Yesterday, we featured two studies (Do I Look Fat ? Lipid Suppression in CSI with Spatial Priors and Highly Undersampled Peripheral K-Space and Accelerated DSI with Compressed Sensing using Adaptive Dictionaries ) that used dictionaries learned as a source of additional information to be used for reconstruction in the MRI world. Today, we have a similar approach but with electron microscopy. I note their use of the dictionary learning SPAMS package whereas the the previous paper this week used K-SVD by Miki Elad and Ron Rubinstein.
Super-resolution using Sparse Representations over Learned Dictionaries: Reconstruction of Brain Structure using Electron Microscopy by Tao Hu, Juan Nunez-Iglesias, Shiv Vitaladevuni, Lou Scheffer, Shan Xu, Mehdi Bolorizadeh, Harald Hess,Richard Fetter, Dmitri Chklovskii. The abstract reads:
A central problem in neuroscience is reconstructing neuronal circuits on the synapse level. Due to a wide range of scales in brain architecture such reconstruction requires imaging that is both high-resolution and high-throughput. Existing electron microscopy (EM) techniques possess required resolution in the lateral plane and either high-throughput or high depth resolution but not both. Here, we exploit recent advances in unsupervised learning and signal processing to obtain high depth-resolution EM images computationally without sacrificing throughput. First, we show that the brain tissue can be represented as a sparse linear combination of localized basis functions that are learned using high-resolution datasets. We then develop compressive sensing-inspired techniques that can reconstruct the brain tissue from very few (typically 5) tomographic views of each section. This enables tracing of neuronal processes and, hence, high throughput reconstruction of neural circuits on the level of individual synapses.
Another paper from some of the same authors ( we featured it already here -with some comments-)
Reconstruction of Sparse Circuits Using Multi-neuronal Excitation (RESCUME) by Tao Hu, Dmitri B. Chklovskii. The abstract reads:
One of the central problems in neuroscience is reconstructing synaptic connectivity in neural circuits. Synapses onto a neuron can be probed by sequentially stimulating potentially pre-synaptic neurons while monitoring the membrane voltage of the post-synaptic neuron. Reconstructing a large neural circuit using such a "brute force" approach is rather time-consuming and inefficient because the connectivity in neural circuits is sparse. Instead, we propose to measure a post-synaptic neuron's voltage while stimulating sequentially random subsets of multiple potentially pre-synaptic neurons. To reconstruct these synaptic connections from the recorded voltage we apply a decoding algorithm recently developed for compressive sensing. Compared to the brute force approach, our method promises significant time savings that grow with the size of the circuit. We use computer simulations to find optimal stimulation parameters and explore the feasibility of our reconstruction method under realistic experimental conditions including noise and non-linear synaptic integration. Multineuronal stimulation allows reconstructing synaptic connectivity just from the spiking activity of post-synaptic neurons, even when sub-threshold voltage is unavailable. By using calcium indicators, voltage-sensitive dyes, or multi-electrode arrays one could monitor activity of multiple postsynaptic neurons simultaneously, thus mapping their synaptic inputs in parallel, potentially reconstructing a complete neural circuit.
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