How advances in neural recording affect data analysis

By Ian H. Stevenson, Konrad Paul Kording. Nature Neuroscience, 2011

622 citations recorded by the source corpus.

Over the last five decades, progress in neural recording techniques has allowed the number of simultaneously recorded neurons to double approximately every 7 years, mimicking Moore's law. Such exponential growth motivates us to ask how data analysis techniques are affected by progressively larger numbers of recorded neurons. Traditionally, neurons are analyzed independently on the basis of their tuning to stimuli or movement. Although tuning curve approaches are unaffected by growing numbers of simultaneously recorded neurons, newly developed techniques that analyze interactions between neurons become more accurate and more complex as the number of recorded neurons increases. Emerging data analysis techniques should consider both the computational costs and the potential for more accurate models associated with this exponential growth of the number of recorded neurons.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/3c58d3a01bd28e4a202d420346ec0a745db3414e

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Citations in the corpus

Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation. (2017, background): “Current intracranial BMIs usually employ extracellular recording methods that allow one to sample and discriminate action potentials generated by hundreds of individual cortical neurons (466, 583, 711, 767).”
On simplicity and complexity in the brave new world of large-scale neuroscience. (2015, background): “4 years [18] in the number of neurons we can simultaneously measure at single cell, single spike-time resolution, we would have to wait more than 100 years before we can observe O(10(6) − 10(9)) neurons typically present in full mammalian circuits controlling complex behaviors [19].”
Fast functional imaging of multiple brain regions in intact zebrafish larvae using Selective Plane Illumination Microscopy (2013, background): “Siliconbased nanoprobes allow electrophysiologists to simultaneously record up to a few hundred neurons (Du et al., 2011; Stevenson and Kording, 2011).”
If deep learning is the answer, what is the question? (2020, background): “As methodological advances have permitted simultaneous recordings from large numbers of neurons 36 , a doctrine has emerged according to which neural representation is dynamically multiplexed across populations 37 .”
Past, present and future of spike sorting techniques (2015, background): “In general, the number of simultaneously recorded neurons has grown exponentially since the 1950s, doubling every 7 years, and currently allowing electrical observation of hundreds of neurons at sub-millisecond timescales (Stevenson and Kording, 2011).”
Representational models: A common framework for understanding encoding, pattern-component, and representational-similarity analysis (2017, background): “The measurement of brain activity is rapidly advancing in terms of spatial and temporal resolution, and in terms of the number of responses that can be measured simultaneously [1].”
Training Excitatory-Inhibitory Recurrent Neural Networks for Cognitive Tasks: A Simple and Flexible Framework (2016, neutral): “At the same time, the novel challenges posed by high-dimensional neural data have led to the development of new methods for analyzing and modeling such data [10 – 12].”
A theory of multineuronal dimensionality, dynamics and measurement (2017, background): “Indeed, exciting advances in recording technology over the last several decades have lead to a type of Moore’s law in neuroscience: an exponential growth in the number of neurons we can simultaneously record with a doubling rate of 7.4 years since the 1960’s [Stevenson and Kording, 2011].”