Three PhD students funded by BBSRC MIBTP. Please find more information on https://sites.google.com/site/jiankliu/join-us 1. Towards a functional model for associative learning and memory formation Drs Jian Liu and Rodrigo Quian Quiroga, CSN/NPB, University of Leicester 2. Neuronal coupling across spatiotemporal scales and dimensions of cortical population activity Drs Michael Okun and Jian Liu, CSN/NPB, University of Leicester 3. Decoding movement from single neurons in motor cortex and their subcortical targets Drs Todor Gerdjikov and Jian Liu, CSN/NPB, University of Leicester

Brief project description: We all know what it feels like to stop ourselves just before making a mistake. How do we stop ourselves from making mistakes? This project will elucidate how the brain detects and stops in-progress mistakes. The postdoctoral scientist will have the opportunity to study single unit spiking and local field potentials in rats during near-mistake movements, which have only been previously studied using EEG in humans. The lab uses a state-of-the-art head-fixed rat-on-a-treadmill paradigm to measure near-mistake behaviors. Rats are trained to run when they see one stimulus and remain immobile when they see another. Near-mistakes occur when the rat initiates an incorrect running response, but realizes their mistake and stops before crossing a response threshold (running distance). Recordings from the anterior cingulate cortex, motor cortex, subthalamic nucleus, and globus pallidus will be used to describe how neural circuits enable response conflict detection and engage immediate action inhibition, as well as adjustments to future behavior after a near-mistake. Optogenetics will be used to link these behavioral neurophysiology findings to the structural connectivity of individual anterior cingulate neurons. Job specifics: • Prior experience with optogenetics experiments is highly beneficial. • Being a capable MATLAB programmer is a strong benefit, but there is also room to improve your programming skills. • Experience with LFP analyses (e.g., cross-region spike-field phase locking) is beneficial. • 5 years (40,800 EUR starting salary with annual raises) funded by the Academy of Finland. • Start date is flexible. The earliest is 1 November 2020. Resources in the lab: • Head-fixed rat-on-a-treadmill behavior with locomotion and pupil size tracking during complex cognitive tasks using visual, auditory, and whisker deflection sensory stimuli • Ultra-flexible, ultra-thin (1 um) multi-electrode probes (with collaborators) • Neuropixels and silicon probe recordings during head-fixed behavior • Active collaborations with computational neuroscientists Resources in Helsinki Institute of Life Science: • AAV Vector, Lenti Virus Vector, and CRISPR/Cas9 Cores • Drug Discovery Unit • Electron Microscopy Core • Small animal SPECT-CT If interested, contact Nelson Totah at nelson.totah@helsinki.fi with your CV and a motivation letter.

A postdoctoral position is available beginning in 2023 to help develop a brain-machine interface for dexterous control of a cortically-controlled robotic arm and hand. The approach involves creating 1) decoding strategies from electrical signals in motor cortex that enable the user to not only control the movements of the arm and hand but also the forces transmitted through the hand and 2) encoding models to convey tactile sensations to the user through intracortical microstimulation of somatosensory cortex.

I’m interested in structure-function relationships in neural circuits and behavior, with a focus on motor and somatosensory areas of the mouse’s cortex involved in controlling forelimb movements. In one line of investigation, we take a bottom-up, cellularly oriented approach and use optogenetics, electrophysiology, and related slice-based methods to dissect cell-type-specific circuits of corticospinal and other neurons in forelimb motor cortex. In another, we take a top-down ethologically oriented approach and analyze the kinematics and cortical correlates of “oromanual” dexterity as mice handle food. I'll discuss recent progress on both fronts.

The cell assembly hypothesis postulates that groups of coordinated neurons form the basis of information processing. Here, we test this hypothesis by analyzing massively parallel spiking activity recorded in monkey motor cortex during a reach-to-grasp experiment for the presence of significant ms-precise spatio-temporal spike patterns (STPs). For this purpose, the parallel spike trains were analyzed for STPs by the SPADE method (Stella et al, 2019, Biosystems), which detects, counts and evaluates spike patterns for their significance by the use of surrogates (Stella et al, 2022 eNeuro). As a result we find STPs in 19/20 data sets (each of 15min) from two monkeys, but only a small fraction of the recorded neurons are involved in STPs. To consider the different behavioral states during the task, we analyzed the data in a quasi time-resolved analysis by dividing the data into behaviorally relevant time epochs. The STPs that occur in the various epochs are specific to behavioral context - in terms of neurons involved and temporal lags between the spikes of the STP. Furthermore we find, that the STPs often share individual neurons across epochs. Since we interprete the occurrence of a particular STP as the signature of a particular active cell assembly, our interpretation is that the neurons multiplex their cell assembly membership. In a related study, we model these findings by networks with embedded synfire chains (Kleinjohann et al, 2022, bioRxiv 2022.08.02.502431).

Modern electrophysiological recordings simultaneously capture single-unit spiking activities of hundreds of neurons. These neurons exhibit highly complex coordination patterns. Where does this complexity stem from? One candidate is the ubiquitous heterogeneity in connectivity of local neural circuits. Studying neural network dynamics in the linearized regime and using tools from statistical field theory of disordered systems, we derive relations between structure and dynamics that are readily applicable to subsampled recordings of neural circuits: Measuring the statistics of pairwise covariances allows us to infer statistical properties of the underlying connectivity. Applying our results to spontaneous activity of macaque motor cortex, we find that the underlying network operates in a strongly recurrent regime. In this regime, network connectivity is highly heterogeneous, as quantified by a large radius of bulk connectivity eigenvalues. Being close to the point of linear instability, this dynamical regime predicts a rich correlation structure, a large dynamical repertoire, long-range interaction patterns, relatively low dimensionality and a sensitive control of neuronal coordination. These predictions are verified in analyses of spontaneous activity of macaque motor cortex and mouse visual cortex. Finally, we show that even microscopic features of connectivity, such as connection motifs, systematically scale up to determine the global organization of activity in neural circuits.

Efficient coding has served as a guiding principle in understanding the neural code. To date, however, it has been explored mainly in the context of peripheral sensory cells with simple tuning curves. By contrast, ‘deeper’ neurons such as grid cells come with more complex tuning properties which imply a different, yet highly efficient, strategy for representing information. I will show that a highly efficient code is not specific to a population of neurons with finely tuned response properties: it emerges robustly in a shallow network with random synapses. Here, the geometry of population responses implies that optimality obtains from a tradeoff between two qualitatively different types of error: ‘local’ errors (common to classical neural population codes) and ‘global’ (or ‘catastrophic’) errors. This tradeoff leads to efficient compression of information from a high-dimensional representation to a low-dimensional one. After describing the theoretical framework, I will use it to re-interpret recordings of motor cortex in behaving monkey. Our framework addresses the encoding of (sensory) information; if time allows, I will comment on ongoing work that focuses on decoding from the perspective of efficient coding.

It is widely known that motor learning leads to reorganization changes in the motor cortex. Recently, we have shown that using navigated transcranial magnetic stimulation (TMS) allows us to reliably trace interactions among motor cortical representations (MCRs) of different upper limb muscles. Using this approach, we investigate changes in the MCRs after fine finger movement training. Our preliminary results demonstrated that areas of the APB and ADM and their overlaps tended to increase after finger independence training. Considering the behavioral data, hand dexterity increased for both hands, but the amplitudes of voluntary contraction of the muscles for the APB and ADM did not change significantly. The behavioral results correspond with a previously described suggestion that hand strength and hand dexterity are not directly related as well as an increase in overlaps between MCRs of the trained muscles supports the idea that voluntary muscle relaxation is an active physiological process.

While non-invasive Brain-to-Computer interface can accurately classify the lateralization of hand moments, the distinction of fingers activation in the same hand is limited by their local and overlapping representation in the motor cortex. In particular, the low signal-to-noise ratio restrains the opportunity to identify meaningful patterns in a supervised fashion. Here we combined Magnetoencephalography (MEG) recordings with advanced decoding strategy to classify finger movements at single trial level. We recorded eight subjects performing a serial reaction time task, where they pressed four buttons with left and right index and middle fingers. We evaluated the classification performance of hand and finger movements with increasingly complex approaches: supervised common spatial patterns and logistic regression (CSP + LR) and unsupervised linear finite convolutional neural network (LF-CNN). The right vs left fingers classification performance was accurate above 90% for all methods. However, the classification of the single finger provided the following accuracy: CSP+SVM : – 68 ± 7%, LF-CNN : 71 ± 10%. CNN methods allowed the inspection of spatial and spectral patterns, which reflected activity in the motor cortex in the theta and alpha ranges. Thus, we have shown that the use of CNN in decoding MEG single trials with low signal to noise ratio is a promising approach that, in turn, could be extended to a manifold of problems in clinical and cognitive neuroscience.

My group pioneered the development of a novel intracortical brain computer interface (iBCI) that decodes muscle activity (EMG) from signals recorded in the motor cortex of animals. We use these synthetic EMG signals to control Functional Electrical Stimulation (FES), which causes the muscles to contract and thereby restores rudimentary voluntary control of the paralyzed limb. In the past few years, there has been much interest in the fact that information from the millions of neurons active during movement can be reduced to a small number of “latent” signals in a low-dimensional manifold computed from the multiple neuron recordings. These signals can be used to provide a stable prediction of the animal’s behavior over many months-long periods, and they may also provide the means to implement methods of transfer learning across individuals, an application that could be of particular importance for paralyzed human users. We have begun to examine the representation within this latent space, of a broad range of behaviors, including well-learned, stereotyped movements in the lab, and more natural movements in the animal’s home cage, meant to better represent a person’s daily activities. We intend to develop an FES-based iBCI that will restore voluntary movement across a broad range of motor tasks without need for intermittent recalibration. However, the nonlinearities and context dependence within this low-dimensional manifold present significant challenges.

We have been developing Brain-Computer Interface (BCI) using electrocorticography (ECoG) [1] , which is recorded by electrodes implanted on brain surface, and magnetoencephalography (MEG) [2] , which records the cortical activities non-invasively, for the clinical applications. The invasive BCI using ECoG has been applied for severely paralyzed patient to restore the communication and motor function. The non-invasive BCI using MEG has been applied as a neurofeedback tool to modulate some pathological neural activities to treat some neuropsychiatric disorders. Although these techniques have been developed for clinical application, BCI is also an important tool to investigate neural function. For example, motor BCI records some neural activities in a part of the motor cortex to generate some movements of external devices. Although our motor system consists of complex system including motor cortex, basal ganglia, cerebellum, spinal cord and muscles, the BCI affords us to simplify the motor system with exactly known inputs, outputs and the relation of them. We can investigate the motor system by manipulating the parameters in BCI system. Recently, we are developing some BCIs to visualize and manipulate our perception and mental imagery. Although these BCI has been developed for clinical application, the BCI will be useful to understand our neural system to generate the perception and imagery. In this talk, I will introduce our study of phantom limb pain [3] , that is controlled by MEG-BCI, and the development of a communication BCI using ECoG [4] , that enable the subject to visualize the contents of their mental imagery. And I would like to discuss how much we can control our cortical activities that represent our perception and mental imagery. These examples demonstrate that BCI is a promising tool to visualize and manipulate the perception and imagery and to understand our consciousness. References 1. Yanagisawa, T., Hirata, M., Saitoh, Y., Kishima, H., Matsushita, K., Goto, T., Fukuma, R., Yokoi, H., Kamitani, Y., and Yoshimine, T. (2012). Electrocorticographic control of a prosthetic arm in paralyzed patients. AnnNeurol 71, 353-361. 2. Yanagisawa, T., Fukuma, R., Seymour, B., Hosomi, K., Kishima, H., Shimizu, T., Yokoi, H., Hirata, M., Yoshimine, T., Kamitani, Y., et al. (2016). Induced sensorimotor brain plasticity controls pain in phantom limb patients. Nature communications 7, 13209. 3. Yanagisawa, T., Fukuma, R., Seymour, B., Tanaka, M., Hosomi, K., Yamashita, O., Kishima, H., Kamitani, Y., and Saitoh, Y. (2020). BCI training to move a virtual hand reduces phantom limb pain: A randomized crossover trial. Neurology 95, e417-e426. 4. Ryohei Fukuma, Takufumi Yanagisawa, Shinji Nishimoto, Hidenori Sugano, Kentaro Tamura, Shota Yamamoto, Yasushi Iimura, Yuya Fujita, Satoru Oshino, Naoki Tani, Naoko Koide-Majima, Yukiyasu Kamitani, Haruhiko Kishima (2022). Voluntary control of semantic neural representations by imagery with conflicting visual stimulation. arXiv arXiv:2112.01223.

Learning is known to induce the formation of new dendritic spines, but despite decades of effort, the functional properties of new spines in vivo remain unknown. Here, using a combination of longitudinal in vivo 2-photon imaging of the glutamate reporter, iGluSnFR, and correlated electron microscopy (CLEM) of dendritic spines on the apical dendrites of L2/3 excitatory neurons in the motor cortex during motor learning, we describe a framework of new spines' formation, survival, and resulting function. Specifically, our data indicate that the potentiation of a subset of clustered, pre-existing spines showing task-related activity in early sessions of learning creates a micro-environment of plasticity within dendrites, wherein multiple filopodia sample the nearby neuropil, form connections with pre-existing boutons connected to allodendritic spines, and are then selected for survival based on co-activity with nearby task-related spines. Thus, the formation and survival of new spines is determined by the functional micro-environment of dendrites. After formation, new spines show preferential co-activation with nearby task-related spines. This synchronous activity is more specific to movements than activation of the individual spines in isolation, and further, is coincident with movements that are more similar to the learned pattern. Thus, new spines functionally engage with their parent clusters to signal the learned movement. Finally, by reconstructing the axons associated with new spines, we found that they synapse with axons previously unrepresented in these dendritic domains, suggesting that the strong local co-activity structure exhibited by new spines is likely not due to axon sharing. Thus, learning involves the binding of new information streams into functional synaptic clusters to subserve the learned behavior.

The study of mirror neurons (MN) has a long way since its discovery on monkeys and later on humans. However, in literature there are inconsistencies on the ways stimuli are presented and on the time of presentation. Which is the best way to present motor movement stimuli? Is it possible to estimate when the mirror neurons effect take place by using Transcranial Magnetic Stimulation at specific time windows? In the current study we test different ways of stimuli presentation (photo and video of hand movements) and brain stimulation (e.g. TMS) delivered on the dominant primary motor cortex (M1) at different time windows. Our aim is to solve this void still present on the field and create a standardized protocol that will generate the strongest mirror neurons response in order to have the way for future studies on the field.

The primary motor cortex (M1) is a major output center for movement execution and motor learning, and its dysfunction contributes to the pathophysiology of Parkinson’s disease (PD). While human studies have indicated that a loss of midbrain dopamine neurons alters M1 activation, the mechanisms underlying this phenomenon remain unclear. Using a mouse model of PD, we uncovered several shifts within M1 circuitry following dopamine depletion, including impaired excitation by thalamocortical afferents and altered excitability. Our findings add to the growing body of literature highlighting M1 as a major contributor in PD, and provide targeted neural substrates for possible therapeutic interventions.

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.

Animals constantly modify their behavior through experience. Flexible behavior is key to our ability to adapt to the ever-changing environment. My laboratory is interested in studying the activity of neuronal ensembles in behaving animals, and how it changes with learning. We have recently set up a paradigm where mice learn to associate sensory information (two different odors) to motor outputs (lick vs no-lick) under head-fixation. We combined this with two-photon calcium imaging, which can monitor the activity of a microcircuit of many tens of neurons simultaneously from a small area of the brain. Imaging the motor cortex during the learning of this task revealed neurons with diverse task-related response types. Intriguingly, different response types were spatially intermingled; even immediately adjacent neurons often had very different response types. As the mouse learned the task under the microscope, the activity coupling of neurons with similar response types specifically increased, even though they are intermingled with neurons with dissimilar response types. This suggests that intermingled subnetworks of functionally-related neurons form in a learning-related way, an observation that became possible with our cutting-edge technique combining imaging and behavior. We are working to extend this study. How plastic are neuronal microcircuits during other forms of learning? How plastic are they in other parts of the brain? What are the cellular and molecular mechanisms of the microcircuit plasticity? Are the observed activity and plasticity required for learning? How does the activity of identified individual neurons change over days to weeks? We are asking these questions, combining a variety of techniques including in vivo two-photon imaging, optogenetics, electrophysiology, genetics and behavior.

Distinct synapses influence one another when they undergo changes, with unclear consequences for neuronal dynamics and function. Here we show that synapses can interact such that excitatory currents are naturally normalised and balanced by inhibitory inputs. This happens when classical spike-timing dependent synaptic plasticity rules are extended by additional mechanisms that incorporate the influence of neighbouring synaptic currents and regulate the amplitude of efficacy changes accordingly. The resulting control of excitatory plasticity by inhibitory activation, and vice versa, gives rise to quick and long-lasting memories as seen experimentally in receptive field plasticity paradigms. In models with additional dendritic structure, we observe experimentally reported clustering of co-active synapses that depends on initial connectivity and morphology. Finally, in recurrent neural networks, rich and stable dynamics with high input sensitivity emerge, providing transient activity that resembles recordings from the motor cortex. Our model provides a general framework for codependent plasticity that frames individual synaptic modifications in the context of population-wide changes, allowing us to connect micro-level physiology with behavioural phenomena.

A central question in motor physiology has been whether motor cortex activity resembles muscle activity, and if not, why not? Over fifty years, extensive observations have failed to provide a concise answer, and the topic remains much debated. To provide a different perspective, we employed a novel behavioral paradigm that affords extensive comparison between time-evolving neural and muscle activity. Single motor-cortex neurons displayed many muscle-like properties, but the structure of population activity was not muscle-like. Unlike muscle activity, neural activity was structured to avoid ’trajectory tangling’: moments where similar activity patterns led to dissimilar future patterns. Avoidance of trajectory tangling was present across tasks and species. Network models revealed a potential reason for this consistent feature: low tangling confers noise robustness. Remarkably, we were able to predict motor cortex activity from muscle activity alone, by leveraging the hypothesis that muscle-like commands are embedded in additional structure that yields low tangling. Our results argue that motor cortex embeds descending commands in additional structure that ensure low tangling, and thus noise-robustness. The dominant structure in motor cortex may thus serve not a representational function (encoding specific variables) but a computational function: ensuring that outgoing commands can be generated reliably. Our results establish the utility of an emerging approach: understanding the structure of neural activity based on properties of population geometry that flow from normative principles such as noise robustness.

To make judgments, brain must be able to infer the state of the world based on often incomplete and ambiguous evidence. To probe neural circuits that perform the computations underlying such judgments, we developed a behavioral task for mice that required them to detect sustained increases in the speed of a continuously varying visual stimulus. In this talk, I will present evidence that the responses of secondary motor cortex to stimulus fluctuations in this task are consistent with updates of the animal’s state of belief that the change has occurred. These results establish a framework for mechanistic inquiries into neural circuits underlying inference during perceptual decision-making.