Biology subjects

MacLean, J. N.

Publications and source records attributed to MacLean, J. N..

17 recordsLinked to original sources

bioRxiv2026

Somatostatin-excitatory coordination emerges with dexterous motor skill learning

Motor skill-learning increases spatio-temporal consistency in neuronal activity in primary motor cortex (M1), and Somatostatin-expressing (SST) interneurons are causally involved in these learning-related activity changes. Yet, how SST-excitatory interactions shape population level activity changes during motor skill acquisition is still unknown. Here, we imaged large populations of excitatory neurons and identified SST or parvalbumin-expressing (PV) interneurons across successive days as mice learned a dexterous, persistently M1-dependent reach-to-grasp task. Sparse, temporally coordinated, trial-resolved, and rate-controlled functional interactions between SST neurons and skill-modulated excitatory neurons emerged with learning and were stronger than those in excitatory-excitatory or PV-excitatory pairs. Interactions strengthened as neurons were recruited to the skill-modulated ensemble and weakened when skill modulation was lost. SST activity also became temporally reliable earlier than excitatory or PV activity, and SST consistency was significantly associated with behavioral performance even after controlling for training session number. Additionally, SST neurons carried more information about movement kinematics than randomly selected subsets of excitatory neurons. Overall, SST neurons became selectively coordinated with behaviorally relevant excitatory populations, revealing a cell-type-specific functional architecture for dexterous motor learning.

neuroscience↗

bioRxiv2026

Natural prey capture reveals flexible reconfiguration of canonical motor-cortical dynamics for online control

The ability to record neural populations during natural behavior now allows us to ask whether canonical motor-cortical dynamics, defined largely in constrained reaches to static targets, also organize self-paced, feedback-rich actions that require continuous correction. We recorded sensorimotor cortex in marmosets freely capturing live prey, a behavior that separates into ballistic quick captures and dynamic pursuits requiring real-time adjustment. Ballistic captures corresponded to canonical cortical reaching dynamics without an instructed delay, including near-orthogonal premovement and movement subspaces. Pursuits did not recruit additional neural dimensions; despite their elaborate kinematics, dynamics evolved within the same low-dimensional subspace. Corrective movements during pursuit reorganized this geometry into a distinct feedback-dominated regime, in which premovement activity weakly predicted movement, trajectory tangling increased, and a smaller, slower rotation restarted from a shifted initial state. Yet all movement types shared a sequential firing-rate backbone, which during ballistic capture was organized across the cortex as a somatotopically directed propagating wave that weakened when movements required online correction. These results show that sensorimotor cortex preserves canonical dynamical structure while flexibly reconfiguring it for online control in natural behavior.

neuroscience↗

bioRxiv2026

Representations of active grasp maintenance emerge during reach-grasp-carry learning in mouse motor cortex

Object manipulation is a dexterous behavior requiring ongoing sensorimotor coordination to establish and maintain a grasp during consecutive movements. Although grasp initiation is known to modulate activity in primary motor cortex (M1), it remains unclear if sustained grasp maintenance is represented in M1, whether it corresponds to an intrinsic M1 state or one that emerges through learning, and how this state contributes to skilled behaviors involving object manipulation. We addressed these questions using two-photon calcium imaging in mice learning a self-initiated, self-paced, reach-grasp-carry task. This task allowed mice to control trial structure and timing, revealing two kinematically similar but functionally distinct carry behaviors: active grasps, in which mice maintained control of a sucrose pellet and received sensory feedback from the held object, and empty grasps, in which mice completed the same carry movement after failing to grasp the pellet. As behavior was refined, empty grasps became less frequent and the M1 representation of active grasp maintenance became more reliable and more distinct from empty grasp. This distinction was evident in single-neuron activity, population activity, and functional connectivity between neurons. These differences were sufficiently robust to support accurate decoding of active and empty grasp state from neural activity and improved encoding of single neuron activity when models incorporated condition-specific functional network coupling. Together, these findings show that learning refines M1 activity into a distinct and reliable representation of active grasp maintenance, revealing sensorimotor integration during object contact as a key neural substrate of learned dexterous behavior.

neuroscience↗

bioRxiv2025

Mechanistic basis of dynamic and heterogeneous divisive normalization in visual cortex

Neocortical computation emerges from the dynamic interplay of excitation and inhibition, operating in a loose balance regime where recurrent and external inputs contribute comparably to neuronal activity. Neurons display broad heterogeneity in synaptic inputs and firing rates, making it essential to explain the full distribution of responses, not just the mean, when elucidating mechanisms of dynamics and computation. We examined divisive normalization in mouse visual cortex using population calcium imaging of excitatory and parvalbumin (PV) inhibitory neurons, combined with computational models of varying complexity. We found that suppression in PV neurons was transiently reduced, driven by the dynamics of subcortical input, and that heterogeneity in suppression strength was linked to population correlations, variability in excitatory-inhibitory balance, and suppression of both subcortical and local cortical inputs. Our results link local recurrent connectivity to the diversity of normalizing responses in cortex, providing a mechanistic basis for functional heterogeneity in this computation.

neuroscience↗

bioRxiv2024

The natural variability of a dexterous motor skill is stably encoded in the cortex of freely behaving mice

Skilled, goal-directed movements exhibit trial-to-trial variability even in experts, particularly in response to dynamic environmental conditions or when perfect repetition is not required for success. Identifying where, to what extent, and how stably this variability is encoded in the nervous system is essential for understanding how learned movements are robustly maintained over time yet flexibly executed on each trial. We record calcium fluorescence activity in forelimb motor cortex (M1), a key node in the multi-areal network responsible for movement control, in freely-moving mice of both sexes as they performed a self-paced, precision reach-to-grasp task. High trial counts and rich single-trial variability enable rigorous statistical analysis of moment-to-moment movement encoding across matched behavioral sets over five days. Approximately 80% of recorded neurons significantly encoded paw, digit, and head movements during reaching, as quantified using linear models. Across days, encoding similarity shows a small but measurable decline that increases with the interval between recording sessions. This drift is heterogeneously distributed across the population, with many neurons retaining high encoding similarity even in sessions four days apart, as assessed using shuffle controls and comparison to encoding for trial-averaged movements. Thus, over the timescale examined, M1 is capable of maintaining stable encoding of movement details at the level of single cells, even for complex, sensory-guided tasks like reach-to-grasp. Together, these results raise the question of whether downstream circuits support consistent behavior by preferentially relying on neurons with greater stability or instead through population-level readout that is robust to a modest level of representational change.

neuroscience↗

bioRxiv2023

Task success in trained spiking neuronal network models coincides with emergence of cross-stimulus-modulated inhibition

The neocortex is composed of spiking neuronal units interconnected in a sparse, recurrent network. Neuronal networks exhibit spiking activity that transforms sensory inputs into appropriate behavioral outputs. In this study, we train biologically realistic spiking neural network (SNN) models to identify the architectural changes which enable task-appropriate computations. Specifically, we employ a binary state change detection task, where each state is defined by motion entropy. This task mirrors behavioral paradigms that mice perform in the lab. SNNs are composed of excitatory and inhibitory units randomly interconnected with connection likelihoods and strengths matched to observations from mouse neocortex. Following training, we discover that SNNs selectively adjust firing rates depending on state, and that excitatory and inhibitory connectivity between input and recurrent layers change in accordance with this rate modulation. Input channels that exhibit bias to one specific motion entropy input develop stronger connections to recurrent excitatory units during training, while channels that exhibit bias to the other input develop stronger connections to inhibitory units. Furthermore, recurrent inhibitory units which positively modulated firing rates to one input strengthened their connections to recurrent units of the opposite modulation. This specific pattern of cross-modulation inhibition emerged as the optimal solution when imposing Dales law throughout training of the SNNs. Removing this constraint led to the absence of the emergence of this architectural solution. This work highlights the critical role of interneurons and the specific architectural patterns of inhibition in shaping dynamics and information processing within neocortical circuits.

neuroscience↗

bioRxiv2023

Normalization in mouse primary visual cortex

When multiple stimuli appear together in the receptive field of a visual cortical neuron, the response is typically close to the average of that neurons response to each individual stimulus. The departure from a linear sum of each individual response is referred to as normalization. In mammals, normalization has been best characterized in the visual cortex of macaques and cats. Here we study visually evoked normalization in the visual cortex of awake mice using optical imaging of calcium indicators in large populations of layer 2/3 (L2/3) V1 excitatory neurons and electrophysiological recordings across layers in V1. Regardless of recording method, mouse visual cortical neurons exhibit normalization to varying degrees. The distributions of normalization strength are similar to those described in cats and macaques, albeit slightly weaker on average.

neuroscience↗

bioRxiv2022

Dynamic Structure Of Motor Cortical Neuron Co-Activity Carries Behaviorally Relevant Information

Skillful, voluntary movements are underpinned by computations performed by networks of interconnected neurons in the primary motor cortex (M1). Computations are reflected by patterns of co-activity between neurons. Using spike time correlations, co-activity can be represented as functional networks (FNs). Here, we show that the structure of FNs constructed from instructed-delay reach trials in non-human primates are behaviorally specific: low dimensional embedding and graph alignment scores show that FNs constructed from closer target reach distances are also closer in network space. We next constructed temporal FNs using short intervals across a trial. We find that temporal FNs traverse a low-dimensional subspace in a reach-specific trajectory. Alignment scores show that FNs become separable and correspondingly, decodable shortly after the instruction cue. Finally, we observe that reciprocal connections in FNs transiently decrease following the instruction cue, suggesting the network momentarily switches from a recurrent system to one that is more feedforward.

neuroscience↗

bioRxiv2022

Sequential addition of neuronal stem cell temporal cohorts generates a feed-forward circuit in the Drosophila larval nerve cord

Understanding how circuits self-assemble starting from neuronal stem cells is a fundamental question in developmental biology. Here, we addressed how neurons from different lineages wire with each other to form a specific circuit motif. To do so, we combined developmental genetics--Twin spot MARCM, Multi-color Flip Out, permanent labeling--with circuit analysis--calcium imaging, connectomics, and network science analyses. We find many lineages are organized into temporal cohorts, which are sets of lineage-related neurons born within a tight time window, and that temporal cohort boundaries have sharp transitions in patterns of input connectivity. We identify a feed-forward circuit motif that encodes the onset of vibration stimuli. This feed-forward circuit motif is assembled by preferential connectivity between temporal cohorts from different neuronal stem cell lineages. Further, connectivity does not follow the often-cited early-to-early, late-to-late model. Instead, the feed-forward motif is formed by sequential addition of temporal cohorts, with circuit output neurons born before circuit input neurons. Further, we generate multiple new tools for the fly community. Ultimately, our data suggest that sequential addition of neurons (with outputs neurons being oldest and input neurons being youngest) could be a fundamental strategy for assembling feed-forward circuits.

developmental biology↗

bioRxiv2022

A sparse set of spikes corresponding to reliable correlations is highly informative of visual stimulus on single trials

Spike trains in cortical neuronal populations vary in number and timing trial-to-trial, rendering a viable single trial coding scheme for sensory information elusive. Correlations between pairs of neocortical neurons can be segmented into either sensory or noise according to their stimulus specificity. Here we show that pairs of spikes, corresponding to reliable sensory correlations in imaged populations in layer 2/3 of mouse visual cortex are particularly informative of visual stimuli. This set of spikes is sparse and exhibits comparable levels of trial-to-trial variance relative to the full spike train. Despite this, correspondence of pairs of spikes to a specific set of sensory correlations identifies spikes that carry more information per spike about the visual stimulus than the full set or any other matched set of spikes. Moreover, this sparse subset is more accurately decoded, regardless of the decoding algorithm employed. Our findings suggest that consistent pairwise correlations between neurons, rather than first-order statistical features of spike trains, may be an organizational principle of a single trial sensory coding scheme.

neuroscience↗

bioRxiv2021

Large-scale algorithmic search identifies stiff and sloppy dimensions in synaptic architectures consistent with murine neocortical wiring

Complex systems can be defined by "sloppy" dimensions, meaning that their behavior is unmodified by large changes to specific parameter combinations, and "stiff" dimensions whose change results in considerable behavioral modification. In the neocortex, sloppiness in synaptic architectures would be crucial to allow for the maintenance of asynchronous irregular spiking dynamics with low firing rates despite a diversity of inputs, states, and both short- and long-term plasticity. Using simulations on neural networks with first-order spiking statistics matched to firing in murine visual cortex while varying connectivity parameters, we determined the stiff and sloppy parameters of synaptic architectures across three classes of input (brief, continuous, and cyclical). Algorithmically-generated connectivity parameter values drawn from a large portion of the parameter space reveal that specific combinations of excitatory and inhibitory connectivity are stiff and that all other architectural details are sloppy. Stiff dimensions are consistent across input classes with self-sustaining synaptic architectures following brief input occupying a smaller subspace as compared to the other input classes. Experimentally estimated connectivity probabilities from mouse visual cortex are consistent with the connectivity correlations found and fall in the same region of the parameter space as architectures identified algorithmically. This suggests that simple statistical descriptions of spiking dynamics are a sufficient and parsimonious description of neocortical activity when examining structure-function relationships at the mesoscopic scale. Additionally, coarse graining cell types does not prevent the generation of accurate, informative, and interpretable models underlying simple spiking activity. This unbiased investigation provides further evidence of the importance of the interrelationship of excitatory and inhibitory connectivity to establish and maintain stable spiking dynamical regimes in the neocortex. Author SummaryConnections between neurons are continuously changing to allow learning and adaptation to new stimuli. However, the ability of neural networks to vary these connections while avoiding excessively high- or low-activity states is still not well understood. We tackled this question by studying how changes in the parameters of connectivity within and between different neuronal populations impacted network activity in computational models. We identified specific combinations of parameters, deemed "stiff", that must be maintained to observe activity consistent with recordings from murine visual cortex, while the rest of the parameters can be varied freely with minimal effects on activity. Our results agree with experimentally measured connectivity statistics demonstrating the importance of balancing opposing forces to maintain activity in a natural regime.

neuroscience↗

bioRxiv2021

M1 dynamics share similar inputs for initiating and correcting movement

Primary motor cortex (M1) plays a central role in voluntary movement, but how it integrates sensory-driven corrective instructions is unclear. We analyzed population activity recorded from M1 of macaques during a sequential arm movement task with target updates requiring online adjustments to the motor plan. Using Latent Factor Analysis via Dynamical Systems (LFADS), we separated neural activity into two components: intrinsic dynamics and inferred external inputs influencing those dynamics. Inferred input timing was more strongly locked to target appearance than to movement onset, suggesting that variable reaction times reflect interactions between inputs and ongoing dynamics. Inferred inputs were tuned similarly for both initial and corrective movements, suggesting a shared input encoding across visually-instructed and corrective movements that was previously obscured by M1 dynamics. Because input inference can suffer from the challenge of nonidentifiability, where different models fit the data indistinguishably, we used ensembles of models with varied hyperparameters to diagnose when inputs are identifiable or nonidentifiable. In the monkey data, ensembles produced consistently similar results, suggesting that inputs could be meaningfully inferred and that their encoding was not simply a result of model bias. These results highlight the challenges of nonidentifiability and the potential of model ensembles to identify inputs in ongoing dynamics, at least in some cases.

neuroscience↗

bioRxiv2021

Validating marker-less pose estimation with 3D x-ray radiography

To reveal the neurophysiological underpinnings of natural movement, neural recordings must be paired with accurate tracking of limbs and postures. Here we validate the accuracy of DeepLabCut (DLC) by comparing it to a 3D x-ray video radiography system that tracks markers placed under the skin (XROMM). We record behavioral data simultaneously with XROMM and high-speed video for DLC as marmosets engage in naturalistic foraging and reconstruct three-dimensional kinematics in a shared coordinate system. We find that DLC tracks position and velocity of 12 markers on the forelimb and torso with low median error (0.272 cm and 1.76 cm/s, respectively) corresponding to 2.9% of the full range of marker positions and 5.9% of the range of speeds. For studies that can accept this relatively small degree of error, DLC and similar marker-less pose estimation tools enable the study of more naturalistic, unconstrained behaviors in many fields including non-human primate motor control. Summary StatementWe validate the accuracy of DeepLabCut, a widely used marker-less pose estimation tool, using a marker-based 3D x-ray radiography system (XROMM).

animal behavior and cognition↗

bioRxiv2021

Chronic wireless neural population recordings with common marmosets

Marmosets are an increasingly important model system for neuroscience in part due to genetic tractability and enhanced cortical accessibility, due to a lissencephalic neocortex. However, many of the techniques generally employed to record neural activity in primates inhibit the expression of natural behaviors in marmosets precluding neurophysiological insights. To address this challenge, we developed methods for recording neural population activity in unrestrained marmosets across multiple ethological behaviors, multiple brain states, and over multiple years. Notably, our flexible methodological design allows for replacing electrode arrays and removal of implants providing alternative experimental endpoints. We validate the method by recording sensorimotor cortical population activity in freely moving marmosets across their natural behavioral repertoire and during sleep. HIGHLIGHTS- Simultaneous and chronic wireless neural population recordings in multiple freely moving marmosets - Neural recording approach enables studies of natural repertoire of behaviors and sleep - Methyl-methacrylate free surgical approach designed to promote biocompatibility and longitudinal success of the implant - Modular headstage configuration requires minimal daily animal handling for daily neural recordings - Alternative experimental endpoints: implant removal, healing, and electrode array replacement

neuroscience↗

bioRxiv2019

A platform for semi-automated voluntary training of common marmosets for behavioral neuroscience

In most cases, behavioral neuroscience studies of the common marmoset employ adaptations of well-established methods used with macaque monkeys. However, in most cases these approaches do not readily generalize to marmosets indicating a need for alternatives. Here we present the development of one such alternate: a platform for semi-automated, voluntary in-home cage behavioral training that allows for the study of naturalistic behaviors. We describe the design and production of a modular behavioral training apparatus using CAD software and digital fabrication. We demonstrate that this apparatus permits voluntary behavioral training and data collection throughout the marmosets waking hours with little experimenter intervention. Further we demonstrate the use of this apparatus to reconstruct the kinematics of the marmosets upper limb movement during natural foraging behavior.\n\nNEW AND NOTEWORTHYThe study of marmosets in neuroscience has grown rapidly and this model organism presents challenges that are unique to this primate species. Here we address those challenges with an innovative platform for semi-automated and voluntary training of common marmosets. The platform allows marmosets to train throughout their waking hours with little to no experimenter intervention. We describe the use of this platform to capture the kinematics of the upper limb during natural foraging behavior and to expand the opportunities for behavioral training beyond the limits of traditional behavioral training sessions. The platform is flexible and can be easily extended to incorporate other motor tasks (e.g. visually cued reaching or manipulandum based tasks) using CAD models and digital fabrication.

neuroscience↗

bioRxiv2019

Recurrent interactions can explain the variance in single trial responses

1.To develop a complete description of sensory encoding, it is necessary to account for trial-to-trial variability in cortical neurons. Using a generalized linear model with terms corresponding to the visual stimulus, mouse running speed, and experimentally measured neuronal correlations, we modeled short term dynamics of L2/3 murine visual cortical neurons to evaluate the relative importance of each factor to neuronal variability within single trials. We find single trial predictions improve most when conditioning on the experimentally measured local correlations in comparison to predictions based on the stimulus or running speed. Specifically, accurate predictions are driven by positively co-varying and synchronously active functional groups of neurons. Including functional groups in the model enhances decoding accuracy of sensory information compared to a model that assumes neuronal independence. Functional groups, in encoding and decoding frameworks, provide an operational definition of Hebbian assemblies in which local correlations largely explain neuronal responses on individual trials.

neuroscience↗

bioRxiv2017

Functional triplet motifs underlie accurate predictions of single-trial responses in populations of tuned and untuned V1 neurons

Visual stimuli are encoded in the activity patterns of neocortical neuronal populations. Trial-averaged neuronal activity is selectively modulated by particular visual stimulus parameters, such as the direction of a moving bar of light, resulting in well-defined tuning properties. However, a large number of neurons in visual cortex remain unmodulated by any given stimulus parameter, and the role of this untuned population is not well understood. Here, we use two-photon calcium imaging to record, in an unbiased manner, from large populations of layer 2/3 excitatory neurons in mouse primary visual cortex to describe co-varying activity on single trials in populations consisting of tuned and untuned neurons. Specifically, we summarize pairwise covariability with an asymmetric partial correlation coefficient, allowing us to analyze the population correlation structure with graph theory. Using the graph neighbors of a neuron, we find that the local population, including tuned and untuned neurons, are able to predict individual neuron activity on a single trial basis and recapitulate average tuning properties of tuned neurons. We also find that a specific functional triplet motif in the graph results in the best predictions, suggesting a signature of informative correlations in these populations. Variance explained in total population activity scales with the number of neurons imaged, suggesting larger sample sizes are required to fully capture local network interactions. In summary, we show that unbiased sampling of the local population can explain single trial response variability as well as trial-averaged tuning properties in V1, and the ability to predict responses is tied to the occurrence of a functional triplet motif.\n\nAuthor summaryV1 populations have historically been characterized by single cell response properties and pairwise co-variability. Many cells, however, do not show obvious dependencies to a given stimulus or behavioral task, and have consequently gone unanalyzed. We densely record from V1 populations to measure how trial-to-trial response variability relates to these previously understudied neurons. We find that individual neurons, regardless of response properties, are inextricably dependent on the population in which they are embedded. By studying patterns of correlations between groups of neurons, we identify a specific triplet motif that predicts this dependence on local population activity. Only by studying large populations simultaneously were we able to find an emergent property of this information. These results imply that understanding how the visual system operates with substantial trial-to-trial variability will necessitate a network perspective that accounts for both visual stimuli and activity in the local population.

neuroscience↗