Transition from Anti-coherence resonance to coherence resonance for mixed-mode oscillations and period-1 firing of nervous system

Identification of dynamics of the mixed-mode oscillations (MMOs), which exhibit transition between oscillations with large and small amplitudes, is very important for nonlinear physics. In this paper, the MMOs with transition between subthreshold oscillations and spikes are investigated in a neuron model. In the absence of noise, the MMOs appear between the resting state and period-1 firing with increasing depolarization current. After introducing white noise, coherence resonance (CR) is evoked from the resting state and non-CR is induced from period-1 firing far from the MMOs, which is consistent with the traditional viewpoint. However, an interesting result that a transition from anti-CR to CR is evoked by noise from both the MMOs and the period-1 firing near the MMOs is acquired, which is characterized by the increase, decrease and increase again of the coefficient of variations of interspike intervals (ISIs) with increasing noise intensity. At small noise intensity, more subthreshold oscillations are evoked by noise to reduce the firing frequency, resulting in faster increase of standard deviation (SD) of ISIs than that of mean value of ISIs, which is the cause for the anti-CR. The decrease of SD is faster for middle noise intensity and is lower for strong noise intensity, which is the cause for the CR. The different stochastic responses of MMOs and period-1 firing nearby at different levels of noise insanity are the dynamical mechanism for the transition from anti-CR to CR. Such results present potential functions of the MMOs and period-1 firing on information processing in the nervous system with noise and extend the conditions for the CR and anti-CR phenomena, which enriches the contents of nonlinear dynamics.

Membrane Resonance in Pyramidal and GABAergic Neurons of the Mouse Perirhinal Cortex

The perirhinal cortex (PRC) is a polymodal associative region of the temporal lobe that works as a gateway between cortical areas and hippocampus. In recent years, an increasing interest arose in the role played by the PRC in learning and memory processes, such as object recognition memory, in contrast with certain forms of hippocampus-dependent spatial and episodic memory. The integrative properties of the PRC should provide all necessary resources to select and enhance the information to be propagated to and from the hippocampus. Among these properties, we explore in this paper the ability of the PRC neurons to amplify the output voltage to current input at selected frequencies, known as membrane resonance. Within cerebral circuits the resonance of a neuron operates as a filter toward inputs signals at certain frequencies to coordinate network activity in the brain by affecting the rate of neuronal firing and the precision of spike timing. Furthermore, the ability of the PRC neurons to resonate could have a fundamental role in generating subthreshold oscillations and in the selection of cortical inputs directed to the hippocampus. Here, performing whole-cell patch-clamp recordings from perirhinal pyramidal neurons and GABAergic interneurons of GAD67-GFP+ mice, we found, for the first time, that the majority of PRC neurons are resonant at their resting potential, with a resonance frequency of 0.5–1.5 Hz at 23°C and of 1.5–2.8 Hz at 36°C. In the presence of ZD7288 (blocker of HCN channels) resonance was abolished in both pyramidal neurons and interneurons, suggesting that Ih current is critically involved in resonance generation. Otherwise, application of TTx (voltage-dependent Na+ channel blocker) attenuates the resonance in pyramidal neurons but not in interneurons, suggesting that only in pyramidal neurons the persistent sodium current has an amplifying effect. These experimental results have also been confirmed by a computational model. From a functional point of view, the resonance in the PRC would affect the reverberating activity between neocortex and hippocampus, especially during slow wave sleep, and could be involved in the redistribution and strengthening of memory representation in cortical regions.

The voltage and spiking responses of subthreshold resonant neurons to structured and fluctuating inputs: resonance, loss of resonance and variability

The communication of oscillatory activity between neurons in a network result from the interplay of the subthreshold oscillatory properties of the participating neurons, when they exist, the properties of the synaptic connectivity and modulatory effects (e.g., oscillatory, deterministic and stochastic fluctuations) capturing identified external activity and unidentified background activity. A necessary step to address the underlying mechanisms is to understand how the response of neurons to period inputs, and external inputs in general, depends on the interplay of the neuronal intrinsic properties and the properties of the input. We address this issues in a systematic manner in the context of the response of neurons to oscillatory and synaptic-like inputs, and we extend our investigation to fluctuating spiking inputs with more realistic distributions of spike times. We use relatively simple neuronal models subject to additive current-based inputs and multiplicative conductance-based synaptic inputs, and we use two types of chirp-like inputs, one consisting of a sequence of cycles with discretely increasing frequencies over time, and the other consisting of the same cycles arranged in an arbitrary order. We develop a number of voltage response metrics to capture the different aspects of the voltage response, including the standard impedance profiles (curves of the impedance amplitude as a function of the input frequency) and the peak-to-trough amplitude envelope (VENV) profiles. We show that Z-resonant cells (cells that exhibit subthreshold resonance in response to sinusoidal inputs) also show VENV-resonance in response to sinusoidal inputs, but generally do not (or very mildly) in response to square-wave and synaptic-like inputs. We also show that responses to conductance-based synaptic-like inputs are attenuated as compared to the response to current-based synaptic-like inputs. These response patterns were strongly dependent on the intrinsic properties of the participating neurons, in particular whether the unperturbed Z-resonant cells had a stable node or a focus. In addition, we show that variability emerges in response to chirp-like inputs with arbitrarily ordered patterns where all signals (trials) in a given protocol have the same frequency content and the only source of uncertainty is the subset of all possible permutations of cycles chosen for a given protocol. This variability is the result of the multiple different ways in which the autonomous transient dynamics is activated across cycles in each signal (different cycle orderings) and across trials. We extend our results to include high-rate Poisson distributed current- and conductance-based synaptic inputs and compare them with similar results using additive Gaussian white noise. We show that the responses to both Poisson-distributed synaptic inputs are attenuated with respect to the responses to Gaussian white noise. For cells that exhibit oscillatory responses to Gaussian white noise (band-pass filters), the response to conductance-based synaptic inputs are low-pass filters, while the response to current-based synaptic inputs may remain band-pass filters, consistent with experimental findings. Our results shed ling on the mechanisms of communication of oscillatory activity among neurons in a network in a network via subthreshold oscillations and resonance and the generation of network resonance.

Excitability and Threshold Mechanism for Enhanced Neuronal Response Induced by Inhibition Preceding Excitation

Postinhibitory facilitation (PIF) of neural firing presents a paradoxical phenomenon that the inhibitory effect induces enhancement instead of reduction of the firing activity, which plays important roles in sound location of the auditory nervous system, awaited theoretical explanations. In the present paper, excitability and threshold mechanism for the PIF phenomenon is presented in the Morris-Lecar model with type I, II, and III excitabilities. Firstly, compared with the purely excitatory stimulations applied to the steady state, the inhibitory preceding excitatory stimulation to form pairs induces the firing rate increased for type II and III excitabilities instead of type I excitability, when the interval between the inhibitory and excitatory stimulation within each pair is suitable. Secondly, the threshold mechanism for the PIF phenomenon is acquired. For type II and III excitabilities, the inhibitory stimulation induces subthreshold oscillations around the steady state. During the middle and ending phase of the ascending part and the beginning phase of the descending part within a period of the subthreshold oscillations, the threshold to evoke an action potential by an excitatory stimulation becomes weaker, which is the cause for the PIF phenomenon. Last, a theoretical estimation for the range of the interval between the inhibitory and excitatory stimulation for the PIF phenomenon is acquired, which approximates half of the intrinsic period of the subthreshold oscillations for the relatively strong stimulations and becomes narrower for the relatively weak stimulations. The interval for the PIF phenomenon is much shorter for type III excitability, which is closer to the experiment observation, due to the shorter period of the subthreshold oscillations. The results present the excitability and threshold mechanism for the PIF phenomenon, which provide comprehensive and deep explanations to the PIF phenomenon.

Imaging Subthreshold Voltage Oscillation With Cellular Resolution in the Inferior Olive in vitro

Voltage imaging with cellular resolution in mammalian brain slices is still a challenging task. Here, we describe and validate a method for delivery of the voltage-sensitive dye ANNINE-6plus (A6+) into tissue for voltage imaging that results in higher signal-to-noise ratio (SNR) than conventional bath application methods. The not fully dissolved dye was injected into the inferior olive (IO) 0, 1, or 7 days prior to acute slice preparation using stereotactic surgery. We find that the voltage imaging improves after an extended incubation period in vivo in terms of labeled volume, homogeneous neuropil labeling with saliently labeled somata, and SNR. Preparing acute slices 7 days after the dye injection, the SNR is high enough to allow single-trial recording of IO subthreshold oscillations using wide-field (network-level) as well as high-magnification (single-cell level) voltage imaging with a CMOS camera. This method is easily adaptable to other brain regions where genetically-encoded voltage sensors are prohibitively difficult to use and where an ultrafast, pure electrochromic sensor, like A6+, is required. Due to the long-lasting staining demonstrated here, the method can be combined, for example, with deep-brain imaging using implantable GRIN lenses.

Bifurcations of Time-Delay-Induced Multiple Transitions Between In-Phase and Anti-Phase Synchronizations in Neurons with Excitatory or Inhibitory Synapses

Time-delay-induced synchronous behaviors and synchronization transitions have been widely investigated for coupled neurons, and they play important roles for physiological functions. In the present study, time-delay-induced synchronized subthreshold oscillations were simulated, and the bifurcations underlying the synchronized behaviors were identified for a pair of coupled FitzHugh–Nagumo neurons. Multiple transitions between in-phase and anti-phase synchronizations induced by the time delay were simulated for the inhibitory and excitatory couplings. Subcritical or supercritical Hopf bifurcations and the stability of the Hopf-bifurcating periodic subthreshold oscillations were acquired using center manifold reduction and normal form theory. The in-phase or anti-phase synchronizations of the stable periodic subthreshold oscillations, which appear for multiple values of the time delay, were interpreted with the related eigenspace. The distributions of the different dynamical behaviors, including the synchronizations and bifurcations in the two-parameter plane of the time delay and coupling strength, were acquired for both types of synapses, and the different roles of the inhibitory and excitatory couplings on the synchronization transitions were compared.

Temporal discrimination from the interaction between dynamic synapses and intrinsic subthreshold oscillations

The interaction between synaptic and intrinsic dynamics can efficiently shape neuronal input-output relationships in response to temporally structured spike trains. We use a neuron model with subthreshold oscillations receiving inputs through a synapse with short-term depression and facilitation to show that the combination of intrinsic subthreshold and synaptic dynamics leads to channel-specific nontrivial responses and recognition of specific temporal structures. We employ the Generalized Integrate-and-Fire (GIF) model, which can be subjected to analytical characterization. We map the temporal structure of spike input trains to the type of spike response, and show how the emergence of nontrivial input-output preferences is modulated by intrinsic and synaptic parameters in a synergistic manner. We demonstrate that these temporal input discrimination properties are robust to noise and to variations in synaptic strength, suggesting that they likely contribute to neuronal computation in biological circuits. Furthermore, we also illustrate the presence of these input-output relationships in conductance-based models.Author summaryNeuronal subthreshold oscillations underlie key aspects of information processing in single neuron and network dynamics. Dynamic synapses provide a channel-specific temporal modulation of input information. We combine a neuron model that displays subthreshold oscillations and a dynamic synapse to analytically assess their interplay in processing trains of spike-mediated synaptic currents. Our results show that the co-action of intrinsic and synaptic dynamics builds nontrivial input-output relationships, which are resistant to noise and to changes in synaptic strength. The discrimination of a precise temporal structure of the input signal is shaped as a function of the joint interaction of intrinsic oscillations and synaptic dynamics. This interaction can result in channel-specific recognition of precise temporal patterns, hence greatly expanding the flexibility and complexity in information processing achievable by individual neurons with respect to temporal discrimination mechanisms based on intrinsic neuronal dynamics alone.

Pulse Inputs Affect Timings of Spikes in Neurons with or Without Time Delays

Inputs can change timings of spikes in neurons. But it is still not clear how input’s parameters for example injecting time of inputs affect timings of neurons. HR neurons receiving both weak and strong inputs are considered. How pulse inputs affecting neurons is studied by using the phase-resetting curve technique. For a single neuron, weak pulse inputs may advance or delay the next spike, while strong pulse inputs may induce subthreshold oscillations depending on parameters such as injecting timings of inputs. The behavior of synchronization in a network with or without coupling delays can be predicted by analysis in a single neuron. Our results can be used to predict the effects of inputs on other spiking neurons.