scholarly journals Firing Rate-dependent Phase Responses of Purkinje Cells Support Transient Oscillations

2019 ◽  
Author(s):  
Yunliang Zang ◽  
Sungho Hong ◽  
Erik De Schutter
Keyword(s):  
Firing Rate ◽  
Network Model ◽  
Rate Adaptation ◽  
Reciprocal Inhibition ◽  
Cerebellar Nuclei ◽  
Spike Timing ◽  
Response Curves ◽  
Rate Dependent ◽  
Network Output ◽  
Transient Oscillations

AbstractBoth spike rate and timing can transmit information in the brain. Phase response curves (PRCs) quantify how a neuron transforms input to output by spike timing. They exhibit strong firing-rate adaptation, but its mechanism and relevance for network output are poorly understood. Using our Purkinje cell (PC) model we demonstrate that the rate adaptation is caused by rate-dependent subthreshold membrane potentials efficiently regulating the activation of Na+ channels. Then we use a realistic PC network model to examine how rate-dependent responses synchronize spikes in the scenario of reciprocal inhibition-caused high-frequency oscillations. The changes in PRC cause oscillations and spike correlations only at high firing rates. The causal role of the PRC is confirmed using a simpler coupled oscillator network model. This mechanism enables transient oscillations between fast-spiking neurons that thereby form PC assemblies. Our work demonstrates that rate adaptation of PRCs can spatio-temporally organize the PC input to cerebellar nuclei.

scholarly journals Firing rate-dependent phase responses of Purkinje cells support transient oscillations

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Yunliang Zang ◽  
Sungho Hong ◽  
Erik De Schutter
Keyword(s):  
Firing Rate ◽  
Network Model ◽  
Rate Adaptation ◽  
Reciprocal Inhibition ◽  
Cerebellar Nuclei ◽  
Spike Timing ◽  
Response Curves ◽  
Rate Dependent ◽  
Network Output ◽  
Transient Oscillations

Both spike rate and timing can transmit information in the brain. Phase response curves (PRCs) quantify how a neuron transforms input to output by spike timing. PRCs exhibit strong firing-rate adaptation, but its mechanism and relevance for network output are poorly understood. Using our Purkinje cell (PC) model, we demonstrate that the rate adaptation is caused by rate-dependent subthreshold membrane potentials efficiently regulating the activation of Na+ channels. Then, we use a realistic PC network model to examine how rate-dependent responses synchronize spikes in the scenario of reciprocal inhibition-caused high-frequency oscillations. The changes in PRC cause oscillations and spike correlations only at high firing rates. The causal role of the PRC is confirmed using a simpler coupled oscillator network model. This mechanism enables transient oscillations between fast-spiking neurons that thereby form PC assemblies. Our work demonstrates that rate adaptation of PRCs can spatio-temporally organize the PC input to cerebellar nuclei.

Amplitude and Frequency Dependence of Spike Timing: Implications for Dynamic Regulation

2003 ◽  
Vol 90 (1) ◽  
pp. 387-394 ◽  
Author(s):  
John D. Hunter ◽  
John G. Milton
Keyword(s):  
Firing Rate ◽  
Phase Locking ◽  
Inhibitory Interneuron ◽  
Spike Timing ◽  
Spike Time ◽  
Frequency Content ◽  
Dynamic Regulation ◽  
Regime Changes ◽  
Rate Dependent ◽  
Cv Measurements

The spike-time reliability of motoneurons in the Aplysia buccal motor ganglion was studied as a function of the frequency content and the relative amplitude of the fluctuations in the neuronal input, calculated as the coefficient of variation (CV). Measurements of spike-time reliability to sinusoidal and aperiodic inputs, as well as simulations of a noisy leaky integrate-and-fire neuron stimulated by spike trains drawn from a periodically modulated process, demonstrate that there are three qualitatively different CV-dependent mechanisms that determine reliability: noise-dominated (CV < 0.05 for Aplysia motoneurons) where spike timing is unreliable regardless of frequency content; resonance-dominated (CV ≈ 0.05–0.25) where reliability is reduced by removal of input frequencies equal to motoneuron firing rate; and amplitude-dominated (CV >0.35) where reliability depends on input frequencies greater than motoneuron firing rate. In the resonance-dominated regime, changes in the activity of the presynaptic inhibitory interneuron B4/5 alter motoneuron spike-time reliability. The increases or decreases in reliability occur coincident with small changes in motoneuron spiking rate due to changes in interneuron activity. Injection of a hyperpolarizing current into the motoneuron reproduces the interneuron-induced changes in reliability. The rate-dependent changes in reliability can be understood from the phase-locking properties of regularly spiking motoneurons to periodic inputs. Our observations demonstrate that the ability of a neuron to support a spike-time code can be actively controlled by varying the properties of the neuron and its input.

PHASE DEPENDENT TRANSITION BETWEEN MULTISTABLE STATES IN A NEURAL NETWORK WITH RECIPROCAL INHIBITION

2004 ◽  
Vol 14 (05) ◽  
pp. 1559-1575 ◽  
Author(s):  
KATSUMI TATENO ◽  
HIDEYUKI TOMONARI ◽  
HATSUO HAYASHI ◽  
SATORU ISHIZUKA
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Synaptic Input ◽  
Reciprocal Inhibition ◽  
Conduction Delay ◽  
Inhibitory Connection ◽  
Pacemaker Neurons ◽  
Excitatory Synaptic Input ◽  
Multistable State

We studied multistable oscillatory states of a small neural network model and switching of an oscillatory mode. In the present neural network model, two pacemaker neurons are reciprocally inhibited with conduction delay; one pacemaker neuron inhibits the other via an inhibitory nonpacemaker interneuron, and vice versa. The small network model shows bifurcations from quasi-periodic oscillation to chaos via period 3 with increase in the synaptic weight of the reciprocal inhibition. The route to chaos in the network model is different from that in the single pacemaker neuron. The network model exhibits several multistable states. In a regime of a weak inhibitory connection, in-phase beat, out-of-phase beat (period 3), and chaotic oscillation coexist at the multistable state. We can switch an oscillatory mode by an excitatory synaptic input to one of the pacemaker neurons through an afferent path. In a strong inhibitory connection regime, in-phase beat and out-of-phase beat (period 4) coexist at the multistable state. An excitatory synaptic input through the afferent path leads to the transition from the in-phase beat to the out-of-phase beat. The transition from the out-of-phase beat to the in-phase beat is induced by an inhibitory synaptic input via interneurons. A conduction delay, furthermore, causes the spontaneous transition from the in-phase beat to the out-of-phase beat. These transitions can be explained by phase response curves.

scholarly journals Properties of precise firing synchrony between synaptically coupled cortical interneurons depend on their mode of coupling

2015 ◽  
Vol 114 (1) ◽  
pp. 624-637 ◽  
Author(s):  
Hang Hu ◽  
Ariel Agmon
Keyword(s):  
Firing Rate ◽  
Electrical Coupling ◽  
Gamma Oscillations ◽  
Inhibitory Synapses ◽  
Data Set ◽  
Temporal Precision ◽  
Gamma Frequency ◽  
Rate Dependent ◽  
Cortical Interneurons

Precise spike synchrony has been widely reported in the central nervous system, but its functional role in encoding, processing, and transmitting information is yet unresolved. Of particular interest is firing synchrony between inhibitory cortical interneurons, thought to drive various cortical rhythms such as gamma oscillations, the hallmark of cognitive states. Precise synchrony can arise between two interneurons connected electrically, through gap junctions, chemically, through fast inhibitory synapses, or dually, through both types of connections, but the properties of synchrony generated by these different modes of connectivity have never been compared in the same data set. In the present study we recorded in vitro from 152 homotypic pairs of two major subtypes of mouse neocortical interneurons: parvalbumin-containing, fast-spiking (FS) interneurons and somatostatin-containing (SOM) interneurons. We tested firing synchrony when the two neurons were driven to fire by long, depolarizing current steps and used a novel synchrony index to quantify the strength of synchrony, its temporal precision, and its dependence on firing rate. We found that SOM-SOM synchrony, driven solely by electrical coupling, was less precise than FS-FS synchrony, driven by inhibitory or dual coupling. Unlike SOM-SOM synchrony, FS-FS synchrony was strongly firing rate dependent and was not evident at the prototypical 40-Hz gamma frequency. Computer simulations reproduced these differences in synchrony without assuming any differences in intrinsic properties, suggesting that the mode of coupling is more important than the interneuron subtype. Our results provide novel insights into the mechanisms and properties of interneuron synchrony and point out important caveats in current models of cortical oscillations.

scholarly journals Sparse bursts optimize information transmission in a multiplexed neural code

2018 ◽  
Vol 115 (27) ◽  
pp. E6329-E6338 ◽  
Author(s):  
Richard Naud ◽  
Henning Sprekeler
Keyword(s):  
Firing Rate ◽  
Cortical Neurons ◽  
Neural Code ◽  
Spike Timing ◽  
Neural Ensemble ◽  
Multiple Input ◽  
Cortical Hierarchy ◽  
Connectivity Patterns ◽  
Rate Coding

Many cortical neurons combine the information ascending and descending the cortical hierarchy. In the classical view, this information is combined nonlinearly to give rise to a single firing-rate output, which collapses all input streams into one. We analyze the extent to which neurons can simultaneously represent multiple input streams by using a code that distinguishes spike timing patterns at the level of a neural ensemble. Using computational simulations constrained by experimental data, we show that cortical neurons are well suited to generate such multiplexing. Interestingly, this neural code maximizes information for short and sparse bursts, a regime consistent with in vivo recordings. Neurons can also demultiplex this information, using specific connectivity patterns. The anatomy of the adult mammalian cortex suggests that these connectivity patterns are used by the nervous system to maintain sparse bursting and optimal multiplexing. Contrary to firing-rate coding, our findings indicate that the physiology and anatomy of the cortex may be interpreted as optimizing the transmission of multiple independent signals to different targets.

scholarly journals GABAA receptors contribute more to rate than temporal coding in the IC of awake mice

2020 ◽  
Vol 123 (1) ◽  
pp. 134-148
Author(s):  
Boris Gourévitch ◽  
Elena J. Mahrt ◽  
Warren Bakay ◽  
Cameron Elde ◽  
Christine V. Portfors
Keyword(s):  
Firing Rate ◽  
Neuronal Response ◽  
Signal Transmission ◽  
Brain Structures ◽  
Temporal Coding ◽  
Model Organisms ◽  
Gabaergic Inhibition ◽  
Response Curves ◽  
Daily Lives ◽  
Complex Sounds

Speech is our most important form of communication, yet we have a poor understanding of how communication sounds are processed by the brain. Mice make great model organisms to study neural processing of communication sounds because of their rich repertoire of social vocalizations and because they have brain structures analogous to humans, such as the auditory midbrain nucleus inferior colliculus (IC). Although the combined roles of GABAergic and glycinergic inhibition on vocalization selectivity in the IC have been studied to a limited degree, the discrete contributions of GABAergic inhibition have only rarely been examined. In this study, we examined how GABAergic inhibition contributes to shaping responses to pure tones as well as selectivity to complex sounds in the IC of awake mice. In our set of long-latency neurons, we found that GABAergic inhibition extends the evoked firing rate range of IC neurons by lowering the baseline firing rate but maintaining the highest probability of firing rate. GABAergic inhibition also prevented IC neurons from bursting in a spontaneous state. Finally, we found that although GABAergic inhibition shaped the spectrotemporal response to vocalizations in a nonlinear fashion, it did not affect the neural code needed to discriminate vocalizations, based either on spiking patterns or on firing rate. Overall, our results emphasize that even if GABAergic inhibition generally decreases the firing rate, it does so while maintaining or extending the abilities of neurons in the IC to code the wide variety of sounds that mammals are exposed to in their daily lives. NEW & NOTEWORTHY GABAergic inhibition adds nonlinearity to neuronal response curves. This increases the neuronal range of evoked firing rate by reducing baseline firing. GABAergic inhibition prevents bursting responses from neurons in a spontaneous state, reducing noise in the temporal coding of the neuron. This could result in improved signal transmission to the cortex.

scholarly journals Spike timing precision changes with spike rate adaptation in the owl's auditory space map

2015 ◽  
Vol 114 (4) ◽  
pp. 2204-2219 ◽  
Author(s):  
Clifford H. Keller ◽  
Terry T. Takahashi
Keyword(s):  
Carrier Frequency ◽  
Time Course ◽  
Rate Adaptation ◽  
Stimulus Level ◽  
Spike Timing ◽  
Spike Rate ◽  
Temporal Precision ◽  
Timing Precision ◽  
Spike Timing Precision

Spike rate adaptation (SRA) is a continuing change of responsiveness to ongoing stimuli, which is ubiquitous across species and levels of sensory systems. Under SRA, auditory responses to constant stimuli change over time, relaxing toward a long-term rate often over multiple timescales. With more variable stimuli, SRA causes the dependence of spike rate on sound pressure level to shift toward the mean level of recent stimulus history. A model based on subtractive adaptation (Benda J, Hennig RM. J Comput Neurosci 24: 113–136, 2008) shows that changes in spike rate and level dependence are mechanistically linked. Space-specific neurons in the barn owl's midbrain, when recorded under ketamine-diazepam anesthesia, showed these classical characteristics of SRA, while at the same time exhibiting changes in spike timing precision. Abrupt level increases of sinusoidally amplitude-modulated (SAM) noise initially led to spiking at higher rates with lower temporal precision. Spike rate and precision relaxed toward their long-term values with a time course similar to SRA, results that were also replicated by the subtractive model. Stimuli whose amplitude modulations (AMs) were not synchronous across carrier frequency evoked spikes in response to stimulus envelopes of a particular shape, characterized by the spectrotemporal receptive field (STRF). Again, abrupt stimulus level changes initially disrupted the temporal precision of spiking, which then relaxed along with SRA. We suggest that shifts in latency associated with stimulus level changes may differ between carrier frequency bands and underlie decreased spike precision. Thus SRA is manifest not simply as a change in spike rate but also as a change in the temporal precision of spiking.

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