with the latency of pursuit in individual trials. We suggest that the latency of the incoming visual motion signals combines with the state of preparatory activity to determine the latency of the transient response that drives eye movement.

neuroscience↗