Signal-to-signal neural networks for improved spike estimation from calcium imaging data

Information processing by a population of neurons is studied using two-photon calcium imaging techniques. A neuronal spike results in an increased intracellular calcium concentration. Fluorescent calcium indicators change their brightness upon a change in the calcium concentration, and this change is captured in the imaging technique. The task of estimating the actual spike positions from the brightness variations is formally referred to as spike estimation. Several signal processing and machine learning-based algorithms have been proposed in the past to solve this problem. However, the task is still far from being solved. Here we present a novel neural network-based data-driven algorithm for spike estimation. Our method takes the fluorescence recording as the input and synthesizes the spike information signal, which is well-correlated with the actual spike positions. Our method outperforms state-of-the-art methods on a standard evaluation framework. We further analyze different components of the model and discuss its benefits.

Timing is Everything

Karen Adam

In this thesis, timing is everything. In the first part, we mean this literally, as we tackle systems that encode information using timing alone. In the second part, we adopt the standard, metaphoric interpretation of this saying and show the importance of choosing the right time to sample a system, when efficiency is a key consideration.Time encoding machines, or, alternately, integrate-and-fire neurons, encode their inputs using spikes, the timings of which depend on the input and therefore hold information about it. These devices can be made more power efficient than their clocked counterparts and have thus been studied in the fields of signal processing, event-based vision, computational neuroscience and machine learning. However, their timing-based spiking output has so far often been considered a nuisance that one must make do with, rather than a potential advantage. In this thesis, we show that this timing-based output equips spiking devices with capabilities that are out of reach for classical encoding and processing systems.We first discover the benefits of time encoding on multi-channel encoding and recovery of a signal: with time encoding, clock alignment is easy to solve, although it poses problems in the classical sampling scenario. Then, we study the time encoding of low-dimensional signals and see that the asynchrony of spikes allows for a lower sample complexity in comparison with synchronous sampling. Thanks to this same asynchrony, time encoding of video results in an entanglement between spatial sampling density and temporal resolution---a relationship which is not present in frame-based video. Finally, we show that the all-or-none nature of the spikes allows training spiking neural networks in a layer-by-layer fashion---a feat that is impossible with clocked, artificial neural networks, due to the credit assignment problem.The second part of this thesis shows that choosing the right timing of samples can be crucial to ensure efficiency when performing nanoscale magnetic sensing. We are given a stochastic process, where each sample at time t follows a Bernoulli distribution, and which is characterized by oscillation frequencies that we are interested in recovering. We search for an optimal approach to sample this process, such that the variance of the frequencies' estimates is minimized, given constraints on the measurement time. The models we assume stem from the field of nanoscale magnetic sensing, where the number of parameters to be estimated varies with the number of spins one is trying to sense. We present an adaptive approach to choosing samples in both the single-spin and two-spin cases and compare the adaptive algorithm's performance to classical approaches to sampling.In both parts of the thesis, we move away from classical amplitude sampling and consider cases where timing takes the forefront and amplitude information is merely binary, to shows that timing can carry information and that it can control the amount of information gain.

EPFL2022

Using Animal Motion Capture to Learn Neural Representations

Semih Günel

Understanding behavior from neural activity is a fundamental goal in neuroscience. It has practical applications in building robust brain-machine interfaces, human-computer interaction, and assisting patients with neurological disabilities. Despite the ever-growing demand driven by the applications, neural action decoding methods still require laborious manual annotations. Furthermore, a different model must be trained for each new individual, requiring even more annotation and overwhelming the resources of most applications. This thesis proposes a self-supervised way to tackle the unsupervised domain adaptation for the neural action decoding problem. Our method does not require manual annotations and can generalize across individuals. To do that, we propose to use animal motion capture to guide the encoding of neural action representations together with a set of neural and behavioral augmentations. We, therefore, divide the thesis into two parts. For the first part, we propose multiple novel ways to learn robust markerless motion capture systems for animals. In the second part, we then use motion capture to learn an unsupervised neural action decoding algorithm.To start, we present a novel unsupervised markerless 2D pose estimation system. Extracting pose information from laboratory animals requires significant annotation labor. Our model requires only unrealistic renderings of the target animal, without any manual pose labeling. While deep learning models cannot be trained on unrealistic renderings of laboratory animals due to the domain gap, we propose a deformation-based generative network transforming the renderings into realistic images while at the same time keeping the pose information intact. Our model enables us to train a pose estimation network on generated but realistic images without manual annotations.Second, we propose a markerless self-calibrating motion capture system on tethered animals. Although extracting a 2D pose is enough for some applications, 2D pose information is incomplete due to information loss from the camera projection. On the other hand, triangulating 3D poses from multiple images requires camera calibration. This tedious process is not ideal for many laboratory setups, for example, a small model organism \textit{Drosophila}. We model a self-calibrating system that uses the animal as the calibration pattern. To make the system even more reliable, we use the shape and skeleton of the animal as the 3D prior, therefore fixing the inference mistakes of the deep learning based methods.Third, we propose a monocular 3D motion capture algorithm for laboratory animals. Although 3D pose information provides all the necessary information about the movement, multi-camera systems require expensive hardware and multi-view synchronization. We propose a monocular system that can still recover 3D pose without requiring multiple views. We test our model on numerous lab animals, including flies, mice, rats, and macaques. We show that our monocular motion capture system performs reliably and on-par with multi-camera motion capture systems.Lastly, we propose a novel unsupervised domain adaptation method for neural decoding of actions, which uses the previously mentioned animal markerless motion capture systems for domain adaptation. We show that our neural action decoding algorithm can successfully generalize across individuals. Furthermore, our method can provide better neural acti

EPFL2022

Towards Verifiable, Generalizable and Efficient Robust Deep Neural Networks.

Chen Liu

In the last decade, deep neural networks have achieved tremendous success in many fields of machine learning.However, they are shown vulnerable against adversarial attacks: well-designed, yet imperceptible, perturbations can make the state-of-the-art deep neural networks output incorrect results.Understanding adversarial attacks and designing algorithms to make deep neural networks robust against these attacks are key steps to building reliable artificial intelligence in real-life applications.In this thesis, we will first formulate the robust learning problem.Based on the notations of empirical robustness and verified robustness, we design new algorithms to achieve both of these types of robustness.Specifically, we investigate the robust learning problem from the optimization perspectives.Compared with classic empirical risk minimization, we show the slow convergence and large generalization gap in robust learning.Our theoretical and numerical analysis indicates that these challenges arise, respectively, from non-smooth loss landscapes and model's fitting hard adversarial instances.Our insights shed some light on designing algorithms for mitigating these challenges.Robust learning has other challenges, such as large model capacity requirements and high computational complexity.To solve the model capacity issue, we combine robust learning with model compression.We design an algorithm to obtain sparse and binary neural networks and make it robust.To decrease the computational complexity, we accelerate the existing adversarial training algorithm and preserve its performance stability.In addition to making models robust, our research provides other benefits.Our methods demonstrate that robust models, compared with non-robust ones, usually utilize input features in a way more similar to the way human beings use them, hence the robust models are more interpretable.To obtain verified robustness, our methods indicate the geometric similarity of the decision boundaries near data points.Our approaches towards reliable artificial intelligence can not only render deep neural networks more robust in safety-critical applications but also make us better aware of how they work.

EPFL2022

Timing is Everything

Karen Adam

EPFL2022

Using Animal Motion Capture to Learn Neural Representations

Semih Günel

EPFL2022

Towards Verifiable, Generalizable and Efficient Robust Deep Neural Networks.

Chen Liu

EPFL2022