Model Of Short-term Plasticity Research Articles

Capture the Moment: High-Speed Imaging With Spiking Cameras Through Short-Term Plasticity.

High-speed imaging can help us understand some phenomena that are too fast to be captured by our eyes. Although ultra-fast frame-based cameras (e.g., Phantom) can record millions of fps at reduced resolution, they are too expensive to be widely used. Recently, a retina-inspired vision sensor, spiking camera, has been developed to record external information at 40, 000 Hz. The spiking camera uses the asynchronous binary spike streams to represent visual information. Despite this, how to reconstruct dynamic scenes from asynchronous spikes remains challenging. In this paper, we introduce novel high-speed image reconstruction models based on the short-term plasticity (STP) mechanism of the brain, termed TFSTP and TFMDSTP. We first derive the relationship between states of STP and spike patterns. Then, in TFSTP, by setting up the STP model at each pixel, the scene radiance can be inferred by the states of the models. In TFMDSTP, we use the STP to distinguish the moving and stationary regions, and then use two sets of STP models to reconstruct them respectively. In addition, we present a strategy for correcting error spikes. Experimental results show that the STP-based reconstruction methods can effectively reduce noise with less computing time, and achieve the best performances on both real-world and simulated datasets.

A CHAOTIC MULTILAYER LIF SCHEME TO MODEL THE PRIMARY VISUAL CORTEX

Precise mathematical modeling of the primary visual cortex (V1) is still a challenging problem. Due to the high similarity of visual system of cat and human, in this paper, we present a hybrid model to track the electrical responses of neurons that are measured by a multi-electrode array implanted in cat V1. The proposed model combines a stochastic phenomenological model with a multilayer leaky integrate-and-fire (LIF) model to predict V1 responses. Since all the existing visual cortex models do not capture the stochastic properties of synaptic changes, the proposed phenomenological model provides input currents for V1 by utilizing continuous chaotic neural equations with a quantization rule. Then a multilayer LIF model is presented to mimic the functions of lateral geniculate nucleus (LGN) and V1 neurons by their corresponding differential equations. The input current in these models is from the presynaptic neurons, which are computed using the LIF model. The LGN-V1 neuronal connections are adopted from previous studies, where the receptive fields (RFs) of LGN neurons converge onto elongated spatial structures that denote RFs of V1 neurons. The main purpose of this paper is to develop a short-term plasticity model that is more consistent with the nature of the LGN and V1 responses compared to state-of-the-art models. Previous studies have not incorporated the stochastic and quantized behaviors of neurons that in the recorded data of implemented electrodes. The experimental results show the ability of the proposed model to accurately predict spikes recorded experimentally, indicating the model outperforms the state-of-the-art method.

Paired-pulse Inhibition and Disinhibition of the Dentate Gyrus Following Orexin Receptors Inactivation in the Basolateral Amygdala.

Introduction:The Basolateral Amygdala (BLA) substantially affects neuronal transmission and synaptic plasticity processes through the dentate gyrus. Orexin neuropeptides play different roles in the sleep/wakefulness cycle, feeding, learning, and memory. The present study aimed to investigate the function of the orexin receptors of the BLA in the hippocampal local interneuron circuits.Methods:For this, the region’s paired-pulse responses from the Dentate Gyrus (DG) were recorded. Within the procedure, SB-334867-A (12μg/0.5μL) and TCS-OX2-29 (10μg/0.5μL (orexin 1 & 2 receptors antagonists, respectively), were administered into both sides of the BLA areas of the rat brain. Dimethyl Sulfoxide (DMSO) was used as the solvent in the control animals with a volume of 0.5μL.Results:Our data indicated that the Paired-pulse (PP) responses were not affected by the inactivation of the orexin receptors of the BLA.Conclusion:Due to not observing any significant changes in the short form of synaptic plasticity, after inactivation of the orexin system of the BLA, we hypothesize that the orexinergic fibers to the basolateral part of the amygdala influence the long-term synaptic efficacy; however, the primary processing of information in short-term plasticity model is not affected by the same system. The elementary processing of the data by the amygdala might happen through the action of other neurotransmitter systems.HighlightsThe neuronal transmission of DG following orexin receptors antagonism of the BLA.Paired-pulse responses were not affected by the orexin 1 receptors antagonism.Paired-pulse responses were not affected by the orexin 2 receptors antagonism.Plain Language SummaryThe orexinergic system has modulatory effects by sending projection fibers to several parts of the brain, such as the hippocampus and amygdala. Orexin neuropeptides activate basolateral amygdala neural circuits during different arousal states. Although, this system plays a vital role in creating appropriate behavioral reactions, the primary processing of the information in short-term plasticity model is not affected by it.

Complementary Effects of Adaptation and Gain Control on Sound Encoding in Primary Auditory Cortex.

An important step toward understanding how the brain represents complex natural sounds is to develop accurate models of auditory coding by single neurons. A commonly used model is the linear-nonlinear spectro-temporal receptive field (STRF; LN model). The LN model accounts for many features of auditory tuning, but it cannot account for long-lasting effects of sensory context on sound-evoked activity. Two mechanisms that may support these contextual effects are short-term plasticity (STP) and contrast-dependent gain control (GC), which have inspired expanded versions of the LN model. Both models improve performance over the LN model, but they have never been compared directly. Thus, it is unclear whether they account for distinct processes or describe one phenomenon in different ways. To address this question, we recorded activity of neurons in primary auditory cortex (A1) of awake ferrets during presentation of natural sounds. We then fit models incorporating one nonlinear mechanism (GC or STP) or both (GC+STP) using this single dataset, and measured the correlation between the models’ predictions and the recorded neural activity. Both the STP and GC models performed significantly better than the LN model, but the GC+STP model outperformed both individual models. We also quantified the equivalence of STP and GC model predictions and found only modest similarity. Consistent results were observed for a dataset collected in clean and noisy acoustic contexts. These results establish general methods for evaluating the equivalence of arbitrarily complex encoding models and suggest that the STP and GC models describe complementary processes in the auditory system.

Neural plasticity is modified over the human menstrual cycle: Combined insight from sensory evoked potential LTP and repetition suppression.

In healthy women, fluctuations in hormones including progesterone and oestradiol lead to functional changes in the brain over the course of each menstrual cycle. Though considerable attention has been directed towards understanding changes in human cognition over the menstrual cycle, changes in underlying processes such as neural plasticity have largely only been studied in animals. In this study we explored predictive coding and repetition suppression via the roving mismatch negativity paradigm as a model of short-term plasticity (Garrido, Kilner, Kiebel, et al., 2009), and Hebbian learning via visual sensory long-term potentiation (LTP) as a model of long-term plasticity (Teyler et al., 2005). Electroencephalography (EEG) was recorded in 20 females during their early follicular and mid-luteal phases. Event-related potential (ERP) analyses were complemented with dynamic causal modelling (DCM) to characterise changes in the underlying neural architecture. More sustained variability in the ERP response to a change in tone during the luteal phase are interpreted as a delayed habituation of the P3a component in the luteal relative to the follicular phase. The additional increased forward connection strength over tone repetitions compared to the follicular phase suggests that, in this phase, females may be less efficient when processing deviations from predicted sensory input (error). In contrast, there appears to be no reliable change in sensory LTP. This suggests that predictive coding, but not Hebbian plasticity is modified in the mid-luteal compared to the follicular phase, at least at the days of the menstrual cycle tested. This finding implicates the human menstrual cycle in complex changes in neural plasticity and provides further evidence for the importance of considering the menstrual cycle when including females in electrophysiological research.

A novel analytical characterization for short-term plasticity parameters in spiking neural networks.

Short-term plasticity (STP) is a phenomenon that widely occurs in the neocortex with implications for learning and memory. Based on a widely used STP model, we develop an analytical characterization of the STP parameter space to determine the nature of each synapse (facilitating, depressing, or both) in a spiking neural network based on presynaptic firing rate and the corresponding STP parameters. We demonstrate consistency with previous work by leveraging the power of our characterization to replicate the functional volumes that are integral for the previous network stabilization results. We then use our characterization to predict the precise transitional point from the facilitating regime to the depressing regime in a simulated synapse, suggesting in vitro experiments to verify the underlying STP model. We conclude the work by integrating our characterization into a framework for finding suitable STP parameters for self-sustaining random, asynchronous activity in a prescribed recurrent spiking neural network. The systematic process resulting from our analytical characterization improves the success rate of finding the requisite parameters for such networks by three orders of magnitude over a random search.

Modeling sound pulse counting in inferior colliculus

The ability of animals to count events or objects and its underlying mechanisms – including the possible existence of a dedicated “number sense” – is a topic of much recent fascination and research interest. A simple computation that frogs execute routinely is counting the number of consecutive sound pulses in a conspecific call that occur with precise and regular timing [1]. Cells signaling that a threshold number of pulses have occurred have been found in the midbrain of anurans [2]. These counting cells will not respond if a single inter-pulse interval is a few milliseconds longer than the baseline interval. What intrinsic or network mechanisms can give rise to such pulse/interval counting? Comparing simplified neuron models with previously published in vivo membrane potential recordings [3], we identify biophysical processes that can explain the observations. First, we consider a model of phasic inhibition made of onset and offset inhibition. Phasic inhibition enhances reset because a longer interval will engender onset and possibly offset inhibition. Second, we consider four mechanisms, namely short-term facilitation of excitation, persistent sodium currents, dendritic NMDA synapses and recurrent connections of cells imbedded in a network. Combining phasic inhibition with either of these mechanisms can qualitatively reproduce the array of recordings for different pulse patterns – including those with pauses that reset the counting – as well as the effect of pharmacologically attenuating inhibition. These results support the hypothesis that prior segmentation of sound via phasic on and off responses underlies the emergence of features such as pulse counting and duration selectivity in the auditory midbrain. Figure 1 Membrane potential response of counting neurons to series of sound pulses. From left to right columns correspond to: experiments, the persistent sodium model, the short-term plasticity model and the dendritic NMDA model.

Short term depression unmasks the ghost frequency.

Short Term Plasticity (STP) has been shown to exist extensively in synapses throughout the brain. Its function is more or less clear in the sense that it alters the probability of synaptic transmission at short time scales. However, it is still unclear what effect STP has on the dynamics of neural networks. We show, using a novel dynamic STP model, that Short Term Depression (STD) can affect the phase of frequency coded input such that small networks can perform temporal signal summation and determination with high accuracy. We show that this property of STD can readily solve the problem of the ghost frequency, the perceived pitch of a harmonic complex in absence of the base frequency. Additionally, we demonstrate that this property can explain dynamics in larger networks. By means of two models, one of chopper neurons in the Ventral Cochlear Nucleus and one of a cortical microcircuit with inhibitory Martinotti neurons, it is shown that the dynamics in these microcircuits can reliably be reproduced using STP. Our model of STP gives important insights into the potential roles of STP in self-regulation of cortical activity and long-range afferent input in neuronal microcircuits.

The Role of Presynaptic Dynamics in Processing of Natural Spike Trains in Hippocampal Synapses

Short-term plasticity (STP) represents a key neuronal mechanism of information processing. In excitatory hippocampal synapses, STP serves as a high-pass filter optimized for the transmission of information-carrying place-field discharges. This STP filter enables synapses to perform a highly nonlinear, switch-like operation permitting the passage and amplification of signals with place-field-like characteristics. Because of the complexity of interactions among STP processes, the synaptic mechanisms underlying this filtering paradigm remain poorly understood. Here, we describe a simple mechanistic model of STP, derived in large part from basic principles of synaptic function, that reproduces this highly nonlinear synaptic behavior. The model, formulated in terms of release probability, considers the interactions between calcium-dependent forms of presynaptic enhancement and their impact on vesicle pool dynamics, which is described using a two-pool model of vesicle recruitment. By considering the interdependency between release probability and various forms of STP, the model attempts to provide a realistic coupling among major presynaptic processes. The model parameters are first determined using synaptic dynamics during constant-frequency stimulation. The model then accurately reproduces all major characteristics of the synaptic filtering paradigm during natural stimulus patterns without free parameters. An elimination approach is then used to identify the contribution of each STP component to synaptic dynamics. Based on this analysis, the model predicts strong calcium dependence of synaptic filtering properties, which is verified experimentally in rat hippocampal slices. This simple model may thus offer a useful framework to further investigate the role of STP in neural computations.

Nonlinear Dynamic Model of CA1 Short-Term Plasticity using Random Impulse Train Stimulation

A comprehensive, quantitative description of the nonlinear dynamic characteristics of the short-term plasticity (STP) in the CA1 hippocampal region is presented. It is based on the Volterra-Poisson modeling approach using random impulse train (RIT) stimuli. In vitro hippocampal slice preparations were used from adult rats. RIT stimuli were applied at the Schaffer collaterals and population spike responses were recorded at the CA1 cell body layer. The computed STP descriptors that capture the nonlinear dynamics of the underlying STP mechanisms were the Volterra-Poisson kernels. The kernels quantified the presence of facilitatory and inhibitory STP behavior in magnitude and duration. A third order Volterra-Poisson STP model was introduced that accurately predicted in-sample and out-of-sample system responses. The proposed model could also accurately predict impulse pair and short impulse train system responses.

Fast vesicle reloading and a large pool sustain high bandwidth transmission at a central synapse

What limits the rate at which sensory information can be transmitted across synaptic connections in the brain? High-frequency signalling is restricted to brief bursts at many central excitatory synapses, whereas graded ribbon-type synapses can sustain release and transmit information at high rates. Here we investigate transmission at the cerebellar mossy fibre terminal, which can fire at over 200 Hz for sustained periods in vivo, yet makes few synaptic contacts onto individual granule cells. We show that connections between mossy fibres and granule cells can sustain high-frequency signalling at physiological temperature. We use fluctuation analysis and pharmacological block of desensitization to identify the quantal determinants of short-term plasticity and combine these with a short-term plasticity model and cumulative excitatory postsynaptic current analysis to quantify the determinants of sustained high-frequency transmission. We show that release is maintained at each release site by rapid reloading of release-ready vesicles from an unusually large releasable pool of vesicles (approximately 300 per site). Our results establish that sustained vesicular release at high rates is not restricted to graded ribbon-type synapses and that mossy fibres are well suited for transmitting broad-bandwidth rate-coded information to the input layer of the cerebellar cortex.

Short-Term Plasticity at Primary Afferent Synapse in Rat Spinal Dorsal Horn and Its Biological Function

Short-term plasticity (STP) is an important element of information processing in neuronal networks. As the first synaptic relay between primary afferent fibers (PAFs) and central neurons, primary afferent synapses in spinal dorsal horn (DH) are essential to the initial processing of somatosensory information. In this research, we examined the STP between Aδ-PAFs and spinal DH neurons by patch-clamp recording. Our results showed that depression dominated the STP at primary afferent synapses. The curves of STP had no significant changes in the presence of bicuculline, CTZ or AP-5. Lowering extracellular Ca²⁺ concentration ([Ca²⁺]_o) from 2.4 to 0.8 mM reduced the depression of synaptic responses at all stimulus rates, while raising [Ca²⁺]_o from 2.4 to 4.0 mM increased the synaptic depression. Increasing the bath temperature from 24 to 32°C clearly reduced the depression of all responses. These results indicate that the observed STP is of presynaptic origin and depends on transmitter release. By fitting the experimental data recorded under different conditions, a model of STP was used to quantitatively characterize the observed STP and to analyze the possible mechanisms underlying the effects of [Ca²⁺]_o and temperature. Furthermore, using a model neuron receiving synaptic inputs, we found that with this form of STP, postsynaptic DH neurons could detect rate changes in both rapidly- and slowly-firing afferents with equal sensitivity. The present study links the intrinsic STP properties of primary afferent synapses with their role in processing neural information, and provides a basis for further research on the STP in spinal DH and its biological function under in vivo conditions.

An efficient method for studying short-term plasticity with random impulse train stimuli

In this article, we introduce an efficient method that models quantitatively nonlinear dynamics associated with short-term plasticity (STP) in biological neural systems. It is based on the Voterra–Wiener modeling approach adapted for special stimulus/response datasets. The stimuli are random impulse trains (RITs) of fixed amplitude and Poisson distributed, variable interimpulse intervals. The class of stimuli, we use can be viewed as a hybrid between the paired impulse approach (variable interimpulse interval between two input impulses) and the fixed frequency approach (impulses repeated at fixed intervals, varying in frequency from one stimulus dataset to the next). The responses are sequences of population spike amplitudes of variable size and are assumed to be contemporaneous with the corresponding impulses in the RITs they are evoked by. The nonlinear dynamics of the mechanisms underlying STP are captured by kernels used to create compact STP models with predictive capabilities. Compared to similar methods in the literature, the method presented in this article provides a comprehensive model of STP with considerable improvement in prediction accuracy and requires shorter experimental data collection time.