Approximate, not perfect synchrony maximizes the downstream effectiveness of excitatory neuronal ensembles

The most basic functional role commonly ascribed to synchrony in the brain is that of amplifying excitatory neuronal signals. The reasoning is straightforward: When positive charge is injected into a leaky target neuron over a time window of positive duration, some of it will have time to leak back out before an action potential is triggered in the target, and it will in that sense be wasted. If the goal is to elicit a firing response in the target using as little charge as possible, it seems best to deliver the charge all at once, i.e., in perfect synchrony. In this article, we show that this reasoning is correct only if one assumes that the input ceases when the target crosses the firing threshold, but before it actually fires. If the input ceases later-for instance, in response to a feedback signal triggered by the firing of the target-the "most economical" way of delivering input (the way that requires the least total amount of input) is no longer precisely synchronous, but merely approximately so. If the target is a heterogeneous network, as it always is in the brain, then ceasing the input "when the target crosses the firing threshold" is not an option, because there is no single moment when the firing threshold is crossed. In this sense, precise synchrony is never optimal in the brain.R01 NS067199 - NINDS NIH HH

The Mechanism of NMDA Receptor Mediated Increased in Gamma Oscillation Frequency

Activation of N-methyl-D-aspartate (NMDA) receptors has been shown to increase the frequency of gamma oscillations in the CA3 region of the hippocampus. The underlying mechanism of the increase however, is unclear. This project utilizes an integrate-and-fire model of the CA3, based on experimental data, to investigate the increase in oscillation frequency. The model was built first without NMDA receptors to simulate carbachol induced oscillations in vitro. Then, NMDA receptors were added to evoke the increase in oscillation frequency. The model shows that a shift in mechanism, from a pyramidal neuron-interneuron feedback loop, to interneuron-interneuron oscillations, is responsible for the increase in gamma oscillation frequency. An interesting relationship between the active NMDA mediated current and instantaneous cycle frequencies points to further areas of study

Corticospinal beta-band synchronization entails rhythmic gain modulation

Rhythmic synchronization of neurons in the beta or gamma band occurs almost ubiquitously, and this synchronization has been linked to numerous nervous system functions. Many respective studies make the implicit assumption that neuronal synchronization affects neuronal interactions. Indeed, when neurons synchronize, their output spikes reach postsynaptic neurons together, trigger coincidence detection mechanisms, and therefore have an enhanced impact. There is ample experimental evidence demonstrating this consequence of neuronal synchronization, but beyond this, beta/gamma-band synchronization within a group of neurons might also modulate the impact of synaptic input to that synchronized group. This would constitute a separate mechanism through which synchronization affects neuronal interactions, but direct in vivo evidence for this putative mechanism is lacking. Here, we demonstrate that synchronized beta-band activity of a neuronal group modulates the efficacy of synaptic input to that group in-phase with the beta rhythm. This response modulation was not an addition of rhythmic activity onto the average response but a rhythmic modulation of multiplicative input gain. Our results demonstrate that beta-rhythmic activity of a neuronal target group multiplexes input gain along the rhythm cycle. The actual gain of an input then depends on the precision and the phase of its rhythmic synchronization to this target, providing one mechanistic explanation for why synchronization modulates interactions

Paradoxical phase response of gamma rhythms facilitates their entrainment in heterogeneous networks

The synchronization of different $\gamma$-rhythms arising in different brain areas has been implicated in various cognitive functions. Here, we focus on the effect of the ubiquitous neuronal heterogeneity on the synchronization of PING (pyramidal-interneuronal network gamma) and ING (interneuronal network gamma) rhythms. The synchronization properties of rhythms depends on the response of their collective phase to external input. We therefore determined the macroscopic phase-response curve for finite-amplitude perturbations (fmPRC), using numerical simulation of all-to-all coupled networks of integrate-and-fire (IF) neurons exhibiting either PING or ING rhythms. We show that the intrinsic neuronal heterogeneity can qualitatively modify the fmPRC. While the phase-response curve for the individual IF-neurons is strictly positive (type I), the fmPRC can be biphasic and exhibit both signs (type II). Thus, for PING rhythms, an external excitation to the excitatory cells can, in fact, delay the collective oscillation of the network, even though the same excitation would lead to an advance when applied to uncoupled neurons. This paradoxical delay arises when the external excitation modifies the internal dynamics of the network by causing additional spikes of inhibitory neurons, whose delaying within-network inhibition outweighs the immediate advance caused by the external excitation. These results explain how intrinsic heterogeneity allows the PING rhythm to become synchronized with a periodic forcing or another PING rhythm for a wider range in the mismatch of their frequencies. We demonstrate a similar mechanism for the synchronization of ING rhythms. Our results identify a potential function of neuronal heterogeneity in the synchronization of coupled $\gamma$-rhythms, which may play a role in neural information transfer via communication through coherence.Comment: 24 pages, 7 Figs, 3 Supp Fig

Attentional effects on local V1 microcircuits explain selective V1-V4 communication

Selective attention implements preferential routing of attended stimuli, likely through increasing the influence of the respective synaptic inputs on higher-area neurons. As the inputs of competing stimuli converge onto postsynaptic neurons, presynaptic circuits might offer the best target for attentional top-down influences. If those influences enabled presynaptic circuits to selectively entrain postsynaptic neurons, this might explain selective routing. Indeed, when two visual stimuli induce two gamma rhythms in V1, only the gamma induced by the attended stimulus entrains gamma in V4. Here, we modeled induced responses with a Dynamic Causal Model for Cross-Spectral Densities and found that selective entrainment can be explained by attentional modulation of intrinsic V1 connections. Specifically, local inhibition was decreased in the granular input layer and increased in the supragranular output layer of the V1 circuit that processed the attended stimulus. Thus, presynaptic attentional influences and ensuing entrainment were sufficient to mediate selective routing

Differences in visually induced MEG oscillations reflect differences in deep cortical layer activity

Neural activity is organized at multiple scales, ranging from the cellular to the whole brain level. Connecting neural dynamics at different scales is important for understanding brain pathology. Neurological diseases and disorders arise from interactions between factors that are expressed in multiple scales. Here, we suggest a new way to link microscopic and macroscopic dynamics through combinations of computational models. This exploits results from statistical decision theory and Bayesian inference. To validate our approach, we used two independent MEG datasets. In both, we found that variability in visually induced oscillations recorded from different people in simple visual perception tasks resulted from differences in the level of inhibition specific to deep cortical layers. This suggests differences in feedback to sensory areas and each subject’s hypotheses about sensations due to differences in their prior experience. Our approach provides a new link between non-invasive brain imaging data, laminar dynamics and top-down control

Distinct oscillatory dynamics underlie different components of hierarchical cognitive control

Action Contro

A Group Analysis of Oscillatory Phase and Phase Synchronization in Cortical Networks

Neuronal oscillatory phase and phase synchronization are two main aspects of neuronal oscillation. Neurophysiological and computational studies have demonstrated that oscillatory phase for individual neurons has quantifiable relationships with neuronal excitation and input stimulus. In order to investigate the issue for neuronal groups, we constructed orientation columns by means of a spiking neural network and introduced six network activity states, pre-stimulus and stimulus periods for comparison. We proposed a new method of spike-LFP (Local Field Potential) phase based on vector addition of point spike-LFP phases to represent oscillatory phase. We also proposed a PPCG (Pairwise Phase Consistency for Group) method to quantify phase synchronization for neuronal groups. As illustrated in the simulation, the characteristics of oscillatory phase and phase synchronization for neuronal groups were consistent with the ones for individual neurons. Preferred orientations and stronger external inputs tended to result in smaller and more concentrated oscillatory phases. No matter individual neurons or neuronal groups, the oscillatory phase decreased monotonically as a function of neuronal excitation and input strength. More importantly, neuronal groups had a competitive advantage over individual neurons, because they can achieve reliable relationship quantification of oscillatory phase for all network activity states, even in weak oscillatory or non-oscillatory states