Optogenetic feedback control of neural activity

Bridging model and experiment in systems neuroscience with Cleo: the Closed-Loop, Electrophysiology, and Optophysiology simulation testbed

Systems neuroscience has experienced an explosion of new tools for reading and writing neural activity, enabling exciting new experiments such as all-optical or closed-loop control that effect powerful causal interventions. At the same time, improved computational models are capable of reproducing behavior and neural activity with increasing fidelity. Unfortunately, these advances have drastically increased the complexity of integrating different lines of research, resulting in the missed opportunities and untapped potential of suboptimal experiments. Experiment simulation can help bridge this gap, allowing model and experiment to better inform each other by providing a low-cost testbed for experiment design, model validation, and methods engineering. Specifically, this can be achieved by incorporating the simulation of the experimental interface into our models, but no existing tool integrates optogenetics, two-photon calcium imaging, electrode recording, and flexible closed-loop processing with neural population simulations. To address this need, we have developed Cleo: the Closed-Loop, Electrophysiology, and Optophysiology experiment simulation testbed. Cleo is a Python package enabling injection of recording and stimulation devices as well as closed-loop control with realistic latency into a Brian spiking neural network model. It is the only publicly available tool currently supporting two-photon and multi-opsin/wavelength optogenetics. To facilitate adoption and extension by the community, Cleo is open-source, modular, tested, and documented, and can export results to various data formats. Here we describe the design and features of Cleo, validate output of individual components and integrated experiments, and demonstrate its utility for advancing optogenetic techniques in prospective experiments using previously published systems neuroscience models.

Pixel-wise programmability enables dynamic high-SNR cameras for high-speed microscopy

High-speed wide-field fluorescence microscopy has the potential to capture biological processes with exceptional spatiotemporal resolution. However, conventional cameras suffer from low signal-to-noise ratio at high frame rates, limiting their ability to detect faint fluorescent events. Here, we introduce an image sensor where each pixel has individually programmable sampling speed and phase, so that pixels can be arranged to simultaneously sample at high speed with a high signal-to-noise ratio. In high-speed voltage imaging experiments, our image sensor significantly increases the output signal-to-noise ratio compared to a low-noise scientific CMOS camera (~2-3 folds). This signal-to-noise ratio gain enables the detection of weak neuronal action potentials and subthreshold activities missed by the standard scientific CMOS cameras. Our camera with flexible pixel exposure configurations offers versatile sampling strategies to improve signal quality in various experimental conditions.

Pixel-wise programmability enables dynamic high-SNR cameras for high-speed microscopy

High-speed wide-field fluorescence microscopy has the potential to capture biological processes with exceptional spatiotemporal resolution. However, conventional cameras suffer from low signal-to-noise ratio at high frame rates, limiting their ability to detect faint fluorescent events. Here, we introduce an image sensor where each pixel has individually programmable sampling speed and phase, so that pixels can be arranged to simultaneously sample at high speed with a high signal-to-noise ratio. In high-speed voltage imaging experiments, our image sensor significantly increases the output signal-to-noise ratio compared to a low-noise scientific CMOS camera (~2-3 folds). This signal-to-noise ratio gain enables the detection of weak neuronal action potentials and subthreshold activities missed by the standard scientific CMOS cameras. Our camera with flexible pixel exposure configurations offers versatile sampling strategies to improve signal quality in various experimental conditions.

Optogenetic Approach in Trigeminal Neuralgia and Potential Concerns: Preclinical Insights

The integration of optogenetics in the trigeminal pain circuitry broadens and reinforces existing pain investigations. Similar to research on spinal neuropathic pain, the exploration of the underlying determinants of orofacial pain is expanding. Optogenetics facilitates more direct, specific, and subtle investigations of the neuronal circuits involved in orofacial pain. One of the most significant concerns of both dentistry and medicine is trigeminal neuralgia (TN) management due to its substantial impact on a patient's quality of life. Our objective is to gather insights from preclinical studies conducted in TN employing an optogenetic paradigm, thereby extending the prospects for in-depth neurobiological research. This review highlights optogenetic research in trigeminal pain circuitry involving TN. We outline the central and peripheral regions associated with pain-that have been investigated using optogenetics in the trigeminal pain circuitry. The study further reports its scope and limitations as well as its potential for future applications from bench to bedside.

Design and validation of a low-cost photomodulator for in vivo photoactivation of a mGluR5 inhibitor

Purpose: Severe side effects prevent the utilization of otherwise promising drugs in treatments. These side effects arise when drugs affect untargeted tissues due to poor target specificity. In photopharmacology, light controls the timing and the location of drug delivery, improving treatment specificity and pharmacokinetic control. Photopharmaceuticals have not seen widespread adoption in part because researchers do not always have access to reliable and reproducible light delivery devices at prices which fit within the larger research budget. Method: In this work, we present a customizable photomodulator for use in both wearable and implantable devices. For experimental validation of the photomodulator, we photolyse JF-NP-26 in rats. Results: We successfully drive in vivo photopharmacology with a tethered photomodulator and demonstrate modifications which enable the photomodulator to operate wirelessly. Conclusion: By documenting our photomodulator development, we hope to introduce researchers to a simple solution which significantly lowers the engineering barriers to photopharmacology research.

Graphical abstract

Researchers present a photomodulator, a device designed to facilitate in vivo photopharmacology. They demonstrate the in vivo capabilities of the photomodulator by photoreleasing raseglurant, an mGluR5 inhibitor, to treat pain in an acute rat model and follow this study by showing how to reconfigure the photomodulator to work wirelessly and interface with other biomedical devices.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13534-023-00334-3.

Opto-electronic feedback control of membrane potential for real-time control of action potentials

To unlock new research possibilities by acquiring control of action potential (AP) morphologies in excitable cells, we developed an opto-electronic feedback loop-based system integrating cellular electrophysiology, real-time computing, and optogenetic approaches and applied it to monolayers of heart muscle cells. This allowed accurate restoration and preservation of cardiac AP morphologies in the presence of electrical perturbations of different origin in an unsupervised, self-regulatory manner, without any prior knowledge of the disturbance. Moreover, arbitrary AP waveforms could be enforced onto these cells. Collectively, these results set the stage for the refinement and application of opto-electronic control systems to enable in-depth investigation into the regulatory role of membrane potential in health and disease.

An unsupervised real-time spike sorting system based on optimized OSort

Objective. The OSort algorithm, a pivotal unsupervised spike sorting method, has been implemented in dedicated hardware devices for real-time spike sorting. However, due to the inherent complexity of neural recording environments, OSort still grapples with numerous transient cluster occurrences during the practical sorting process. This leads to substantial memory usage, heavy computational load, and complex hardware architectures, especially in noisy recordings and multi-channel systems.Approach. This study introduces an optimized OSort algorithm (opt-OSort) which utilizes correlation coefficient (CC), instead of Euclidean distance as classification criterion. TheCCmethod not only bolsters the robustness of spike classification amidst the diverse and ever-changing conditions of physiological and recording noise environments, but also can finish the entire sorting procedure within a fixed number of cluster slots, thus preventing a large number of transient clusters. Moreover, the opt-OSort incorporates two configurable validation loops to efficiently reject cluster outliers and track recording variations caused by electrode drifting in real-time.Main results. The opt-OSort significantly reduces transient cluster occurrences by two orders of magnitude and decreases memory usage by 2.5-80 times in the number of pre-allocated transient clusters compared with other hardware implementations of OSort. The opt-OSort maintains an accuracy comparable to offline OSort and other commonly-used algorithms, with a sorting time of 0.68µs as measured by the hardware-implemented system in both simulated datasets and experimental data. The opt-OSort's ability to handle variations in neural activity caused by electrode drifting is also demonstrated.Significance. These results present a rapid, precise, and robust spike sorting solution suitable for integration into low-power, portable, closed-loop neural control systems and brain-computer interfaces.

Bayesian target optimisation for high-precision holographic optogenetics

Two-photon optogenetics has transformed our ability to probe the structure and function of neural circuits. However, achieving precise optogenetic control of neural ensemble activity has remained fundamentally constrained by the problem of off-target stimulation (OTS): the inadvertent activation of nearby non-target neurons due to imperfect confinement of light onto target neurons. Here we propose a novel computational approach to this problem called Bayesian target optimisation. Our approach uses nonparametric Bayesian inference to model neural responses to optogenetic stimulation, and then optimises the laser powers and optical target locations needed to achieve a desired activity pattern with minimal OTS. We validate our approach in simulations and using data from in vitro experiments, showing that Bayesian target optimisation considerably reduces OTS across all conditions we test. Together, these results establish our ability to overcome OTS, enabling optogenetic stimulation with substantially improved precision.

Synchronization, clustering, and weak chimeras in a densely coupled transcription-based oscillator model for split circadian rhythms

The synchronization dynamics for the circadian gene expression in the suprachiasmatic nucleus is investigated using a transcriptional circadian clock gene oscillator model. With global coupling in constant dark (DD) conditions, the model exhibits a one-cluster phase synchronized state, in dim light (dim LL), bistability between one- and two-cluster states and in bright LL, a two-cluster state. The two-cluster phase synchronized state, where some oscillator pairs synchronize in-phase, and some anti-phase, can explain the splitting of the circadian clock, i.e., generation of two bouts of daily activities with certain species, e.g., with hamsters. The one- and two-cluster states can be reached by transferring the animal from DD or bright LL to dim LL, i.e., the circadian synchrony has a memory effect. The stability of the one- and two-cluster states was interpreted analytically by extracting phase models from the ordinary differential equation models. In a modular network with two strongly coupled oscillator populations with weak intragroup coupling, with appropriate initial conditions, one group is synchronized to the one-cluster state and the other group to the two-cluster state, resulting in a weak-chimera state. Computational modeling suggests that the daily rhythms in sleep-wake depend on light intensity acting on bilateral networks of suprachiasmatic nucleus (SCN) oscillators. Addition of a network heterogeneity (coupling between the left and right SCN) allowed the system to exhibit chimera states. The simulations can guide experiments in the circadian rhythm research to explore the effect of light intensity on the complexities of circadian desynchronization.

High-precision monitoring of and feedback control over drug concentrations in the brains of freely moving rats

Knowledge of drug concentrations in the brains of behaving subjects remains constrained on a number of dimensions, including poor temporal resolution and lack of real-time data. Here, however, we demonstrate the ability of electrochemical aptamer-based sensors to support seconds-resolved, real-time measurements of drug concentrations in the brains of freely moving rats. Specifically, using such sensors, we achieve <4 μM limits of detection and 10-s resolution in the measurement of procaine in the brains of freely moving rats, permitting the determination of the pharmacokinetics and concentration-behavior relations of the drug with high precision for individual subjects. In parallel, we have used closed-loop feedback-controlled drug delivery to hold intracranial procaine levels constant (±10%) for >1.5 hours. These results demonstrate the utility of such sensors in (i) the determination of the site-specific, seconds-resolved neuropharmacokinetics, (ii) enabling the study of individual subject neuropharmacokinetics and concentration-response relations, and (iii) performing high-precision control over intracranial drug levels.

All-optical closed-loop voltage clamp for precise control of muscles and neurons in live animals

Excitable cells can be stimulated or inhibited by optogenetics. Since optogenetic actuation regimes are often static, neurons and circuits can quickly adapt, allowing perturbation, but not true control. Hence, we established an optogenetic voltage-clamp (OVC). The voltage-indicator QuasAr2 provides information for fast, closed-loop optical feedback to the bidirectional optogenetic actuator BiPOLES. Voltage-dependent fluorescence is held within tight margins, thus clamping the cell to distinct potentials. We established the OVC in muscles and neurons of Caenorhabditis elegans, and transferred it to rat hippocampal neurons in slice culture. Fluorescence signals were calibrated to electrically measured potentials, and wavelengths to currents, enabling to determine optical I/V-relationships. The OVC reports on homeostatically altered cellular physiology in mutants and on Ca²⁺-channel properties, and can dynamically clamp spiking in C. elegans. Combining non-invasive imaging with control capabilities of electrophysiology, the OVC facilitates high-throughput, contact-less electrophysiology in individual cells and paves the way for true optogenetic control in behaving animals.

Self-tuned criticality: Controlling a neuron near its bifurcation point via temporal correlations

Previous work showed that the collective activity of large neuronal networks can be tamed to remain near its critical point by a feedback control that maximizes the temporal correlations of the mean-field fluctuations. Since such correlations behave similarly near instabilities across nonlinear dynamical systems, it is expected that the principle should control also low-dimensional dynamical systems exhibiting continuous or discontinuous bifurcations from fixed points to limit cycles. Here we present numerical evidence that the dynamics of a single neuron can be controlled in the vicinity of its bifurcation point. The approach is tested in two models: a two-dimensional generic excitable map and the paradigmatic FitzHugh-Nagumo neuron model. The results show that in both cases, the system can be self-tuned to its bifurcation point by modifying the control parameter according to the first coefficient of the autocorrelation function.

Intertwining Neuropathogenic Impacts of Aberrant Circadian Rhythm and Impaired Neuroregenerative Plasticity in Huntington’s Disease: Neurotherapeutic Significance of Chemogenetics

Huntington’s disease (HD) is a progressive neurodegenerative disorder characterized by abnormal progressive involuntary movements, cognitive deficits, sleep disturbances, and psychiatric symptoms. The onset and progression of the clinical symptoms have been linked to impaired adult neurogenesis in the brains of subjects with HD, due to the reduced neurogenic potential of neural stem cells (NSCs). Among various pathogenic determinants, an altered clock pathway appears to induce the dysregulation of neurogenesis in neurodegenerative disorders. Notably, gamma-aminobutyric acid (GABA)-ergic neurons that express the vasoactive intestinal peptide (VIP) in the brain play a key role in the regulation of circadian rhythm and neuroplasticity. While an abnormal clock gene pathway has been associated with the inactivation of GABAergic VIP neurons, recent studies suggest the activation of this neuronal population in the brain positively contributes to neuroplasticity. Thus, the activation of GABAergic VIP neurons in the brain might help rectify the irregular circadian rhythm in HD. Chemogenetics refers to the incorporation of genetically engineered receptors or ion channels into a specific cell population followed by its activation using desired chemical ligands. The recent advancement of chemogenetic-based approaches represents a potential scientific tool to rectify the aberrant circadian clock pathways. Considering the facts, the defects in the circadian rhythm can be rectified by the activation of VIP-expressing GABAergic neurons using chemogenetics approaches. Thus, the chemogenetic-based rectification of an abnormal circadian rhythm may facilitate the neurogenic potentials of NSCs to restore the neuroregenerative plasticity in HD. Eventually, the increased neurogenesis in the brain can be expected to mitigate neuronal loss and functional deficits.

Preconfigured dynamics in the hippocampus are guided by embryonic birthdate and rate of neurogenesis

The incorporation of new information into the hippocampal network is likely to be constrained by its innate architecture and internally generated activity patterns. However, the origin, organization and consequences of such patterns remain poorly understood. In the present study we show that hippocampal network dynamics are affected by sequential neurogenesis. We birthdated CA1 pyramidal neurons with in utero electroporation over 4 embryonic days, encompassing the peak of hippocampal neurogenesis, and compared their functional features in freely moving adult mice. Neurons of the same birthdate displayed distinct connectivity, coactivity across brain states and assembly dynamics. Same-birthdate neurons exhibited overlapping spatial representations, which were maintained across different environments. Overall, the wiring and functional features of CA1 pyramidal neurons reflected a combination of birthdate and the rate of neurogenesis. These observations demonstrate that sequential neurogenesis during embryonic development shapes the preconfigured forms of adult network dynamics.

An improved platform for cultured neuronal network electrophysiology: multichannel optogenetics integrated with MEAs

Cultured neuronal networks (CNNs) are powerful tools for studying how neuronal representation and adaptation emerge in networks of controlled populations of neurons. To ensure the interaction of a CNN and an artificial setting, reliable operation in both open and closed loops should be provided. In this study, we integrated optogenetic stimulation with microelectrode array (MEA) recordings using a digital micromirror device and developed an improved research tool with a 64-channel interface for neuronal network control and data acquisition. We determined the ideal stimulation parameters including light intensity, frequency, and duty cycle for our configuration. This resulted in robust and reproducible neuronal responses. We also demonstrated both open and closed loop configurations in the new platform involving multiple bidirectional channels. Unlike previous approaches that combined optogenetic stimulation and MEA recordings, we did not use binary grid patterns, but assigned an adjustable-size, non-binary optical spot to each electrode. This approach allowed simultaneous use of multiple input-output channels and facilitated adaptation of the stimulation parameters. Hence, we advanced a 64-channel interface in that each channel can be controlled individually in both directions simultaneously without any interference or interrupts. The presented setup meets the requirements of research in neuronal plasticity, network encoding and representation, closed-loop control of firing rate and synchronization. Researchers who develop closed-loop control techniques and adaptive stimulation strategies for network activity will benefit much from this novel setup.

Advantages, Pitfalls, and Developments of All Optical Interrogation Strategies of Microcircuits in vivo

The holy grail for every neurophysiologist is to conclude a causal relationship between an elementary behaviour and the function of a specific brain area or circuit. Our effort to map elementary behaviours to specific brain loci and to further manipulate neural activity while observing the alterations in behaviour is in essence the goal for neuroscientists. Recent advancements in the area of experimental brain imaging in the form of longer wavelength near infrared (NIR) pulsed lasers with the development of highly efficient optogenetic actuators and reporters of neural activity, has endowed us with unprecedented resolution in spatiotemporal precision both in imaging neural activity as well as manipulating it with multiphoton microscopy. This readily available toolbox has introduced a so called all-optical physiology and interrogation of circuits and has opened new horizons when it comes to precisely, fast and non-invasively map and manipulate anatomically, molecularly or functionally identified mesoscopic brain circuits. The purpose of this review is to describe the advantages and possible pitfalls of all-optical approaches in system neuroscience, where by all-optical we mean use of multiphoton microscopy to image the functional response of neuron(s) in the network so to attain flexible choice of the cells to be also optogenetically photostimulated by holography, in absence of electrophysiology. Spatio-temporal constraints will be compared toward the classical reference of electrophysiology methods. When appropriate, in relation to current limitations of current optical approaches, we will make reference to latest works aimed to overcome these limitations, in order to highlight the most recent developments. We will also provide examples of types of experiments uniquely approachable all-optically. Finally, although mechanically non-invasive, all-optical electrophysiology exhibits potential off-target effects which can ambiguate and complicate the interpretation of the results. In summary, this review is an effort to exemplify how an all-optical experiment can be designed, conducted and interpreted from the point of view of the integrative neurophysiologist.

Discovering sparse control strategies in neural activity

Biological circuits such as neural or gene regulation networks use internal states to map sensory input to an adaptive repertoire of behavior. Characterizing this mapping is a major challenge for systems biology. Though experiments that probe internal states are developing rapidly, organismal complexity presents a fundamental obstacle given the many possible ways internal states could map to behavior. Using C. elegans as an example, we propose a protocol for systematic perturbation of neural states that limits experimental complexity and could eventually help characterize collective aspects of the neural-behavioral map. We consider experimentally motivated small perturbations—ones that are most likely to preserve natural dynamics and are closer to internal control mechanisms—to neural states and their impact on collective neural activity. Then, we connect such perturbations to the local information geometry of collective statistics, which can be fully characterized using pairwise perturbations. Applying the protocol to a minimal model of C. elegans neural activity, we find that collective neural statistics are most sensitive to a few principal perturbative modes. Dominant eigenvalues decay initially as a power law, unveiling a hierarchy that arises from variation in individual neural activity and pairwise interactions. Highest-ranking modes tend to be dominated by a few, “pivotal” neurons that account for most of the system’s sensitivity, suggesting a sparse mechanism of collective control.

Spiking Adaptive Dynamic Programming Based on Poisson Process for Discrete-Time Nonlinear Systems

In this article, a new iterative spiking adaptive dynamic programming (SADP) method based on the Poisson process is developed to solve optimal impulsive control problems. For a fixed time interval, combining the Poisson process and the maximum likelihood estimation (MLE), the three-tuple of state, spiking interval, and probability of Poisson distribution can be computed, and then, the iterative value functions and iterative control laws can be obtained. A property analysis method is developed to show that the value functions converge to optimal performance index function as the iterative index increases from zero to infinity. Finally, two simulation examples are given to verify the effectiveness of the developed algorithm.

A dynamic clamp protocol to artificially modify cell capacitance

Dynamics of excitable cells and networks depend on the membrane time constant, set by membrane resistance and capacitance. Whereas pharmacological and genetic manipulations of ionic conductances of excitable membranes are routine in electrophysiology, experimental control over capacitance remains a challenge. Here, we present capacitance clamp, an approach that allows electrophysiologists to mimic a modified capacitance in biological neurons via an unconventional application of the dynamic clamp technique. We first demonstrate the feasibility to quantitatively modulate capacitance in a mathematical neuron model and then confirm the functionality of capacitance clamp in in vitro experiments in granule cells of rodent dentate gyrus with up to threefold virtual capacitance changes. Clamping of capacitance thus constitutes a novel technique to probe and decipher mechanisms of neuronal signaling in ways that were so far inaccessible to experimental electrophysiology.

Inhibition allocates spikes during hippocampal ripples

Sets of spikes emitted sequentially across neurons constitute fundamental pulse packets in neural information processing, including offline memory replay during hippocampal sharp-wave ripples (SWRs). The relative timing of neuronal spikes is fine-tuned in each spike sequence but can vary between different sequences. However, the microcircuitry mechanism that enables such flexible spike sequencing remains unexplored. We recorded the membrane potentials of multiple hippocampal CA1 pyramidal cells in mice and found that the neurons were transiently hyperpolarized prior to SWRs. The pre-SWR hyperpolarizations were spatiotemporally heterogeneous, and larger hyperpolarizations were associated with later spikes during SWRs. Intracellular blockade of Cl--mediated inhibition reduced pre-SWR hyperpolarizations and advanced spike times. Single-unit recordings also revealed that the pre-SWR firing rates of inhibitory interneurons predicted the SWR-relevant spike times of pyramidal cells. Thus, pre-SWR inhibitory activity determines the sequential spike times of pyramidal cells and diversifies the repertoire of sequence patterns.

Modeling multiscale causal interactions between spiking and field potential signals during behavior

Objective. Brain recordings exhibit dynamics at multiple spatiotemporal scales, which are measured with spike trains and larger-scale field potential signals. To study neural processes, it is important to identify and model causal interactions not only at a single scale of activity, but also across multiple scales, i.e. between spike trains and field potential signals. Standard causality measures are not directly applicable here because spike trains are binary-valued but field potentials are continuous-valued. It is thus important to develop computational tools to recover multiscale neural causality during behavior, assess their performance on neural datasets, and study whether modeling multiscale causalities can improve the prediction of neural signals beyond what is possible with single-scale causality. Approach. We design a multiscale model-based Granger-like causality method based on directed information and evaluate its success both in realistic biophysical spike-field simulations and in motor cortical datasets from two non-human primates (NHP) performing a motor behavior. To compute multiscale causality, we learn point-process generalized linear models that predict the spike events at a given time based on the history of both spike trains and field potential signals. We also learn linear Gaussian models that predict the field potential signals at a given time based on their own history as well as either the history of binary spike events or that of latent firing rates. Main results. We find that our method reveals the true multiscale causality network structure in biophysical simulations despite the presence of model mismatch. Further, models with the identified multiscale causalities in the NHP neural datasets lead to better prediction of both spike trains and field potential signals compared to just modeling single-scale causalities. Finally, we find that latent firing rates are better predictors of field potential signals compared with the binary spike events in the NHP datasets. Significance. This multiscale causality method can reveal the directed functional interactions across spatiotemporal scales of brain activity to inform basic science investigations and neurotechnologies.

Closed-loop control of neural spike rate of cultured neurons using a thermoplasmonics-based photothermal neural stimulation

Objective.Photothermal neural stimulation has been developed in a variety of interfaces as an alternative technology that can perturb neural activity. The demonstrations of these techniques have heavily relied on open-loop stimulation or complete suppression of neural activity. To extend the controllability of photothermal neural stimulation, combining it with a closed-loop system is required. In this work, we investigated whether photothermal suppression mechanism can be used in a closed-loop system to reliably modulate neural spike rate to non-zero setpoints.Approach. To incorporate the photothermal inhibition mechanism into the neural feedback system, we combined a thermoplasmonic stimulation platform based on gold nanorods (GNRs) and near-infrared illuminations (808 nm, spot size: 2 mm or 200μm in diameter) with a proportional-integral (PI) controller. The closed-loop feedback control system was implemented to track predetermined target spike rates of hippocampal neuronal networks cultured on GNR-coated microelectrode arrays.Main results. The closed-loop system for neural spike rate control was successfully implemented using a PI controller and the thermoplasmonic neural suppression platform. Compared to the open-loop control, the target-channel spike rates were precisely modulated to remain constant or change in a sinusoidal form in the range below baseline spike rates. The spike rate response behaviors were affected by the choice of the controller gain. We also demonstrated that the functional connectivity of a synchronized bursting network could be altered by controlling the spike rate of one of the participating channels.Significance.The thermoplasmonic feedback controller proved that it can precisely modulate neural spike rate of neural activityin vitro. This technology can be used for studying neuronal network dynamics and might provide insights in developing new neuromodulation techniques in clinical applications.

Probing neural codes with two-photon holographic optogenetics

Optogenetics ushered in a revolution in how neuroscientists interrogate brain function. Because of technical limitations, the majority of optogenetic studies have used low spatial resolution activation schemes that limit the types of perturbations that can be made. However, neural activity manipulations at finer spatial scales are likely to be important to more fully understand neural computation. Spatially precise multiphoton holographic optogenetics promises to address this challenge and opens up many new classes of experiments that were not previously possible. More specifically, by offering the ability to recreate extremely specific neural activity patterns in both space and time in functionally defined ensembles of neurons, multiphoton holographic optogenetics could allow neuroscientists to reveal fundamental aspects of the neural codes for sensation, cognition and behavior that have been beyond reach. This Review summarizes recent advances in multiphoton holographic optogenetics that substantially expand its capabilities, highlights outstanding technical challenges and provides an overview of the classes of experiments it can execute to test and validate key theoretical models of brain function. Multiphoton holographic optogenetics could substantially accelerate the pace of neuroscience discovery by helping to close the loop between experimental and theoretical neuroscience, leading to fundamental new insights into nervous system function and disorder.

Optogenetically Controlled Activity Pattern Determines Survival Rate of Developing Neocortical Neurons

A substantial proportion of neurons undergoes programmed cell death (apoptosis) during early development. This process is attenuated by increased levels of neuronal activity and enhanced by suppression of activity. To uncover whether the mere level of activity or also the temporal structure of electrical activity affects neuronal death rates, we optogenetically controlled spontaneous activity of synaptically-isolated neurons in developing cortical cultures. Our results demonstrate that action potential firing of primary cortical neurons promotes neuronal survival throughout development. Chronic patterned optogenetic stimulation allowed to effectively modulate the firing pattern of single neurons in the absence of synaptic inputs while maintaining stable overall activity levels. Replacing the burst firing pattern with a non-physiological, single pulse pattern significantly increased cell death rates as compared to physiological burst stimulation. Furthermore, physiological burst stimulation led to an elevated peak in intracellular calcium and an increase in the expression level of classical activity-dependent targets but also decreased Bax/BCL-2 expression ratio and reduced caspase 3/7 activity. In summary, these results demonstrate at the single-cell level that the temporal pattern of action potentials is critical for neuronal survival versus cell death fate during cortical development, besides the pro-survival effect of action potential firing per se.

Automatic synchronisation of the cell cycle in budding yeast through closed-loop feedback control

The cell cycle is the process by which eukaryotic cells replicate. Yeast cells cycle asynchronously with each cell in the population budding at a different time. Although there are several experimental approaches to synchronise cells, these usually work only in the short-term. Here, we build a cyber-genetic system to achieve long-term synchronisation of the cell population, by interfacing genetically modified yeast cells with a computer by means of microfluidics to dynamically change medium, and a microscope to estimate cell cycle phases of individual cells. The computer implements a controller algorithm to decide when, and for how long, to change the growth medium to synchronise the cell-cycle across the population. Our work builds upon solid theoretical foundations provided by Control Engineering. In addition to providing an avenue for yeast cell cycle synchronisation, our work shows that control engineering can be used to automatically steer complex biological processes towards desired behaviours similarly to what is currently done with robots and autonomous vehicles.

State-space optimal feedback control of optogenetically driven neural activity

Objective.The rapid acceleration of tools for recording neuronal populations and targeted optogenetic manipulation has enabled real-time, feedback control of neuronal circuits in the brain. Continuously-graded control of measured neuronal activity poses a wide range of technical challenges, which we address through a combination of optogenetic stimulation and a state-space optimal control framework implemented in the thalamocortical circuit of the awake mouse.Approach.Closed-loop optogenetic control of neurons was performed in real-time via stimulation of channelrhodopsin-2 expressed in the somatosensory thalamus of the head-fixed mouse. A state-space linear dynamical system model structure was used to approximate the light-to-spiking input-output relationship in both single-neuron as well as multi-neuron scenarios when recording from multielectrode arrays. These models were utilized to design state feedback controller gains by way of linear quadratic optimal control and were also used online for estimation of state feedback, where a parameter-adaptive Kalman filter provided robustness to model-mismatch.Main results.This model-based control scheme proved effective for feedback control of single-neuron firing rate in the thalamus of awake animals. Notably, the graded optical actuation utilized here did not synchronize simultaneously recorded neurons, but heterogeneity across the neuronal population resulted in a varied response to stimulation. Simulated multi-output feedback control provided better control of a heterogeneous population and demonstrated how the approach generalizes beyond single-neuron applications.Significance.To our knowledge, this work represents the first experimental application of state space model-based feedback control for optogenetic stimulation. In combination with linear quadratic optimal control, the approaches laid out and tested here should generalize to future problems involving the control of highly complex neural circuits. More generally, feedback control of neuronal circuits opens the door to adaptively interacting with the dynamics underlying sensory, motor, and cognitive signaling, enabling a deeper understanding of circuit function and ultimately the control of function in the face of injury or disease.

Thalamic state influences timing precision in the thalamocortical circuit

Sensory signals from the outside world are transduced at the periphery, passing through thalamus before reaching cortex, ultimately giving rise to the sensory representations that enable us to perceive the world. The thalamocortical circuit is particularly sensitive to the temporal precision of thalamic spiking due to highly convergent synaptic connectivity. Thalamic neurons can exhibit burst and tonic modes of firing that strongly influence timing within the thalamus. The impact of these changes in thalamic state on sensory encoding in the cortex, however, remains unclear. Here, we investigated the role of thalamic state on timing in the thalamocortical circuit of the vibrissa pathway in the anesthetized rat. We optogenetically hyperpolarized thalamus while recording single unit activity in both thalamus and cortex. Tonic spike-triggered analysis revealed temporally precise thalamic spiking that was locked to weak white-noise sensory stimuli, whereas thalamic burst spiking was associated with a loss in stimulus-locked temporal precision. These thalamic state-dependent changes propagated to cortex such that the cortical timing precision was diminished during the hyperpolarized (burst biased) thalamic state. Although still sensory driven, the cortical neurons became significantly less precisely locked to the weak white-noise stimulus. The results here suggests a state-dependent differential regulation of spike timing precision in the thalamus that could gate what signals are ultimately propagated to cortex.NEW & NOTEWORTHY The majority of sensory signals are transmitted through the thalamus. There is growing evidence of complex thalamic gating through coordinated firing modes that have a strong impact on cortical sensory representations. Optogenetic hyperpolarization of thalamus pushed it into burst firing that disrupted precise time-locked sensory signaling, with a direct impact on the downstream cortical encoding, setting the stage for a timing-based thalamic gate of sensory signaling.

Living Things Are Not (20th Century) Machines: Updating Mechanism Metaphors in Light of the Modern Science of Machine Behavior

One of the most useful metaphors for driving scientific and engineering progress has been that of the “machine.” Much controversy exists about the applicability of this concept in the life sciences. Advances in molecular biology have revealed numerous design principles that can be harnessed to understand cells from an engineering perspective, and build novel devices to rationally exploit the laws of chemistry, physics, and computation. At the same time, organicists point to the many unique features of life, especially at larger scales of organization, which have resisted decomposition analysis and artificial implementation. Here, we argue that much of this debate has focused on inessential aspects of machines – classical properties which have been surpassed by advances in modern Machine Behavior and no longer apply. This emerging multidisciplinary field, at the interface of artificial life, machine learning, and synthetic bioengineering, is highlighting the inadequacy of existing definitions. Key terms such as machine, robot, program, software, evolved, designed, etc., need to be revised in light of technological and theoretical advances that have moved past the dated philosophical conceptions that have limited our understanding of both evolved and designed systems. Moving beyond contingent aspects of historical and current machines will enable conceptual tools that embrace inevitable advances in synthetic and hybrid bioengineering and computer science, toward a framework that identifies essential distinctions between fundamental concepts of devices and living agents. Progress in both theory and practical applications requires the establishment of a novel conception of “machines as they could be,” based on the profound lessons of biology at all scales. We sketch a perspective that acknowledges the remarkable, unique aspects of life to help re-define key terms, and identify deep, essential features of concepts for a future in which sharp boundaries between evolved and designed systems will not exist.

Layer 6 ensembles can selectively regulate the behavioral impact and layer-specific representation of sensory deviants

Predictive models can enhance the salience of unanticipated input. Here, we tested a key potential node in neocortical model formation in this process, layer (L) 6, using behavioral, electrophysiological and imaging methods in mouse primary somatosensory neocortex. We found that deviant stimuli enhanced tactile detection and were encoded in L2/3 neural tuning. To test the contribution of L6, we applied weak optogenetic drive that changed which L6 neurons were sensory responsive, without affecting overall firing rates in L6 or L2/3. This stimulation selectively suppressed behavioral sensitivity to deviant stimuli, without impacting baseline performance. This stimulation also eliminated deviance encoding in L2/3 but did not impair basic stimulus responses across layers. In contrast, stronger L6 drive inhibited firing and suppressed overall sensory function. These findings indicate that, despite their sparse activity, specific ensembles of stimulus-driven L6 neurons are required to form neocortical predictions, and to realize their behavioral benefit.

Rodent and fly models in behavioral neuroscience: An evaluation of methodological advances, comparative research, and future perspectives

The assessment of behavioral outcomes is a central component of neuroscientific research, which has required continuous technological innovations to produce more detailed and reliable findings. In this article, we provide an in-depth review on the progress and future implications for three model organisms (mouse, rat, and Drosophila) essential to our current understanding of behavior. By compiling a comprehensive catalog of popular assays, we are able to compare the diversity of tasks and usage of these animal models in behavioral research. This compilation also allows for the evaluation of existing state-of-the-art methods and experimental applications, including optogenetics, machine learning, and high-throughput behavioral assays. We go on to discuss novel apparatuses and inter-species analyses for centrophobism, feeding behavior, aggression and mating paradigms, with the goal of providing a unique view on comparative behavioral research. The challenges and recent advances are evaluated in terms of their translational value, ethical procedures, and trustworthiness for behavioral research.

Variational Online Learning of Neural Dynamics

New technologies for recording the activity of large neural populations during complex behavior provide exciting opportunities for investigating the neural computations that underlie perception, cognition, and decision-making. Non-linear state space models provide an interpretable signal processing framework by combining an intuitive dynamical system with a probabilistic observation model, which can provide insights into neural dynamics, neural computation, and development of neural prosthetics and treatment through feedback control. This brings with it the challenge of learning both latent neural state and the underlying dynamical system because neither are known for neural systems a priori. We developed a flexible online learning framework for latent non-linear state dynamics and filtered latent states. Using the stochastic gradient variational Bayes approach, our method jointly optimizes the parameters of the non-linear dynamical system, the observation model, and the black-box recognition model. Unlike previous approaches, our framework can incorporate non-trivial distributions of observation noise and has constant time and space complexity. These features make our approach amenable to real-time applications and the potential to automate analysis and experimental design in ways that testably track and modify behavior using stimuli designed to influence learning.

Twister3: a simple and fast microwire twister

Objective.

Twisted wire probes (TWPs, e.g. stereotrodes and tetrodes) provide a cheap and reliable method for obtaining high quality, multiple single-unit neural recordings in freely moving animals. Despite their ubiquity, TWPs are constructed using a tedious procedure consisting of manually folding, turning, and fusing microwire. This imposes a significant labor burden on research personnel who use TWPs in their experiments.

Approach.

To address this issue, we created Twister3, an open-source microwire twisting machine. This machine features a quick-draw wire feeder that eliminates manual wire folding, an auto-aligning motor attachment mechanism which results in consistently straight probes, and a high speed motor for rapid probe turning.

Main results.

Twister3 greatly increases the speed and repeatability of constructing twisted microwire probes compared to existing options. Users with less than one hour of experience using the device were able to make ~70 tetrodes per hour, on average. It is cheap, well documented, and all associated designs and source code are open-source.

Significance.

Twister3 significantly reduces the labor burden of creating high-quality TWPs so electrophysiologists can spend more of their time performing recordings rather than making probes. Therefore, this device is of interest to any lab performing TWP neural recordings, for example, using microdrives.

A VTA to Basal Amygdala Dopamine Projection Contributes to Signal Salient Somatosensory Events during Fear Learning

The amygdala is a brain area critical for the formation of fear memories. However, the nature of the teaching signal(s) that drive plasticity in the amygdala are still under debate. Here, we use optogenetic methods to investigate the contribution of ventral tegmental area (VTA) dopamine neurons to auditory-cued fear learning in male mice. Using anterograde and retrograde labeling, we found that a sparse and relatively evenly distributed population of VTA neurons projects to the basal amygdala (BA). In vivo optrode recordings in behaving mice showed that many VTA neurons, among them putative dopamine neurons, are excited by footshocks, and acquire a response to auditory stimuli during fear learning. Combined cfos imaging and retrograde labeling in dopamine transporter (DAT) Cre mice revealed that a large majority of BA projectors (>95%) are dopamine neurons, and that BA projectors become activated by the tone-footshock pairing of fear learning protocols. Finally, silencing VTA dopamine neurons, or their axon terminals in the BA during the footshock, reduced the strength of fear memory as tested 1 d later, whereas silencing the VTA-central amygdala (CeA) projection had no effect. Thus, VTA dopamine neurons projecting to the BA contribute to fear memory formation, by coding for the saliency of the footshock event and by signaling such events to the basal amygdala.SIGNIFICANCE STATEMENT Powerful mechanisms of fear learning have evolved in animals and humans to enable survival. During fear conditioning, a sensory cue, such as a tone (the conditioned stimulus), comes to predict an innately aversive stimulus, such as a mild footshock (the unconditioned stimulus). A brain representation of the unconditioned stimulus must act as a teaching signal to instruct plasticity of the conditioned stimulus representation in fear-related brain areas. Here we show that dopamine neurons in the VTA that project to the basal amygdala contribute to such a teaching signal for plasticity, thereby facilitating the formation of fear memories. Knowledge about the role of dopamine in aversively motivated plasticity might allow further insights into maladaptive plasticities that underlie anxiety and post-traumatic stress disorders in humans.

Representation of visual landmarks in retrosplenial cortex

The process by which visual information is incorporated into the brain’s spatial framework to represent landmarks is poorly understood. Studies in humans and rodents suggest that retrosplenial cortex (RSC) plays a key role in these computations. We developed an RSC-dependent behavioral task in which head-fixed mice learned the spatial relationship between visual landmark cues and hidden reward locations. Two-photon imaging revealed that these cues served as dominant reference points for most task-active neurons and anchored the spatial code in RSC. This encoding was more robust after task acquisition. Decoupling the virtual environment from mouse behavior degraded spatial representations and provided evidence that supralinear integration of visual and motor inputs contributes to landmark encoding. V1 axons recorded in RSC were less modulated by task engagement but showed surprisingly similar spatial tuning. Our data indicate that landmark representations in RSC are the result of local integration of visual, motor, and spatial information.

When moving through a city, people often use notable or familiar landmarks to help them navigate. Landmarks provide us with information about where we are and where we need to go next. But despite the ease with which we – and most other animals – use landmarks to find our way around, it remains unclear exactly how the brain makes this possible.

One area that seems to have a key role is the retrosplenial cortex, which is located deep within the back of the brain in humans. This area becomes more active when animals use visual landmarks to navigate. It is also one of the first brain regions to be affected in Alzheimer's disease, which may help to explain why patients with this condition can become lost and disoriented, even in places they have been many times before.

To find out how the retrosplenial cortex supports navigation, Fischer et al. measured its activity in mice exploring a virtual reality world. The mice ran through simulated corridors in which visual landmarks indicated where hidden rewards could be found. The activity of most neurons in the retrosplenial cortex was most strongly influenced by the mouse’s position relative to the landmark; for example, some neurons were always active 10 centimeters after the landmark.

In other experiments, when the landmarks were present but no longer indicated the location of a reward, the same neurons were much less active. Fischer et al. also measured the activity of the neurons when the mice were running with nothing shown on the virtual reality, and when they saw a landmark but did not run. Notably, the activity seen when the mice were using the landmarks to find rewards was greater than the sum of that recorded when the mice were just running or just seeing the landmark without a reward, making the “landmark response” an example of so-called supralinear processing.

Fischer et al. showed that visual centers of the brain send information about landmarks to retrosplenial cortex. But only the latter adjusts its activity depending on whether the mouse is using that landmark to navigate. These findings provide the first evidence for a “landmark code” at the level of neurons and lay the foundations for studying impaired navigation in patients with Alzheimer's disease. By showing that retrosplenial cortex neurons combine different types of input in a supralinear fashion, the results also point to general principles for how neurons in the brain perform complex calculations.

Somatic and Dendritic Encoding of Spatial Variables in Retrosplenial Cortex Differs during 2D Navigation

Active amplification of organized synaptic inputs in dendrites can endow individual neurons with the ability to perform complex computations. However, whether dendrites in behaving animals perform independent local computations is not known. Such activity may be particularly important for complex behavior, where neurons integrate multiple streams of information. Head-restrained imaging has yielded important insights into cellular and circuit function, but this approach limits behavior and the underlying computations. We describe a method for full-featured 2-photon imaging in awake mice during free locomotion with volitional head rotation. We examine head direction and position encoding in simultaneously imaged apical tuft dendrites and their respective cell bodies in retrosplenial cortex, an area that encodes multi-modal navigational information. Activity in dendrites was not determined solely by somatic activity but reflected distinct navigational variables, fulfilling the requirements for dendritic computation. Our approach provides a foundation for studying sub-cellular processes during complex behaviors.

A Closed-Loop Optogenetic Stimulation Device

Closed-loop optogenetic stimulation devices deliver optical stimulations based on real-time measurement and analysis of neural responses to stimulations. However, the use of large bench-top and tethered devices hinders the naturalistic test environment, which is crucial in pre-clinical neuroscience studies involving small rodent subjects. This paper presents a tetherless, lightweight and miniaturized head-mountable closed-loop optogenetic stimulation device. The device consists of three hardware modules: a hybrid electrode, an action potential detector, and an optogenetic stimulator. In addition, the device includes three software modules: a feature extractor, a control algorithm, and a pulse generator. The details of the design, implementation, and bench-testing of the device are presented. Furthermore, an in vitro test environment is formed using synthetic neural signals, wherein the device is validated for its closed-loop performance. During the in vitro validation, the device was able to identify abnormal neural signals, and trigger optical stimulation. On the other hand, it was able to also distinguish normal neural signals and inhibit optical stimulation. The overall power consumption of the device is 24 mW. The device measures 6 mm in radius and weighs 0.44 g excluding the power source.

Low-latency single channel real-time neural spike sorting system based on template matching

Recent technical advancements in neural engineering allow for precise recording and control of neural circuits simultaneously, opening up new opportunities for closed-loop neural control. In this work, a rapid spike sorting system was developed based on template matching to rapidly calculate instantaneous firing rates for each neuron in a multi-unit extracellular recording setting. Cluster templates were first generated by a desktop computer using a non-parameter spike sorting algorithm (Super-paramagnetic clustering) and then transferred to a field-programmable gate array digital circuit for rapid sorting through template matching. Two different matching techniques–Euclidean distance (ED) and correlational matching (CM)–were compared for the accuracy of sorting and the performance of calculating firing rates. The performance of the system was first verified using publicly available artificial data and was further confirmed with pre-recorded neural spikes from an anesthetized Mongolian gerbil. Real-time recording and sorting from an awake mouse were also conducted to confirm the system performance in a typical behavioral neuroscience experimental setting. Experimental results indicated that high sorting accuracies were achieved for both template-matching methods, but CM can better handle spikes with non-Gaussian spike distributions, making it more robust for in vivo recording. The technique was also compared to several other off-line spike sorting algorithms and the results indicated that the sorting accuracy is comparable but sorting time is significantly shorter than these other techniques. A low sorting latency of under 2 ms and a maximum spike sorting rate of 941 spikes/second have been achieved with our hybrid hardware/software system. The low sorting latency and fast sorting rate allow future system developments of neural circuit modulation through analyzing neural activities in real-time.

From Novel Technology to Novel Applications: Comment on "An Integrated Brain-Machine Interface Platform With Thousands of Channels" by Elon Musk and Neuralink

Collapse

Optimizing optogenetic stimulation protocols in auditory corticofugal neurons based on closed-loop spike feedback

OBJECTIVE

Optogenetics provides a means to probe functional connections between brain areas. By activating a set of presynaptic neurons and recording the activity from a downstream brain area, one can establish the sign and strength of a feedforward connection. One challenge is that there are virtually limitless patterns that can be used to stimulate a presynaptic brain area. Functional influences on downstream brain areas can depend not just on whether presynaptic neurons were activated, but how they were activated. Corticofugal axons from the auditory cortex (ACtx) heavily innervate the auditory tectum, the inferior colliculus (IC). Here, we sought to determine whether different modes of corticocollicular activation could titrate the strength of feedforward modulation of sound processing in IC neurons.

APPROACH

We used multi-channel electrophysiology and optogenetics to record from multiple regions of the IC in awake head-fixed mice while optogenetically stimulating ACtx neurons expressing Chronos, an ultra-fast channelrhodopsin. To identify cortical activation patterns associated with the strongest effects on IC firing rates, we employed a closed-loop evolutionary optimization procedure that tailored the voltage command signal sent to the laser based on spike feedback from single IC neurons.

MAIN RESULTS

Within minutes, our evolutionary search procedure converged on ACtx stimulation configurations that produced more effective and widespread enhancement of IC unit activity than generic activation parameters. Cortical modulation of midbrain spiking was bi-directional, as the evolutionary search procedure could be programmed to converge on activation patterns that either suppressed or enhanced sound-evoked IC firing rate.

SIGNIFICANCE

This study introduces a closed-loop optimization procedure to probe functional connections between brain areas. Our findings demonstrate that the influence of descending feedback projections on subcortical sensory processing can vary both in sign and degree depending on how cortical neurons are activated in time.

A Wireless Electro-Optic Headstage With a 0.13- μm CMOS Custom Integrated DWT Neural Signal Decoder for Closed-Loop Optogenetics

We present a wireless electro-optic headstage that uses a 0.13- μm CMOS custom integrated circuit (IC) implementing a digital neural decoder (ND-IC) for enabling real-time closed-loop (CL) optogenetics. The ND-IC processes the neural activity data using three digital cores: 1) the detector core detects and extracts the action potential (AP) of individual neurons by using an adaptive threshold; 2) the data compression core compresses the detected AP by using an efficient Symmlet-2 discrete wavelet transform (DWT) processor for decreasing the amount of data to be transmitted by the low-power wireless link; and 3) the classification core sorts the compressed AP into separated clusters on the fly according to their wave shapes. The ND-IC encompasses several innovations: 1) the compression core decreases the complexity from O(n ²) to O(n · log(n)) compared to the previous solutions, while using two times less memory, thanks to the use of a new coefficient sorting tree; and 2) the AP classification core reuses both the compressed DWT coefficients to perform implicit dimensionality reduction, which allows for performing intensive signal processing on-chip, while increasing power and hardware efficiency. This core also reuses the signal standard deviation already computed by the AP detector core as threshold for performing automatic AP sorting. The headstage also introduces innovations by enabling a new wireless CL scheme between the neural data acquisition module and the optical stimulator. Our CL scheme uses the AP sorting and timing information produced by the ND-IC for detecting complex firing patterns within the brain. The headstage is also smaller (1.13 cm ³), lighter (3.0 g with a 40 mAh battery) and less invasive than the previous solutions, while providing a measured autonomy of 2h40, with the ND-IC. The whole system and the ND-IC are first validated in vivo in the LD thalamus of a Long-Evans rat, and then in freely-moving CL experiments involving a mouse virally expressing ChR2-mCherry in inhibitory neurons of the prelimbic cortex, and the results show that our system works well within an in vivo experimental setting with a freely moving mouse.

Design strategies for dynamic closed-loop optogenetic neurocontrol in vivo

OBJECTIVE

Controlling neural activity enables the possibility of manipulating sensory perception, cognitive processes, and body movement, in addition to providing a powerful framework for functionally disentangling the neural circuits that underlie these complex phenomena. Over the last decade, optogenetic stimulation has become an increasingly important and powerful tool for understanding neural circuit function, owing to the ability to target specific cell types and bidirectionally modulate neural activity. To date, most stimulation has been provided in open-loop or in an on/off closed-loop fashion, where previously-determined stimulation is triggered by an event. Here, we describe and demonstrate a design approach for precise optogenetic control of neuronal firing rate modulation using feedback to guide stimulation continuously.

APPROACH

Using the rodent somatosensory thalamus as an experimental testbed for realizing desired time-varying patterns of firing rate modulation, we utilized a moving average exponential filter to estimate firing rate online from single-unit spiking measured extracellularly. This estimate of instantaneous rate served as feedback for a proportional integral (PI) controller, which was designed during the experiment based on a linear-nonlinear Poisson (LNP) model of the neuronal response to light.

MAIN RESULTS

The LNP model fit during the experiment enabled robust closed-loop control, resulting in good tracking of sinusoidal and non-sinusoidal targets, and rejection of unmeasured disturbances. Closed-loop control also enabled manipulation of trial-to-trial variability.

SIGNIFICANCE

Because neuroscientists are faced with the challenge of dissecting the functions of circuit components, the ability to maintain control of a region of interest in spite of changes in ongoing neural activity will be important for disambiguating function within networks. Closed-loop stimulation strategies are ideal for control that is robust to such changes, and the employment of continuous feedback to adjust stimulation in real-time can improve the quality of data collected using optogenetic manipulation.

Conceptual Evolution of Cell Signaling

During the last 100 years, cell signaling has evolved into a common mechanism for most physiological processes across systems. Although the majority of cell signaling principles were initially derived from hormonal studies, its exponential growth has been supported by interdisciplinary inputs, e.g., from physics, chemistry, mathematics, statistics, and computational fields. As a result, cell signaling has grown out of scope for any general review. Here, we review how the messages are transferred from the first messenger (the ligand) to the receptor, and then decoded with the help of cascades of second messengers (kinases, phosphatases, GTPases, ions, and small molecules such as cAMP, cGMP, diacylglycerol, etc.). The message is thus relayed from the membrane to the nucleus where gene expression ns, subsequent translations, and protein targeting to the cell membrane and other organelles are triggered. Although there are limited numbers of intracellular messengers, the specificity of the response profiles to the ligands is generated by the involvement of a combination of selected intracellular signaling intermediates. Other crucial parameters in cell signaling are its directionality and distribution of signaling strengths in different pathways that may crosstalk to adjust the amplitude and quality of the final effector output. Finally, we have reflected upon its possible developments during the coming years.

Rho-kinase inhibition has antidepressant-like efficacy and expedites dendritic spine pruning in adolescent mice

Adolescence represents a critical period of neurodevelopment, defined by structural and synaptic pruning within the prefrontal cortex. While characteristic of typical development, this structural instability may open a window of vulnerability to developing neuropsychiatric disorders, including depression. Thus, therapeutic interventions that support or expedite neural remodeling in adolescence may be advantageous. Here, we inhibited the neuronally-expressed cytoskeletal regulatory factor Rho-kinase (ROCK), focusing primarily on the clinically-viable ROCK inhibitor fasudil. ROCK inhibition had rapid antidepressant-like effects in adolescent mice, and its efficacy was comparable to ketamine and fluoxetine. It also modified levels of the antidepressant-related signaling factors, tropomyosin/tyrosine receptor kinase B and Akt, as well as the postsynaptic marker PSD-95, in the ventromedial prefrontal cortex (vmPFC). Meanwhile, adolescent-typical dendritic spine pruning on excitatory pyramidal neurons in the vmPFC was expedited. Further, vmPFC-specific shRNA-mediated reduction of ROCK2, the dominant ROCK isoform in the brain, had antidepressant-like consequences. We cautiously suggest that ROCK inhibitors may have therapeutic potential for adolescent-onset depression.

Myopic control of neural dynamics

Manipulating the dynamics of neural systems through targeted stimulation is a frontier of research and clinical neuroscience; however, the control schemes considered for neural systems are mismatched for the unique needs of manipulating neural dynamics. An appropriate control method should respect the variability in neural systems, incorporating moment to moment “input” to the neural dynamics and behaving based on the current neural state, irrespective of the past trajectory. We propose such a controller under a nonlinear state-space feedback framework that steers one dynamical system to function as through it were another dynamical system entirely. This “myopic” controller is formulated through a novel variant of a model reference control cost that manipulates dynamics in a short-sighted manner that only sets a target trajectory of a single time step into the future (hence its myopic nature), which omits the need to pre-calculate a rigid and computationally costly neural feedback control solution. To demonstrate the breadth of this control’s utility, two examples with distinctly different applications in neuroscience are studied. First, we show the myopic control’s utility to probe the causal link between dynamics and behavior for cognitive processes by transforming a winner-take-all decision-making system to operate as a robust neural integrator of evidence. Second, an unhealthy motor-like system containing an unwanted beta-oscillation spiral attractor is controlled to function as a healthy motor system, a relevant clinical example for neurological disorders.

Stimulating a neural system and observing its effect through simultaneous observation offers the promise to better understand how neural systems perform computations, as well as for the treatment of neurological disorders. A powerful perspective for understanding a neural system’s behavior undergoing stimulation is to conceptualize them as dynamical systems, which considers the global effect that stimulation has on the brain, rather than only assessing what impact it has on the recorded signal from the brain. With this more comprehensive perspective comes a central challenge of determining what requirements need to be satisfied to harness neural observations and then stimulate to make one dynamical system function as another one entirely. This could lead to applications such as neural stimulators that make a diseased brain behave like its healthy counterpart, or to make a neural system previously capable of only hasty decision making to wait and accumulate more evidence for a more informed decision. In this work we explore the implications of this new perspective on neural stimulation and derive a simple prescription for using neural observations to inform stimulation protocol that makes one neural system behave like another one.

A Versatile and Open-Source Rapid LED Switching System for One-Photon Imaging and Photo-Activation

Combining fluorescence and transmitted light sources for microscopy is an invaluable method in cellular neuroscience to probe the molecular and cellular mechanisms of cells. This approach enables the targeted recording from fluorescent reporter protein expressing neurons or glial cells in brain slices and fluorescence-assisted electrophysiological recordings from subcellular structures. However, the existing tools to mix multiple light sources in one-photon microscopy are limited. Here, we present the development of several microcontroller devices that provide temporal and intensity control of light emitting diodes (LEDs) for computer controlled microscopy illumination. We interfaced one microcontroller with μManager for rapid and dynamic overlay of transmitted and fluorescent images. Moreover, on the basis of this illumination system we implemented an electronic circuit to combine two pulsed LED light sources for fast (up to 1 kHz) ratiometric calcium (Ca²⁺) imaging. This microcontroller enabled the calibration of intracellular Ca²⁺ concentration and furthermore the combination of Ca²⁺ imaging with optogenetic activation. The devices are based on affordable components and open-source hardware and software. Integration into existing bright-field microscope systems will take ∼1 day. The microcontroller based LED imaging substantially advances conventional illumination methods by limiting light exposure and adding versatility and speed.

Long-Term Activity Dynamics of Single Neurons and Networks

The firing rate of neuronal spiking in vitro and in vivo significantly varies over extended timescales, characterized by long-memory processes and complex statistics, and appears in spontaneous as well as evoked activity upon repeated stimulus presentation. These variations in response features and their statistics, in face of repeated instances of a given physical input, are ubiquitous in all levels of brain-behavior organization. They are expressed in single neuron and network response variability but even appear in variations of subjective percepts or psychophysical choices and have been described as stemming from history-dependent, stochastic, or rate-determined processes.But what are the sources underlying these temporally rich variations in firing rate? Are they determined by interactions of the nervous system as a whole, or do isolated, single neurons or neuronal networks already express these fluctuations independent of higher levels? These questions motivated the application of a method that allows for controlled and specific long-term activation of a single neuron or neuronal network, isolated from higher levels of cortical organization.This chapter highlights the research done in cultured cortical networks to study (1) the inherent non-stationarity of neuronal network activity, (2) single neuron response fluctuations and underlying processes, and (3) the interface layer between network and single cell, the non-stationary efficacy of the ensemble of synapses impinging onto the observed neuron.

Closed-loop all-optical interrogation of neural circuits in vivo

Understanding the causal relationship between neural activity and behavior requires the ability to perform rapid and targeted interventions in ongoing activity. Here we describe a closed-loop all-optical strategy for dynamically controlling neuronal activity patterns in awake mice. We rapidly tailored and delivered two-photon optogenetic stimulation based on online readout of activity using simultaneous two-photon imaging, thus enabling the manipulation of neural circuit activity 'on the fly' during behavior.