Towards the neural population doctrine.

By Saurabh Saxena, John P. Cunningham. 2019

322 citations recorded by the source corpus.

Across neuroscience, large-scale data recording and population-level analysis methods have experienced explosive growth. While the underlying hardware and computational techniques have been well reviewed, we focus here on the novel science that these technologies have enabled. We detail four areas of the field where the joint analysis of neural populations has significantly furthered our understanding of computation in the brain: correlated variability, decoding, neural dynamics, and artificial neural networks. Together, these findings suggest an exciting trend towards a new era where neural populations are understood to be the essential unit of computation in many brain regions, a classic idea that has been given new life.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Electrical and Electronic Engineering

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Citations in the corpus

Orthogonal representations for robust context-dependent task performance in brains and neural networks (2022, neutral): “The question of whether neural codes are task agnostic or task specific speaks to core problems in neural theory with widespread implications for understanding the coding properties of neurons and neural populations (Yuste, 2015; Saxena and Cunningham, 2019).”
Complementary roles of dimensionality and population structure in neural computations (2020, background): “A newly emerging paradigm has therefore posited that neural computations need instead to be understood in terms of collective dynamics in the state space of joint activity of all neurons [Buonomano and Maass, 2009; Rigotti et al., 2013; Mante et al., 2013; Gallego et al., 2017; Remington et al., 201”
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 .”
Computation Through Neural Population Dynamics (2020, background): “This approach has helped us to understand cortical responses that are dominated by intrinsic neural dynamics, as opposed to sensory input dynamics (Seely et al. 2016), and has been explored over the past 15 years in a variety of contexts (Briggman et al. 2005, 2006; Broome et al. 2006; Churchland…”