Dynamical response properties of neocortical neurons to conductance‐driven time‐varying inputs

Abstract

Ensembles of cortical neurons can track fast-varying inputs and relay them in their spike trains, far beyond the cut-off imposed by membrane passive electrical properties and mean firing rates. Initially explored in silico and later demonstrated experimentally, investigating how neurons respond to sinusoidally modulated stimuli provides a deeper insight into spike initiation mechanisms and information processing than conventional F-I curve methodologies. Besides net membrane currents, physiological synaptic inputs can also induce a stimulus-dependent modulation of the total membrane conductance, which is not reproduced by standard current-clamp protocols. Here, we investigated whether rat cortical neurons can track fast temporal modulations over a noisy conductance background. We also determined input-output transfer properties over a range of conditions, including: distinct presynaptic activation rates, postsynaptic firing rates and variability and type of temporal modulations. We found a very broad signal transfer bandwidth across all conditions, similar large cut-off frequencies and power-law attenuations of fast-varying inputs. At slow and intermediate input modulations, the response gain decreased for increasing output mean firing rates. The gain also decreased significantly for increasing intensities of background synaptic activity, thus generalising earlier studies on F-I curves. We also found a direct correlation between the action potentials' onset rapidness and the neuronal bandwidth. Our novel results extend previous investigations of dynamical response properties to non-stationary and conductance-driven conditions, and provide computational neuroscientists with a novel set of observations that models must capture when aiming to replicate cortical cellular excitability.

Correlation Transfer by Layer 5 Cortical Neurons Under Recreated Synaptic InputsIn Vitro

“…Theoretical arguments (de la Rocha et al, 2007, Litwin-Kumar et al, 2011 explain these modulations in correlation transfer by associated shifts in the dynamical transfer functions of each cell in the pair (Kondgen et al, 2008, Linaro et al, 2018. This is consistent with studies showing that intrinsic properties-such as whether the neuron acts like an integrator or a coincidence detector (Konig et al, 1996)-also determine correlation transfer (Hong et al, 2012).…”

“…16 into eq. 14: (Fourcaud and Brunel, 2002, Kondgen et al, 2008, Testa-Silva et al, 2014, Linaro et al, 2018. For these two reasons,…”

“…For some experiments, we thus probed the ratecurrent curve of each neuron, in addition to the other stimulation protocol, and we employed its local slope to approximate the neuron linear response function at the low frequencies needed to estimate spike-count covariances over long windows. In fact, although fast experimental techniques for the characterization of A È 9 ( ) and A È = ( ) are available (Higgs andSpain, 2009, Ilin et al, 2013), using the full transfer function is anyway not practical: A È 9 ( ) and A È = ( ) are in fact modulated by the mean firing rate 9 and = of the neurons (Linaro et al, 2018) and their complete estimate, across a sufficiently wide range of firing rates, is incompatible with the limited duration of each experiment.…”

The location of the axon initial segment affects the bandwidth of spike initiation dynamics

“…The dynamics of AP initiation and its underlying time-scales have been themes of intense investigation in rodent and human cortical neurons, both experimentally [1][2][3][4][5][6][7][8] and theoretically [6,[9][10][11][12]. Investigations have focused particularly on the shape of the somatic AP, its rapidity at onset, and on its underlying biophysics [1,6,10,13].…”

Dynamic Gain Analysis Reveals Encoding Deficiencies in Cortical Neurons That Recover from Hypoxia-Induced Spreading Depolarizations

“…Thus, at the single-cell level, circuit function depends on the ability of the individual neuron to detect subtle, correlated high-frequency input activity that is hidden in intense, asynchronous background noise, and to encode it in the timing of the output spikes (London et al, 2010). It was recently discovered that cortical neurons exhibit an ultrafast response, as manifested by a high bandwidth dynamic gain, such that the firing across the population can follow rapidly changing, time-dependent inputs with millisecond precision and even lock to fast rhythms in the frequency range of sharp wave ripples (hundreds of Hertz) as precisely as to lowfrequency oscillations (Silberberg et al, 2004;Köndgen et al, 2008;Boucsein et al, 2009;Spain, 2009, 2011;Broicher et al, 2012;Ilin et al, 2013;Testa-Silva et al, 2014;Doose et al, 2016;Nikitin et al, 2017;Lazarov et al, 2018;Linaro et al, 2018). We now report that the dynamic gain of cortical neurons that recover from SD is significantly narrowed.…”