Reconstruction of Sparse Circuits Using Multi-neuronal Excitation (RESCUME)

@inproceedings{Hu2009ReconstructionOS,
  title={Reconstruction of Sparse Circuits Using Multi-neuronal Excitation (RESCUME)},
  author={Tao Hu and Anthony M. Leonardo and Dmitri B. Chklovskii},
  booktitle={NIPS},
  year={2009}
}
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… 

Figures from this paper

Compressive Sensing Inference of Neuronal Network Connectivity in Balanced Neuronal Dynamics
TLDR
By tuning the network dynamical regime, it is determined that the highest fidelity reconstructions are achievable in the balanced state and the methodology developed is expected to be generalizable for alternative model networks as well as experimental paradigms.
A Bayesian compressed-sensing approach for reconstructing neural connectivity from subsampled anatomical data
TLDR
A statistical approach for connectivity reconstruction that relies on relatively easy-to-obtain measurements using fluorescent probes such as synaptic markers, cytoplasmic dyes, transsynaptic tracers, or activity-dependent dyes and shows that the spatial heterogeneity and biological variability in the connectivity matrix can be estimated using the same method.
Online neural connectivity estimation with ensemble stimulation.
TLDR
By stimulating small ensembles of neurons, it is shown that it is possible to recover binarized network connectivity with a number of tests that grows only logarithmically with population size under minimal statistical assumptions, and it is proved that the approach can be related to Variational Bayesian inference on the binary connection weights.
Bayesian Inference and Online Experimental Design for Mapping Neural Microcircuits
TLDR
This work develops a method for efficiently inferring posterior distributions over synaptic strengths in neural microcircuits and develops an online optimal design algorithm for choosing which neurons to stimulate at each trial.
Online Neural Connectivity Estimation with Noisy Group Testing
TLDR
By stimulating small ensembles of neurons, it is shown that it is possible to recover binarized network connectivity with a number of tests that grows only logarithmically with population size under minimal statistical assumptions, and it is proved that the approach, which reduces to an efficiently solvable convex optimization problem, is equivalent to Variational Bayesian inference on the binary connection weights.
Neural Reconstruction with Approximate Message Passing (NeuRAMP)
TLDR
The proposed estimation algorithm models the neurons via a graphical model and then estimates the parameters in the model using a recently-developed generalized approximate message passing (GAMP) method, which is validated on estimation of linear nonlinear Poisson models for receptive fields of salamander retinal ganglion cells.
A Compressed Sensing Framework for Efficient Dissection of Neural Circuits
TLDR
This work describes a compressed sensing-based framework in combination with non-specific genetic tools to infer candidate neurons controlling behaviors with fewer measurements than previously thought possible and suggests that compressed sensing approaches can be used to identify key nodes in complex biological networks.
Inference About Functional Connectivity From Multiple Neural Spike Trains
TLDR
The preliminary results show the effect of sample size, connection strength and basis set on functional connectivity inference, and the proposed plan is to combine two families of methods, i.e. point process-generalized linear model based methods and graph theory based methods, to develop procedure that can be used to infer functional connectivity network given limited amount of data.
Neural mass spatio-temporal modeling from high-density electrode array recordings
TLDR
This work proposes a novel method for systematically identifying neural mass models that is particularly well-suited for high-density micro-electrocorticographic (μECoG) data, and can automatically uncover the underlying components in the neural populations.
Statistical mechanics of complex neural systems and high dimensional data
TLDR
A pedagogical review of a collection of ideas and theoretical methods arising at the intersection of statistical physics, computer science and neurobiology to illustrate how statistical physics and computer science might provide a lens through which to uncover emergent computational functions buried deep within the dynamical complexities of neuronal networks.
...
...

References

SHOWING 1-10 OF 58 REFERENCES
Reverse optical trawling for synaptic connections in situ.
TLDR
A new method is introduced to unveil the network connectivity among dozens of neurons in brain slice preparations by statistically screening the neurons that exhibited calcium transients immediately before the postsynaptic inputs and identifying the presynaptic cells that made synaptic connections onto the patch-clamped neurons.
Highly Nonrandom Features of Synaptic Connectivity in Local Cortical Circuits
TLDR
The local cortical network structure can be viewed as a skeleton of stronger connections in a sea of weaker ones, likely to play an important role in network dynamics and should be investigated further.
Optical probing of neuronal circuits with calcium indicators.
TLDR
It is demonstrated that neurons that display somatic calcium transients time-locked to the spikes of a trigger neuron can be monosynaptically connected to it.
A network of tufted layer 5 pyramidal neurons.
TLDR
These studies suggest that a local network of TL5 neurons within a cortical module of diameter 300 microns consists of a few hundred neurons that are extensively inter-connected with reciprocal feedback from at least first-, second- and third-order target neurons.
Analyzing Functional Connectivity Using a Network Likelihood Model of Ensemble Neural Spiking Activity
TLDR
A discrete-time version of Chornoboy, Schramm, and Karr's maximum likelihood method for the simultaneous analysis of multiple pair-wise interactions among an ensemble of neurons is devised that includes a new, efficient computational strategy, a principled method to compute starting values, and a principled stopping criterion.
A 256×256 CMOS Microelectrode Array for Extracellular Neural Stimulation of Acute Brain Slices
TLDR
The focus of this work is on stimulation and achieving stable electrical interfaces between acute slices and a high-density active CMOS MEA.
Physiology and anatomy of synaptic connections between thick tufted pyramidal neurones in the developing rat neocortex.
TLDR
Dual voltage recordings were made from pairs of adjacent, synaptically connected thick tufted layer 5 pyramidal neurones in brain slices of young rat (14‐16 days) somatosensory cortex to examine the physiological properties of unitary EPSPs and the axonal and dendritic anatomy of both projecting and target neurones was uniform.
An evaluation of causes for unreliability of synaptic transmission.
  • C. Allen, C. Stevens
  • Biology
    Proceedings of the National Academy of Sciences of the United States of America
  • 1994
TLDR
Probabilistic release mechanisms at low capacity synapses are the main cause of unreliability of synaptic transmission on CA1 hippocampal pyramidal neurons.
Photostimulation using caged glutamate reveals functional circuitry in living brain slices.
  • E. Callaway, L. Katz
  • Biology
    Proceedings of the National Academy of Sciences of the United States of America
  • 1993
TLDR
Caged glutamate-based photostimulation eliminates artifacts and limitations inherent in conventional stimulation methods, including stimulation of axons of passage, desensitization, and poor temporal resolution of "puffer" pipettes, and current artifacts of iontophoretic application.
Computational subunits in thin dendrites of pyramidal cells
TLDR
This work combined confocal imaging and dual-site focal synaptic stimulation of identified thin dendrites in rat neocortical pyramidal neurons found that nearby inputs on the same branch summed sigmoidally, whereas widely separated inputs or inputs to different branches summed linearly.
...
...