Publications

Population-level neural correlates of flexible avoidance learning in medial prefrontal cortex.

Benjamin Ehret, Roman Boehringer, Elizabeth A. Amadei, Maria R. Cervera, Christian Henning, Aniruddh Galgali, Valerio Mante, Benjamin F. Grewe (Nature Neuroscience, in press)
 
Abstract

The medial prefrontal cortex (mPFC) has been proposed to link sensory inputs and behavioral outputs to mediate the execution of learned behaviors. However, how such a link is implemented has remained unclear. To measure prefrontal neural correlates of sensory stimuli and learned behaviors, we performed population calcium imaging during a novel tone-signaled active avoidance paradigm in mice. We developed a novel analysis approach based on dimensionality reduction and decoding that allowed us to identify and isolate population activity patterns related the tone stimulus, learned avoidance actions and general motion.While tone-related activity was not informative about behavior, avoidance-related activity was predictive of upcoming avoidance actions. Moreover, avoidance-related activity distinguished between two different learned avoidance actions, consistent with a model in which mPFC contributes to the selection between different goal-directed actions. Overall, our results suggest that mPFC circuit dynamics transform sensory inputs into specific behavioral outputs through distributed population-level computations.

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MC Layer Normalization for calibrated uncertainty in Deep Learning

Thomas Frick, Diego Antognini, Ioana Giurgiu, Benjamin F. Grewe, Cristiano Malossi, Rong J.B. Zhu, Mattia Rigotti (TMLR, in press)
 
Abstract

Efficiently estimating the uncertainty of neural network predictions has become an increasingly important challenge as machine learning models are adopted for high-stakes industrial applications where shifts in data distribution may occur. Thus, calibrated prediction uncertainty is crucial to determine when to trust a model’s output and when to discard them as implausible. We propose a novel deep learning module – MC Layer Normalization – that acts as a drop-in replacement for Layer Normalization blocks and endows a neural network with uncertainty estimation capabilities. Our method is motivated from an approximate Bayesian perspective, but it is simple to deploy with no significant computational overhead thanks to an efficient one-shot approximation of Monte Carlo integration at prediction time. To evaluate the effectiveness of our module, we conduct experiments in two distinct settings. First, we investigate its potential to replace existing methods such as MC-Dropout and Prediction-Time Batch Normalization. Second, we explore its suitability for use cases where such conventional modules are either unsuitable or sub-optimal for certain tasks (as is the case with modules based on Batch Normalization, which is incompatible for instance with transformers). We empirically demonstrate the competitiveness of our module in terms of prediction accuracy and uncertainty calibration on established out-of-distribution image classification benchmarks, as well as its flexibility by applying it on tasks and architectures where previous methods are unsuitable.

Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directions

Peng Yan, Ahmed Abdulkadir, Paul-Philipp Luley, Matthias Rosenthal, Gerrit A. Schatte, Benjamin F. Grewe, Thilo Stadelmann (IEEE Access 2024)
 
Abstract

Automating the monitoring of industrial processes has the potential to enhance efficiency and optimize quality by promptly detecting abnormal events and thus facilitating timely interventions. Deep learning, with its capacity to discern non-trivial patterns within large datasets, plays a pivotal role in this process. Standard deep learning methods are suitable to solve a specific task given a specific type of data. During training, deep learning demands large volumes of labeled data. However, due to the dynamic nature of the industrial processes and environment, it is impractical to acquire large-scale labeled data for standard deep learning training for every slightly different case anew. Deep transfer learning offers a solution to this problem. By leveraging knowledge from related tasks and accounting for variations in data distributions, the transfer learning framework solves new tasks with little or even no additional labeled data. The approach bypasses the need to retrain a model from scratch for every new setup and dramatically reduces the labeled data requirement. This survey first provides an in-depth review of deep transfer learning, examining the problem settings of transfer learning and classifying the prevailing deep transfer learning methods. Moreover, we delve into applications of deep transfer learning in the context of a broad spectrum of time series anomaly detection tasks prevalent in primary industrial domains, e.g., manufacturing process monitoring, predictive maintenance, energy management, and infrastructure facility monitoring. We discuss the challenges and limitations of deep transfer learning in industrial contexts and conclude the survey with practical directions and actionable suggestions to address the need to leverage diverse time series data for anomaly detection in an increasingly dynamic production environment.

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Striatum-projecting prefrontal cortex neurons support working memory maintenance

Maria Wilhelm, Yaroslav Sych, Aleksejs Fomins, José Luis Alatorre Warren, Christopher Lewis, Laia Serratosa Capdevila, Roman Boehringer, Elisabeth A. Amadei, Benjamin Grewe, Eoin C. O’Connor, Bnjamin J. Hall, Fritjof Helmchen  (Nature Communications , 2023)
 
Abstract

Neurons in the medial prefrontal cortex (mPFC) are functionally linked to working memory (WM) but how distinct projection pathways contribute to WM remains unclear. Based on optical recordings, optogenetic perturbations, and pharmacological interventions in male mice, we report here that dorsomedial striatum (dmStr)-projecting mPFC neurons are essential for WM maintenance, but not encoding or retrieval, in a T-maze spatial memory task. Fiber photometry of GCaMP6m-labeled mPFC→dmStr neurons revealed strongest activity during the maintenance period, and optogenetic inhibition of these neurons impaired performance only when applied during this period. Conversely, enhancing mPFC→dmStr pathway activity—via pharmacological suppression of HCN1 or by optogenetic activation during the maintenance period—alleviated WM impairment induced by NMDA receptor blockade. Moreover, cellular-resolution miniscope imaging revealed that >50% of mPFC→dmStr neurons are active during WM maintenance and that this subpopulation is distinct from neurons active during encoding and retrieval. In all task periods, neuronal sequences were evident. Striatum-projecting mPFC neurons thus critically contribute to spatial WM maintenance.

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Bio-inspired, task-free continual learning through activity
regularization.

Francesco Lässig, Pau Vilimelis Aceituno, Martino Sorbaro, Benjamin F. Grewe (Biological Cybernetics, 2023)

Abstract

The ability to sequentially learn multiple tasks without forgetting is a key skill of biological brains, whereas it represents a major challenge to the field of deep learning. To avoid catastrophic forgetting, various continual learning (CL) approaches have been devised. However, these usually require discrete task boundaries. This requirement seems biologically implausible and often limits the application of CL methods in the real world where tasks are not always well defined. Here, we take inspiration from neuroscience, where sparse, non-overlapping neuronal representations have been suggested to prevent catastrophic forgetting. As in the brain, we argue that these sparse representations should be chosen on the basis of feed forward (stimulus-specific) as well as top-down (context-specific) information. To implement such selective sparsity, we use a bio-plausible form of hierarchical credit assignment known as Deep Feedback Control (DFC) and combine it with a winner-take-all sparsity mechanism. In addition to sparsity, we introduce lateral recurrent connections within each layer to further protect previously learned representations. We evaluate the new sparse-recurrent version of DFC on the split-MNIST computer vision benchmark and show that only the combination of sparsity and intra-layer recurrent connections improves CL performance with respect to standard backpropagation. Our method achieves similar performance to well-known CL methods, such as Elastic Weight Consolidation and Synaptic Intelligence, without requiring information about task boundaries. Overall, we showcase the idea of adopting computational principles from the brain to derive new, task-free learning algorithms for CL.

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Meta-Learning via Classifier (-free) Guidance.

Elvis Nava, Seijin Kobayashi, Yifei Yin, Robert K. Katzschmann, Benjamin F. Grewe (TMLR, in press)

Abstract

We introduce meta-learning algorithms that perform zero-shot weight-space adaptation of neural network models to unseen tasks. Our methods repurpose the popular generative image synthesis techniques of natural language guidance and diffusion models to generate neural network weights adapted for tasks. We first train an unconditional generative hypernetwork model to produce neural network weights; then we train a second “guidance” model that, given a natural language task description, traverses the hypernetwork latent space to find high-performance task-adapted weights in a zero-shot manner. We explore two alternative approaches for latent space guidance: “HyperCLIP”-based classifier guidance and a conditional Hypernetwork Latent Diffusion Model (“HyperLDM”), which we show to benefit from the classifier-free guidance technique common in image generation. Finally, we demonstrate that our approaches outperform existing multi-task and meta-learning methods in a series of zero-shot learning experiments on our Meta-VQA dataset.

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AI to the Rescue of Voltage Imaging.

Jerome Lecoq, Kaspar Podgorsky, Benjamin F. Grewe (Cell Reports Methodes, Spotlight, 2023, in press)

Abstract

In a recent issue of Nature Methods, Platisa et al. present an approach for long-term, in vivo population voltage imaging with single spike resolution across a local population of 100 neurons. Key to this step forward was the combination of a customized high-speed two-photon microscope with an optimized, positive-going, genetically encoded voltage indicator and a tailored machine learning denoising algorithm.

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Learning Cortical Hierarchies with Temporal Hebbian Updates.

Pau Vilimelis Aceituno, Matilde Tristany Farinha, Reinhard Loidl, Benjamin F. Grewe (Frontiers. Comp. Neurosc, 2023)

Abstract

A key driver of mammalian intelligence is the ability to represent incoming sensory information across multiple abstraction levels. For example, in the visual ventral stream, incoming signals are first represented as low-level edge filters and then transformed into high-level object representations. Similar hierarchical structures routinely emerge in artificial neural networks (ANNs) trained for object recognition tasks, suggesting that similar structures may underlie biological neural networks. However, the classical ANN training algorithm, backpropagation, is considered biologically implausible, and thus alternative biologically plausible training methods have been developed such as Equilibrium Propagation, Deep Feedback Control, Supervised Predictive Coding, and Dendritic Error Backpropagation. Several of those models propose that local errors are calculated for each neuron by comparing apical and somatic activities. Notwithstanding, from a neuroscience perspective, it is not clear how a neuron could compare compartmental signals. Here, we propose a solution to this problem in that we let the apical feedback signal change the postsynaptic firing rate and combine this with a differential Hebbian update, a rate-based version of classical spiking time-dependent plasticity (STDP). We prove that weight updates of this form minimize two alternative loss functions that we prove to be equivalent to the error-based losses used in machine learning: the inference latency and the amount of top-down feedback necessary. Moreover, we show that the use of differential Hebbian updates works similarly well in other feedback-based deep learning frameworks such as Predictive Coding or Equilibrium Propagation. Finally, our work removes a key requirement of biologically plausible models for deep learning and proposes a learning mechanism that would explain how temporal Hebbian learning rules can implement supervised hierarchical learning.

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Homomorphism Autoencoder – Learning Group Structured Representations from Observed Transitions.

Hamza Keurti, Hsiao-Ru Pan, Michel Besserve, Benjamin F. Grewe, Bernhard Schölkopf, (ICML, 2023)

Abstract

How can agents learn internal models that veridically represent interactions with the real world is a largely open question. As machine learning is moving towards representations containing not just observational but also interventional knowledge, we study this problem using tools from representation learning and group theory. We propose methods enabling an agent acting upon the world to learn internal representations of sensory information that are consistent with actions that modify it. We use an autoencoder equipped with a group representation acting on its latent space, trained using an equivariance-derived loss in order to enforce a suitable homomorphism property on the group representation. In contrast to existing work, our approach does not require prior knowledge of the group and does not restrict the set of actions the agent can perform. We motivate our method theoretically, and show empirically that it can learn a group representation of the actions, thereby capturing the structure of the set of transformations applied to the environment. We further show that this allows agents to predict the effect of sequences of future actions with improved accuracy.

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Deep Brain Imaging in the Move

Jerome Lecoq, Roman Boehringer, Benjamin F. Grewe, (Nat. Methods, News&Views, 2023)
Abstract
In this issue, Zhao et al.and Klioutchnikov et al. introduce new types of miniaturized microscopes that are capable of three-photon imaging deep in the brains of freely moving mice. Their work builds upon recent developments in multiphoton in vivo calcium imaging that demonstrated the capability to record large populations of neurons (from thousands to hundreds of thousands) deep in the brains of head-fixed mice. These developments revealed the tremendous potential of optical methods to enable large-scale, dense recordings of deep brain activity in longitudinal studies of complex neural dynamics across learning. To study neuronal correlates during natural behaviors in freely moving animals, miniaturized versions of single-photon (1P, Fig. 1a,b), two-photon (2P, Fig. 1c,d) and three-photon (3P, Fig. 1e) microscopes have been developed. One of the major advantages of such miniaturization is that large-scale optical recordings can be integrated in a straightforward manner into well-established rodent behavior paradigms. These include pavlovian, instrumental and avoidance conditioning as well as anxiety, social, maternal and spatial learning paradigms.
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Fast Aquatic Swimmer Optimization with Differentiable Projective Dynamics and Neural Network Hydrodynamic Models

Elvis Nava; John Z. Zhang; Mike Y. Michelis; Tao Du; Pingchuan Ma; Benjamin F. Grewe; Wojciech Matusik; Robert K. Katzschmann. (NeurRips 2021, spotlight)

Abstract

Aquatic locomotion is a classic fluid-structure interaction (FSI) problem of interest to biologists and engineers. Solving the fully coupled FSI equations for incompressible Navier-Stokes and finite elasticity is computationally expensive. Optimizing robotic swimmer design within such a system generally involves cumbersome, gradient-free procedures on top of the already costly simulation. To address this challenge we present a novel, fully differentiable hybrid approach to FSI that combines a 2D direct numerical simulation for the deformable solid structure of the swimmer and a physics-constrained neural network surrogate to capture hydrodynamic effects of the fluid. For the deformable solid simulation of the swimmer’s body, we use state-of-the-art techniques from the field of computer graphics to speed up the finite-element method (FEM). For the fluid simulation, we use a U-Net architecture trained with a physics-based loss function to predict the flow field at each time step. The pressure and velocity field outputs from the neural network are sampled around the boundary of our swimmer using an immersed boundary method (IBM) to compute its swimming motion accurately and efficiently. We demonstrate the computational efficiency and differentiability of our hybrid simulator on a 2D carangiform swimmer. Due to differentiability, the simulator can be used for computational design of controls for soft bodies immersed in fluids via direct gradient-based optimization.

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Minimizing Control for Credit Assignment with Strong Feedback

Alexander Meulemans; Matilde Tristany Farinha; Maria R. Cervera; João Sacramento; Benjamin F. Grewe. (ICLM, 2022)

Abstract

The success of deep learning ignited interest in whether the brain learns hierarchical representations using gradient-based learning. However, current biologically plausible methods for gradient-based credit assignment in deep neural networks need infinitesimally small feedback signals, which is problematic in biologically realistic noisy environments and at odds with experimental evidence in neuroscience showing that top-down feedback can significantly influence neural activity. Building upon deep feedback control (DFC), a recently proposed credit assignment method, we combine strong feedback influences on neural activity with gradient-based learning and show that this naturally leads to a novel view on neural network optimization. Instead of gradually changing the network weights towards configurations with low output loss, weight updates gradually minimize the amount of feedback required from a controller that drives the network to the supervised output label. Moreover, we show that the use of strong feedback in DFC allows learning forward and feedback connections simultaneously, using learning rules fully local in space and time. We complement our theoretical results with experiments on standard computer-vision benchmarks, showing competitive performance to backpropagation as well as robustness to noise. Overall, our work presents a fundamentally novel view of learning as control minimization, while sidestepping biologically unrealistic assumptions.

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Different encoding of reward location in dorsal and intermediate hippocampus

Przemyslaw Jarzebowski; Audrey Hay; Benjamin F. Grewe; Ole Paulsen. (Current Biology, 2022)

Abstract

Hippocampal place cells fire at specific locations in the environment. They form a cognitive map that encodes spatial relations in the environment, including reward locations. As part of this encoding, dorsal CA1 (dCA1) place cells accumulate at reward. The encoding of learned reward location could vary between the dorsal and intermediate hippocampus, which differ in gene expression and cortical and subcortical connectivity. While the dorsal hippocampus is critical for spatial navigation, the involvement of intermediate CA1 (iCA1) in spatial navigation might depend on task complexity and learning phase. The intermediate-to-ventral hippocampus regulates reward-seeking, but little is known about the involvement in reward-directed navigation. Here, we compared the encoding of learned reward locations in dCA1 and iCA1 during spatial navigation. We used calcium imaging with a head-mounted microscope to track the activity of CA1 cells over multiple days during which mice learned different reward locations. In dCA1, the fraction of active place cells increased in anticipation of reward, but the pool of active cells changed with the reward location. In iCA1, the same cells anticipated multiple reward locations. Our results support a model in which the dCA1 cognitive map incorporates a changing population of cells that encodes reward proximity through increased population activity, while iCA1 provides a reward-predictive code through a dedicated subpopulation. Both of these location-invariant codes persisted over time, and together they provide a dual hippocampal reward location code, assisting goal-directed navigation.

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Credit Assignment in Neural Networks through Deep Feedback Control

Alexander Meulemans; Matilde Tristany Farinha; Javier García Ordóñez; Pau Vilimelis Aceituno; João Sacramento; Benjamin F. Grewe (NeurRips 2021, spotlight)

Abstract

The success of deep learning sparked interest in whether the brain learns by using similar techniques for assigning credit to each synaptic weight for its contribution to the network output. However, the majority of current attempts at biologically- plausible learning methods are either non-local in time, require highly specific connectivity motifs, or have no clear link to any known mathematical optimization method. Here, we introduce Deep Feedback Control (DFC), a new learning method that uses a feedback controller to drive a deep neural network to match a desired output target and whose control signal can be used for credit assignment. The resulting learning rule is fully local in space and time and approximates Gauss- Newton optimization for a wide range of feedback connectivity patterns. To further underline its biological plausibility, we relate DFC to a multi-compartment model of cortical pyramidal neurons with a local voltage-dependent synaptic plasticity rule, consistent with recent theories of dendritic processing. By combining dynamical system theory with mathematical optimization theory, we provide a strong theoretical foundation for DFC that we corroborate with detailed results on toyexperiments and standard computer-vision benchmarks.

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Posterior Meta-Replay for Continual Learning

Christian Henning; Maria R. Cervera; Francesco D’Angelo; Johannes von Oswald; Regina Traber; Benjamin Ehret; Seijin Kobayashi; Benjamin F. Grewe; João Sacramento (NeurRips 2021)

Abstract
The success of deep learning sparked interest in whether the brain learns by using similar techniques for assigning credit to each synaptic weight for its contribution to the network output. However, the majority of current attempts at biologically- plausible learning methods are either non-local in time, require highly specific connectivity motifs, or have no clear link to any known mathematical optimization method. Here, we introduce Deep Feedback Control (DFC), a new learning method that uses a feedback controller to drive a deep neural network to match a desired output target and whose control signal can be used for credit assignment. The resulting learning rule is fully local in space and time and approximates Gauss- Newton optimization for a wide range of feedback connectivity patterns. To further underline its biological plausibility, we relate DFC to a multi-compartment model of cortical pyramidal neurons with a local voltage-dependent synaptic plasticity rule, consistent with recent theories of dendritic processing. By combining dynamical system theory with mathematical optimization theory, we provide a strong theoretical foundation for DFC that we corroborate with detailed results on toyexperiments and standard computer-vision benchmarks.
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Continual Learning in Recurrent Neural Networks

Benjamin Ehret, Christian Henning, Maria R. Cervera, Alexander Meulemans, Johannes von Oswald, Benjamin F. Grewe (ICLR, 2021)

Abstract

While a diverse collection of continual learning (CL) methods has been proposed to prevent catastrophic forgetting, a thorough investigation of their effectiveness for processing sequential data with recurrent neural networks (RNNs) is lacking. Here, we provide the first comprehensive evaluation of established CL methods on a variety of sequential data benchmarks. Specifically, we shed light on the particularities that arise when applying weight-importance methods, such as elastic weight consolidation, to RNNs. In contrast to feedforward networks, RNNs iteratively reuse a shared set of weights and require working memory to process input samples. We show that the performance of weight-importance methods is not directly affected by the length of the processed sequences, but rather by high working memory requirements, which lead to an increased need for stability at the cost of decreased plasticity for learning subsequent tasks. We additionally provide theoretical arguments supporting this interpretation by studying linear RNNs. Our study shows that established CL methods can be successfully ported to the recurrent case, and that a recent regularization approach based on hypernetworks outperforms weight-importance methods, thus emerging as a promising candidate for CL in RNNs. Overall, we provide insights on the differences between CL in feedforward networks and RNNs, while guiding towards effective solutions to tackle CL on sequential data.

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Neural networks with late-phase weights

Johannes von Oswald, Seijin Kobayashi, Alexander Meulemans, Christian Henning, Benjamin F. Grewe, João Sacramento (ICLR, 2021)

Abstract

The largely successful method of training neural networks is to learn their weights using some variant of stochastic gradient descent (SGD). Here, we show that the solutions found by SGD can be further improved by ensembling a subset of the weights in late stages of learning. At the end of learning, we obtain back a single model by taking a spatial average in weight space. To avoid incurring increased computational costs, we investigate a family of low-dimensional late-phase weight models which interact multiplicatively with the remaining parameters. Our results show that augmenting standard models with late-phase weights improves generalization in established benchmarks such as CIFAR-10/100, ImageNet and enwik8. These findings are complemented with a theoretical analysis of a noisy quadratic problem which provides a simplified picture of the late phases of neural network learning.

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Fast retrograde access to projection neuron circuits underlying vocal learning in songbirds.

Daniel Düring, Falk Dittrich, Mariana D. Rocha, Ryosuke O. Tachibana, Chihiro Mori, Kazou Okanoya, Roman Boehringer, Benjamin Ehret, Benjamin F. Grewe, Melanie Rauch, Jean C. Paterna, Robert Kasper, Manfred Gahr, Richard H.R. Hahnloser (Cell Reports, 2020)

Abstract

Understanding the structure and function of neural circuits underlying speech and language is a vital step toward better treatments for diseases of these systems.  Songbirds, among the few animal orders that share with humans the ability to learn vocalizations from a conspecific, have provided many insights into the neural mechanisms of vocal development. However, research into vocal learning circuits has been hindered by a lack of tools for rapid genetic targeting of specific neuron populations to meet the quick pace of develop-mental learning. Here, we present a viral tool that enables fast and efficient retrograde access to projection neuron populations. In zebra finches, Bengalese finches, canaries, and mice, we demonstrate fast retrograde labeling of cortical or dopaminergic neurons. We further demonstrate the suitability of our construct for detailed morphological analysis, for in vivo imaging of calcium activity, and for multi-color brainbow labeling.

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Deep learning-based behavioral analysis reaches human accuracy and is capable of outperforming commercial solutions.

Oliver Sturman, Lukas von Ziegler, Christa Schläppi, Furkan Akyol, Mattia Privitera, Daria Slominski, Christina Grimm, Laetitia Thieren, Valerio Zerbi, Benjamin F. Grewe & Johannes Bohacek  (Nature Neuropsych., 2020)

Abstract

To study brain function, preclinical research heavily relies on animal monitoring and the subsequent analyses of behavior. Commercial platforms have enabled semi high-throughput behavioral analyses by automating animal tracking, yet they poorly recognize ethologically relevant behaviors and lack the flexibility to be employed in variable testing environments. Critical advances based on deep-learning and machine vision over the last couple of years now enable markerless tracking of individual body parts of freely moving rodents with high precision. Here, we compare the performance of commercially available platforms (EthoVision XT14, Noldus; TSE Multi-Conditioning System, TSE Systems) to cross-verified human annotation. We provide a set of videos—carefully annotated by several human raters—of three widely used behavioral tests (open field test, elevated plus maze, forced swim test). Using these data, we then deployed the pose estimation software DeepLabCut to extract skeletal mouse representations. Using simple post-analyses, we were able to track animals based on their skeletal representation in a range of
classic behavioral tests at similar or greater accuracy than commercial behavioral tracking systems. We then developed supervised machine learning classifiers that integrate the skeletal representation with the manual annotations. This new combined approach allows us to score ethologically relevant behaviors with similar accuracy to humans, the current gold standard, while outperforming commercial solutions. Finally, we show that the resulting machine learning approach eliminates variation both within and between human annotators. In summary, our approach helps to improve the quality and accuracy of behavioral data, while outperforming commercial systems at a fraction of the cost.

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A Theoretical framework for Target Propagation.

Alexander Meulemans, Francesco S. Carzaniga, Johan A.K. Suykens, João Sacramento, Benjamin F. Grewe  (NeurRips, 2020, spotlight)

Abstract

The success of deep learning, a brain-inspired form of AI, has sparked interest in understanding how the brain could similarly learn across multiple layers of neurons. However, the majority of biologically-plausible learning algorithms have not yet reached the performance of backpropagation (BP), nor are they built on strong theoretical foundations. Here, we analyze target propagation (TP), a popular but not yet fully understood alternative to BP, from the standpoint of mathematical optimization. Our theory shows that TP is closely related to Gauss-Newton optimization and thus substantially differs from BP. Furthermore, our analysis reveals a fundamental limitation of difference target propagation (DTP), a well-known variant of TP, in the realistic scenario of non-invertible neural networks. We provide a first solution to this problem through a novel reconstruction loss that improves feedback weight training, while simultaneously introducing architectural flexibility by allowing for direct feedback connections from the output to each hidden layer. Our theory is corroborated by experimental results that show significant improvements in performance and in the alignment of forward weight updates with loss gradients, compared to DTP.

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Continual Learning with Hypernetworks.

Johannes von Oswald, Christian Henning, Benjamin F. Grewe, João Sacramento  (ICLR, 2020, spotlight)

Abstract

Artificial neural networks suffer from catastrophic forgetting when they are se quentially trained on multiple tasks. To overcome this problem, we present a novel approach based on task-conditioned hypernetworks, i.e., networks that generate the weights of a target model based on task identity. Continual learning (CL) is less difficult for this class of models thanks to a simple key feature: instead of recalling the input-output relations of all previously seen data, task-conditioned hypernetworks only require rehearsing task-specific weight realizations, which can be maintained in memory using a simple regularizer. Besides achieving state-of-the-art performance on standard CL benchmarks, additional experiments on long task sequences reveal that task-conditioned hypernetworks display a very large capacity to retain previous memories. Notably, such long memory lifetimes are achieved in a compressive regime, when the number of trainable hypernetwork weights is comparable or smaller than target network size. We provide insight into the structure of low-dimensional task embedding spaces (the input space of the hypernetwork) and show that task-conditioned hypernetworks demonstrate transfer learning. Finally, forward information transfer is further supported by empirical results on a challenging CL benchmark based on the CIFAR-10/100 image datasets.

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Wide. Deep. Fast. Recent Advances in vivo Multi-Photon Microscopy of Neuronal Activity.

Jérôme Lecoq, Natalia Orlova, Benjamin F. Grewe.  (Journal Neurosc. 2019, REVIEW)

Abstract
Multiphoton microscopy (MPM) has emerged as one of the most powerful and widespread technologies to monitor the activity of neuronal networks in awake, behaving animals over long periods of time. MPM development spanned across decades and crucially depended on the concurrent improvement of calcium indicators that report neuronal activity as well as surgical protocols, head fixation approaches, and innovations in optics and microscopy technology. Here we review the last decade of MPM development and highlight how in vivo imaging has matured and diversified, making it now possible to concurrently monitor thousands of neurons across connected brain areas or, alternatively, small local networks with sampling rates in the kilohertz range. This review includes different laser scanning approaches, such as multibeam technologies as well as recent developments to image deeper into neuronal tissues using new, long-wavelength laser sources. As future development will critically depend on our ability to resolve and discriminate individual neuronal spikes, we will also describe a simple framework that allows performing quantitative comparisons between the reviewed MPM instruments. Finally, we provide our own opinion on how the most  recent MPM developments can be leveraged at scale to enable the next generation of discoveries in brain function.
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Amygdala neuronal ensembles dynamically encode behavioral states.

Jan Gründemann, Yael Bitterman, Tingjia Lu, Sabine Krabbe, Benjamin F. Grewe, Mark J Schnitzer, Andreas Lüthi (Science 2019)

Abstract

Internal states, including affective or homeostatic states, are important behavioral motivators. The amygdala regulates motivated behaviors, yet how distinct states are represented in amygdala circuits is unknown. By longitudinally imaging neural calcium dynamics in freely moving mice across different environments, we identified  opponent changes in activity levels of two major, nonoverlapping populations of basal amygdala principal neurons. This population signature does not report global anxiety but predicts switches between exploratory and nonexploratory, defensive states. Moreover, the amygdala separately processes external stimuli and internal states and broadcasts state information via several output pathways to larger brain networks. Our findings extend the concept of thalamocortical “brain-state” coding to include affective and exploratory states and provide an entry point into the state dependency of brain function and behavior in defined circuits.

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An amygdalar neural ensemble that encodes the unpleasantness of pain

Gregory Corder, Biafra Ahanonu, Benjamin F. Grewe, Dong Wang, Mark J Schnitzer, Grégory Scherrer  (Science 2019)

Abstract

Pain is an unpleasant experience. How the brain’s affective neural circuits attribute this aversive quality to nociceptive information remains unknown. By means of time-lapse in vivo calcium imaging and neural activity manipulation in freely behaving mice encountering noxious stimuli, we identified a distinct neural ensemble in the basolateral amygdala that encodes the negative affective valence of pain. Silencing this nociceptive ensemble alleviated pain affective-motivational behaviors without altering the detection of noxious stimuli, withdrawal reflexes, anxiety, or reward. Following peripheral nerve injury, innocuous stimuli activated this nociceptive ensemble to drive dysfunctional perceptual changes associated with neuropathic pain, including pain aversion to light touch (allodynia). These results identify the amygdalar representations of noxious stimuli that are functionally required for the negative affective qualities of acute and chronic pain perception.

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Diametric neural ensemble dynamics in parkinsonian and dyskinetic states.

Jones G. Parker, Jesse D. Marshall, Biafra Ahanonu, Yu-Wei Wu, Tony Hyun Kim, Benjamin F. Grewe, Yanping Zhang, Jin Zhong Li, Jun B. Ding, Michael D. Ehlers & Mark J. Schnitzer (Nature 2018)

Abstract

Loss of dopamine in Parkinson’s disease is hypothesized to impede movement by inducing hypo- and hyperactivity in striatal spiny projection neurons (SPNs) of the direct (dSPNs) and indirect (iSPNs) pathways in the basal ganglia, respectively. The opposite imbalance might underlie hyperkinetic abnormalities, such as dyskinesia caused by treatment of Parkinson’s disease with the dopamine precursor l-DOPA. Here we monitored thousands of SPNs in behaving mice, before and after dopamine depletion and during l-DOPA-induced dyskinesia. Normally, intermingled clusters of dSPNs and iSPNs coactivated before movement. Dopamine depletion unbalanced SPN activity rates and disrupted the movement-encoding iSPN clusters. Matching their clinical efficacy, l-DOPA or agonism of the D 2 dopamine receptor reversed these abnormalities more effectively than agonism of the D 1 dopamine receptor. The opposite pathophysiology arose in l-DOPA-induced dyskinesia, during which iSPNs showed hypoactivity and dSPNs showed unclustered hyperactivity. Therefore, both the spatiotemporal profiles and rates of SPN activity appear crucial to striatal function, and next-generation treatments for basal ganglia disorders should target both facets of striatal activity

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Neuronal Representation of Social Information in the Medial Amygdala of Awake Behaving Mice.

Ying Li, Alexander Mathis, Benjamin F. Grewe, Jessica A. Osterhout, Biafra Ahanonu, Mark J. Schnitzer, Venkatesh N. Murthy, Catherine Dulac (Cell 2017)

Abstract

The medial amygdala (MeA) plays a critical role in processing species- and sex-specific signals that trigger social and defensive behaviors. However, the principles by which this deep brain structure encodes social information is poorly understood. We used a miniature microscope to image the Ca2+ dynamics of large neural ensembles in awake behaving mice and tracked the responses of MeA neurons over several months. These recordings revealed spatially intermingled subsets of MeA neurons with distinct temporal dynamics. The encoding of social information in the MeA differed between males and females and relied on information from both individual cells and neuronal populations. By performing long-term Ca 2+ imaging across different social contexts, we found that sexual experience triggers lasting and sex-specific changes in MeA activity, which, in males, involve signaling by oxytocin. These findings reveal basic principles underlying the brain’s repre sentation of social information and its modulation by intrinsic and extrinsic factors.

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Social Behavior shapes hypothalamic neural ensemble representations of conspecific sex.

Ryan Remedios, Ann Kennedy, Mariel Zelikowsky, Benjamin F. Grewe, Mark J. Schnitzer and David J. Anderson (Nature 2017)

Abstract

All animals possess a repertoire of innate (or instinctive 1,2 ) behaviours, which can be performed without training. Whether such behaviours are mediated by anatomically distinct and/or genetically specified neural pathways remains unknown 3–5 . Here we report that neural representations within the mouse hypothalamus, that underlie innate social behaviours, are shaped by social experience. Oestrogen receptor 1-expressing (Esr1+) neurons in the ventrolateral subdivision of the ventromedial hypothalamus (VMHvl) control mating and fighting in rodents 6–8 . We used microendoscopy 9 to image Esr1+ neuronal activity in the VMHvl of male mice engaged in these social behaviours. In sexually and socially experienced adult males, divergent and characteristic neural ensembles represented male versus female conspecifics. However, in inexperienced adult males, male and female intruders activated overlapping neuronal populations. Sex-specific neuronal ensembles gradually separated as the mice acquired social and sexual experience. In mice permitted to investigate but not to mount or attack conspecifics, ensemble divergence did not occur. However, 30 minutes of sexual experience with a female was sufficient to promote the separation of male and female ensembles and to induce an attack response 24 h later. These observations uncover an unexpected social experience-dependent component to the formation of hypothalamic neural assemblies controlling innate social behaviours. More generally, they reveal plasticity and dynamic coding in an evolutionarily ancient deep subcortical structure that is traditionally viewed as a ‘hard-wired’ system.

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Neural dynamics underlying a long-term associative memory.

Benjamin F. Grewe, Jan Gründeman, Jerome Lecoq, Lacey Kitch, Jones G Parker, Jesse D. Marschall, Jin-Zhong Li, Andreas Lüthi and Mark J. Schnitzer  (Nature 2017)

Abstract

The brain’s ability to associate different stimuli is vital for long-term memory, but how neural ensembles encode associative memories is unknown. Here we studied how cell ensembles in the basal and lateral amygdala encode associations between conditioned and unconditioned stimuli (CS and US, respectively). Using a miniature fluorescence microscope, we tracked the Ca 2+ dynamics of ensembles of amygdalar neurons during fear learning and extinction over 6 days in behaving mice. Fear conditioning induced both up- and down-regulation of individual cells’ CS-evoked responses. This bi-directional plasticity mainly occurred after conditioning, and reshaped the neural ensemble representation of the CS to become more similar to the US representation. During extinction training with repetitive CS presentations, the CS representation became more distinctive without reverting to its original form. Throughout the experiments, the strength of the ensemble-encoded CS–US association predicted the level of behavioural conditioning in each mouse. These findings support a supervised learning model in which activation of the US representation guides the transformation of the CS representation.

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Distinct Hippocampal Pathways Mediate Dissociable Roles of Context in Memory Retrieval.

Chun Xu, Sabine Krabbe, Jan Gründemann, Paolo Botta, John Fadok, Fumitaka Osakada, Dieter Saur, Benjamin F. Grewe, Mark J. Schnitzer, Ed Callaway, Andreas Lüthi  (Cell 2016)

Abstract

Memories about sensory experiences are tightly linked to the context in which they were formed. Memory contextualization is fundamental for the selection of appropriate behavioral reactions needed for survival, yet the underlying neuronal circuits are poorly understood. By combining trans-synaptic viral tracing and optogenetic manipulation, we found that the ventral hippocampus (vHC) and the amygdala, two key brain structures encoding context and emotional experiences, interact via multiple parallel pathways. A projection from the vHC to the basal amygdala mediates fear behavior elicited by a conditioned context, whereas a parallel projection from a distinct subset of vHC neurons onto midbrain-projecting neurons in the central amygdala is necessary for context-dependent retrieval of cued fear memories. Our findings demonstrate that two fundamentally distinct roles of context in fear memory retrieval are processed by distinct vHC output pathways, thereby allowing for the formation of robust contextual fear memories while preserving context-dependent behavioral flexibility.

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4A/Nogo-A influences the distribution of Kir4.1 but is not essential for potassium conductance in retinal Müller glia.

Sandrine Joly, Dana A. Dodd, Benjamin F. Grewe, Vincent PernetReticulon (Neurosc. Letters 2016)

Abstract

In the adult retina, we have previously shown that Nogo-A was highly expressed in Müller glia. However, the role of Nogo-A in the glial cell physiology is not clear. In this study, we investigated the possible influence that Nogo-A may exert on other polarized molecules in Müller cells, in particular inwardly rec-tifying potassium channel 4.1 (Kir4.1) and aquaporin 4 (AQP4) that respectively control potassium andwater exchange in glial cells. Our results showed that adenovirus-mediated Nogo-A overexpression withAdNogo-A increased the immunofluorescent signal of Kir4.1 in rat Müller cell line 1 (rMC-1) cells but didnot change its expression level by Western blotting. In vivo, AdNogo-A induced ectopic Kir4.1 immunore-activity throughout the radial processes of Müller cells compared with AdLacZ control virus. Surprisingly,AdNogo-A did not modify the distribution of Dp71 and AQP4 that are common binding partners for Kir4.1in the dystrophin-associated protein (DAP) complex anchored at the plasma membrane of Müller glia.Immunoprecipitation experiments revealed molecular interactions between Nogo-A and Kir4.1. In Nogo-A KO mouse retinae, the distribution of Kir4.1 was not different from that observed in Wild-Type (WT)animals. In addition, potassium conductance did not change in freshly dissociated Nogo-A KO Müllerglia compared with WT cells. In summary, the increase of Nogo-A expression can selectively influencethe distribution of Kir4.1 in glia but is not essential for Kir4.1-mediated potassium conductance at theplasma membrane in physiological conditions. Nogo-A-Kir4.1 interactions may, however, contribute topathological processes taking place in the retina, for instance, after ischemia.

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High-speed recording of neural spikes in awake mice and flies with a fluorescent voltage sensor.

Yiyang Gong, Cheng Huang, Jin Zhong Li, Benjamin F. Grewe, Yanping Zhang, Stephan Eismann and Mark J. Schnitzer (Science, 2015)

Abstract

Genetically encoded voltage indicators (GEVIs) are a promising technology forfluorescence readout of millisecond-scale neuronal dynamics. Previous GEVIs hadinsufficient signaling speed and dynamic range to resolve action potentials in liveanimals. We coupled fast voltage-sensing domains from a rhodopsin protein to brightfluorophores through resonance energy transfer. The resulting GEVIs are sufficiently brightand fast to report neuronal action potentials and membrane voltage dynamics in awakemice and flies, resolving fast spike trains with 0.2-millisecond timing precision at spikedetection error rates orders of magnitude better than previous GEVIs. In vivo imagingrevealed sensory-evoked responses, including somatic spiking, dendritic dynamics,and intracellular voltage propagation. These results empower in vivo optical studiesof neuronal electrophysiology and coding and motivate further advancements inhigh-speed microscopy.

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Cellular Level Brain Imaging in Behaving Mammals: An Engineering Approach.

Elizabeth J. O. Hamel, Benjamin F. Grewe, Jones G. Parker, Mark J. Schnitzer (Neuron 2015, REVIEW)

Abstract

Fluorescence imaging offers expanding capabilities for recording neural dynamics in behaving mammals, including the means to monitor hundreds of cells targeted by genetic type or connectivity, track cells over weeks, densely sample neurons within local microcircuits, study cells too inactive to isolate in extracellular electrical recordings, and visualize activity in dendrites, axons, or dendritic spines. We discuss recent progress and future directions for imaging in behaving mammals from a systems engineering perspective, whichseeks holistic consideration of fluorescent indicators, optical instrumentation, and computational analyses.Today, genetically encoded indicators of neural Ca 2+ dynamics are widely used, and those of trans-membrane voltage are rapidly improving. Two complementary imaging paradigms involve conventional microscopes for studying head-restrained animals and head-mounted miniature microscopes for imaging in freely behaving animals. Overall, the field has attained sufficient sophistication that increased cooperation between those designing new indicators, light sources, microscopes, and computational analyses would greatlybenefit future progress.

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Visualizing mammalian brain area interactions by dual-axis two-photon calcium imaging.

Jerome Lecoq, Joan, Dejan Vučinić, Benjamin F. Grewe, Huyn Kim, Jin-Zhong Li, Lacey Kitch and Mark J. Schnitzer (Nat. Neurosc., 2014)

Abstract

Fluorescence Ca2+ imaging enables large-scale recordings of neural activity, but collective dynamics across mammalian brain regions are generally inaccessible within single fields of view. Here we introduce a two-photon microscope possessing two articulated arms that can simultaneously image two brain areas (~0.38 mm2 each), either nearby or distal, using microendoscopes. Concurrent Ca2+ imaging of ~100–300 neurons in primary visual cortex (V1) and lateromedial (LM) visual area in behaving mice revealed that the variability in LM neurons’ visual responses was strongly dependent on that in V1, suggesting that fluctuations in sensory responses propagate through extended cortical networks.

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High-speed two-photon calcium imaging of neuronal population activity using acousto-optic deflectors.

Benjamin F. Grewe and Fritjof Helmchen (Cold Spring Harb Protoc, 2014)

Abstract

Two-photon calcium imaging of neuronal populations allows optical measurements of spiking activity in living animals. However, laser-scanning microscopes with galvanometric scan mirrors are too slow to capture population activity on a millisecond timescale. This protocol describes a two-photon microscope that is based on two-dimensional laser scanning with acousto-optic deflectors (AODs), enabling high-speed in vivo recording of neuronal population activity at temporal resolutions of several hundred hertz. The detailed construction plan of the AOD-based microscope is accompanied by equally detailed optimization procedures. We also introduce a novel random-access pattern scanning (RAPS) technique for high-speed in vivo measurements of neuronal population activity. AOD-based RAPS can measure calcium transients in neocortical neuronal populations, revealing spike trains with near-millisecond precision. The current limitations of the AOD-based microscope are discussed, and we provide an outlook of its future applications.

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Two-photon optogenetic toolbox for fast inhibition, excitation and bistable modulation.

Rohit Prakash, Ofer Yizhar, Benjamin F. Grewe, Charu Ramakrishnan, Nancy Wang, Inbal Goshen, Adam M. Packer, Darcy S. Peterka, Raphael Yuste, Mark J. Schnitzer and Karl Deisseroth (Nat. Methodes, 2012)

Abstract

Optogenetics with microbial opsin genes has enabled high- speed control of genetically specified cell populations in intact tissue. however, it remains a challenge to independently control subsets of cells within the genetically targeted population. although spatially precise excitation of target molecules can be achieved using two-photon laser-scanning microscopy (tPLSm) hardware, the integration of two-photon excitation with optogenetics has thus far required specialized equipment or scanning and has not yet been widely adopted. here we take a complementary approach, developing opsins with custom kinetic, expression and spectral properties uniquely suited to scan times typical of the raster approach that is ubiquitous in tPLSm laboratories. We use a range of culture, slice and mammalian in vivo preparations to demonstrate the versatility of this toolbox, and we quantitatively map parameter space for fast excitation, inhibition and bistable control. together these advances may help enable broad adoption of integrated optogenetic and tPLSm technologies across  experimental fields and systems.

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Selective Regulation of NR2B by Protein Phosphatase-1 for the Control of the NMDA Receptor in Neuroprotection

Mélissa Farinelli, Fabrice Heitz, Benjamin F. Grewe, Shiva Tyagarajan, Fritjof Helmchen and Isabelle Mansuy (PLoS One, 2012)

Abstract

An imbalance between pro-survival and pro-death pathways in brain cells can lead to neuronal cell death and neurodegeneration. While such imbalance is known to be associated with alterations in glutamatergic and Ca 2+ signaling, the underlying mechanisms remain undefined. We identified the protein Ser/Thr phosphatase protein phosphatase-1 (PP1), an enzyme associated with glutamate receptors, as a key trigger of survival pathways that can prevent neuronal death and neurodegeneration in the adult hippocampus. We show that PP1a overexpression in hippocampal neurons limits NMDA receptor overactivation and Ca 2+ overload during an excitotoxic event, while PP1 inhibition favors Ca 2+ overload and cell death. The protective effect of PP1 is associated with a selective dephosphorylation on a residue phosphorylated by CaMKIIa on the NMDA receptor subunit NR2B, which promotes pro-survival pathways and associated transcriptional programs. These results reveal a novel contributor to the mechanisms of neuroprotection and underscore the importance of PP1-dependent dephosphorylation in these mechanisms. They provide a new target for the development of potential therapeutic treatment of neurodegeneration.

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Enhancement of CA3 hippocampal network activity by activation of group II metabotropic glutamate receptors.

Jeanne Ster, José María Mateos, Benjamin F. Grewe, Guyllaume Coiret, Fritjof Helmchen and Urs Gerber (PLoS One, 2012)

Abstract

Impaired function or expression of group II metabotropic glutamate receptors (mGluRIIs) is observed in brain disorders such as schizophrenia. This class of receptor is thought to modulate activity of neuronal circuits primarily by inhibiting neurotransmitter release. Here, we characterize a postsynaptic excitatory response mediated by somato-dendritic mGluRIIs in hippocampal CA3 pyramidal cells and in stratum oriens interneurons. The specific mGluRII agonists DCG-IV or LCCG-1 induced an inward current blocked by the mGluRII antagonist LY341495. Experiments with transgenic mice revealed a significant reduction of the inward current in mGluR3−/− but not in mGluR2−/− mice. The excitatory response was associated with periods of synchronized activity at theta frequency. Furthermore, cholinergically induced network oscillations exhibited decreased frequency when mGluRIIs were blocked. Thus, our data indicate that hippocampal responses are modulated not only by presynaptic mGluRIIs that reduce glutamate release but also by postsynaptic mGluRIIs that depolarize
neurons and enhance CA3 network activity.

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Fast two-layer two-photon imaging of neuronal cell populations using an electrically tunable lens.

Benjamin F. Grewe, Fabian F. Voigt, Marcel van ’t Hoff and Fritjof Helmchen (Biomedical Opt. Express, 2011)

Abstract

Functional two-photon Ca2+-imaging is a versatile tool to study the dynamics of neuronal populations in brain slices and living animals. However, population imaging is typically restricted to a single two-dimensional image plane. By introducing an electrically tunable lens into the excitation path of a two-photon  microscope we were able to realize fast axial focus shifts within 15 ms. The maximum axial scan range was 0.7 mm employing a 40x NA0.8 water immersion  objective, plenty for typically required ranges of 0.2–0.3 mm. By combining the axial scanning method with 2D acousto-optic frame scanning and random-access scanning, we measured neuronal population activity of about 40 neurons across two imaging planes separated by 40 μm and achieved scan rates up to 20–30 Hz. The method presented is easily applicable and allows upgrading of existing two-photon microscopes for fast 3D scanning.

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High-speed in vivo calcium imaging reveals spike trains in neuronal networks with near-millisecond precision.

Benjamin F. Grewe, Dominik Langer, Björn Kampa and Fritjof Helmchen (Nature Methods, 2010)

Abstract

two-photon calcium imaging of neuronal populations enables optical recording of spiking activity in living animals, but standard laser scanners are too slow to accurately determine spike times. here we report in vivo imaging in mouse neocortex with greatly improved temporal resolution using random-access scanning with acousto-optic deflectors. We obtained fluorescence measurements from 34–91 layer 2/3 neurons at a 180–490 hz sampling rate. We detected single action potential–evoked calcium transients with signal-to-noise ratios of 2–5 and determined spike times with near-millisecond precision and 5–15 ms confidence intervals. An automated ‘peeling’ algorithm enabled reconstruction of complex spike trains from fluorescence traces up to 20–30 hz frequency, uncovering spatiotemporal trial-to-trial variability of sensory responses in barrel cortex and visual cortex. By revealing spike sequences in neuronal populations on a fast time scale, high-speed calcium imaging will facilitate optical studies of information processing in brain microcircuits.

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Backpropagation of realistic action potential output along apical and basal dendrites of slender-tufted L5A pyramidal neurons.

Benjamin F. Grewe, Audrey Bonan and Andreas Frick (Front in Cell Neurosc, 2010)

Abstract

Pyramidal neurons of layer 5A are a major neocortical output type and clearly distinguished from layer 5B pyramidal neurons with respect to morphology, in vivo firing patterns, and connectivity; yet knowledge of their dendritic properties is scant. We used a combination of whole-cell recordings and Ca2+ imaging techniques in vitro to explore the specific dendritic signaling role of physiological action potential patterns recorded in vivo in layer 5A pyramidal neurons of the whisker-related ‘barrel cortex’. Our data provide evidence that the temporal structure of physiological action potential patterns is crucial for an effective invasion of the main apical dendrites up to the major branch point. Both the critical frequency enabling action potential trains to invade efficiently and the dendritic calcium profile changed during postnatal development. In contrast to the main apical dendrite, the more passive properties of the short basal and apical tuft dendrites prevented an efficient back-propagation. Various Ca2+ channel types contributed to the enhanced calcium signals during high-frequency firing activity, whereas A-type K+ and BKCa channels strongly suppressed it. Our data support models in which the interaction of synaptic input with action potential output is a function of the timing, rate and pattern of action potentials, and dendritic location.

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Optical probing of neuronal ensemble activity.

 Benjamin F. Grewe and Fritjof Helmchen (Curr. Opin. Neurobiology, 2009, REVIEW)

Abstract

Neural computations are implemented in densely interconnected networks of excitable neurons as temporal sequences of coactive neuronal ensembles. Ensemble activity is produced by the interaction of external stimuli with internalstates but has been difficult to directly study in the past. Currently, high-resolution optical imaging techniques are emerging as powerful tools to investigate neuronal ensembles in living animals and to characterize their spatiotemporal properties. Here we review recent advances of two-photon calcium imaging and highlight ongoing technical improvements as well as emerging applications. Significant progress has been made in the extent and speed of imaging and in the adaptation of imaging techniques to awake animals. These advances facilitate studies of the functional organization of local neural networks, their experience-dependent reconfiguration, and their functional impairment in diseases. Optical probing of neuronal ensemble dynamics in vivo thus promises to reveal fundamental principles of neural circuit function and dysfunction.

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