Biology subjects

Kashefi, M.

Publications and source records attributed to Kashefi, M..

10 recordsLinked to original sources

bioRxiv2026

Reward-driven emergence of auditory pattern encoding in the primate motor system

The ability to anticipate rhythmic patterns is fundamental to human experience, enabling music appreciation, speech comprehension, and dancing in sync to music. How the brain learns to use acoustic information to guide motor behavior remains a key question, with its neural underpinnings and evolutionary origins debated, especially in non-human primates. Reward contingencies improve monkeys ability to synchronize with musical beats with near-human accuracy. However, humans and certain birds spontaneously synchronize with musical beats. Behavioural work in monkeys suggests that the brains reward system can encode rhythmic patterns if sufficiently reinforced, known as the intrinsic reward hypothesis. To investigate the neural mechanism of how the reward gates rhythmic pattern learning, we recorded neural activity from the globus pallidus interna (GPi) of a rhesus macaque during passive listening to auditory patterns. We found that GPis ability to encode auditory patterns in a monkey is shaped by reward association. Specifically, during passive listening (Experiment 1), with reward only at the end of each trial, GPi showed at most a weak response to the auditory tone patterns that was not evident in single neurons or population responses, ruling out spontaneous entrainment of motor activity to auditory patterns. When each tone was simultaneously accompanied by a liquid reward (Experiment 2), GPi showed clear responses to the tone patterns. To test if the tone responses persisted, the reward contingency was subsequently removed (Experiment 3). Strikingly, although the reward was delivered only at the trials end as in Experiment 1, GPi neurons continued to track the occurrence of the tones. Besides GPi, we also recorded single-neuron activity across the primary somatosensory (S1), primary motor (M1), dorsal premotor (PMd), supplementary motor (SMA), pre-supplementary motor (preSMA) cortices, and the medial geniculate body (MGB) of the same animal in experiments 1 and 2. We found that during passive listening (Experiment 1) with a reward only at the end of each trial, primarily the MGB, not motor areas, responded to the auditory tone patterns, ruling out spontaneous entrainment of motor activity to auditory patterns across the primate motor system. When each tone was simultaneously accompanied by a liquid reward (Experiment 2), all areas except S1 showed clear responses to the tone patterns. These findings suggest that while basic sensory processing of tone patterns occurs in the MGB, the GPi exhibits reward-driven encoding of auditory patterns that persists after reward withdrawal. This reorganization of neural responsiveness shows that the capacity for auditory association can be elicited in the motor system through appropriate motivational contexts.

neuroscience↗

bioRxiv2025

Sequence preparation is not always associated with a reaction time cost

The extent to which a sequence of movements is prepared before initiating the first movement is a longstanding question in motor neuroscience. The observation that reaction time (RT) increases for longer sequences has been used as evidence of sequence preparation - reflecting the additional demands of preparing multiple movements before initiating a sequential action. However, many processes contribute to RT, making it unclear whether the observed RT increases specifically reflect sequence preparation. For example, with longer sequences, participants face greater ambiguity in selecting their first movement in the sequence. Here, we test how much of the observed RT increases can be explained by the first-target ambiguity when reaching toward spatial targets. In our paradigm, we independently manipulate: (i) the number of future targets displayed, (ii) the number of targets to be acquired, and (iii) the spatial arrangement of the targets. This approach allows us to vary the demands of sequence preparation and first-target ambiguity, thereby enabling a direct assessment of their respective influence on RT. We report that RT increases with additional sequence elements but that this effect is fully explained by the ambiguity in selecting the first reach target. That is, sequence preparation causes no RT increase. In fact, when first-target ambiguity is eliminated, RT is constant across the number of displayed targets even though kinematic analysis reveals that participants have prepared a sequence. Together, these results indicate that preparing multiple reaches to spatial targets does not impose additional temporal costs relative to preparing a single reach.

neuroscience↗

bioRxiv2025

Compositional neural dynamics during reaching

The complex mechanics of the arm make the neural control of reaching inherently posture dependent. Because previous reaching studies confound reach direction with final posture, it remains unknown how neural population dynamics in the motor cortex account for arm posture. Here we address this gap with high-density neural recordings and a reaching task in which the same targets serve as start points on some trials and end points on others. We show that neural population dynamics in monkey primary motor cortex and dorsal premotor cortex exhibit a compositional structure with three components that enable posture-dependent control: first, a posture subspace containing fixed points visited whenever the arm is in a specific posture; second, rotational dynamics that transition between these fixed points, systematically organized so that similar rotations produce similar movements while continuously updating the posture representation; third, a condition-independent shift dimension that tracks trial progression across all movements. This compositional structure advances the population-level account of how motor cortical dynamics support skilled reaching.

neuroscience↗

bioRxiv2024

Motor sequence learning involves better prediction of the next action andoptimization of movement trajectories

Learning new sequential movements is a fundamental skill for many animals. Although the behavioral manifestations of sequence learning are clear, the underlying mechanisms remain poorly understood. Motor sequence learning may arise from three distinct processes: (1) improved execution of individual movements independent of their sequential context; (2) enhanced anticipation of "what" movement should be executed next, enabling faster initiation; and (3) the development of motoric sequence-specific representations that encode "how" movements should be optimally performed within a sequence. However, many existing paradigms conflate the "what" and "how" components of learning, as participants often acquire both the sequence content (what to do) and its execution (how to do it). This overlap obscures the distinct contributions of each mechanism to motor sequence learning. In this study, we disentangled these mechanisms using a continuous reaching task. Performance in trained sequences was compared to random sequences to rule out improvements attributable solely to isolated movement execution. By also varying how many upcoming targets were visible we assessed the role of anticipation in learning. When participants could only see one future target, improvements were mostly due to them learning which target would come next. When they could see four future targets, participants immediately demonstrated fast movement times and increased movement smoothness, surpassing late-stage performance in the one target condition. Crucially, even with full visibility of future targets, participants showed further sequence-specific learning caused by a continuous optimization of movement trajectories. Follow-up experiments revealed that the learned sequence representations were effector-specific and encoded contextual information of four movements or longer. Our paradigm enables a clear dissociation between the "what" and "how" components of motor sequence learning and provides compelling evidence for the development of effector-specific sequence representations that guide optimal movement execution.

neuroscience↗

bioRxiv2024

Sensory expectations shape neural population dynamics in motor circuits

The neural basis of movement preparation has been extensively studied during self-initiated actions where motor cortical activity during preparation shows a lawful relationship to the parameters of the subsequent action1,2. However, movements are regularly triggered and constantly corrected based on sensory inputs caused by disturbances to the body or environment. Since such disturbances are often predictable and since preparing for disturbances would make movements better, we hypothesized that expectations about sensory inputs also influence preparatory activity in motor circuits. Here we show that when humans and monkeys are probabilistically cued about the direction of a future mechanical perturbation, they incorporate sensory expectations into their movement preparation and improve their corrective responses. Using high-density neural recordings, we establish that sensory expectations are widespread across the brain, including the motor cortical areas involved in preparing self-initiated actions. The geometry of these preparatory signals in the neural population state is simple, scaling directly with the probability of each perturbation direction. After perturbation onset, a condition-independent perturbation signal shifts the neural state leading to rapid responses that initially reflect sensory expectations. Based on neural networks coupled to a biomechanical model of the arm3, we show that this neural geometry emerges through training, but only when the incoming sensory information indicating perturbation direction coincides with - or is preceded by - a condition-independent signal indicating that a perturbation has occurred. Thus, motor circuit dynamics are shaped by future sensory inputs, providing clear empirical support for the idea that movement is governed by the sophisticated manipulation of sensory feedback4.

neuroscience↗

bioRxiv2023

Neural correlates of online action preparation

When performing movements in rapid succession, the brain needs to coordinate preparation of an upcoming action with ongoing execution. Here we identify the processes and brain areas involved in this ability. Human participants performed pairs of single-finger presses or three-finger chords in settings where they had to prepare the second movement before or after the first response. Despite matched perceptual and movement requirements, 7T functional MRI revealed increased brain activity in regions along the intra-parietal sulcus and ventral visual stream when actions overlapped. Multivariate analyses suggested that these areas were involved in stimulus identification and action selection. In contrast, the dorsal premotor cortex, known to be involved in planning upcoming movements, showed no discernible signs of heightened activity. This observation suggests that the bottleneck during simultaneous action control and preparation arises at the level of stimulus identification and action selection, whereas movement planning in the premotor cortex can unfold concurrently with execution without interference between the two processes. SummaryThe brains ability to prepare for upcoming actions while controlling ongoing movements is a crucial evolutionary adaptation of the action system. However, the neural basis of online action preparation remains largely unknown. We found that superior-parietal and occipito-temporal areas exhibited heightened activation during online preparation. Surprisingly, the dorsal premotor cortex, known to be a crucial structure in motor planning, did not display additional activation for overlapping actions. These findings imply that while motor planning within the premotor cortex can occur in parallel with the execution of ongoing movement, the parallel stimulus identification and action selection in the posterior parietal cortex requires additional neural processes. HighlightsO_LIIndividuals prepare upcoming actions while simultaneously controlling ongoing movement C_LIO_LIWhen these two processes overlap, superior-parietal and occipito-temporal areas show increased activation C_LIO_LIMultivariate analysis suggests that increased activation arises to resolve simultaneous stimulus identification and action selection C_LIO_LIThe premotor cortex, known to be involved in motor planning, coordinates simultaneous planning and execution without extra neural engagement C_LI

neuroscience↗

bioRxiv2023

Interaction of multiple future movement plans in sequential reaching

Real world actions often comprise of a series of movements that cannot be entirely planned before initiation. When these actions are executed rapidly, the planning of multiple future movements needs to occur simultaneously with the ongoing action. How the brain solves this task remains unknown. Here we address this question with a new sequential arm reaching paradigm that manipulates how many future reaches are available for planning while controlling execution of the ongoing reach. We show that participants plan at least two future reaches simultaneously with an ongoing reach. Further, the planning processes of the two future reaches are not independent of one another. Evidence that the planning processes interact is two-fold. First, correcting for a visual perturbation of the ongoing reach target is slower when more future reaches are planned. Second, the curvature of the current reach is modified based on the next reach only when their planning processes temporally overlap. These interactions between future planning processes may enable smooth production of sequential actions by linking individual segments of a long sequence at the level of motor planning.

neuroscience↗

bioRxiv2023

Myomatrix arrays for high-definition muscle recording

Neurons coordinate their activity to produce an astonishing variety of motor behaviors. Our present understanding of motor control has grown rapidly thanks to new methods for recording and analyzing populations of many individual neurons over time. In contrast, current methods for recording the nervous systems actual motor output - the activation of muscle fibers by motor neurons - typically cannot detect the individual electrical events produced by muscle fibers during natural behaviors and scale poorly across species and muscle groups. Here we present a novel class of electrode devices ("Myomatrix arrays") that record muscle activity at unprecedented resolution across muscles and behaviors. High-density, flexible electrode arrays allow for stable recordings from the muscle fibers activated by a single motor neuron, called a "motor unit", during natural behaviors in many species, including mice, rats, primates, songbirds, frogs, and insects. This technology therefore allows the nervous systems motor output to be monitored in unprecedented detail during complex behaviors across species and muscle morphologies. We anticipate that this technology will allow rapid advances in understanding the neural control of behavior and in identifying pathologies of the motor system.

neuroscience↗

bioRxiv2023

MotorNet: a Python toolbox for controlling differentiable biomechanical effectors with artificial neural networks

Artificial neural networks (ANNs) are a powerful class of computational models for unravelling neural mechanisms of brain function. However, for neural control of movement, they currently must be integrated with software simulating biomechanical effectors, leading to limiting impracticalities: (1) researchers must rely on two different platforms and (2) biomechanical effectors are not generally differentiable, constraining researchers to reinforcement learning algorithms despite the existence and potential biological relevance of faster training methods. To address these limitations, we developed MotorNet, an open-source Python toolbox for creating arbitrarily complex, differentiable, and biomechanically realistic effectors that can be trained on user-defined motor tasks using ANNs. MotorNet is designed to meet several goals: ease of installation, ease of use, a high-level user-friendly API, and a modular architecture to allow for flexibility in model building. MotorNet requires no dependencies outside Python, making it easy to get started with. For instance, it allows training ANNs on typically used motor control models such as a two joint, six muscle, planar arm within minutes on a typical desktop computer. MotorNet is built on PyTorch and therefore can implement any network architecture that is possible using the PyTorch framework. Consequently, it will immediately benefit from advances in artificial intelligence through PyTorch updates. Finally, it is open source, enabling users to create and share their own improvements, such as new effector and network architectures or custom task designs. MotorNets focus on higher order model and task design will alleviate overhead cost to initiate computational projects for new researchers by providing a standalone, ready-to-go framework, and speed up efforts of established computational teams by enabling a focus on concepts and ideas over implementation.

neuroscience↗

bioRxiv2020

Real-Time Observation of Structure and Dynamics during the Liquid-to-Solid Transition of FUS LC

Many of the proteins found in pathological protein fibrils also exhibit tendencies for liquid-liquid phase separation (LLPS) both in vitro and in cells. The mechanisms underlying the connection between these phase transitions have been challenging to study due to the heterogeneous and dynamic nature of the states formed during the maturation of LLPS protein droplets into gels and solid aggregates. Here, we interrogate the liquid-to-solid transition of the low complexity domain of the RNA binding protein FUS (FUS LC), which has been shown to adopt LLPS, gel-like, and amyloid states. We employ magic-angle spinning (MAS) NMR spectroscopy which has allowed us to follow these transitions in real time and with residue specific resolution. We observe the development of {beta}-sheet structure through the maturation process and show that the final state of FUS LC fibrils produced through LLPS is distinct from that grown from fibrillar seeds. We also apply our methodology to FUS LC G156E, a clinically relevant FUS mutant that exhibits accelerated fibrillization rates. We observe significant changes in dynamics during the transformation of the FUS LC G156E construct and begin to unravel the sequence specific contributions to this phenomenon with computational studies of the phase separated state of FUS LC and FUS LC G156E. SignificanceThe presence of protein aggregates and plaques in the brain is a common pathological sign of neurodegenerative disease. Recent work has revealed that many of the proteins found in these aggregates can also form liquid-liquid droplets and gels. While the interconversion from one state to another can have vast implications for cell function and disease, the molecular mechanisms that underlie these processes are not well understood. Here, we combine MAS NMR spectroscopy with other biophysical and computational tools to follow the transitions of the stress response protein FUS. This approach has allowed us to observe real-time changes in structure and dynamics as the protein undergoes these transitions, and to reveal the intricate effects of disease-relevant mutations on the transformation process.

biophysics↗