Imperial College London

ProfessorPier LuigiDragotti

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Professor of Signal Processing
 
 
 
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Contact

 

+44 (0)20 7594 6192p.dragotti

 
 
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Location

 

814Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
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197 results found

Song P, Verinaz Jadan H, Howe C, Quicke P, Foust A, Dragotti PLet al., 3D Localization for Light-Field Microscopy via Convolutional Sparse Coding on Epipolar Images, IEEE transactions on computational imaging, ISSN: 2333-9403

Journal article

Leung VCH, Huang J-J, Dragotti PL, 2020, Reconstruction of FRI signals using deep neural network approaches, 2020 International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2020), Publisher: IEEE

Finite Rate of Innovation (FRI) theory considers sampling and reconstruction of classes of non-bandlimited continuous signals that have a small number of free parameters, such as a stream of Diracs. The task of reconstructing FRI signals from discrete samples is often transformed into a spectral estimation problem and solved using Prony's method and matrix pencil method which involve estimating signal subspaces. They achieve an optimal performance given by the Cramér-Rao bound yet break down at a certain peak signal-to-noise ratio (PSNR). This is probably due to the so-called subspace swap event. In this paper, we aim to alleviate the subspace swap problem and investigate alternative approaches including directly estimating FRI parameters using deep neural networks and utilising deep neural networks as denoisers to reduce the noise in the samples. Simulations show significant improvements on the breakdown PSNR over existing FRI methods, which still outperform learning-based approaches in medium to high PSNR regimes.

Conference paper

Deng X, Dragotti PL, 2020, Deep convolutional neural network for multi-modal image restoration and fusion., IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN: 0162-8828

In this paper, we propose a novel deep convolutional neural network to solve the general multi-modal image restoration (MIR) and multi-modal image fusion (MIF) problems. Different from other methods based on deep learning, our network architecture is designed by drawing inspirations from a new proposed multi-modal convolutional sparse coding (MCSC) model. The key feature of the proposed network is that it can automatically split the common information shared among different modalities, from the unique information that belongs to each single modality, and is therefore denoted with CU-Net, i.e., Common and Unique information splitting network. Specifically, the CU-Net is composed of three modules, i.e., the unique feature extraction module (UFEM), common feature preservation module (CFPM), and image reconstruction module (IRM). The architecture of each module is derived from the corresponding part in the MCSC model, which consists of several learned convolutional sparse coding (LCSC) blocks. Extensive numerical results verify the effectiveness of our method on a variety of MIR and MIF tasks, including RGB guided depth image super-resolution, flash guided non-flash image denoising, multi-focus and multi-exposure image fusion.

Journal article

Alexandru R, Dragotti PL, 2020, Reconstructing Classes of Non-Bandlimited Signals From Time Encoded Information, IEEE TRANSACTIONS ON SIGNAL PROCESSING, Vol: 68, Pages: 747-763, ISSN: 1053-587X

Journal article

Song P, Deng X, Mota JFC, Deligiannis N, Dragotti PL, Rodrigues MRDet al., 2020, Multimodal Image Super-Resolution via Joint Sparse Representations Induced by Coupled Dictionaries, IEEE Transactions on Computational Imaging, Vol: 6, Pages: 57-72, ISSN: 2573-0436

Journal article

Lawson M, Brookes M, Dragotti PL, 2019, Scene estimation from a swiped image, IEEE Transactions on Computational Imaging, Vol: 5, Pages: 540-555, ISSN: 2333-9403

The image blurring that results from moving a camera with the shutter open is normally regarded as undesirable. However, the blurring of the images encapsulates information which can be extracted to recover the light rays present within the scene. Given the correct recovery of the light rays that resulted in a blurred image, it is possible to reconstruct images of the scene from different camera locations. Therefore, rather than resharpening an image with motion blur, the goal of this paper is to recover the information needed to resynthesise images of the scene from different viewpoints. Estimation of the light rays within a scene is achieved by using a layer-based model to represent objects in the scene as layers, and by using an extended level set method to segment the blurred image into planes at different depths. The algorithm described in this paper has been evaluated on real and synthetic images to produce an estimate of the underlying Epipolar Plane Image.

Journal article

Erdemir E, Dragotti PL, Gunduz D, 2019, Privacy-Aware Location Sharing with Deep Reinforcement Learning

© 2019 IEEE. Location-based services (LBSs) have become widely popular. Despite their utility, these services raise concerns for privacy since they require sharing location information with untrusted third parties. In this work, we study privacy-utility trade-off in location sharing mechanisms. Existing approaches are mainly focused on privacy of sharing a single location or myopic location trace privacy; neither of them taking into account the temporal correlations between the past and current locations. Although these methods preserve the privacy for the current time, they may leak significant amount of information at the trace level as the adversary can exploit temporal correlations in a trace. We propose an information theoretically optimal privacy preserving location release mechanism that takes temporal correlations into account. We measure the privacy leakage by the mutual information between the user's true and released location traces. To tackle the history-dependent mutual information minimization, we reformulate the problem as a Markov decision process (MDP), and solve it using asynchronous actor-critic deep reinforcement learning (RL).

Conference paper

Kotzagiannidis MS, Dragotti PL, 2019, Sampling and reconstruction of sparse signals on circulant graphs – an introduction to graph-FRI, Applied and Computational Harmonic Analysis, Vol: 47, Pages: 539-565, ISSN: 1096-603X

With the objective of employing graphs toward a more generalized theory of signal processing, we present a novel sampling framework for (wavelet-)sparse signals defined on circulant graphs which extends basic properties of Finite Rate of Innovation (FRI) theory to the graph domain, and can be applied to arbitrary graphs via suitable approximation schemes. At its core, the introduced Graph-FRI-framework states that any K-sparse signal on the vertices of a circulant graph can be perfectly reconstructed from its dimensionality-reduced representation in the graph spectral domain, the Graph Fourier Transform (GFT), of minimum size 2K. By leveraging the recently developed theory of e-splines and e-spline wavelets on graphs, one can decompose this graph spectral transformation into the multiresolution low-pass filtering operation with a graph e-spline filter, with subsequent transformation to the spectral graph domain; this allows to infer a distinct sampling pattern, and, ultimately, the structure of an associated coarsened graph, which preserves essential properties of the original, including circularity and, where applicable, the graph generating set.

Journal article

Deng X, Dragotti PL, 2019, Deep coupled ISTA network for multi-modal image super-resolution, IEEE Transactions on Image Processing, Vol: 29, Pages: 1683-1698, ISSN: 1057-7149

Given a low-resolution (LR) image, multi-modal image super-resolution (MISR) aims to find the high-resolution (HR) version of this image with the guidance of an HR image from another modality. In this paper, we use a model-based approach to design a new deep network architecture for MISR. We first introduce a novel joint multi-modal dictionary learning (JMDL) algorithm to model cross-modality dependency. In JMDL, we simultaneously learn three dictionaries and two transform matrices to combine the modalities. Then, by unfolding the iterative shrinkage and thresholding algorithm (ISTA), we turn the JMDL model into a deep neural network, called deep coupled ISTA network. Since the network initialization plays an important role in deep network training, we further propose a layer-wise optimization algorithm (LOA) to initialize the parameters of the network before running back-propagation strategy. Specifically, we model the network initialization as a multi-layer dictionary learning problem, and solve it through convex optimization. The proposed LOA is demonstrated to effectively decrease the training loss and increase the reconstruction accuracy. Finally, we compare our method with other state-of-the-art methods in the MISR task. The numerical results show that our method consistently outperforms others both quantitatively and qualitatively at different upscaling factors for various multi-modal scenarios.

Journal article

Perez-Nieves N, Leung VCH, Dragotti PL, Goodman DFMet al., 2019, Advantages of heterogeneity of parameters in spiking neural network training, 2019 Conference on Cognitive Computational Neuroscience, Publisher: Cognitive Computational Neuroscience

It is very common in studies of the learning capabilities of spiking neural networks (SNNs) to use homogeneous neural and synaptic parameters (time constants, thresholds, etc.). Even in studies in which these parameters are distributed heterogeneously, the advantages or disadvantages of the heterogeneity have rarely been studied in depth. By contrast, in the brain, neurons and synapses are highly diverse, leading naturally to the hypothesis that this heterogeneity may be advantageous for learning. Starting from two state-of-the-art methods for training spiking neural networks (Nicola & Clopath, 2017, Shrestha & Orchard 2018}, we found that adding parameter heterogeneity reduced errors when the network had to learn more complex patterns, increased robustness to hyperparameter mistuning, and reduced the number of training iterations required. We propose that neural heterogeneity may be an important principle for brains to learn robustly in real world environments with highly complex structure, and where task-specific hyperparameter tuning may be impossible. Consequently, heterogeneity may also be a good candidate design principle for artificial neural networks, to reduce the need for expensive hyperparameter tuning as well as for reducing training time.

Conference paper

Kotzagiannidis MS, Dragotti PL, 2019, Splines and Wavelets on Circulant Graphs, Applied and Computational Harmonic Analysis, Vol: 47, Pages: 481-515, ISSN: 1096-603X

We present novel families of wavelets and associated filterbanks for the analysis and representation of functions defined on circulant graphs. In this work, we leverage the inherent vanishing moment property of the circulant graph Laplacian operator, and by extension, the e-graph Laplacian, which is established as a parameterization of the former with respect to the degree per node, for the design of vertex-localized and critically-sampled higher-order graph (e-)spline wavelet filterbanks, which can reproduce and annihilate classes of (exponential) polynomial signals on circulant graphs. In addition, we discuss similarities and analogies of the detected properties and resulting constructions with splines and spline wavelets in the Euclidean domain. Ultimately, we consider generalizations to arbitrary graphs in the form of graph approximations, with focus on graph product decompositions. In particular, we proceed to show how the use of graph products facilitates a multi-dimensional extension of the proposed constructions and properties.

Journal article

Leung VCH, Huang J-J, Dragotti PL, 2019, Reconstruction of FRI Signals using Deep Neural Networks, Signal Processing with Adaptive Sparse Structured Representations (SPARS 2019)

Finite Rate of Innovation (FRI) theory considers sampling and reconstruction of classes of non-bandlimited signals, such as streams of Diracs. Widely used FRI reconstruction methods including Prony's method and matrix pencil method involve Singular Value Decomposition (SVD). When samples are corrupted with noise, they achieve an optimal performance given by the Cramér-Rao bound yet break down at a certain Signal-to-Noise Ratio (SNR) due to the so-called subspace swap problem. In this paper, we investigate a deep neural network approach for FRI signal reconstruction that directly learns a transformation from signal samples to FRI parameters. Simulations show significant improvement on the breakdown SNR over existing FRI methods.

Conference paper

Alexandru R, Dragotti PL, 2019, Rumour source detection in social networks using partial observations, IEEE Global Conference on Signal and Information Processing 2018, Publisher: IEEE

The spread of information on graphs has beenextensively studied in engineering, biology, and economics. Re-cently, however, several authors have started to address themore challenging inverse problem, of localizing the origin of anepidemic, given observed traces of infection. In this paper, weintroduce a novel technique to estimate the location of a sourceof multiple epidemics on a general graph, assuming knowledgeof the start times of rumours, and using observations from asmall number of monitors.

Conference paper

Alexandru R, Dragotti PL, 2019, Diffusion Source Detection in a Network using Partial Observations, Conference on Wavelets and Sparsity XVIII, Publisher: SPIE-INT SOC OPTICAL ENGINEERING, ISSN: 0277-786X

Conference paper

Deng X, Dragotti PL, 2019, COUPLED ISTA NETWORK FOR MULTI-MODAL IMAGE SUPER-RESOLUTION, 44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 1862-1866, ISSN: 1520-6149

Conference paper

Huang J-J, Dragotti PL, 2019, A DEEP DICTIONARY MODEL TO PRESERVE AND DISENTANGLE KEY FEATURES IN A SIGNAL, 44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 3702-3706, ISSN: 1520-6149

Conference paper

Erdemir E, Dragotti PL, Gunduz D, 2019, PRIVACY-COST TRADE-OFF IN A SMART METER SYSTEM WITH A RENEWABLE ENERGY SOURCE AND A RECHARGEABLE BATTERY, 44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 2687-2691, ISSN: 1520-6149

Conference paper

Alexandru R, Dragotti PL, 2019, TIME-BASED SAMPLING AND RECONSTRUCTION OF NON-BANDLIMITED SIGNALS, 44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 7948-7952, ISSN: 1520-6149

Conference paper

Alexandru R, Malhotra P, Reynolds S, Dragotti PLet al., 2018, Estimating the topology of neural networks from distributed observations., European Signal Processing Conference 2018, Publisher: IEEE

We address the problem of estimating the effective connectivity of the brain network, using the input stimulus model proposed by Izhikevich in [1], which accurately reproduces the behaviour of spiking and bursting biological neurons, whilst ensuring computational simplicity. We first analyse the temporal dynamics of neural networks, showing that the spike propagation within the brain can be modelled as a diffusion process. This helps prove the suitability of NetRate algorithm proposed by Rodriguez in [2] to infer the structure of biological neural networks. Finally, we present simulation results using synthetic data to verify the performance of the topology estimation algorithm.

Conference paper

Dragotti P, Huang J, 2018, Photo realistic image completion via dense correspondence, IEEE Transactions on Image Processing, Vol: 27, Pages: 5234-5247, ISSN: 1057-7149

In this paper, we propose an image completion algorithm based on dense correspondence between the input image and an exemplar image retrieved from the Internet. Contrary to traditional methods which register two images according to sparse correspondence, in this paper, we propose a hierarchical PatchMatch method that progressively estimates a dense correspondence, which is able to capture small deformations between images. The estimated dense correspondence has usually large occlusion areas that correspond to the regions to be completed. A nearest neighbor field (NNF) interpolation algorithm interpolates a smooth and accurate NNF over the occluded region. Given the calculated NNF, the correct image content from the exemplar image is transferred to the input image. Finally, as there could be a color difference between the completed content and the input image, a color correction algorithm is applied to remove the visual artifacts. Numerical results show that our proposed image completion method can achieve photo realistic image completion results.

Journal article

Reynolds SC, abrahamsson T, sjostrom PJ, Schultz S, Dragotti PLet al., 2018, CosMIC: a consistent metric for spike inference from calcium imaging, Neural Computation, Vol: 30, Pages: 2726-2756, ISSN: 0899-7667

In recent years, the development of algorithms to detect neuronal spiking activity from two-photon calcium imaging data has received much attention. Meanwhile, few researchers have examined the metrics used to assess the similarity of detected spike trains with the ground truth. We highlight the limitations of the two most commonly used metrics, the spike train correlation and success rate, and propose an alternative, which we refer to as CosMIC. Rather than operating on the true and estimated spike trains directly, the proposed metric assesses the similarity of the pulse trains obtained from convolution of the spike trains with a smoothing pulse. The pulse width, which is derived from the statistics of the imaging data, reflects the temporal tolerance of the metric. The final metric score is the size of the commonalities of the pulse trains as a fraction of their average size. Viewed through the lens of set theory, CosMIC resembles a continuous Sørensen-Dice coefficient — an index commonly used to assess the similarity of discrete, presence/absence data. We demonstrate the ability of the proposed metric to discriminate the precision and recall of spike train estimates. Unlike the spike train correlation, which appears to reward overestimation, the proposed metric score is maximised when the correct number of spikes have been detected. Furthermore, we show that CosMIC is more sensitive to the temporal precision of estimates than the success rate.

Journal article

Deng X, Huang J, Liu M, Dragotti PLet al., 2018, U-FRESH: AN FRI-BASED SINGLE IMAGE SUPER RESOLUTION ALGORITHM AND AN APPLICATION IN IMAGE COMPRESSION, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 1807-1811

Conference paper

Huang J-J, Dragotti PL, 2018, A DEEP DICTIONARY MODEL FOR IMAGE SUPER-RESOLUTION, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 6777-6781

Conference paper

Yang G, Yu S, Hao D, Slabaugh G, Dragotti PL, Ye X, Liu F, Arridge S, Keegan J, Guo Y, Firmin Det al., 2018, DAGAN: deep de-aliasing generative adversarial networks for fast compressed sensing MRI reconstruction, IEEE Transactions on Medical Imaging, Vol: 37, Pages: 1310-1321, ISSN: 0278-0062

Compressed Sensing Magnetic Resonance Imaging (CS-MRI) enables fast acquisition, which is highly desirable for numerous clinical applications. This can not only reduce the scanning cost and ease patient burden, but also potentially reduce motion artefacts and the effect of contrast washout, thus yielding better image quality. Different from parallel imaging based fast MRI, which utilises multiple coils to simultaneously receive MR signals, CS-MRI breaks the Nyquist-Shannon sampling barrier to reconstruct MRI images with much less required raw data. This paper provides a deep learning based strategy for reconstruction of CS-MRI, and bridges a substantial gap between conventional non-learning methods working only on data from a single image, and prior knowledge from large training datasets. In particular, a novel conditional Generative Adversarial Networks-based model (DAGAN) is proposed to reconstruct CS-MRI. In our DAGAN architecture, we have designed a refinement learning method to stabilise our U-Net based generator, which provides an endto-end network to reduce aliasing artefacts. To better preserve texture and edges in the reconstruction, we have coupled the adversarial loss with an innovative content loss. In addition, we incorporate frequency domain information to enforce similarity in both the image and frequency domains. We have performed comprehensive comparison studies with both conventional CSMRI reconstruction methods and newly investigated deep learning approaches. Compared to these methods, our DAGAN method provides superior reconstruction with preserved perceptual image details. Furthermore, each image is reconstructed in about 5 ms, which is suitable for real-time processing.

Journal article

Dragotti PL, 2018, A Brief Message From the New Editor-in-Chief, IEEE TRANSACTIONS ON SIGNAL PROCESSING, Vol: 66, Pages: 1952-1952, ISSN: 1053-587X

Journal article

Lu Y, Onativia J, Dragotti P, 2018, Sparse representation in Fourier and local bases Using ProSparse: a probabilistic analysis, IEEE Transactions on Information Theory, Vol: 64, Pages: 2639-2647, ISSN: 0018-9448

Finding the sparse representation of a signal in an overcomplete dictionary has attracted a lot of attention over the past years. This paper studies ProSparse, a new polynomial complexity algorithm that solves the sparse representation problem when the underlying dictionary is the union of a Vandermonde matrix and a banded matrix. Unlike our previous work which establishes deterministic (worst-case) sparsity bounds for ProSparse to succeed, this paper presents a probabilistic average-case analysis of the algorithm. Based on a generatingfunction approach, closed-form expressions for the exact success probabilities of ProSparse are given. The success probabilities are also analyzed in the high-dimensional regime. This asymptotic analysis characterizes a sharp phase transition phenomenon regarding the performance of the algorithm.

Journal article

Schuck R, Go MA, Garasto S, Reynolds S, Dragotti PL, Schultz SRet al., 2018, Multiphoton minimal inertia scanning for fast acquisition of neural activity signals, Journal of Neural Engineering, Vol: 15, ISSN: 1741-2552

Objective: Multi-photon laser scanning microscopy provides a powerful tool for monitoring the spatiotemporal dynamics of neural circuit activity. It is, however, intrinsically a point scanning technique. Standard raster scanning enables imaging at subcellular resolution; however, acquisition rates are limited by the size of the field of view to be scanned. Recently developed scanning strategies such as Travelling Salesman Scanning (TSS) have been developed to maximize cellular sampling rate by scanning only select regions in the field of view corresponding to locations of interest such as somata. However, such strategies are not optimized for the mechanical properties of galvanometric scanners. We thus aimed to develop a new scanning algorithm which produces minimal inertia trajectories, and compare its performance with existing scanning algorithms.
 Approach: We describe here the Adaptive Spiral Scanning (SSA) algorithm, which fits a set of near-circular trajectories to the cellular distribution to avoid inertial drifts of galvanometer position. We compare its performance to raster scanning and TSS in terms of cellular sampling frequency and signal-to-noise ratio (SNR).
 Main Results: Using surrogate neuron spatial position data, we show that SSA acquisition rates
 are an order of magnitude higher than those for raster scanning and generally exceed those achieved by TSS for neural densities comparable with those found in the cortex. We show that this result also holds true for in vitro hippocampal mouse brain slices bath loaded with the synthetic calcium dye Cal-520 AM. The ability of TSS to "park" the laser on each neuron along the scanning trajectory, however, enables higher SNR than SSA when all targets are precisely scanned. Raster scanning has the highest SNR but at a substantial cost in number of cells scanned. To understand the impact of sampling rate and SNR on functional calcium imaging, we used the Crame ́r-Rao Bound on e

Journal article

Markovsky I, Dragotti PL, 2018, Using Hankel Structured Low-Rank Approximation for Sparse Signal Recovery, 14th International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 479-487, ISSN: 0302-9743

Conference paper

Reynolds SC, Abrahamsson T, Schuck R, Sjöström PJ, Schultz SR, Dragotti PLet al., 2017, ABLE: an Activity-Based Level Set Segmentation Algorithm for Two-Photon Calcium Imaging Data, Eneuro, Vol: 4, ISSN: 2373-2822

We present an algorithm for detecting the location of cells from two-photon calcium imaging data. In our framework, multiple coupled active contours evolve, guided by a model-based cost function, to identify cell boundaries. An active contour seeks to partition a local region into two subregions, a cell interior and exterior, in which all pixels have maximally ‘similar’ time courses. This simple, local model allows contours to be evolved predominantly independently. When contours are sufficiently close, their evolution is coupled, in a manner that permits overlap. We illustrate the ability of the proposed method to demix overlapping cells on real data. The proposed framework is flexible, incorporating no prior information regarding a cell’s morphology or stereotypical temporal activity, which enables the detection of cells with diverse properties. We demonstrate algorithm performance on a challenging mouse in vitro dataset, containing synchronously spiking cells, and a manually labelled mouse in vivo dataset, on which ABLE achieves a 67.5% success rate.

Journal article

Schuck R, Go MA, Garasto S, Reynolds S, Dragotti PL, Schultz SRet al., 2017, Multiphoton minimal inertia scanning for fast acquisition of neural activity signals, Publisher: Cold Spring Harbor Laboratory

<jats:title>Abstract</jats:title><jats:p>Multi-photon laser scanning microscopy provides a powerful tool for monitoring the spatiotemporal dynamics of neural circuit activity. It is, however, intrinsically a point scanning technique. Standard raster scanning enables imaging at subcellular resolution; however, acquisition rates are limited by the size of the field of view to be scanned. Recently developed scanning strategies such as Travelling Salesman Scanning (TSS) have been developed to maximize cellular sampling rate by scanning only regions of interest in the field of view corresponding to locations of interest such as somata. However, such strategies are not optimized for the mechanical properties of galvanometric scanners. We describe here the Adaptive Spiral Scanning (SSA) algorithm, which fits a set of near-circular trajectories to the cellular distribution to avoid inertial drifts of galvanometer position. We compare its performance to raster scanning and TSS in terms of cellular sampling frequency and signal-to-noise ratio (SNR). Using surrogate neuron spatial position data, we show that SSA acquisition rates are an order of magnitude higher than those for raster scanning and generally exceed those achieved by TSS for neural densities comparable with those found in the cortex. We show that this result also holds true for <jats:italic>in vitro</jats:italic> hippocampal mouse brain slices bath loaded with the synthetic calcium dye Cal-520 AM. The ability of TSS to ”park” the laser on each neuron along the scanning trajectory, however, enables higher SNR than SSA when all targets are precisely scanned. Raster scanning has the highest SNR but at a substantial cost in number of cells scanned. To understand the impact of sampling rate and SNR on functional calcium imaging, we used the Cramér-Rao Bound on evoked calcium traces recorded simultaneously with electrophysiology traces to calculate the lower bound estima

Working paper

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