Biology subjects

Joshua, M.

Publications and source records attributed to Joshua, M..

12 recordsLinked to original sources

bioRxiv2025

From optimality to reality: decision-making in basal ganglia output

Value-based decision-making requires translating abstract representations of alternative values into concrete decisions. Although this process is widespread, the neural mechanisms underlying this transformation remain unclear. Here, we address this question by examining neural dynamics in the substantia nigra pars reticulata (SNpr) of two monkeys performing a decision-making task. We found a dynamic shift in SNpr activity during the decision process. Before choice, SNpr activity encoded the optimal selection regardless of the eventual action taken, representing the action and value of the optimal choice even when the non-optimal target was ultimately selected. During choice, the SNpr shifted to code the actual selection made by the monkey. This pattern was reinforced by trials with suboptimal choices, revealing a clear transition in SNpr coding from representing the abstract value of the optimal selection to encoding the chosen action, thus suggesting a neural mechanism that transforms value representations into concrete decisions.

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bioRxiv2025

Enhanced reward coding and condition-independent dynamics in optogenetically identified corticostriatal neurons in monkeys

The basal ganglia are considered to be the site where cortical sensorimotor and dopaminergic reward information interact to potentiate and select actions. This had led to the assumption that cortical inputs to the basal ganglia encode sensorimotor states rather than reward or choice signals. We tested this hypothesis by studying the coding properties of neurons in the frontal eye field of monkeys that were optogenetically identified as being connected to the basal ganglia. We found that neurons already contained information about expected rewards and selected actions. Further, the reward and condition-independent modulations were stronger in connected neurons than in other neurons in the same area. These findings indicate that reward, choice, and sensorimotor information are integrated already in the inputs to the basal ganglia, implying that the basal ganglia play a role in manipulating rather than generating reward and choice signals.

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bioRxiv2023

Population coding of distinct categories of behavior in the frontal eye field

Brain regions frequently contribute to the control of diverse behaviors. Within these regions, individual neurons often respond to various motor tasks, suggesting that the ability to control multiple behaviors is rooted in the organization of neuronal populations. To study this organization, we examined how the frontal eye field (FEF) encodes distinct behaviors by recording the activity of 1200 neurons during various eye movement tasks in two female fascicularis monkeys. We focused on three behaviors: smooth pursuit, pursuit suppression, and saccades. We found that single neurons tended to respond in all three tasks, thus challenging the notion that the FEF is organized into task-specific clusters. We then identified the low-dimensional subspaces that contained most of the population activity during each behavior and quantified the extent of overlap between these spaces across behaviors. Population activity during pursuit and pursuit suppression exhibited a substantial overlap, with highly correlated directional tuning at the single-neuron level, as reflected by similar outcomes for the population decoders. These distinct behaviors combined with similar encoding suggest that the suppression of movement occurs mostly downstream from the FEF. By contrast, pursuit and saccades mostly occupied orthogonal subspaces, prompting an independent linear readout of saccades and pursuit. Thus overall, these results indicate that distinct behaviors can exhibit either separate or overlapping population codes within a specific brain region, hence emphasizing the importance of the system-level organization of behavior. Significance statementHow do brain areas control multiple behaviors? We investigated the organization of the FEF in monkeys during three different types of eye movements: smooth pursuit, pursuit suppression, and saccades. We found that individual neurons tended to respond to all three tasks, thus challenging a task-specific FEF cluster organization. Further, the low-dimensional subspaces that contained most of the population activity during pursuit and pursuit suppression overlapped substantially, implying that movement suppression occurs downstream from the FEF. In contrast, pursuit and saccades occupied orthogonal subspaces, prompting independent linear readouts. These results underscore the importance of adopting a system-level perspective to comprehend how diverse behaviors are encoded in the brain.

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bioRxiv2023

High-Dimensional Encoding of Movement by Single Neurons in Basal Ganglia Output

The Substantia Nigra pars reticulata (SNpr), an output structure of the basal ganglia, has been hypothesized to gate the execution of movements. Previous studies focusing mostly on saccadic eye movements have reported that SNpr neurons are tonically active and either pause or increase their firing during movements, consistent with the gating role. We recorded activity in the SNpr of two monkeys during smooth pursuit and saccadic eye movements. SNpr neurons exhibited highly diverse reaction patterns during pursuit, including frequent increases and decreases in firing rate, uncorrelated responses in different movement directions and in reward conditions resulting in the high dimensional activity of single neurons. These diverse temporal patterns surpassed those in other oculomotor areas in the frontal cortex, basal ganglia, and cerebellum. These results suggest that temporal properties of the responses enrich the coding capacity of the basal ganglia output beyond gating or permitting movement.

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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

The basal ganglia achieve a higher signal-to-noise ratio than the cerebellum by sharpening reward and movement signals

The basal ganglia and the cerebellum are major subcortical structures in the motor system. The basal ganglia have been cast as the reward center of the motor system, whereas the cerebellum is thought to be involved in adjusting sensorimotor parameters. Recent findings of reward signals in the cerebellum have challenged this dichotomous view. To compare the basal ganglia and the cerebellum directly, we recorded from oculomotor regions in both structures from the same monkeys. We partitioned the trial-by-trial variability of the neurons into reward and eye-movement signals to compare the coding across structures. Reward expectation and movement signals were the most pronounced in the output structure of the basal ganglia, intermediate in the cerebellum, and the smallest in the input structure of the basal ganglia. These findings suggest that reward and movement information is sharpened through the basal ganglia, resulting in a higher signal-to-noise ratio than in the cerebellum.

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bioRxiv2021

Neural correlations are attenuated in the transformation from the frontal cortex to movement

Correlated activity between neurons can cause variability in behavior across trials. The extent to which correlated activity affects behavior depends on the properties of its translation into movement. We developed a novel method that estimates the contribution of correlations in the frontal eye field (FEF) to pursuit eye movements. We defined a distance metric between the behavior on different trials. Based on this metric, we applied a sequence of shuffles to the neuronal responses, allowing trials to be matched with increasingly distant trials. Correlations between neurons were strongly attenuated when applying even the most constrained shuffle. Thus, only a small fraction of FEF correlations affects the behavior. We used simulations to validate our approach and demonstrate its generalizability over different models. We show that the attenuation of correlated activity through the motor pathway could stem from the interplay between the structure of the correlations and the decoder of FEF activity.

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bioRxiv2021

Fixational drift is driven by diffusive dynamics in central neural circuitry

During fixation and between saccades, our eyes undergo diffusive random motion called fixational drift [1]. The role of fixational drift in visual coding and inference has been debated in the past few decades, but the mechanisms that underlie this motion remained unknown. In particular, it has been unclear whether fixational drift arises from peripheral sources, or from central sources within the brain. Here we show that fixational drift is correlated with neural activity, and identify its origin in central neural circuitry within the oculomotor system. We analyzed a large data set of ocular motoneuron (OMN) recordings in the rhesus monkey, alongside precise measurements of eye position [2, 3], and found that most of the variance of fixational eye drifts must arise upstream of the OMNs. The diffusive statistics of the motion points to the oculomotor integrator, a memory circuit responsible for holding the eyes still between saccades, as a likely source of the motion. Theoretical modeling, constrained by the parameters of the primate oculomotor system, supports this hypothesis by accounting for the amplitude as well as the statistics of the motion. Thus, we propose that fixational ocular drift provides a direct observation of diffusive dynamics in a neural circuit responsible for storage of continuous parameter memory in persistent neural activity. The identification of a mechanistic origin for fixational drift is likely to advance the understanding of its role in visual processing and inference.

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bioRxiv2020

Passive motor learning: Oculomotor adaptation in the absence of feedback on behavioral errors

Motor adaptation is commonly thought to be a trial-and-error process in which accuracy of movement improves with repetition of behavior. We challenged this view by testing whether erroneous movements are necessary for motor adaptation. In the eye movement system, the association between motor command and errors can be disentangled, since errors in the predicted stimulus trajectory can be perceived even without movements. We modified a smooth pursuit eye movement adaptation paradigm in which monkeys learn to make an eye movement that predicts an upcoming change in target direction. We trained monkeys to fixate on a target while covertly, an additional target initially moved in one direction and then changed direction after 250 ms. Monkeys showed a learned response to infrequent probe trials in which they were instructed to follow the moving target. Further experiments confirmed that probing learning or residual eye movement during fixation did not drive learning. These results show that movement is not necessary for motor adaptation and provide an animal model for studying how passive learning is implemented. The standard model assumes that the interaction between movement and error signals in the cerebellum underlies adaptive learning. Our results indicate that either sensory inputs are sufficient for driving learning in the cerebellum or that learning is implemented partly outside the cerebellum.

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bioRxiv2019

Using extracellular low frequency signals to improve the spike sorting of cerebellar complex spikes

The challenge of spike sorting has been addressed by numerous electrophysiological studies. These methods tend to focus on the information conveyed by the high frequencies, but ignore the potentially informative signals at lower frequencies. Activation of Purkinje cells in the cerebellum by input from the climbing fibers results in a large amplitude dendritic spike concurrent with a high frequency burst known as a complex spike. Due to the variability in the high frequency component of complex spikes, previous methods have struggled to sort these complex spikes in an accurate and reliable way. However, complex spikes have a prominent extracellular low frequency signal generated by the input from the climbing fibers. We exploited this to improve complex spike sorting by applying Principal Component Analysis (PCA) on the low frequencies of the signal and show that the low frequency first PC achieves a better separation of the complex spikes from noise. The low frequency data are more effective in detecting events entering into the analysis, and therefore can be harnessed to analyze the data with a larger signal to noise ratio. These two advantages make our method more effective for complex spike sorting. Our characterization of the dendritic low frequency components of complex spikes can be applied in other studies to gain insights into processing in the cerebellum.

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bioRxiv2019

Cerebellar climbing fibers encode expected reward size

Climbing fiber inputs to the cerebellum encode error signals that instruct learning. Recently, evidence has accumulated to suggest that the cerebellum is also involved in the processing of reward. To study how rewarding events are encoded, we recorded the activity of climbing fibers when monkeys were engaged in an eye movement task. At the beginning of each trial, the monkeys were cued the size of the reward that would be delivered upon successful completion of the trial. We found increased climbing fiber activity during cue presentation when information about reward size was first made available. Reward size did not modulate activity at reward delivery. These results indicate that climbing fibers encode the expected reward size and suggest a general role of the cerebellum in associative learning beyond error correction.

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bioRxiv2018

Coordinated cerebellar climbing fiber activity is gated by behavioral context in a voluntary motor regime

The prevailing model of cerebellar learning states that climbing fibers (CFs) are both driven by, and serve to correct, erroneous motor output. However, this model is grounded largely in studies of behaviors that utilize hardwired neural pathways to link sensory input to motor output. To test whether this model applies to more flexible learning regimes that require arbitrary sensorimotor associations, we have developed a cerebellar-dependent motor learning paradigm compatible with both mesoscale and single dendrite resolution calcium imaging in mice. Here, we find that CFs are preferentially driven by and more time-locked to correctly executed movements and other task parameters that predict reward outcome, exhibiting widespread correlated activity within parasagittal processing zones that is governed by these predictions. Together, such CF activity patterns are well-suited to drive learning by providing predictive instructional input consistent with an unsigned reinforcement learning signal that does not rely exclusively on motor errors.

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