Slow Resting State Fluctuations Enhance Neuronal and Behavioral Responses to Looming Sounds.

We investigate both experimentally and using a computational model how the power of the electroencephalogram (EEG) recorded in human subjects tracks the presentation of sounds with acoustic intensities that increase exponentially (looming) or remain constant (flat). We focus on the link between this EEG tracking response, behavioral reaction times and the time scale of fluctuations in the resting state, which show considerable inter-subject variability. Looming sounds are shown to generally elicit a sustained power increase in the alpha and beta frequency bands. In contrast, flat sounds only elicit a transient upsurge at frequencies ranging from 7 to 45 Hz. Likewise, reaction times (RTs) in an audio-tactile task at different latencies from sound onset also present significant differences between sound types. RTs decrease with increasing looming intensities, i.e. as the sense of urgency increases, but remain constant with stationary flat intensities. We define the reaction time variation or "gain" during looming sound presentation, and show that higher RT gains are associated with stronger correlations between EEG power responses and sound intensity. Higher RT gain further entails higher relative power differences between loom and flat in the alpha and beta bands. The full-width-at-half-maximum of the autocorrelation function of the eyes-closed resting state EEG also increases with RT gain. The effects are topographically located over the central and frontal electrodes. A computational model reveals that the increase in stimulus-response correlation in subjects with slower resting state fluctuations is expected when EEG power fluctuations at each electrode and in a given band are viewed as simple coupled low-pass filtered noise processes jointly driven by the sound intensity. The model assumes that the strength of stimulus-power coupling is proportional to RT gain in different coupling scenarios, suggesting a mechanism by which slower resting state fluctuations enhance EEG response and shorten reaction times.

Emergent bimodal firing patterns implement different encoding strategies during gamma-band oscillations.

Upon sensory stimulation, primary cortical areas readily engage in narrow-band rhythmic activity between 30 and 90 Hz, the so-called gamma oscillations. Here we show that, when embedded in a balanced network, type-I excitable neurons entrained to the collective rhythm show a discontinuity in their firing-rates between a slow and a fast spiking mode. This jump in the spiking frequencies is characteristic to type II neurons, but is not present in the frequency-current curve (f-I curve) of isolated type I neurons. Therefore, this rate bimodality arises as an emerging network property in type I population models. We have studied the mechanisms underlying the generation of these two firing modes, in order to reproduce the spiking activity of in vivo cortical recordings, which is known to be highly irregular and sparse. We have also analyzed the relation between afferent inputs and the single unit activity, and between the latter and the local field potential (LFP) phase, in order to establish how the collective dynamics modulates the spiking activity of the individual neurons. Our results reveal that the inhibitory-excitatory balance allows two encoding mechanisms, for input rate variations and LFP phase, to coexist within the network.

Phase-response approach to firing-rate selectivity in neurons with subthreshold oscillations.

Subthreshold oscillations provide neurons with a filtering mechanism that allows their membrane potential to respond selectively to oscillatory currents depending on their frequency. On the other hand, the phase of such oscillations is known to affect the precise timing at which action potentials can be elicited by input spikes. Here we study the combined effect of these two properties by examining the response of a model neuron to periodic spike trains of defined frequency, in the presence of subthreshold oscillatory activity. Numerical results show a marked resonance with the input firing rate, irrespective of the initial relative phase between the input spike train and the intrinsic subthreshold oscillation. This behavior can be understood in terms of a delayed phase transition curve, from which an iterative map can be built that describes the evolution of the phase response to the periodic succession of input spike perturbations. Depending on the input period, the map exhibits stationary, periodic, or chaotic dynamics that predict in a quantitative way the response of the neuron to the spike train. Propagation of the spike train through a chain of neurons is also examined, and the resonant behavior is seen to be enhanced upon propagation.