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1.
Annu Rev Neurosci ; 43: 441-464, 2020 07 08.
Article in English | MEDLINE | ID: mdl-32283996

ABSTRACT

As acquiring bigger data becomes easier in experimental brain science, computational and statistical brain science must achieve similar advances to fully capitalize on these data. Tackling these problems will benefit from a more explicit and concerted effort to work together. Specifically, brain science can be further democratized by harnessing the power of community-driven tools, which both are built by and benefit from many different people with different backgrounds and expertise. This perspective can be applied across modalities and scales and enables collaborations across previously siloed communities.


Subject(s)
Big Data , Brain/physiology , Computational Biology , Nerve Net/physiology , Animals , Computational Biology/methods , Databases, Genetic , Gene Expression/physiology , Humans
2.
Nat Methods ; 20(6): 935-944, 2023 Jun.
Article in English | MEDLINE | ID: mdl-37169928

ABSTRACT

Learning is thought to involve changes in glutamate receptors at synapses, submicron structures that mediate communication between neurons in the central nervous system. Due to their small size and high density, synapses are difficult to resolve in vivo, limiting our ability to directly relate receptor dynamics to animal behavior. Here we developed a combination of computational and biological methods to overcome these challenges. First, we trained a deep-learning image-restoration algorithm that combines the advantages of ex vivo super-resolution and in vivo imaging modalities to overcome limitations specific to each optical system. When applied to in vivo images from transgenic mice expressing fluorescently labeled glutamate receptors, this restoration algorithm super-resolved synapses, enabling the tracking of behavior-associated synaptic plasticity with high spatial resolution. This method demonstrates the capabilities of image enhancement to learn from ex vivo data and imaging techniques to improve in vivo imaging resolution.


Subject(s)
Neurons , Synapses , Mice , Animals , Synapses/physiology , Image Enhancement , Mice, Transgenic , Neuronal Plasticity
3.
Nat Methods ; 19(4): 470-478, 2022 04.
Article in English | MEDLINE | ID: mdl-35347320

ABSTRACT

Population recordings of calcium activity are a major source of insight into neural function. Large datasets require automated processing, but this can introduce errors that are difficult to detect. Here we show that popular time course-estimation algorithms often contain substantial misattribution errors affecting 10-20% of transients. Misattribution, in which fluorescence is ascribed to the wrong cell, arises when overlapping cells and processes are imperfectly defined or not identified. To diagnose misattribution, we develop metrics and visualization tools for evaluating large datasets. To correct time courses, we introduce a robust estimator that explicitly accounts for contaminating signals. In one hippocampal dataset, removing contamination reduced the number of place cells by 15%, and 19% of place fields shifted by over 10 cm. Our methods are compatible with other cell-finding techniques, empowering users to diagnose and correct a potentially widespread problem that could alter scientific conclusions.


Subject(s)
Calcium , Neurons , Algorithms , Calcium/metabolism , Calcium Signaling , Hippocampus/metabolism , Neurons/metabolism
4.
Neural Comput ; : 1-31, 2024 Oct 08.
Article in English | MEDLINE | ID: mdl-39383030

ABSTRACT

The sparse coding model posits that the visual system has evolved to efficiently code natural stimuli using a sparse set of features from an overcomplete dictionary. The original sparse coding model suffered from two key limitations, however: (1) computing the neural response to an image patch required minimizing a nonlinear objective function via recurrent dynamics and (2) fitting relied on approximate inference methods that ignored uncertainty. Although subsequent work has developed several methods to overcome these obstacles, we propose a novel solution inspired by the variational autoencoder (VAE) framework. We introduce the sparse coding variational autoencoder (SVAE), which augments the sparse coding model with a probabilistic recognition model parameterized by a deep neural network. This recognition model provides a neurally plausible feedforward implementation for the mapping from image patches to neural activities and enables a principled method for fitting the sparse coding model to data via maximization of the evidence lower bound (ELBO). The SVAE differs from standard VAEs in three key respects: the latent representation is overcomplete (there are more latent dimensions than image pixels), the prior is sparse or heavy-tailed instead of gaussian, and the decoder network is a linear projection instead of a deep network. We fit the SVAE to natural image data under different assumed prior distributions and show that it obtains higher test performance than previous fitting methods. Finally, we examine the response properties of the recognition network and show that it captures important nonlinear properties of neurons in the early visual pathway.

5.
Nat Methods ; 14(4): 420-426, 2017 Apr.
Article in English | MEDLINE | ID: mdl-28319111

ABSTRACT

Two-photon laser scanning microscopy of calcium dynamics using fluorescent indicators is a widely used imaging method for large-scale recording of neural activity in vivo. Here, we introduce volumetric two-photon imaging of neurons using stereoscopy (vTwINS), a volumetric calcium imaging method that uses an elongated, V-shaped point spread function to image a 3D brain volume. Single neurons project to spatially displaced 'image pairs' in the resulting 2D image, and the separation distance between projections is proportional to depth in the volume. To demix the fluorescence time series of individual neurons, we introduce a modified orthogonal matching pursuit algorithm that also infers source locations within the 3D volume. We illustrated vTwINS by imaging neural population activity in the mouse primary visual cortex and hippocampus. Our results demonstrated that vTwINS provides an effective method for volumetric two-photon calcium imaging that increases the number of neurons recorded while maintaining a high frame rate.


Subject(s)
Imaging, Three-Dimensional/methods , Microscopy, Fluorescence, Multiphoton/methods , Neurons/physiology , Visual Cortex/cytology , Algorithms , Animals , Calcium/analysis , Calcium/metabolism , Female , Hippocampus/cytology , Hippocampus/physiology , Male , Mice, Transgenic , Microscopy, Confocal/instrumentation , Microscopy, Confocal/methods , Microscopy, Fluorescence, Multiphoton/instrumentation , Molecular Imaging/methods , Visual Cortex/physiology
6.
Neural Comput ; 30(4): 1012-1045, 2018 04.
Article in English | MEDLINE | ID: mdl-29381442

ABSTRACT

Neurons in many brain areas exhibit high trial-to-trial variability, with spike counts that are overdispersed relative to a Poisson distribution. Recent workĀ (Goris, Movshon, & Simoncelli, 2014 ) has proposed to explain this variability in terms of a multiplicative interaction between a stochastic gain variable and a stimulus-dependent Poisson firing rate, which produces quadratic relationships between spike count mean and variance. Here we examine this quadratic assumption and propose a more flexible family of models that can account for a more diverse set of mean-variance relationships. Our model contains additive gaussian noise that is transformed nonlinearly to produce a Poisson spike rate. Different choices of the nonlinear function can give rise to qualitatively different mean-variance relationships, ranging from sublinear to linear to quadratic. Intriguingly, a rectified squaring nonlinearity produces a linear mean-variance function, corresponding to responses with a constant Fano factor. We describe a computationally efficient method for fitting this model to data and demonstrate that a majority of neurons in a V1 population are better described by a model with a nonquadratic relationship between mean and variance. Finally, we demonstrate a practical use of our model via an application to Bayesian adaptive stimulus selection in closed-loop neurophysiology experiments, which shows that accounting for overdispersion can lead to dramatic improvements in adaptive tuning curve estimation.


Subject(s)
Action Potentials/physiology , Brain/cytology , Models, Neurological , Neurons/physiology , Algorithms , Humans , Normal Distribution , Stochastic Processes
7.
Neural Comput ; 26(6): 1198-235, 2014 Jun.
Article in English | MEDLINE | ID: mdl-24684446

ABSTRACT

Cortical networks are hypothesized to rely on transient network activity to support short-term memory (STM). In this letter, we study the capacity of randomly connected recurrent linear networks for performing STM when the input signals are approximately sparse in some basis. We leverage results from compressed sensing to provide rigorous nonasymptotic recovery guarantees, quantifying the impact of the input sparsity level, the input sparsity basis, and the network characteristics on the system capacity. Our analysis demonstrates that network memory capacities can scale superlinearly with the number of nodes and in some situations can achieve STM capacities that are much larger than the network size. We provide perfect recovery guarantees for finite sequences and recovery bounds for infinite sequences. The latter analysis predicts that network STM systems may have an optimal recovery length that balances errors due to omission and recall mistakes. Furthermore, we show that the conditions yielding optimal STM capacity can be embodied in several network topologies, including networks with sparse or dense connectivities.


Subject(s)
Memory, Short-Term/physiology , Models, Neurological , Nerve Net/physiology , Cerebral Cortex , Humans , Neural Networks, Computer , Nonlinear Dynamics
8.
bioRxiv ; 2024 Apr 28.
Article in English | MEDLINE | ID: mdl-38260339

ABSTRACT

Accurate tracking of the same neurons across multiple days is crucial for studying changes in neuronal activity during learning and adaptation. Advances in high density extracellular electrophysiology recording probes, such as Neuropixels, provide a promising avenue to accomplish this goal. Identifying the same neurons in multiple recordings is, however, complicated by non-rigid movement of the tissue relative to the recording sites (drift) and loss of signal from some neurons. Here we propose a neuron tracking method that can identify the same cells independent of firing statistics, that are used by most existing methods. Our method is based on between-day non-rigid alignment of spike sorted clusters. We verified the same cell identity in mice using measured visual receptive fields. This method succeeds on datasets separated from one to 47 days, with an 84% average recovery rate.

9.
Elife ; 122024 Jul 10.
Article in English | MEDLINE | ID: mdl-38985568

ABSTRACT

Accurate tracking of the same neurons across multiple days is crucial for studying changes in neuronal activity during learning and adaptation. Advances in high-density extracellular electrophysiology recording probes, such as Neuropixels, provide a promising avenue to accomplish this goal. Identifying the same neurons in multiple recordings is, however, complicated by non-rigid movement of the tissue relative to the recording sites (drift) and loss of signal from some neurons. Here, we propose a neuron tracking method that can identify the same cells independent of firing statistics, that are used by most existing methods. Our method is based on between-day non-rigid alignment of spike-sorted clusters. We verified the same cell identity in mice using measured visual receptive fields. This method succeeds on datasets separated from 1 to 47 days, with an 84% average recovery rate.


Subject(s)
Neurons , Animals , Neurons/physiology , Mice , Electrophysiology/methods , Electrophysiological Phenomena , Action Potentials/physiology , Cell Tracking/methods
10.
J Neurosci Methods ; 403: 110033, 2024 03.
Article in English | MEDLINE | ID: mdl-38056633

ABSTRACT

BACKGROUND: Functional ultrasound imaging (fUS) is an emerging imaging technique that indirectly measures neural activity via changes in blood volume. Chronic fUS imaging during cognitive tasks in freely moving animals faces multiple exceptional challenges: performing large durable craniotomies with chronic implants, designing behavioral experiments matching the hemodynamic timescale, stabilizing the ultrasound probe during freely moving behavior, accurately assessing motion artifacts, and validating that the animal can perform cognitive tasks while tethered. NEW METHOD: We provide validated solutions for those technical challenges. In addition, we present standardized step-by-step reproducible protocols, procedures, and data processing pipelines. Finally, we present proof-of-concept analysis of brain dynamics during a decision making task. RESULTS: We obtain stable recordings from which we can robustly decode task variables from fUS data over multiple months. Moreover, we find that brain wide imaging through hemodynamic response is nonlinearly related to cognitive variables, such as task difficulty, as compared to sensory responses previously explored. COMPARISON WITH EXISTING METHODS: Computational pipelines in fUS are nascent and we present an initial development of a full processing pathway to correct and segment fUS data. CONCLUSIONS: Our methods provide stable imaging and analysis of behavior with fUS that will enable new experimental paradigms in understanding brain-wide dynamics in naturalistic behaviors.


Subject(s)
Brain , Rodentia , Animals , Brain/diagnostic imaging , Brain/physiology , Ultrasonography , Motion , Cognition
11.
bioRxiv ; 2024 Jul 09.
Article in English | MEDLINE | ID: mdl-39026717

ABSTRACT

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

12.
Neural Comput ; 24(12): 3317-39, 2012 Dec.
Article in English | MEDLINE | ID: mdl-22970876

ABSTRACT

The sparse coding hypothesis has generated significant interest in the computational and theoretical neuroscience communities, but there remain open questions about the exact quantitative form of the sparsity penalty and the implementation of such a coding rule in neurally plausible architectures. The main contribution of this work is to show that a wide variety of sparsity-based probabilistic inference problems proposed in the signal processing and statistics literatures can be implemented exactly in the common network architecture known as the locally competitive algorithm (LCA). Among the cost functions we examine are approximate l(p) norms (0 ≤ p ≤ 2), modified l(p)-norms, block-l1 norms, and reweighted algorithms. Of particular interest is that we show significantly increased performance in reweighted l1 algorithms by inferring all parameters jointly in a dynamical system rather than using an iterative approach native to digital computational architectures.


Subject(s)
Algorithms , Artificial Intelligence , Computer Simulation , Somatosensory Cortex/physiology , Animals , Humans
13.
Neurophotonics ; 9(4): 041402, 2022 Oct.
Article in English | MEDLINE | ID: mdl-35937186

ABSTRACT

Functional optical imaging in neuroscience is rapidly growing with the development of optical systems and fluorescence indicators. To realize the potential of these massive spatiotemporal datasets for relating neuronal activity to behavior and stimuli and uncovering local circuits in the brain, accurate automated processing is increasingly essential. We cover recent computational developments in the full data processing pipeline of functional optical microscopy for neuroscience data and discuss ongoing and emerging challenges.

14.
IEEE Trans Image Process ; 31: 3509-3524, 2022.
Article in English | MEDLINE | ID: mdl-35533160

ABSTRACT

Optical imaging of calcium signals in the brain has enabled researchers to observe the activity of hundreds-to-thousands of individual neurons simultaneously. Current methods predominantly use morphological information, typically focusing on expected shapes of cell bodies, to better identify neurons in the field-of-view. The explicit shape constraints limit the applicability of automated cell identification to other important imaging scales with more complex morphologies, e.g., dendritic or widefield imaging. Specifically, fluorescing components may be broken up, incompletely found, or merged in ways that do not accurately describe the underlying neural activity. Here we present Graph Filtered Temporal Dictionary (GraFT), a new approach that frames the problem of isolating independent fluorescing components as a dictionary learning problem. Specifically, we focus on the time-traces-the main quantity used in scientific discovery-and learn a time trace dictionary with the spatial maps acting as the presence coefficients encoding which pixels the time-traces are active in. Furthermore, we present a novel graph filtering model which redefines connectivity between pixels in terms of their shared temporal activity, rather than spatial proximity. This model greatly eases the ability of our method to handle data with complex non-local spatial structure. We demonstrate important properties of our method, such as robustness to morphology, simultaneously detecting different neuronal types, and implicitly inferring number of neurons, on both synthetic data and real data examples. Specifically, we demonstrate applications of our method to calcium imaging both at the dendritic, somatic, and widefield scales.


Subject(s)
Algorithms , Calcium , Brain/diagnostic imaging , Brain/physiology , Neurons
15.
Neuron ; 110(2): 328-349.e11, 2022 01 19.
Article in English | MEDLINE | ID: mdl-34776042

ABSTRACT

Recent work has highlighted that many types of variables are represented in each neocortical area. How can these many neural representations be organized together without interference and coherently maintained/updated through time? We recorded from excitatory neural populations in posterior cortices as mice performed a complex, dynamic task involving multiple interrelated variables. The neural encoding implied that highly correlated task variables were represented by less-correlated neural population modes, while pairs of neurons exhibited a spectrum of signal correlations. This finding relates to principles of efficient coding, but notably utilizes neural population modes as the encoding unit and suggests partial whitening of task-specific information where different variables are represented with different signal-to-noise levels. Remarkably, this encoding function was multiplexed with sequential neural dynamics yet reliably followed changes in task-variable correlations throughout the trial. We suggest that neural circuits can implement time-dependent encodings in a simple way using random sequential dynamics as a temporal scaffold.


Subject(s)
Neurons , Animals , Mice , Neurons/physiology
16.
J Neurosci Methods ; 358: 109173, 2021 07 01.
Article in English | MEDLINE | ID: mdl-33839190

ABSTRACT

BACKGROUND: The past decade has seen a multitude of new in vivo functional imaging methodologies. However, the lack of ground-truth comparisons or evaluation metrics makes the large-scale, systematic validation vital to the continued development and use of optical microscopy impossible. NEW-METHOD: We provide a new framework for evaluating two-photon microscopy methods via in silico Neural Anatomy and Optical Microscopy (NAOMi) simulation. Our computationally efficient model generates large anatomical volumes of mouse cortex, simulates neural activity, and incorporates optical propagation and scanning to create realistic calcium imaging datasets. RESULTS: We verify NAOMi simulations against in vivo two-photon recordings from mouse cortex. We leverage this in silico ground truth to directly compare different segmentation algorithms and optical designs. We find modern segmentation algorithms extract strong neural time-courses comparable to estimation using oracle spatial information, but with an increase in the false positive rate. Comparison between optical setups demonstrate improved resilience to motion artifacts in sparsely labeled samples using Bessel beams, increased signal-to-noise ratio and cell-count using low numerical aperture Gaussian beams and nuclear GCaMP, and more uniform spatial sampling with temporal focusing versus multi-plane imaging. COMPARISON WITH EXISTING METHODS: NAOMi is a first-of-its kind framework for assessing optical imaging modalities. Existing methods are either anatomical simulations or do not address functional imaging. Thus there is no competing method for simulating realistic functional optical microscopy data. CONCLUSIONS: By leveraging the rich accumulated knowledge of neural anatomy and optical physics, we provide a powerful new tool to assess and develop important methods in neural imaging.


Subject(s)
Calcium , Microscopy , Algorithms , Animals , Artifacts , Computer Simulation , Mice
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