Literature DB >> 31327712

Scaling Principles of Distributed Circuits.

Shyam Srinivasan1, Charles F Stevens2.   

Abstract

Identifying shared quantitative features of a neural circuit across species is important for 3 reasons. Often expressed in the form of power laws and called scaling relationships [1, 2], they reveal organizational principles of circuits, make insights gleaned from model systems widely applicable, and explain circuit performance and function, e.g., visual circuits [3, 4]. The visual circuit is topographic [5, 6], wherein retinal neurons target and activate predictable spatial loci in primary visual cortex. The brain, however, contains many circuits, where neuronal targets and activity are unpredictable and distributed throughout the circuit, e.g., olfactory circuits, in which glomeruli (or mitral cells) in the olfactory bulb synapse with neurons distributed throughout the piriform cortex [7-10]. It is unknown whether such circuits, which we term distributed circuits, are scalable. To determine whether distributed circuits scale, we obtained quantitative descriptions of the olfactory bulb and piriform cortex in six mammals using stereology techniques and light microscopy. Two conserved features provide evidence of scalability. First, the number of piriform neurons n and bulb glomeruli g scale as n∼g3/2. Second, the average number of synapses between a bulb glomerulus and piriform neuron is invariant at one. Using theory and modeling, we show that these two features preserve the discriminatory ability and precision of odor information across the olfactory circuit. As both abilities depend on circuit size, manipulating size provides evolution with a way to adapt a species to its niche without designing developmental programs de novo. These principles might apply to other distributed circuits like the hippocampus.
Copyright © 2019. Published by Elsevier Ltd.

Entities:  

Keywords:  Piriform cortex; comparative; discrimination; distributed circuit; neuronal density; olfactory bulb; precision; scaling; synaptic connectivity; topographic

Year:  2019        PMID: 31327712     DOI: 10.1016/j.cub.2019.06.046

Source DB:  PubMed          Journal:  Curr Biol        ISSN: 0960-9822            Impact factor:   10.834


  6 in total

1.  The unreasonable effectiveness of deep learning in artificial intelligence.

Authors:  Terrence J Sejnowski
Journal:  Proc Natl Acad Sci U S A       Date:  2020-01-28       Impact factor: 11.205

Review 2.  Odor coding in piriform cortex: mechanistic insights into distributed coding.

Authors:  Robin M Blazing; Kevin M Franks
Journal:  Curr Opin Neurobiol       Date:  2020-05-15       Impact factor: 6.627

3.  What the odor is not: Estimation by elimination.

Authors:  Vijay Singh; Martin Tchernookov; Vijay Balasubramanian
Journal:  Phys Rev E       Date:  2021-08       Impact factor: 2.529

4.  Connectivity and dynamics in the olfactory bulb.

Authors:  David E Chen Kersen; Gaia Tavoni; Vijay Balasubramanian
Journal:  PLoS Comput Biol       Date:  2022-02-07       Impact factor: 4.475

5.  Developmental and evolutionary constraints on olfactory circuit selection.

Authors:  Naoki Hiratani; Peter E Latham
Journal:  Proc Natl Acad Sci U S A       Date:  2022-03-09       Impact factor: 11.205

Review 6.  Olfactory dysfunction in aging and neurodegenerative diseases.

Authors:  Xiuli Dan; Noah Wechter; Samuel Gray; Joy G Mohanty; Deborah L Croteau; Vilhelm A Bohr
Journal:  Ageing Res Rev       Date:  2021-07-27       Impact factor: 11.788

  6 in total

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