Coherent Spike Research Articles

Coherence resonance in globally coupled neuronal networks with different neuron numbers

Because a brain consists of tremendous neuronal networks with different neuron numbers ranging from tens to tens of thousands, we study the coherence resonance due to ion channel noises in globally coupled neuronal networks with different neuron numbers. We confirm that for all neuronal networks with different neuron numbers there exist the array enhanced coherence resonance and the optimal synaptic conductance to cause the maximal spiking coherence. Furthermoremore, the enhancement effects of coupling on spiking coherence and on optimal synaptic conductance are almost the same, regardless of the neuron numbers in the neuronal networks. Therefore for all the neuronal networks with different neuron numbers in the brain, relative weak synaptic conductance (0.1 mS/cm2) is sufficient to induce the maximal spiking coherence and the best sub-threshold signal encoding.

Coherence for perception of target vs clutter in bat sonar: The role of neuroethology as pathfinder for neuroscience

Coherence for perception of target vs clutter in bat sonar: The role of neuroethology as pathfinder for neuroscience

Coherence resonance and bi-resonance by time-periodic coupling strength in Hodgkin-Huxley neuron networks

We study the effect of time-periodic coupling strength on the spiking coherence of Newman-Watts networks of Hodgkin-Huxley (HH) neurons with non-Gaussian noise. It is found that the spiking can exhibit coherence resonance (CR) when the extent of deviation of non-Gaussian noise from Gaussian noise and the amplitude of the coupling strength are varied. In particular, coherence bi-resonance (CBR) is observed when the frequency of the coupling strength is varied, and the CBR is always observed when the frequency is equal to, or a multiple of, the spiking period, manifesting as the locking between the frequencies of the spiking and the coupling strength. The results show that a time-periodic coupling strength may play a more constructive and efficient role in enhancing the spiking coherence of the neuronal networks than a constant coupling strength. These findings provide insight into the role of time-periodic coupling strength for enhancing the time precision of information processing in neuronal networks.

EXTERNAL NON-GAUSSIAN NOISE-ENHANCED COLLECTIVE INTRINSIC SPIKING COHERENCE IN AN ARRAY OF STOCHASTIC HODGKIN–HUXLEY NEURONS

In this Letter, we study the effect of the interaction of external non-Gaussian noise and channel noise on the temporal coherence of the collective intrinsic spiking of an array of bi-directionally coupled stochastic Hodgkin–Huxley (HH) neurons, mainly investigating how the non-Gaussian noise's deviation q from Gaussian distribution affects the spiking coherence and coherence resonance (CR) induced by channel noise and neuron number. It is found that the spiking coherence for small channel noise and the CR induced by channel noise or by neuron number change with the variation of q. As q is increased, the spiking with smaller channel noise becomes more ordered in time, and the CR by channel noise moves to bigger patch sizes. Furthermore, there is CR phenomenon when neuron number is varied, and the CR can occur in smaller channel noise when q is increased. These results show that appropriate external non-Gaussian noise can enhance and optimize the temporal coherence of the collective spiking of the coupled neurons when channel noise is sufficiently small, and can help the collective spiking with smaller channel noise reach the most ordered performance at an optimal neuron number. The mechanism underlying the phenomena is briefly discussed in terms of the property of the non-Gaussian noise. These findings could help to better understand the joint roles of external non-Gaussian noise and channel noise in the collective spiking activity of an array of coupled stochastic neurons.

ENHANCEMENT OF INTRINSIC SPIKING COHERENCE BY EXTERNAL NON-GAUSSIAN NOISE IN A STOCHASTIC HODGKIN–HUXLEY NEURON

In this paper, we study the effect of external non-Gaussian noise on the temporal coherence of the intrinsic spiking induced by the channel noise in a stochastic Hodgkin–Huxley neuron. It is found that, for a sufficiently large membrane patch, the intrinsic spiking coherence can be enhanced by the proper values of non-Gaussian noise's strength, correlation time, or deviation from Gaussian distribution. And that the intrinsic spiking can exhibit coherence resonance when the noise's strength is optimal. This implies that the channel noise-induced intrinsic spiking may become more or the most ordered in time with the assistance of the external non-Gaussian noise. These results show that the external non-Gaussian noise can play a constructive role for improving the time precision of information processing in stochastic neurons.

Multiple coherence resonance induced by time-periodic coupling in stochastic Hodgkin–Huxley neuronal networks

In this paper, we study the effect of time-periodic coupling strength (TPCS) on the spiking coherence of Newman-Watts small-world networks of stochastic Hodgkin-Huxley (HH) neurons and investigate the relations between the coupling strength and channel noise when coherence resonance (CR) occurs. It is found that, when the amplitude of TPCS is varied, the spiking induced by channel noise can exhibit CR and coherence bi-resonance (CBR), and the CR moves to a smaller patch area (bigger channel noise) when the amplitude increases; when the frequency of TPCS is varied, the intrinsic spiking can exhibit CBR and multiple CR, and the CR always occurs when the frequency is equal to or multiple of the spiking period, manifesting as the locking between the frequencies of the intrinsic spiking and the coupling strength. These results show that TPCS can greatly enhance and optimize the intrinsic spiking coherence, and favors the spiking with bigger channel noise to exhibit CR. This implies that, compared to constant coupling strength, TPCS may play a more efficient role for improving the time precision of the information processing in stochastic neuronal networks.

Effect of Sparse Random Connectivity on the Stochastic Spiking Coherence of Inhibitory Subthreshold Neurons

Effect of Sparse Random Connectivity on the Stochastic Spiking Coherence of Inhibitory Subthreshold Neurons

Influence of Coupling Delay on Noise Induced Coherent Oscillations in Excitable Systems

Influence of small time-delays in coupling between noisy excitable systems on the coherence resonance and self-induced stochastic resonance is studied. Parameters of delayed coupled deterministic excitable units are chosen such that the system has only one attractor, namely the stationary state, for any value of the coupling and the time-lag. Addition of white noise induces qualitatively different types of coherent oscillations, and we analyzed the influence of coupling time-delay on the properties of these coherent oscillations. The main conclusion is that time-lag τ≥1, but still smaller than the refractory period, and sufficiently strong coupling drastically change signal to noise ratio in the quantitative and qualitative way. An interval of noise values implies quite large signal to noise ratio and different types of noise induced coherence are greatly enhanced. We also observed coincident spiking for small noise intensity and time-lag proportional to the inter-spike interval of the coherent spike trains. On the other hand, time-lags τ<1 and/or weak coupling induce negligible changes in the properties of the stochastic coherence.

Information coding by single spikes and bursts in thalamocortical relay neurons

The thalamus modulates the information flow to and from the cortex. The basal ganglia provide the thalamus with inhibitory input, whereas the cortex sends excitatory connections to the thalamus. Thalamocortical relay (TCR) neurons can fire both single spikes and bursts: the latter ones after a low threshold T-type calcium current is activated from a sufficiently hyperpolarized membrane potential. The question what these spikes and bursts code for and how the basal ganglia can influence this coding still remains open. Information processing of TCR neurons was investigated in a computational model and compared with data recorded from TCR neurons in-vitro patched in rat thalamic brain slices. Gaussian noise superimposed on a DC current that put the neuron in different firing regimes (bursting or spiking) was injected into the TCR neuron and several computational tools to measure the reliability, precision and information content of resulting spike trains and coherence between input and output were used to analyze the recorded spike trains. Reverse correlation techniques (Spike-Triggered Average (STA) and Spike-Triggered Covariance (STC)) revealed what input features the neurons were most sensitive to in the different regimes. The results of the analysis show that the two firing regimes of the TCR neurons, bursting mode and spiking mode, represent different coding strategies. At a low membrane potential, the neuron is in bursting mode, in which bursts phase-lock preferentially to low frequencies in the input signal, up to about 20 Hz, and single spikes show little reliability. The STA and filters of the STC analysis show that bursts are a response to long-lasting (up to 200 ms) input signals with a strong negative component. At more depolarized membrane potentials the neuron shows a gradual change from a bursting to a spiking regime. In this second regime single spikes are more precise and more reliable, earlier in time, and phase lock to input frequencies up to 60 Hz. The peaks of the STA become narrower, which reflects a shorter integration time and lower input resistance of the neuron. The STC filters show that spikes are more selective for fast fluctuations in this regime. These results show that even at the single neuron level the thalamus can use different coding strategies depending on whether it is in a more hyperpolarized state, for instance as a consequence of strong basal ganglia input, or in a more depolarized state, induced by strong cortical input. This implies that single neurons can change their encoding regime depending on the background activity of the surrounding network.

Influence of time delay and channel blocking on multiple coherence resonance in Hodgkin–Huxley neuron networks

Toxins such as tetraethylammonium (TEA) and tetrodotoxin (TTX) may reduce the number of working potassium and sodium ion channels by poisoning and making them blocked, respectively. In this paper, we study how channel blocking (CB) affects the time delay-induced multiple coherence resonance (MCR), i.e., a phenomenon that the spiking of neuronal networks intermittently reaches the most ordered state, in stochastic Hodgkin–Huxley neuron networks. It is found that potassium and sodium CB have distinct effects. For potassium CB, the MCR occurs more frequently as the CB develops, but for sodium CB the MCR is badly impaired and only the first coherence resonance (CR) holds and, consequently, the MCR evolves into a single CR as sodium CB develops. We found for sodium CB the spiking becomes disordered at larger delay lengths, which may be the reason for the destruction of the MCR. The underlying mechanism is briefly discussed in terms of distinct effects of potassium and sodium CB on the spiking activity. These results show that potassium CB can increase the frequency of MCR with time delay, but sodium CB may suppress and even destroy the delay-induced MCR. These findings may help to understand the joint effects of CB and time delay on the spiking coherence of neuronal networks.

Effect of the Topology and Delayed Interactions in Neuronal Networks Synchronization

As important as the intrinsic properties of an individual nervous cell stands the network of neurons in which it is embedded and by virtue of which it acquires great part of its responsiveness and functionality. In this study we have explored how the topological properties and conduction delays of several classes of neural networks affect the capacity of their constituent cells to establish well-defined temporal relations among firing of their action potentials. This ability of a population of neurons to produce and maintain a millisecond-precise coordinated firing (either evoked by external stimuli or internally generated) is central to neural codes exploiting precise spike timing for the representation and communication of information. Our results, based on extensive simulations of conductance-based type of neurons in an oscillatory regime, indicate that only certain topologies of networks allow for a coordinated firing at a local and long-range scale simultaneously. Besides network architecture, axonal conduction delays are also observed to be another important factor in the generation of coherent spiking. We report that such communication latencies not only set the phase difference between the oscillatory activity of remote neural populations but determine whether the interconnected cells can set in any coherent firing at all. In this context, we have also investigated how the balance between the network synchronizing effects and the dispersive drift caused by inhomogeneities in natural firing frequencies across neurons is resolved. Finally, we show that the observed roles of conduction delays and frequency dispersion are not particular to canonical networks but experimentally measured anatomical networks such as the macaque cortical network can display the same type of behavior.

Persistent membranous cross correlations due to the multiplicity of gates in ion channels

Ion channels in excitable cells reveal spontaneous intermittent opening and closing. As the membrane area reduces, this stochasticity enables spontaneous firing and elevates the cell's ability to fire at weaker stimuli. A multiple number of gates are accommodated in each individual ion channel. Here we investigate the possible impact of that gate multiplicity on the cell's function specifically when the membrane area is of limited size. It is shown that a non-trivially persistent correlation then takes place between the transmembrane voltage fluctuations (also between the fluctuations in the gating variables) and the component of open channel fluctuations attributed to the above gate multiplicity. This cross correlation persistency is found to be playing a major augmentative role in the elevation of the cell's excitability and spontaneous firing; without the persistency, the cell would be much less excitable. The cross correlation persistency is also found to enhance spike coherence. The stochastic Hodgkin-Huxley equations, put forward by Fox and Lu, are addressed in the context of their recognized failure to produce accurate enough statistics of spike generation. Our results indicate that the major source of that inaccuracy is the incapability of the stochastic Hodgkin-Huxley description to reflect the above cross correlation persistency.

Statistical-mechanical measure of stochastic spiking coherence in a population of inhibitory subthreshold neurons

By varying the noise intensity, we study stochastic spiking coherence (i.e., collective coherence between noise-induced neural spikings) in an inhibitory population of subthreshold neurons (which cannot fire spontaneously without noise). This stochastic spiking coherence may be well visualized in the raster plot of neural spikes. For a coherent case, partially-occupied "stripes" (composed of spikes and indicating collective coherence) are formed in the raster plot. This partial occupation occurs due to "stochastic spike skipping" which is well shown in the multi-peaked interspike interval histogram. The main purpose of our work is to quantitatively measure the degree of stochastic spiking coherence seen in the raster plot. We introduce a new spike-based coherence measure M ( s ) by considering the occupation pattern and the pacing pattern of spikes in the stripes. In particular, the pacing degree between spikes is determined in a statistical-mechanical way by quantifying the average contribution of (microscopic) individual spikes to the (macroscopic) ensemble-averaged global potential. This "statistical-mechanical" measure M ( s ) is in contrast to the conventional measures such as the "thermodynamic" order parameter (which concerns the time-averaged fluctuations of the macroscopic global potential), the "microscopic" correlation-based measure (based on the cross-correlation between the microscopic individual potentials), and the measures of precise spike timing (based on the peri-stimulus time histogram). In terms of M ( s ), we quantitatively characterize the stochastic spiking coherence, and find that M ( s ) reflects the degree of collective spiking coherence seen in the raster plot very well. Hence, the "statistical-mechanical" spike-based measure M ( s ) may be used usefully to quantify the degree of stochastic spiking coherence in a statistical-mechanical way.

Influence of interaction delays on noise-induced coherence in excitable systems

Influence of the interaction time delay on the noise-induced system size resonance in a system of all-to-all electrically coupled FitzHugh-Nagumo excitable neurons is studied. It is observed that small time lags decrease and that large time lags increase the coherence of spiking. Bifurcations of the system's stationary state are used to explain the observed nonmonotonic dependence of coherence on the time lag.

Spiking Regularity and Coherence in Complex Hodgkin–Huxley Neuron Networks

We study the effects of the strength of coupling between neurons on the spiking regularity and coherence in a complex network with randomly connected Hodgkin–Huxley neurons driven by colored noise. It is found that for the given topology realization and colored noise correlation time, there exists an optimal strength of coupling, at which the spiking regularity of the network reaches the best level. Moreover, when the temporal regularity reaches the best level, the spatial coherence of the system has already increased to a relatively high level. In addition, for the given number of neurons and noise correlation time, the values of average regularity and spatial coherence at the optimal strength of coupling are nearly independent of the topology realization. Furthermore, there exists an optimal value of colored noise correlation time at which the spiking regularity can reach its best level. These results may be helpful for understanding of the real neuron world.

Optimal network configuration for maximal coherence resonance in excitable systems

We analyze the coherence resonance phenomenon in an ensemble of noise-driven excitable neurons giving special attention to the role of the interaction topology. The neural architecture is modeled using a spatially embedded network in which we can tune the network organization between scale-free-like with dominating long-range connections and a network with mostly adjacent neurons connected. We found that besides an optimal noise intensity, also an optimal network configuration exists at which the largest average coherence of noise-induced spikes is achieved. Furthermore, we show that long- as well as short-range interactions between neurons should exist in order to achieve the optimal response of the neuronal network.

Effects and applications of ultrashort-lived prehydrated electrons in radiation biology and radiotherapy of cancer

The subpicosecond-lived prehydrated electron (e(pre)(-)) is a fascinating species in radiation biology and radiotherapy of cancer. Using femtosecond time-resolved laser spectroscopy, we have recently resolved that e(pre)(-) states are electronically excited states and have lifetimes of approximately 180 fs and approximately 550 fs, after the identification and removal of a coherence spike, respectively. Notably, the weakly bound e(pre)(-) (< 0 eV) has the highest yield among all the radicals generated in the cell during ionizing radiation. Recently, it has been demonstrated that dissociative electron transfer (DET) reactions of e(pre)(-) can lead to important biological effects. By direct observation of the transition states of the DET reactions, we have showed that DET reactions of e(pre)(-) play key roles in bond breakage of nucleotides and in activations of halopyrimidines as potential hypoxic radiosensitizers and of the chemotherapeutic drug cisplatin in combination with radiotherapy. This review discusses all of these findings, which may lead to improved strategies in radiotherapy of cancer, radioprotection of humans and in discovery of new anticancer drugs.

Probing the Ultrafast Solution Dynamics of a Cyanine Dye in an Organic Solvent Interfaced with Water

Dependence of ultrafast dynamics on the excited state evolution and ground state recovery of a cyanine dye (IR125) in dichloromethane (DCM) solvent interfaced with neat water is presented. We use degenerate pump-probe transient absorption spectroscopy to show that the excited-state dynamics of the dye molecule is strongly dependent on the position of the measurements from bulk DCM solution to the solution near the water layer. The decay component of the transient corresponding to the excited state lifetime increases from bulk DCM solution to its interface with water. Such results show that the effect of the presence of water layer over the dye solution in DCM extends several micrometers, indicating the surfactant nature of the IR125 molecules, and provides us a measure of the penetration of water into the DCM layer. The initial ultrafast decay component (coherent spike) directly correlates to the pulse-width of our near-transform limited pulses used in these experiments. This approach of measuring the excited state decay of a dye across an immiscible liquid interface can provide important characteristics of microtransport across such interfaces.

Channel block-optimized spiking activity of Hodgkin–Huxley neurons on random networks

In this paper, we numerically study the effect of channel block on the spiking temporal coherence and spatial synchronization on Hodgkin–Huxley (HH) neuron networks. It is found that under sodium CB the spike coherence is badly reduced, and the synchronization can, depending on the network randomness (the fraction of random shortcuts), be either enhanced or reduced, while, under potassium CB, the spike coherence can be enhanced but the synchronization is reduced. Interestingly, for certain networks of relatively large randomness, the neuron firings can achieve the best temporal coherence at an optimal potassium CB. These results show that under certain conditions channel blocking can increase and optimize the spike coherence and the synchronization on the complex HH neuron networks, whereby the neurons would exhibit a better and the best sub-threshold signal encoding.

Enhancement of spike coherence by the departure from Gaussian noise in a Hodgkin-Huxley neuron

Experimental study has shown that non-Gaussian noise exists in sensory systems like neurons. The departure from Gaussian behavior is a characteristic parameter of non-Gaussian noise. In this paper, we have numerically studied the effect of a particular kind of non-Gaussian colored noise (NGN), especially its departure q from Gaussian noise (q = 1), on the spiking activity in a deterministic Hodgkin-Huxley (HH) neuron driven by sub-threshold periodic stimulus. Simulation results show that the departure q can affect the spiking activity induced by noise intensity D. For smaller q values, the minimum in the variation coefficient (CV) as a function of noise intensity (D) becomes smaller, showing that D-induced stochastic resonance (SR) becomes strengthened. Meanwhile, depending on the value of D, q can either enhance or reduce the spiking regularity. Interestingly, CV changes non-monotonously with varying q and passes through a minimum at an intermediate q, representing the presence of “departure-induced SR”. This result shows that appropriate departures of the NGN can enhance the spike coherence in the HH neuron. Since the departure of the NGN determines the probability distribution and hence may denote the type of the noise, “departure-induced SR” shows that different types of noise can enhance the spike coherence, and hence may improve the timing precision of sub-threshold signal encoding in the HH neuron.