Automated High-Throughput Characterization of Single Neurons by Means of Simplified Spiking Models

Pozzorini, Christian; Mensi, Skander; Hagens, Olivier; Naud, Richard; Koch, Christof; Gerstner, Wulfram

doi:10.1371/journal.pcbi.1004275

Cited by 83 publications

(148 citation statements)

References 70 publications

Supporting

Mentioning

145

Contrasting

Order By: Relevance

“…This is a very simple model for a neuron with spike frequency adaptation. We refer to (Gerstner et al, 2014;Pozzorini et al, 2015;Gouwens et al, 2018) for experimental data and other neuron models.…”

Section: Network Modelsmentioning

confidence: 99%

A solution to the learning dilemma for recurrent networks of spiking neurons

Bellec

Scherr

Subramoney

et al. 2019

Preprint

View full text Add to dashboard Cite

Recurrently connected networks of spiking neurons underlie the astounding information processing capabilities of the brain. But in spite of extensive research, it has remained open how learning through synaptic plasticity could be organized in such networks. We argue that two pieces of this puzzle were provided by experimental data from neuroscience. A new mathematical insight tells us how they need to be combined to enable network learning through gradient descent. The resulting learning method -called e-prop -approaches the performance of BPTT (backpropagation through time), the best known method for training recurrent neural networks in machine learning. But in contrast to BPTT, e-prop is biologically plausible. In addition, it elucidates how brain-inspired new computer chips -that are drastically more energy efficient -can be enabled to learn.

show abstract

Section: Network Modelsmentioning

confidence: 99%

A solution to the learning dilemma for recurrent networks of spiking neurons

Bellec

Scherr

Subramoney

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…For more complex models, the fitting of the mechanisms is iterative (similar to that in the work in ref. 9) followed by a final optimization step (similar to that in the work in ref. 10) to fine-tune parameters.…”

Section: Models Of Individual Neuronsmentioning

confidence: 99%

Inferring cortical function in the mouse visual system through large-scale systems neuroscience

Hawrylycz

Anastassiou

Arkhipov

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

View full text Add to dashboard Cite

The scientific mission of the Project MindScope is to understand neocortex, the part of the mammalian brain that gives rise to perception, memory, intelligence, and consciousness. We seek to quantitatively evaluate the hypothesis that neocortex is a relatively homogeneous tissue, with smaller functional modules that perform a common computational function replicated across regions. We here focus on the mouse as a mammalian model organism with genetics, physiology, and behavior that can be readily studied and manipulated in the laboratory. We seek to describe the operation of cortical circuitry at the computational level by comprehensively cataloging and characterizing its cellular building blocks along with their dynamics and their cell type-specific connectivities. The project is also building large-scale experimental platforms (i.e., brain observatories) to record the activity of large populations of cortical neurons in behaving mice subject to visual stimuli. A primary goal is to understand the series of operations from visual input in the retina to behavior by observing and modeling the physical transformations of signals in the corticothalamic system. We here focus on the contribution that computer modeling and theory make to this long-term effort.T he neocortical sheet is a layered structure with a thickness that varies by a factor of two to three, whereas its surface area varies by 50,000 between the small smoky shrew and the massive blue whale. A unique hallmark of mammals, neocortex is a highly versatile, scalable, ∼2D computational tissue that excels at real time sensory processing across modalities and making and storing associations as well as planning and producing complex motor patterns. Neocortex consists of smaller modular units (columnar circuits that reach across the depth of cortex) broadly repeated iteratively across the cortical sheet. These modules vary considerably in their connectivity and properties between regions, with some controversy whether there is, indeed, a single canonical function performed by any and all neocortical columns.A deep understanding of cortex necessitates measuring relevant biophysical variables, such as recording action and membrane potentials, and relating them to genetically identified cell types. Mapping, observing, and intervening in widespread but highly specific cellular activity are more readily accomplished in the mouse, Mus musculus, than in the human brain. The brain of the laboratory mouse is more than three orders of magnitude smaller than the human brain in weight (0.4 vs. 1,350 g) and contains 71 million vs. 86 billion nerve cells for the entire brain and 14 million vs. 16 billion nerve cells for neocortex (1).The Allen Institute for Brain Science is engaged in a 10-y, highthroughput, milestone-driven effort to characterize all cortical cell types for the mouse cortex to build a small number of distinct experimental and computational platforms, called brain observatories, for studying behaving mice and to construct abstract and biophysically realisti...

show abstract

“…The generalized integrate and fire method (Pozzorini et al, 2015) provides an extension of the GLM method to account for both spiking and subthreshold dynamics, as would be obtained from an intracellular measurement of membrane potential. The generalized integrate and fire method incorporates a term that filters the membrane potential as it evolves over time that is equivalent to the stimulus feature vector in the GLM.…”

Section: Discussionmentioning

confidence: 99%

Analysis of Neuronal Spike Trains, Deconstructed

Aljadeff

Lansdell

Fairhall

et al. 2016

Neuron

View full text Add to dashboard Cite

As information flows through the brain, neuronal firing progresses from encoding the world as sensed by the animal to driving the motor output of subsequent behavior. One of the more tractable goals of quantitative neuroscience is to develop predictive models that relate the sensory or motor streams with neuronal firing. Here we review and contrast analytical tools used to accomplish this task. We focus on classes of models in which the external variable is compared with one or more feature vectors to extract a low-dimensional representation, the history of spiking and other variables are potentially incorporated, and these factors are nonlinearly transformed to predict the occurrences of spikes. We illustrate these techniques in application to datasets of different degrees of complexity. In particular, we address the fitting of models in the presence of strong correlations in the external variable, as occurs in natural sensory stimuli and in movement. Spectral correlation between predicted and measured spike trains is introduced to contrast the relative success of different methods.

show abstract

Automated High-Throughput Characterization of Single Neurons by Means of Simplified Spiking Models

Cited by 83 publications

References 70 publications

A solution to the learning dilemma for recurrent networks of spiking neurons

A solution to the learning dilemma for recurrent networks of spiking neurons

Inferring cortical function in the mouse visual system through large-scale systems neuroscience

Analysis of Neuronal Spike Trains, Deconstructed

Contact Info

Product

Resources

About