Ultrafast Population Encoding by Cortical Neurons

Abstract

The processing speed of the brain depends on the ability of neurons to rapidly relay input changes. Prior theoretical and experimental studies of the time scale of population firing rate responses arrived at controversial conclusions, some advocating an ultra-fast response scale while others arguing for an inherent disadvantage of mean encoded signals for rapid detection of the stimulus onset. Here we assessed the time scale of population firing rate responses of neocortical neurons in experiments performed in the time domain and the frequency domain in vitro and in vivo. We show that populations of neocortical neurons can alter their firing rate within 1 millisecond in response to somatically delivered weak current signals presented on a fluctuating background. Signals with amplitudes of miniature postsynaptic currents can be robustly and rapidly detected in the population firing. We further show that population firing rate of neocortical neurons in vitro and in vivo can reliably encode weak signals varying at frequencies up to ~200–300 Hz, or ~50 times faster than the firing rate of individual neurons. These results provide coherent evidence for the ultra-fast, millisecond time-scale of cortical population responses. Notably, fast responses to weak stimuli are limited to the mean encoding. Rapid detection of current variance changes requires extraordinarily large signal amplitudes. Our study presents conclusive evidence showing that cortical neurons are capable of rapidly relaying subtle mean current signals. This provides a vital mechanism for the propagation of rate-coded information within and across brain areas.

Cortical Specializations Underlying Fast Computations

“…Neurons of both types can phase-lock their firing to high frequencies: layer 2/3 pyramids up to ~300 to 400 Hz, and layer 5 pyramids to even higher frequencies of up to ~600 to 700 Hz. These results agree with a number of reports on experimental measurements of frequency response functions in pyramidal neurons from different areas of the neocortex (Boucsein and others 2009; Broicher and others 2012; Higgs and Spain 2009; 2011; Ilin and others 2013; Kondgen and others 2008; Tchumatchenko and others 2011). In a linear system, characteristics of the response in time domain and frequency domain are related.…”

“…Detection time can be also decreased by increasing the signal amplitude, for example by increasing input synchrony. These dependences, initially predicted theoretically (Brunel and others 2001; Fourcaud-Trocme and Brunel 2005; Fourcaud-Trocme and others 2003; Huang and others 2012; Naundorf and others 2005; Wei and Wolf 2011), have now been supported by experimental evidence from neocortical neurons (Boucsein and others 2009; Higgs and Spain 2011; Ilin and others 2013; Ilin and others 2014; Kondgen and others 2008; Tchumatchenko and others 2011). …”

Spontaneous emergence of fast attractor dynamics in a model of developing primary visual cortex

“…By contrast, estimating the overall activity of a large population of neurons requires much less time, since one can simply count the number of spiking neurons firing within a short interval (essentially replacing temporal integration with spatial integration). This might explain how large populations of neurons can respond to a change in input current extremely fast, within the first few milliseconds of stimulation—much faster than individual membrane voltage dynamics34. Thus, the organization of neural activity into discrete collective patterns might conceivably represent a sacrifice of potential representativity in exchange for much faster decoding by downstream neurons—trading discriminative power for temporal precision.…”

Static frequency tuning accounts for changes in neural synchrony evoked by transient communication signals

“…Because P-unit action potentials lock onto the EOD (Hagiwara and Morita 1963), their membrane time constant is likely to be shorter than a single EOD period (ϳ1 ms) and thus potentially explains the ability of the P-units to quickly follow such a mean-coded signal. Cortical neurons also have been shown to rapidly follow mean-coded signals (Boucsein et al 2009;Tchumatchenko et al 2011). A variancecoded transmission that was suggested for rapid signal transmission (Silberberg et al 2004) is therefore not required.…”