Temporal whitening by power-law adaptation in neocortical neurons

Abstract

Neural signaling requires a large amount of metabolic energy 1 . Consequently, neurons are thought to communicate using efficient codes in which redundant information is discarded 2 . Theories of efficient coding 3 successfully predict several features of sensory systems. At early stages of visual processing, inputs coming from the external world are decorrelated in both space and time [4][5][6][7] ; through sensory adaptation 8 , codes are dynamically modified so as to maximize information transmission [9][10][11][12] ; and sensory adaptation on multiple timescales 11,13,14 could possibly reflect the statistics of the external world 15 .Sensory adaptation is at least partially a result of intrinsic properties of individual neurons and, in particular, of SFA. SFA is not only observed at the early stages of sensory processing, but is also widespread in cortical neurons embedded in highly recurrent networks. Often modeled by a single process with one specific timescale 16,17 , SFA also occurs on multiple timescales [18][19][20] . In pyramidal neurons of the rat somatosensory cortex, three or more processing steps away from sensory receptors, SFA is scale free 21 , meaning that the effective speed at which individual neurons adapt is not fixed but depends on the input. Scale-free adaptation can be captured by simple threshold models with a power law-decaying spike-triggered process 22 that possibly describes the combined action of Na + -channel inactivation [23][24][25] and ion channels mediating adaptation currents [26][27][28] .Thus, three questions arise. First, can the temporal features of spiketriggered currents and spike-triggered changes in firing threshold, possibly spanning multiple timescales, be directly extracted from experimental data? Second, can SFA be explained by these spiketriggered effects? And finally, do the timescales of SFA match the temporal statistics of the inputs received by individual neurons? If temporal characteristics of inputs and SFA were matched, SFA could lead to a perfect decorrelation of the information contained in one spike with that of the previous one of the same neuron, a phenomenon known as temporal whitening 29 . Temporal whitening in turn implies that, at a high signal-to-noise ratio (SNR), information transmission is enhanced 30 . RESULTSThe question of whether SFA is optimally designed for efficient coding can only be addressed if both the dynamics of SFA and the statistical properties of the inputs generated in biologically relevant situations are known. We used a combined theoretical and experimental approach to extract the dynamics of spike-triggered processes and SFA directly from in vitro recordings of cortical neurons. We then analyzed the synaptically driven membrane potential dynamics recorded in vivo from somatosensory neurons during active whisker sensation (data from ref. 31). Our overall goal was to study whether adaptation optimally removes the temporal correlations in the input to single neurons embedded in the highly recurrent network of the cortex.SFA i...

Balanced ionotropic receptor dynamics support signal estimation via voltage-dependent membrane noise

“…The elimination of postsynaptic negative SCs is a signature of such matching and is thus at least consistent with the Nesse et al (2010) theory. A similar effect has been observed at the single-neuron level in cortex, although it is implemented by an entirely different biophysical mechanism (Pozzorini et al 2013). Their analysis strongly suggested that power-law spike frequency adaptation performs temporal whitening of inputs.…”

Electrosensory processing inApteronotus albifrons: implications for general and specific neural coding strategies across wave-type weakly electric fish species

“…7F). Such phase constancy is characteristic of fractional differentiation (Lundstrom et al 2008;Pozzorini et al 2013), which is thought to optimize coding as discussed below. We thus fit a power law to the gain curves (see METHODS) and found similar fractional differentiation exponents for ON-and OFF-type pyramidal cells (ON: α = 0.35 ± 0.03; OFF: α = 0.32 ± 0.05, Mann-Whitney Utest P = 0.585).…”

Statistical structure of neural spiking under non-Poissonian or other non-white stimulation

“…A number of studies have reported membrane potential fluctuations that indicate synaptic input with a power law spectrum (Pozzorini et al 2013;Destexhe et al 2003). Direct injection of power-law currents have also been used to mimic the statistics of natural stimuli .…”