Weak universality in sensory tradeoffs

Abstract

For many organisms, the number of sensory neurons is largely determined during development, before strong environmental cues are present. This is despite the fact that environments can fluctuate drastically both from generation to generation and within an organism's lifetime. How can organisms get by by hard coding the number of sensory neurons? We approach this question using rate-distortion theory. A combination of simulation and theory suggests that when environments are large, the rate-distortion function-a proxy for material costs, timing delays, and energy requirements-depends only on coarse-grained environmental statistics that are expected to change on evolutionary, rather than ontogenetic, time scales.

The evolution of lossy compression

“…Marzen & DeDeo [41] focused on the insensitivity of the rate–distortion function to particular choices of the distortion measure; here, we focus on the scaling of the rate–distortion function with environmental complexity.…”

“…We tackle this problem in a simplified set-up previously studied in [41]. As in [42], model organisms have (i) an adaptive representation system, which we refer to as a ‘sensory codebook’, and that specifies how an environmental state affects, probabilistically, the internal state of the organism and (ii) two constants: a rate , r , at which the perceptual apparatus gains information from the environment, and a gain , β , which quantifies the costs of doing so.…”

Information theory, predictability and the emergence of complex life

“…Key aspects of information theory relate deeply to formulations in statistical physics (Jaynes, 1957a,b;Parrondo et al, 2015) and there have been several calls to further integrate information theory in biological research (Joyce, 2002(Joyce, , 2012Krakauer, 2011;Maynard-Smith, 2000;Nurse, 2008;Walker & Davies, 2012). This theory shall play important roles in population or ecosystems dynamics, in regulatory genomics, and in chemical signal processing among others (Adami, 2012;Bergstrom & Lachmann, 2004;Dall et al, 2005;Dall & Johnstone, 2002;Donaldson-Matasci et al, 2010;Donalson-Matasci et al, 2008;Evans et al, 2015;Friston, 2013;Hidalgo et al, 2014;Kussell & Leibler, 2005;Marzen & DeDeo, 2016;McNamara & Houston, 1987;Rivoire & Leibler, 2011;Sartori et al, 2014;Segré et al, 2000Segré et al, , 2001Szathmáry, 1989;Tkačik & Bialek, 2014), but a unifying approach is far from complete. Given its generality and power, information theory has also been used to address problems that connect Darwinian evolution and far from equilibrium thermodynamics (Drossel, 2001;England, 2013;Goldenfeld & Woese, 2010;Nicolis & Prigogine, 1977;Perunov et al, 2014).…”

“…Key aspects of information theory relate deeply to formulations in statistical physics [12][13][14] and there have been several calls to further integrate information theory in biological research [15][16][17][18][19][20][21][22][23]. This theory shall play important roles in population or ecosystems dynamics, in regulatory genomics, and in chemical signal processing among others [7,[24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41], but a unifying approach is far from complete. Given its generality and power, information theory has also been used to address problems that connect Darwinian evolution and far from equilibrium thermodynamics [42][43][44][45][46].…”

Fate of Duplicated Neural Structures

“…A thermodynamically viable and evolutionarily successful organism must: first, optimize its interface with environmental inputs [ 203 , 204 ] as well as its responding behavior. Second, optimize its internal organization so that the input can be mapped into the output as cheaply as possible.…”