The lab of Rune W. Berg is looking for a highly motivated and dynamic researcher for a 3-year position to start January 1st, 2024. The topic is the neuroscience of motor control with a focus on locomotion and spinal circuitry and connections with the brain. The person will be performing the following: 1) experimental recording of neurons in the brain and spinal cord of awake behaving rats using Neuropixels and Neuronexus electrodes combined with optogenetics. 2) Analyze the large amount of data generated from these experiments, including tissue processing. 3) Participate in the development of the new theory of motor control.

The lab of Rune W. Berg is looking for a highly motivated and dynamic researcher for a 3-year position to start January 1st, 2024. The topic is the neuroscience of motor control with a focus on locomotion and spinal circuitry and connections with the brain. The person will be performing the following: 1) experimental recording of neurons in the brain and spinal cord of awake behaving rats using Neuropixels and Neuronexus electrodes combined with optogenetics. 2) Analyze the large amount of data generated from these experiments, including tissue processing. 3) Participate in the development of the new theory of motor control.

All animal locomotion is rhythmic,whether it is achieved through undulatory movement of the whole body or the coordination of articulated limbs. Neurobiologists have long studied locomotor circuits that produce rhythmic activity with non-rhythmic input, also called central pattern generators (CPGs). However, the cellular and microcircuit implementation of a walking CPG has not been described for any limbed animal. New comprehensive connectomes of the fruit fly ventral nerve cord (VNC) provide an opportunity to study rhythmogenic walking circuits at a synaptic scale.We use a data-driven network modeling approach to identify and characterize a putative walking CPG in the Drosophila leg motor system.

Every movement we make requires us to precisely coordinate muscle activity across our body in space and time. In this talk I will describe our efforts to understand how the brain generates flexible, coordinated movement. We have taken a behavior-centric approach to this problem, starting with the development of quantitative frameworks for mouse locomotion (LocoMouse; Machado et al., eLife 2015, 2020) and locomotor learning, in which mice adapt their locomotor symmetry in response to environmental perturbations (Darmohray et al., Neuron 2019). Combined with genetic circuit dissection, these studies reveal specific, cerebellum-dependent features of these complex, whole-body behaviors. This provides a key entry point for understanding how neural computations within the highly stereotyped cerebellar circuit support the precise coordination of muscle activity in space and time. Finally, I will present recent unpublished data that provide surprising insights into how cerebellar circuits flexibly coordinate whole-body movements in dynamic environments.

Through recent advances in microscopy, we now have an unprecedented view of the brain and body of the fruit fly Drosophila melanogaster. We now know the connectivity at single neuron resolution across the whole brain. How do we translate these new measurements into a deeper understanding of how the brain processes sensory information and produces behavior? I will describe two computational efforts to model the brain and the body of the fruit fly. First, I will describe a new modeling method which makes highly accurate predictions of neural activity in the fly visual system as measured in the living brain, using only measurements of its connectivity from a dead brain [1], joint work with Jakob Macke. Second, I will describe a whole body physics simulation of the fruit fly which can accurately reproduce its locomotion behaviors, both flight and walking [2], joint work with Google DeepMind.

Navigation requires orienting oneself relative to landmarks in the environment, evaluating relevant sensory data, remembering goals, and convert all this information into motor commands that direct locomotion. I will present models, highly constrained by connectomic, physiological and behavioral data, for how these functions are accomplished in the fly brain.

To effectively move in complex and changing environments, animals must control locomotor speed and gait, while precisely coordinating and adapting limb movements to the terrain. The underlying neuronal control is facilitated by circuits in the spinal cord, which integrate supraspinal commands and afferent feedback signals to produce coordinated rhythmic muscle activations necessary for stable locomotion. I will present a series of computational models investigating dynamics of central neuronal interactions as well as a neuromechanical model that integrates neuronal circuits with a model of the musculoskeletal system. These models closely reproduce speed-dependent gait expression and experimentally observed changes following manipulation of multiple classes of genetically-identified neuronal populations. I will discuss the utility of these models in providing experimentally testable predictions for future studies.

Running, swimming, or flying through the world, animals are constantly making decisions while on the move—decisions that allow them to choose where to eat, where to hide, and with whom to associate. Despite this most studies have considered only on the outcome of, and time taken to make, decisions. Motion is, however, crucial in terms of how space is represented by organisms during spatial decision-making. Employing a range of new technologies, including automated tracking, computational reconstruction of sensory information, and immersive ‘holographic’ virtual reality (VR) for animals, experiments with fruit flies, locusts and zebrafish (representing aerial, terrestrial and aquatic locomotion, respectively), I will demonstrate that this time-varying representation results in the emergence of new and fundamental geometric principles that considerably impact decision-making. Specifically, we find that the brain spontaneously reduces multi-choice decisions into a series of abrupt (‘critical’) binary decisions in space-time, a process that repeats until only one option—the one ultimately selected by the individual—remains. Due to the critical nature of these transitions (and the corresponding increase in ‘susceptibility’) even noisy brains are extremely sensitive to very small differences between remaining options (e.g., a very small difference in neuronal activity being in “favor” of one option) near these locations in space-time. This mechanism facilitates highly effective decision-making, and is shown to be robust both to the number of options available, and to context, such as whether options are static (e.g. refuges) or mobile (e.g. other animals). In addition, we find evidence that the same geometric principles of decision-making occur across scales of biological organisation, from neural dynamics to animal collectives, suggesting they are fundamental features of spatiotemporal computation.

Although bradykinesia, tremor, and rigidity are hallmark motor defects in Parkinson’s disease (PD) patients, they also experience motor learning impairments and non-motor symptoms such as depression. The neural basis for these different PD symptoms are not well understood. While current treatments are effective for locomotion deficits in PD, therapeutic strategies targeting motor learning deficits and non-motor symptoms are lacking. We found that distinct parafascicular (PF) thalamic subpopulations project to caudate putamen (CPu), subthalamic nucleus (STN), and nucleus accumbens (NAc). While PF-->CPu and PF-->STN circuits are critical for locomotion and motor learning respectively, inhibition of the PF-->NAc circuit induced a depression-like state. While chemogenetically manipulating CPu-projecting PF neurons led to a long-term restoration of locomotion, optogenetic long-term potentiation at PF-->STN synapses restored motor learning behavior in PD model mice. Furthermore, activation of NAc-projecting PF neurons rescued depression-like PD phenotypes. Importantly, we identified nicotinic acetylcholine receptors capable of modulating PF circuits to rescue different PD phenotypes. Thus, targeting PF thalamic circuits may be an effective strategy for treating motor and non-motor deficits in PD.

The medial entorhinal cortex (MEC) hosts many of the brain’s circuit elements for spatial navigation and episodic memory, operations that require neural activity to be organized across long durations of experience. While location is known to be encoded by a plethora of spatially tuned cell types in this brain region, little is known about how the activity of entorhinal cells is tied together over time. Among the brain’s most powerful mechanisms for neural coordination are network oscillations, which dynamically synchronize neural activity across circuit elements. In MEC, theta and gamma oscillations provide temporal structure to the neural population activity at subsecond time scales. It remains an open question, however, whether similarly coordination occurs in MEC at behavioural time scales, in the second-to-minute regime. In this talk I will show that MEC activity can be organized into a minute-scale oscillation that entrains nearly the entire cell population, with periods ranging from 10 to 100 seconds. Throughout this ultraslow oscillation, neural activity progresses in periodic and stereotyped sequences. The oscillation sometimes advances uninterruptedly for tens of minutes, transcending epochs of locomotion and immobility. Similar oscillatory sequences were not observed in neighboring parasubiculum or in visual cortex. The ultraslow periodic sequences in MEC may have the potential to couple its neurons and circuits across extended time scales and to serve as a scaffold for processes that unfold at behavioural time scales.

Here, we present a protocol for using a versatile treadmill system to measure locomotion and neural activity at high temporal resolution in head-fixed mice. We first describe the assembly of the treadmill system. We then detail surgical implantation of the headplate on the mouse skull, followed by habituation of mice to locomotion on the treadmill system. The system is compact, movable, and simple to synchronize with other data streams, making it ideal for monitoring brain activity in diverse behavioral frameworks. https://dx.doi.org/10.1016/j.xpro.2022.101701

I am working on color vision in Drosophila, identifying behaviors that involve color vision and understanding the neural circuits supporting them (Longden 2016). I have a long-term interest in understanding how neural computations operate reliably under changing circumstances, be they external changes in the sensory context, or internal changes of state such as hunger and locomotion. On internal state-modulation of sensory processing, I have shown how hunger alters visual motion processing in blowflies (Longden et al. 2014), and identified a role for octopamine in modulating motion vision during locomotion (Longden and Krapp 2009, 2010). On responses to external cues, I have shown how one kind of uncertainty in the motion of the visual scene is resolved by the fly (Saleem, Longden et al. 2012), and I have identified novel cells for processing translation-induced optic flow (Longden et al. 2017). I like working with colleagues who use different model systems, to get at principles of neural operation that might apply in many species (Ding et al. 2016, Dyakova et al. 2015). I like work motivated by computational principles - my background is computational neuroscience, with a PhD on models of memory formation in the hippocampus (Longden and Willshaw, 2007).

Hippocampal pyramidal cells have been widely studied during locomotion, when theta oscillations are present, and during short wave ripples at rest, when replay takes place. However, we find a subset of pyramidal cells that are preferably active during rest, in the absence of theta oscillations and short wave ripples. We recorded these cells using two-photon imaging in dorsal CA1 of the hippocampus of mice, during a virtual reality object location recognition task. During locomotion, the cells show a similar level of activity as control cells, but their activity increases during rest, when this population of cells shows highly synchronous, oscillatory activity at a low frequency (0.1-0.4 Hz). In addition, during both locomotion and rest these cells show place coding, suggesting they may play a role in maintaining a representation of the current location, even when the animal is not moving. We performed simultaneous electrophysiological and calcium recordings, which showed a higher correlation of activity between the LFO and the hippocampal cells in the 0.1-0.4 Hz low frequency band during rest than during locomotion. However, the relationship between the LFO and calcium signals varied between electrodes, suggesting a localized effect. We used the Allen Brain Observatory Neuropixels Visual Coding dataset to further explore this. These data revealed localised low frequency oscillations in CA1 and DG during rest. Overall, we show a novel population of hippocampal cells, and a novel oscillatory band of activity in hippocampus during rest.

The ability to reach, grasp, and manipulate objects is a remarkable expression of motor skill, and the loss of this ability in injury, stroke, or disease can be devastating. These behaviors are controlled by the coordinated activity of tens of millions of neurons distributed across many CNS regions, including the primary motor cortex. While many studies have characterized the activity of single cortical neurons during reaching, the principles governing the dynamics of large, distributed neural populations remain largely unknown. Recent work in primates has suggested that during the execution of reaching, motor cortex may autonomously generate the neural pattern controlling the movement, much like the spinal central pattern generator for locomotion. In this seminar, I will describe recent work that tests this hypothesis using large-scale neural recording, high-resolution behavioral measurements, dynamical systems approaches to data analysis, and optogenetic perturbations in mice. We find, by contrast, that motor cortex requires strong, continuous, and time-varying thalamic input to generate the neural pattern driving reaching. In a second line of work, we demonstrate that the cortico-cerebellar loop is not critical for driving the arm towards the target, but instead fine-tunes movement parameters to enable precise and accurate behavior. Finally, I will describe my future plans to apply these experimental and analytical approaches to the adaptive control of locomotion in complex environments.

Neuronal computations are matched to optimally encode the sensory information that is available and relevant for the animal. However, the physical distribution of sensory information is often shaped by the animal’s own behavior. One prominent example is the encoding of optic flow fields that are generated during self-motion of the animal and will therefore depend on the type of locomotion. How evolution has matched computational resources to the behavioral constraints of an animal is not known. Here we use in vivo two photon imaging to record from a population of >3.500 local-direction selective cells. Our data show that the local direction-selective T4/T5 neurons in Drosophila form a population code that is matched to represent optic flow fields generated during translational and rotational self-motion of the fly. This coding principle for optic flow is reminiscent to the population code of local direction-selective ganglion cells in the mouse retina, where four direction-selective ganglion cells encode four different axes of self-motion encountered during walking (Sabbah et al., 2017). However, in flies we find six different subtypes of T4 and T5 cells that, at the population level, represent six axes of self-motion of the fly. The four uniformly tuned T4/T5 subtypes described previously represent a local snapshot (Maisak et al. 2013). The encoding of six types of optic flow in the fly as compared to four types of optic flow in mice might be matched to the high degrees of freedom encountered during flight. Thus, a population code for optic flow appears to be a general coding principle of visual systems, resulting from convergent evolution, but matching the individual ethological constraints of the animal.

When animals navigate on a two-dimensional (2D) surface, many neurons in the medial entorhinal cortex (MEC) are activated as the animal passes through multiple locations (‘firing fields’) arranged in a hexagonal lattice that tiles the locomotion-surface; these neurons are known as grid cells. However, although our world is three-dimensional (3D), the 3D volumetric representation in MEC remains unknown. Here we recorded MEC cells in freely-flying bats and found several classes of spatial neurons, including 3D border cells, 3D head-direction cells, and neurons with multiple 3D firing-fields. Many of these multifield neurons were 3D grid cells, whose neighboring fields were separated by a characteristic distance – forming a local order – but these cells lacked any global lattice arrangement of their fields. Thus, while 2D grid cells form a global lattice – characterized by both local and global order – 3D grid cells exhibited only local order, thus creating a locally ordered metric for space. We modeled grid cells as emerging from pairwise interactions between fields, which yielded a hexagonal lattice in 2D and local order in 3D – thus describing both 2D and 3D grid cells using one unifying model. Together, these data and model illuminate the fundamental differences and similarities between neural codes for 3D and 2D space in the mammalian brain.

For spatial orientation, heading information can be processed in two different frames of reference, a self-centered egocentric or a viewpoint allocentric frame of reference. Using the most efficient frame of reference is in particular important if an animal migrates over large distances, as it the case for the monarch butterfly (Danaus plexippus). These butterflies employ a sun compass to travel over more than 4,000 kilometers to their destination in central Mexico. We developed tetrode recordings from the heading-direction network of tethered flying monarch butterflies that were allowed to orient with respect to a sun stimulus. We show that the neurons switch their frame of reference depending on the animal’s locomotion state. In quiescence, the heading-direction cells encode a sun bearing in an egocentric reference frame, while during active flight, the heading-direction is encoded within an allocentric reference frame. By switching to an allocentric frame of reference during flight, monarch butterflies convert the sun to a global compass cue for long-distance navigation, an ideal strategy for maintaining a migratory heading.

Over 60 years of research has established that medial temporal lobe structures, including the hippocampus and entorhinal cortex, are necessary for the formation of episodic memories (i.e. memories of specific personal events that occur in spatial and temporal context). While prior work to establish the neural mechanisms underlying episodic memory has largely focused on questions related spatial context, recently we have begun to investigate how these brain structures could be involved in encoding aspects of temporal context. In particular, we have focused on how medial entorhinal cortex, a structure well known for its role in spatial memory, may also be involved in encoding interval time. To answer this question we have developed an instrumental paradigm for head-fixed mice that requires both immobile interval timing and locomotion-dependent navigation behavior. By combining this behavioral paradigm with large-scale cellular resolution functional imaging and optogenetic-mediated inactivation, our results suggest that MEC is required for learning of interval timing behavior and that interval timing could be mediated through regular, sequential neural activity of a distinct subpopulation of neurons in MEC that encode elapsed time during periods of immobility (Heys and Dombeck, 2018; Heys et al, 2020; Issa et al., 2020). In this talk, I will discuss these findings and discuss our on-going work to investigate the principles underlying the role of medial temporal lobe structures in timing behavior and episodic memory.

The brain is remarkably flexible, and appears to instantly reconfigure its processing depending on what’s needed to solve a task at hand: fMRI studies indicate that distal brain areas appear to fluidly couple and decouple with one another depending on behavioral context. But the structural architecture of the brain is comprised of long-range axonal projections that are relatively fixed by adulthood. How does the global dynamism evident in fMRI recordings manifest at a cellular level? To bridge the gap between the activity of single neurons and cortex-wide networks, we correlated electrophysiological recordings of individual neurons in primary visual (V1) and retrosplenial (RSP) associational cortex with activity across dorsal cortex, recorded simultaneously using widefield calcium imaging. We found that individual neurons in both cortical areas independently engaged in different distributed cortical networks depending on the animal’s behavioral state, suggesting that locomotion puts cortex into a more sensory driven mode relevant for navigation.

The medial entorhinal cortex (MEC) supports the brain’s representation of space with distinct cell types whose firing is tuned to features of the environment (grid, border, and object-vector cells) or navigation (head-direction and speed cells). While the firing properties of these functionally-distinct cell types are well characterized, how they interact with one another remains unknown. To determine how activity self-organizes in the MEC network, we tested mice in a spontaneous locomotion task under sensory-deprived conditions. Using 2-photon calcium imaging, we monitored the activity of large populations of MEC neurons in head-fixed mice running on a wheel in darkness, in the absence of external sensory feedback tuned to navigation. We unveiled the presence of motifs that involve the sequential activation of cells in layer II of MEC (MEC-L2). We call these motifs waves. Waves lasted tens of seconds to minutes, were robust, swept through the entire network of active cells and did not exhibit any anatomical organization. Furthermore, waves did not map the position of the mouse on the wheel and were not restricted to running epochs. The majority of MEC-L2 neurons participate in this global sequential dynamics, that ties all functional cell types together. We found the waves in the most lateral region of MEC, but not in adjacent areas such as PaS or in a sensory cortex such as V1.

My research at the interface of neurobiology, biomechanics, and behavior seeks to understand how the timing precision of sensory input structures locomotor output. My lab studies the flight behavior of the fruit fly, Drosophila melanogaster, combining powerful genetic tools available for labeling and manipulating neural circuits with cutting-edge imaging in awake, behaving animals. This work has the potential to fundamentally reshape understanding of the evolution of insect flight, as well as highlight the tremendous importance of timing in the context of locomotion. Timing is crucial to the nervous system. The ability to rapidly detect and process subtle disturbances in the environment determines whether an animal can attain its next meal or successfully navigate complex, unpredictable terrain. While previous work on various animals has made tremendous strides uncovering the specialized neural circuits used to resolve timing differences with sub-microsecond resolution, it has focused on the detection of timing differences in sensory systems. Understanding of how the timing of motor output is structured by precise sensory input remains poor. My research focuses on an organ unique to fruit flies, called the haltere, that serves as a bridge for detecting and acting on subtle timing differences, helping flies execute rapid maneuvers. Understanding how this relatively simple insect canperform such impressive aerial feats demands an integrative approach that combines physics, muscle mechanics, neuroscience, and behavior. This unique, powerful approach will reveal the general principles that govern sensorimotor processing.

Today, considerable information is available on the organization and operation of the neural networks that generate the motor output for animal locomotion, such as swimming, walking, or flying. In recent years, the question of which neural mechanisms are responsible for task-specific and flexible adaptations of locomotor patterns has gained increased attention in the field of motor control. I will report on advances we made with respect to this topic for walking in insects, i.e. the leg muscle control system of phasmids and fruit flies. I will present insights into the neural basis of speed control, heading, walking direction, and the role of ground contact in insect walking, both for local control and intersegmental coordination. For these changes in motor activity modifications in the processing of sensory feedback signals play a pivotal role, for instance for movement and load signals in heading and curve walking or for movement signals that contribute to intersegmental coordination. Our recent findings prompt future investigations that aim to elucidate the mechanisms by which descending and intersegmental signals interact with local networks in the generation of motor flexibility during walking in animals.

Maintaining posture is a vital challenge for all freely-moving organisms. As animals grow, their relationship to destabilizing physical forces changes. How does the nervous system deal with this ongoing challenge? Vertebrates use highly conserved vestibular reflexes to stabilize the body. We established the larval zebrafish as a new model system to understand the development of the vestibular reflexes responsible for balance. In this talk, I will begin with the biophysical challenges facing baby fish as they learn to swim. I’ll briefly review published work by David Ehrlich, Ph.D., establishing a fundamental relationship between postural stability and locomotion. The bulk of the talk will highlight unpublished work by Kyla Hamling. She discovered that a small (~50) population of molecularly-defined brainstem neurons called vestibulo-spinal cells act as a nexus for postural development. Her loss-of-function experiments show that these neurons contribute more to postural stability as animals grow older. I’ll end with brief highlights from her ongoing work examining tilt-evoked responses of these neurons using 2-photon imaging and the consequences of downstream activity in the spinal cord using single-objective light-sheet (SCAPE) microscopy

Animals rely on an internal sense of body position and movement to effectively control motor behaviour. This sense of proprioception is mediated by diverse populations of internal mechanosensory neurons distributed throughout the body. My lab is trying to understand how proprioceptive stimuli are detected by sensory neurons, integrated and transformed in central circuits, and used to guide motor output. We approach these questions using genetic tools, in vivo two-photon imaging, and patch-clamp electrophysiology in Drosophila. We recently found that the axons of fly leg proprioceptors are organized into distinct functional projections that contain topographic representations of specific kinematic features: one group of axons encodes tibia position, another encodes movement direction, and a third encodes bidirectional movement and vibration frequency. Whole-cell recordings from downstream neurons reveal that position, movement, and directional information remain segregated in central circuits. These feedback signals then converge upon motor neurons that control leg muscles during walking. Overall, our findings reveal how a low-dimensional stimulus – the angle of a single leg joint – is encoded by a diverse population of mechanosensory neurons. Specific proprioceptive parameters are initially processed by parallel pathways, but are ultimately integrated to influence motor output. This architecture may help to maximize information transmission, processing speed, and robustness, which are critical for feedback control of the limbs during adaptive locomotion.