Communication in Neuronal Networks

By Simon Barry Laughlin, Terrence J. Sejnowski. 2003

934 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

The plastic human brain cortex. (2005, background): “Neuronal networks provide a most energy efficient, spatially compact, and precise means to process input signals and generate responses (Laughlin & Sejnowski 2003).”
Communication dynamics in complex brain networks (2017, background): “Understanding how brain function emerges from patterns of communication between large numbers of neuronal elements stands as one of the enduring challenges of modern neuroscience 1–3 .”
Deep Learning With Spiking Neurons: Opportunities and Challenges (2018, background): “The computational eﬃciency of SNNs can be observed in brains that can solve complex tasks while requiring less power than a dim light bulb (Laughlin and Sejnowski, 2003).”
Network hubs in the human brain. (2013, background): “The ‘richness’ of hubs Both theoretical and empirical observations have led to the hypothesis that the architecture of neural systems is shaped by a trade-off between the optimization of wiring cost and the efficiency of neural communication [112,146,155,156].”
Efficiency and Cost of Economical Brain Functional Networks (2007, background): “It is well-recognised that the metabolic costs of building and functionally resourcing the brain are large in proportion to the total energy budget of the body [ 25 ].”
Interneuron Diversity series: Circuit complexity and axon wiring economy of cortical interneurons. (2004, background): “However, random connectivity is not economic because in larger brains distant connections require longer axon lengths and/or more effective myelination, and transmitting action potentials over large distances is energetically costly [10, 15 ].”
The unreasonable effectiveness of deep learning in artificial intelligence (2020, background): “A switching network routes information between sensory and motor areas that can be rapidly reconfigured to meet ongoing cognitive demands (18).”
Network Oscillations: Emerging Computational Principles (2006, methodology): “…generation is energetically expensive, this mechanism may have evolved to minimize energy expenditure during computation and communication (Laughlin and Sejnowski, 2003), which might also explain why it is evolutionarily conserved from mollusks to the human brain (Gelperin, 2006; Kahana,…”
Generative models of the human connectome (2015, positive): “The observation that geometry partly explains the topology of brain networks is in line with in a large literature on wiring minimization (Mitchison, 1991; Laughlin and Sejnowski, 2003; Cherniak et al., 2004; Samu et al., 2014), and has been appreciated in modeling studies of both human brain networ”
A Resilient, Low-Frequency, Small-World Human Brain Functional Network with Highly Connected Association Cortical Hubs (2006, background): “It seems likely a priori (Laughlin and Sejnowski, 2003; Sporns et al., 2004; Sporns and Zwi, 2004; Kaiser and Hilgetag, 2004a,b) that similar physical constraints (aging or connection costs) might apply to the formation of whole-brain functional networks and thus account for the empirical form of…”