Our research goal is to understand, on the whole-brain but single cell level, how entire neural circuits generate adaptable behavior and how plasticity reorganizes the functional properties of these circuits to implement learned changes in behavior. We image from almost all neurons in the brain simultaneously, which gives us the opportunity to go beyond studying subsets of cells and allows us to monitor the activity of all cells in the brain of a vertebrate, during behavior.
We use advances in microscopy, genetics and virtual reality to study the neural basis of adaptive behaviors. A general goal of the lab is to address the question: Are neural circuits hard-wired to generate a fixed pattern of behavior in response to a stimulus? In most cases, the answer is no — animals continuously adapt their behavior to changing environments. This includes basic changes in the environment, such as luminosity, and changes in the reward structure as well as changes in the physics of the body and the environment. This adaptability is the key to the successful function of the central nervous system in driving behavior. How is this continuous learning and adaptation implemented on the circuit level? How does the function of neural networks change when an animal is confronted with a change in the environment, and by what mechanism is this mediated? How does information get transmitted across brain regions, and how does this change when the fish adapts to new situations? We aim to tackle these questions on the whole-brain, single-cell level for multiple behaviors, using a combination of imaging, and perturbing, neuronal activity during fictive virtual-reality behavior.
Questions? ahrensm at janelia dot hhmi dot org
Misha Ahrens Group Leader
Davis Bennett Graduate Student
Takashi Kawashima Postdoctoral Associate
Yu Mu Postdoctoral Associate
Sujatha Narayan Research Staff
Chao-Tsung Yang Research Staff
Nikita Vladimirov Research Staff
Understanding brain function requires monitoring and interpreting the activity of large networks of neurons during behavior. Advances in recording technology are greatly increasing the size and complexity of neural data. Analyzing such data will pose a fundamental bottleneck for neuroscience. We present a library of analytical tools called Thunder built on the open-source Apache Spark platform for large-scale distributed computing. The library implements a variety of univariate and multivariate analyses with a modular, extendable structure well-suited to interactive exploration and analysis development. We demonstrate how these analyses find structure in large-scale neural data, including whole-brain light-sheet imaging data from fictively behaving larval zebrafish, and two-photon imaging data from behaving mouse. The analyses relate neuronal responses to sensory input and behavior, run in minutes or less and can be used on a private cluster or in the cloud. Our open-source framework thus holds promise for turning brain activity mapping efforts into biological insights.
Brain function relies on communication between large populations of neurons across multiple brain areas, a full understanding of which would require knowledge of the time-varying activity of all neurons in the central nervous system. Here we use light-sheet microscopy to record activity, reported through the genetically encoded calcium indicator GCaMP5G, from the entire volume of the brain of the larval zebrafish in vivo at 0.8 Hz, capturing more than 80% of all neurons at single-cell resolution. Demonstrating how this technique can be used to reveal functionally defined circuits across the brain, we identify two populations of neurons with correlated activity patterns. One circuit consists of hindbrain neurons functionally coupled to spinal cord neuropil. The other consists of an anatomically symmetric population in the anterior hindbrain, with activity in the left and right halves oscillating in antiphase, on a timescale of 20 s, and coupled to equally slow oscillations in the inferior olive.
Optogenetic tools can be used to manipulate neuronal activity in a reversible and specific manner. In recent years, such methods have been applied to uncover causal relationships between activity in specified neuronal circuits and behavior in the larval zebrafish. In this small, transparent, genetic model organism, noninvasive manipulation and monitoring of neuronal activity with light is possible throughout the nervous system. Here we review recent work in which these new tools have been applied to zebrafish, and discuss some of the existing challenges of these approaches.
Prior Publications (11)
A full understanding of nervous system function requires recording from large populations of neurons during naturalistic behaviors. Here we enable paralyzed larval zebrafish to fictively navigate two-dimensional virtual environments while we record optically from many neurons with two-photon imaging. Electrical recordings from motor nerves in the tail are decoded into intended forward swims and turns, which are used to update a virtual environment displayed underneath the fish. Several behavioral features-such as turning responses to whole-field motion and dark avoidance-are well-replicated in this virtual setting. We readily observed neuronal populations in the hindbrain with laterally selective responses that correlated with right or left optomotor behavior. We also observed neurons in the habenula, pallium, and midbrain with response properties specific to environmental features. Beyond single-cell correlations, the classification of network activity in such virtual settings promises to reveal principles of brainwide neural dynamics during behavior.
Discrete populations of brainstem spinal projection neurons (SPNs) have been shown to exhibit behavior-specific responses during locomotion [1-9], suggesting that separate descending pathways, each dedicated to a specific behavior, control locomotion. In an alternative model, a large variety of motor outputs could be generated from different combinations of a small number of basic motor pathways. We examined this possibility by studying the precise role of ventromedially located hindbrain SPNs (vSPNs) in generating turning behaviors. We found that unilateral laser ablation of vSPNs reduces the tail deflection and cycle period specifically during the first undulation cycle of a swim bout, whereas later tail movements are unaffected. This holds true during phototaxic , optomotor , dark-flash-induced , and spontaneous turns , suggesting a universal role of these neurons in controlling turning behaviors. Importantly, we found that the ablation not only abolishes turns but also results in a dramatic increase in the number of forward swims, suggesting that these neurons transform forward swims into turns by introducing turning kinematics into a basic motor pattern of symmetric tail undulations. Finally, we show that vSPN activity is direction specific and graded by turning angle. Together, these results provide a clear example of how a specific motor pattern can be transformed into different behavioral events by the graded activation of a small set of SPNs.
Identification of Nonvisual Photomotor Response Cells in the Vertebrate Hindbrain.The Journal of neuroscience : the official journal of the Society for Neuroscience 2013
D. Kokel, T. W. Dunn, M. B. Ahrens, R. Alshut, C. J. Cheung, L. Saint-Amant, G. Bruni, R. Mateus, T. J. Ham, T. Shiraki, Y. Fukada, D. Kojima, J. J. Yeh, R. Mikut, J. Lintig, F. Engert, and R. T. Peterson The Journal of neuroscience : the official journal of the Society for Neuroscience, 33:3834-3843 (2013)
Nonvisual photosensation enables animals to sense light without sight. However, the cellular and molecular mechanisms of nonvisual photobehaviors are poorly understood, especially in vertebrate animals. Here, we describe the photomotor response (PMR), a robust and reproducible series of motor behaviors in zebrafish that is elicited by visual wavelengths of light but does not require the eyes, pineal gland, or other canonical deep-brain photoreceptive organs. Unlike the relatively slow effects of canonical nonvisual pathways, motor circuits are strongly and quickly (seconds) recruited during the PMR behavior. We find that the hindbrain is both necessary and sufficient to drive these behaviors. Using in vivo calcium imaging, we identify a discrete set of neurons within the hindbrain whose responses to light mirror the PMR behavior. Pharmacological inhibition of the visual cycle blocks PMR behaviors, suggesting that opsin-based photoreceptors control this behavior. These data represent the first known light-sensing circuit in the vertebrate hindbrain.
A fundamental question in neuroscience is how entire neural circuits generate behaviour and adapt it to changes in sensory feedback. Here we use two-photon calcium imaging to record the activity of large populations of neurons at the cellular level, throughout the brain of larval zebrafish expressing a genetically encoded calcium sensor, while the paralysed animals interact fictively with a virtual environment and rapidly adapt their motor output to changes in visual feedback. We decompose the network dynamics involved in adaptive locomotion into four types of neuronal response properties, and provide anatomical maps of the corresponding sites. A subset of these signals occurred during behavioural adjustments and are candidates for the functional elements that drive motor learning. Lesions to the inferior olive indicate a specific functional role for olivocerebellar circuitry in adaptive locomotion. This study enables the analysis of brain-wide dynamics at single-cell resolution during behaviour.
Sensory stimulation can systematically bias the perceived passage of time, but why and how this happens is mysterious. In this report, we provide evidence that such biases may ultimately derive from an innate and adaptive use of stochastically evolving dynamic stimuli to help refine estimates derived from internal timekeeping mechanisms. A simplified statistical model based on probabilistic expectations of stimulus change derived from the second-order temporal statistics of the natural environment makes three predictions. First, random noise-like stimuli whose statistics violate natural expectations should induce timing bias. Second, a previously unexplored obverse of this effect is that similar noise stimuli with natural statistics should reduce the variability of timing estimates. Finally, this reduction in variability should scale with the interval being timed, so as to preserve the overall Weber law of interval timing. All three predictions are borne out experimentally. Thus, in the context of our novel theoretical framework, these results suggest that observers routinely rely on sensory input to augment their sense of the passage of time, through a process of Bayesian inference based on expectations of change in the natural environment.
The representation of acoustic stimuli in the brainstem forms the basis for higher auditory processing. While some characteristics of this representation (e.g. tuning curve) are widely accepted, it remains a challenge to predict the firing rate at high temporal resolution in response to complex stimuli. In this study we explore models for in vivo, single cell responses in the medial nucleus of the trapezoid body (MNTB) under complex sound stimulation. We estimate a family of models, the multilinear models, encompassing the classical spectrotemporal receptive field and allowing arbitrary input-nonlinearities and certain multiplicative interactions between sound energy and its short-term auditory context. We compare these to models of more traditional type, and also evaluate their performance under various stimulus representations. Using the context model, 75% of the explainable variance could be predicted based on a cochlear-like, gamma-tone stimulus representation. The presence of multiplicative contextual interactions strongly reduces certain inhibitory/suppressive regions of the linear kernels, suggesting an underlying nonlinear mechanism, e.g. cochlear or synaptic suppression, as the source of the suppression in MNTB neuronal responses. In conclusion, the context model provides a rich and still interpretable extension over many previous phenomenological models for modeling responses in the auditory brainstem at submillisecond resolution.
Nonlinearities and contextual influences in auditory cortical responses modeled with multilinear spectrotemporal methods.The Journal of neuroscience : the official journal of the Society for Neuroscience 2008
M. B. Ahrens, J. F. Linden, and M. Sahani The Journal of neuroscience : the official journal of the Society for Neuroscience, 28:1929-42 (2008)
The relationship between a sound and its neural representation in the auditory cortex remains elusive. Simple measures such as the frequency response area or frequency tuning curve provide little insight into the function of the auditory cortex in complex sound environments. Spectrotemporal receptive field (STRF) models, despite their descriptive potential, perform poorly when used to predict auditory cortical responses, showing that nonlinear features of cortical response functions, which are not captured by STRFs, are functionally important. We introduce a new approach to the description of auditory cortical responses, using multilinear modeling methods. These descriptions simultaneously account for several nonlinearities in the stimulus-response functions of auditory cortical neurons, including adaptation, spectral interactions, and nonlinear sensitivity to sound level. The models reveal multiple inseparabilities in cortical processing of time lag, frequency, and sound level, and suggest functional mechanisms by which auditory cortical neurons are sensitive to stimulus context. By explicitly modeling these contextual influences, the models are able to predict auditory cortical responses more accurately than are STRF models. In addition, they can explain some forms of stimulus dependence in STRFs that were previously poorly understood.
Many perceptual processes and neural computations, such as speech recognition, motor control and learning, depend on the ability to measure and mark the passage of time. However, the processes that make such temporal judgements possible are unknown. A number of different hypothetical mechanisms have been advanced, all of which depend on the known, temporally predictable evolution of a neural or psychological state, possibly through oscillations or the gradual decay of a memory trace. Alternatively, judgements of elapsed time might be based on observations of temporally structured, but stochastic processes. Such processes need not be specific to the sense of time; typical neural and sensory processes contain at least some statistical structure across a range of time scales. Here, we investigate the statistical properties of an estimator of elapsed time which is based on a simple family of stochastic process.
We describe a class of models that predict how the instantaneous firing rate of a neuron depends on a dynamic stimulus. The models utilize a learnt pointwise nonlinear transform of the stimulus, followed by a linear filter that acts on the sequence of transformed inputs. In one case, the nonlinear transform is the same at all filter lag-times. Thus, this "input nonlinearity" converts the initial numerical representation of stimulus value to a new representation that provides optimal input to the subsequent linear model. We describe algorithms that estimate both the input nonlinearity and the linear weights simultaneously; and present techniques to regularise and quantify uncertainty in the estimates. In a second approach, the model is generalized to allow a different nonlinear transform of the stimulus value at each lag-time. Although more general, this model is algorithmically more straightforward to fit. However, it has many more degrees of freedom than the first approach, thus requiring more data for accurate estimation. We test the feasibility of these methods on synthetic data, and on responses from a neuron in rodent barrel cortex. The models are shown to predict responses to novel data accurately, and to recover several important neuronal response properties.
Our understanding of the input-output function of single cells has been substantially advanced by biophysically accurate multi-compartmental models. The large number of parameters needing hand tuning in these models has, however, somewhat hampered their applicability and interpretability. Here we propose a simple and well-founded method for automatic estimation of many of these key parameters: 1) the spatial distribution of channel densities on the cell’s membrane; 2) the spatiotemporal pattern of synaptic input; 3) the channels’ reversal potentials; 4) the intercompartmental conductances; and 5) the noise level in each compartment. We assume experimental access to: a) the spatiotemporal voltage signal in the dendrite (or some contiguous subpart thereof, e.g. via voltage sensitive imaging techniques), b) an approximate kinetic description of the channels and synapses present in each compartment, and c) the morphology of the part of the neuron under investigation. The key observation is that, given data a)-c), all of the parameters 1)-4) may be simultaneously inferred by a version of constrained linear regression; this regression, in turn, is efficiently solved using standard algorithms, without any “local minima” problems despite the large number of parameters and complex dynamics. The noise level 5) may also be estimated by standard techniques. We demonstrate the method’s accuracy on several model datasets, and describe techniques for quantifying the uncertainty in our estimates.
Biophysically accurate multicompartmental models of individual neurons have significantly advanced our understanding of the input-output function of single cells. These models depend on a large number of parameters that are difficult to estimate. In practice, they are often hand-tuned to match measured physiological behaviors, thus raising questions of identifiability and interpretability. We propose a statistical approach to the automatic estimation of various biologically relevant parameters, including 1) the distribution of channel densities, 2) the spatiotemporal pattern of synaptic input, and 3) axial resistances across extended dendrites. Recent experimental advances, notably in voltage-sensitive imaging, motivate us to assume access to: i) the spatiotemporal voltage signal in the dendrite and ii) an approximate description of the channel kinetics of interest. We show here that, given i and ii, parameters 1-3 can be inferred simultaneously by nonnegative linear regression; that this optimization problem possesses a unique solution and is guaranteed to converge despite the large number of parameters and their complex nonlinear interaction; and that standard optimization algorithms efficiently reach this optimum with modest computational and data requirements. We demonstrate that the method leads to accurate estimations on a wide variety of challenging model data sets that include up to about 10(4) parameters (roughly two orders of magnitude more than previously feasible) and describe how the method gives insights into the functional interaction of groups of channels.