Literature DB >> 8868566

Energy efficient neural codes.

W B Levy1, R A Baxter.   

Abstract

In 1969 Barlow introduced the phrase "economy of impulses" to express the tendency for successive neural systems to use lower and lower levels of cell firings to produce equivalent encodings. From this viewpoint, the ultimate economy of impulses is a neural code of minimal redundancy. The hypothesis motivating our research is that energy expenditures, e.g., the metabolic cost of recovering from an action potential relative to the cost of inactivity, should also be factored into the economy of impulses. In fact, coding schemes with the largest representational capacity are not, in general, optimal when energy expenditures are taken into account. We show that for both binary and analog neurons, increased energy expenditure per neuron implies a decrease in average firing rate if energy efficient information transmission is to be maintained.

Mesh:

Year:  1996        PMID: 8868566     DOI: 10.1162/neco.1996.8.3.531

Source DB:  PubMed          Journal:  Neural Comput        ISSN: 0899-7667            Impact factor:   2.026


  120 in total

1.  Energy-efficient neuronal computation via quantal synaptic failures.

Authors:  William B Levy; Robert A Baxter
Journal:  J Neurosci       Date:  2002-06-01       Impact factor: 6.167

2.  How does connectivity between cortical areas depend on brain size? Implications for efficient computation.

Authors:  Jan Karbowski
Journal:  J Comput Neurosci       Date:  2003 Nov-Dec       Impact factor: 1.621

3.  Energy-based stochastic control of neural mass models suggests time-varying effective connectivity in the resting state.

Authors:  Roberto C Sotero; Amir Shmuel
Journal:  J Comput Neurosci       Date:  2011-11-01       Impact factor: 1.621

4.  Local non-linear interactions in the visual cortex may reflect global decorrelation.

Authors:  Simo Vanni; Tom Rosenström
Journal:  J Comput Neurosci       Date:  2010-04-27       Impact factor: 1.621

5.  Metabolic cost as a unifying principle governing neuronal biophysics.

Authors:  Andrea Hasenstaub; Stephani Otte; Edward Callaway; Terrence J Sejnowski
Journal:  Proc Natl Acad Sci U S A       Date:  2010-06-23       Impact factor: 11.205

6.  Precise feature based time scales and frequency decorrelation lead to a sparse auditory code.

Authors:  Chen Chen; Heather L Read; Monty A Escabí
Journal:  J Neurosci       Date:  2012-06-20       Impact factor: 6.167

7.  Testing the odds of inherent vs. observed overdispersion in neural spike counts.

Authors:  Wahiba Taouali; Giacomo Benvenuti; Pascal Wallisch; Frédéric Chavane; Laurent U Perrinet
Journal:  J Neurophysiol       Date:  2015-10-07       Impact factor: 2.714

8.  Preconfigured, skewed distribution of firing rates in the hippocampus and entorhinal cortex.

Authors:  Kenji Mizuseki; György Buzsáki
Journal:  Cell Rep       Date:  2013-08-29       Impact factor: 9.423

9.  The integration of multiple stimulus features by V1 neurons.

Authors:  Alexander Grunewald; Evelyn K Skoumbourdis
Journal:  J Neurosci       Date:  2004-10-13       Impact factor: 6.167

10.  How much the eye tells the brain.

Authors:  Kristin Koch; Judith McLean; Ronen Segev; Michael A Freed; Michael J Berry; Vijay Balasubramanian; Peter Sterling
Journal:  Curr Biol       Date:  2006-07-25       Impact factor: 10.834

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