Quantifying impacts of short-term plasticity on neuronal information transfer

Information Load from Neuromediator Diffusion to Extrasynaptic Space: The Interplay between the Injection Frequency and Clearance

In our study, we simulate the release of glutamate, a neurotransmitter, from the presynaptic cell by modeling the diffusion of glutamate into both synaptic and extrasynaptic space around the synapse. We have also incorporated a new factor into our model: convection. This factor represents the process by which the body clears glutamate from the synapse. Due to this process, the physiological mechanisms that typically prevent glutamate from spreading beyond the synapse are altered. This results in a different distribution of glutamate concentrations, with higher levels outside the synapse than inside it. The variety of biological effects that occur in response to this extrasynaptic glutamate highlights the importance of preventing neurotransmitters from spreading beyond the synapse. We aim to explain the physical reasons behind these biological effects, which are observed as excitotoxicity. Our results show that preventing the spread of glutamate outside the synapse increases the amount of information exchanged within the synapse and its surroundings for frequencies of glutamate release up to 30-50 Hz, followed by a decrease. Additionally, we find that the rate at which glutamate is cleared from the synapse is effective at relatively low levels (≤0.5 nm/μs in our calculation grid) and remains constant at higher levels.

An unsupervised STDP-based spiking neural network inspired by biologically plausible learning rules and connections

The backpropagation algorithm has promoted the rapid development of deep learning, but it relies on a large amount of labeled data and still has a large gap with how humans learn. The human brain can quickly learn various conceptual knowledge in a self-organized and unsupervised manner, accomplished through coordinating various learning rules and structures in the human brain. Spike-timing-dependent plasticity (STDP) is a general learning rule in the brain, but spiking neural networks (SNNs) trained with STDP alone is inefficient and perform poorly. In this paper, taking inspiration from short-term synaptic plasticity, we design an adaptive synaptic filter and introduce the adaptive spiking threshold as the neuron plasticity to enrich the representation ability of SNNs. We also introduce an adaptive lateral inhibitory connection to adjust the spikes balance dynamically to help the network learn richer features. To speed up and stabilize the training of unsupervised spiking neural networks, we design a samples temporal batch STDP (STB-STDP), which updates weights based on multiple samples and moments. By integrating the above three adaptive mechanisms and STB-STDP, our model greatly accelerates the training of unsupervised spiking neural networks and improves the performance of unsupervised SNNs on complex tasks. Our model achieves the current state-of-the-art performance of unsupervised STDP-based SNNs in the MNIST and FashionMNIST datasets. Further, we tested on the more complex CIFAR10 dataset, and the results fully illustrate the superiority of our algorithm. Our model is also the first work to apply unsupervised STDP-based SNNs to CIFAR10. At the same time, in the small-sample learning scenario, it will far exceed the supervised ANN using the same structure.

Multiscale modeling of presynaptic dynamics from molecular to mesoscale

Chemical synapses exhibit a diverse array of internal mechanisms that affect the dynamics of transmission efficacy. Many of these processes, such as release of neurotransmitter and vesicle recycling, depend strongly on activity-dependent influx and accumulation of Ca²⁺. To model how each of these processes may affect the processing of information in neural circuits, and how their dysfunction may lead to disease states, requires a computationally efficient modelling framework, capable of generating accurate phenomenology without incurring a heavy computational cost per synapse. Constructing a phenomenologically realistic model requires the precise characterization of the timing and probability of neurotransmitter release. Difficulties arise in that functional forms of instantaneous release rate can be difficult to extract from noisy data without running many thousands of trials, and in biophysical synapses, facilitation of per-vesicle release probability is confounded by depletion. To overcome this, we obtained traces of free Ca²⁺ concentration in response to various action potential stimulus trains from a molecular MCell model of a hippocampal Schaffer collateral axon. Ca²⁺ sensors were placed at varying distance from a voltage-dependent calcium channel (VDCC) cluster, and Ca²⁺ was buffered by calbindin. Then, using the calcium traces to drive deterministic state vector models of synaptotagmin 1 and 7 (Syt-1/7), which respectively mediate synchronous and asynchronous release in excitatory hippocampal synapses, we obtained high-resolution profiles of instantaneous release rate, to which we applied functional fits. Synchronous vesicle release occurred predominantly within half a micron of the source of spike-evoked Ca²⁺ influx, while asynchronous release occurred more consistently at all distances. Both fast and slow mechanisms exhibited multi-exponential release rate curves, whose magnitudes decayed exponentially with distance from the Ca²⁺ source. Profile parameters facilitate on different time scales according to a single, general facilitation function. These functional descriptions lay the groundwork for efficient mesoscale modelling of vesicular release dynamics.

Most information transmission between neurons in the brain occurs via release of neurotransmitter from synaptic vesicles. In response to a presynaptic spike, calcium influx at the active zone of a synapse can trigger the release of neurotransmitter with a certain probability. These stochastic release events may occur immediately after a spike or with some delay. As calcium accumulates from one spike to the next, the probability of release may increase (facilitate) for subsequent spikes. This process, known as short-term plasticity, transforms the spiking code to a release code, underlying much of the brain’s information processing. In this paper, we use an accurate, detailed model of presynaptic molecular physiology to characterize these processes at high precision in response to various spike trains. We then apply model reduction to the results to obtain a phenomenological model of release timing, probability, and facilitation, which can perform as accurately as the molecular model but with far less computational cost. This mesoscale model of spike-evoked release and facilitation helps to bridge the gap between microscale molecular dynamics and macroscale information processing in neural circuits. It can thus benefit large scale modelling of neural circuits, biologically inspired machine learning models, and the design of neuromorphic chips.

Short-Term Synaptic Plasticity Makes Neurons Sensitive to the Distribution of Presynaptic Population Firing Rates

The ability to discriminate spikes that encode a particular stimulus from spikes produced by background activity is essential for reliable information processing in the brain. We describe how synaptic short-term plasticity (STP) modulates the output of presynaptic populations as a function of the distribution of the spiking activity and find a strong relationship between STP features and sparseness of the population code, which could solve this problem. Furthermore, we show that feedforward excitation followed by inhibition (FF-EI), combined with target-dependent STP, promote substantial increase in the signal gain even for considerable deviations from the optimal conditions, granting robustness to this mechanism. A simulated neuron driven by a spiking FF-EI network is reliably modulated as predicted by a rate analysis and inherits the ability to differentiate sparse signals from dense background activity changes of the same magnitude, even at very low signal-to-noise conditions. We propose that the STP-based distribution discrimination is likely a latent function in several regions such as the cerebellum and the hippocampus.

Short-term synaptic depression can increase the rate of information transfer at a release site

The release of neurotransmitters from synapses obeys complex and stochastic dynamics. Depending on the recent history of synaptic activation, many synapses depress the probability of releasing more neurotransmitter, which is known as synaptic depression. Our understanding of how synaptic depression affects the information efficacy, however, is limited. Here we propose a mathematically tractable model of both synchronous spike-evoked release and asynchronous release that permits us to quantify the information conveyed by a synapse. The model transits between discrete states of a communication channel, with the present state depending on many past time steps, emulating the gradual depression and exponential recovery of the synapse. Asynchronous and spontaneous releases play a critical role in shaping the information efficacy of the synapse. We prove that depression can enhance both the information rate and the information rate per unit energy expended, provided that synchronous spike-evoked release depresses less (or recovers faster) than asynchronous release. Furthermore, we explore the theoretical implications of short-term synaptic depression adapting on longer time scales, as part of the phenomenon of metaplasticity. In particular, we show that a synapse can adjust its energy expenditure by changing the dynamics of short-term synaptic depression without affecting the net information conveyed by each successful release. Moreover, the optimal input spike rate is independent of the amplitude or time constant of synaptic depression. We analyze the information efficacy of three types of synapses for which the short-term dynamics of both synchronous and asynchronous release have been experimentally measured. In hippocampal autaptic synapses, the persistence of asynchronous release during depression cannot compensate for the reduction of synchronous release, so that the rate of information transmission declines with synaptic depression. In the calyx of Held, the information rate per release remains constant despite large variations in the measured asynchronous release rate. Lastly, we show that dopamine, by controlling asynchronous release in corticostriatal synapses, increases the synaptic information efficacy in nucleus accumbens.

Fatigue is an intrinsic property of living systems and synapses are no exception. Synaptic depression reduces the ability of synapses to release vesicles in response to an incoming action potential. Whether synaptic depression simply reflects the exhaustion of neuronal resources or whether it serves some additional function is still an open question. We ask how synaptic depression modulates the information transfer between neurons by keeping the synapse in an appropriate operating range. Using a tractable mathematical model for synaptic depression of both synchronous spike-evoked and asynchronous release of neurotransmitter, we derive a closed-form expression for the mutual information rate. Depression, it turns out, can both enhance or impair information transfer, depending on the relative level of depression for synchronous spike-evoked and asynchronous releases. We also study the compromise a synapse makes between its energy consumption and the rate of information transmission. Interestingly, we show that synaptic depression can regulate energy use without affecting the information (measured in bits) per synaptic release. By applying our mathematical framework to experimentally measured synapses, we show that some synapses can compensate for intrinsic variability in asynchronous release rates; moreover, we show how neuromodulators such as dopamine act to improve the information transmission rate.

Phase changes in neuronal postsynaptic spiking due to short term plasticity

In the brain, the postsynaptic response of a neuron to time-varying inputs is determined by the interaction of presynaptic spike times with the short-term dynamics of each synapse. For a neuron driven by stochastic synapses, synaptic depression results in a quite different postsynaptic response to a large population input depending on how correlated in time the spikes across individual synapses are. Here we show using both simulations and mathematical analysis that not only the rate but the phase of the postsynaptic response to a rhythmic population input varies as a function of synaptic dynamics and synaptic configuration. Resultant phase leads may compensate for transmission delays and be predictive of rhythmic changes. This could be particularly important for sensory processing and motor rhythm generation in the nervous system.

The synapses that connect neurons in the brain are far from being simple relay points that pass a signal from one neuron to another. There is now much evidence that long term changes in the strength of such connections, which determines the amplitude of the received signal, underpin learning and memory in the brain. However, signal amplitudes also fluctuate on fast time scales of milliseconds to seconds due to a variety of particular presynaptic mechanisms that regulate the release of neurotransmitter from the presynaptic terminal. Understanding the signal filtering properties of this short-term plasticity (STP) is a challenge and requires theoretical models. Aspects such as rate filtering and information transfer have been studied. Here we explore the effects of STP on the phase of a receiving neuron’s response to oscillating input and show that short-term depression can result in a frequency-dependent phase lead. This may be particularly important in the processing of rhythmic visual and auditory signals and producing rhythmic motor outputs.

The Mechanisms and Functions of Synaptic Facilitation

The ability of the brain to store and process information relies on changing the strength of connections between neurons. Synaptic facilitation is a form of short-term plasticity that enhances synaptic transmission for less than a second. Facilitation is a ubiquitous phenomenon thought to play critical roles in information transfer and neural processing. Yet our understanding of the function of facilitation remains largely theoretical. Here we review proposed roles for facilitation and discuss how recent progress in uncovering the underlying molecular mechanisms could enable experiments that elucidate how facilitation, and short-term plasticity in general, contributes to circuit function and animal behavior.

Information Rate Analysis of a Synaptic Release Site Using a Two-State Model of Short-Term Depression

Synapses are the communication channels for information transfer between neurons; these are the points at which pulse-like signals are converted into the stochastic release of quantized amounts of chemical neurotransmitter. At many synapses, prior neuronal activity depletes synaptic resources, depressing subsequent responses of both spontaneous and spike-evoked releases. We analytically compute the information transmission rate of a synaptic release site, which we model as a binary asymmetric channel. Short-term depression is incorporated by assigning the channel a memory of depth one. A successful release, whether spike evoked or spontaneous, decreases the probability of a subsequent release; if no release occurs on the following time step, the release probabilities recover back to their default values. We prove that synaptic depression can increase the release site's information rate if spontaneous release is more strongly depressed than spike-evoked release. When depression affects spontaneous and evoked release equally, the information rate must invariably decrease, even when the rate is normalized by the resources used for synaptic transmission. For identical depression levels, we analytically disprove the hypothesis, at least in this simplified model, that synaptic depression serves energy- and information-efficient encoding.

Fast response and high sensitivity to microsaccades in a cascading-adaptation neural network with short-term synaptic depression

Microsaccades are very small eye movements during fixation. Experimentally, they have been found to play an important role in visual information processing. However, neural responses induced by microsaccades are not yet well understood and are rarely studied theoretically. Here we propose a network model with a cascading adaptation including both retinal adaptation and short-term depression (STD) at thalamocortical synapses. In the neural network model, we compare the microsaccade-induced neural responses in the presence of STD and those without STD. It is found that the cascading with STD can give rise to faster and sharper responses to microsaccades. Moreover, STD can enhance response effectiveness and sensitivity to microsaccadic spatiotemporal changes, suggesting improved detection of small eye movements (or moving visual objects). We also explore the mechanism of the response properties in the model. Our studies strongly indicate that STD plays an important role in neural responses to microsaccades. Our model considers simultaneously retinal adaptation and STD at thalamocortical synapses in the study of microsaccade-induced neural activity, and may be useful for further investigation of the functional roles of microsaccades in visual information processing.

Modeling the influence of short term depression in vesicle release and stochastic calcium channel gating on auditory nerve spontaneous firing statistics

We propose several modifications to an existing computational model of stochastic vesicle release in inner hair cell ribbon synapses, with the aim of producing simulated auditory nerve fiber spiking data that more closely matches empirical data. Specifically, we studied the inter-spike-interval (ISI) distribution, and long and short term ISI correlations in spontaneous spiking in post-synaptic auditory nerve fibers. We introduced short term plasticity to the pre-synaptic release probability, in a manner analogous to standard stochastic models of cortical short term synaptic depression. This modification resulted in a similar distribution of vesicle release intervals to that estimated from empirical data. We also introduced a biophysical stochastic model of calcium channel opening and closing, but showed that this model is insufficient for generating a match with empirically observed spike correlations. However, by combining a phenomenological model of channel noise and our short term depression model, we generated short and long term correlations in auditory nerve spontaneous activity that qualitatively match empirical data.

Stochastic information transfer from cochlear implant electrodes to auditory nerve fibers

Cochlear implants, also called bionic ears, are implanted neural prostheses that can restore lost human hearing function by direct electrical stimulation of auditory nerve fibers. Previously, an information-theoretic framework for numerically estimating the optimal number of electrodes in cochlear implants has been devised. This approach relies on a model of stochastic action potential generation and a discrete memoryless channel model of the interface between the array of electrodes and the auditory nerve fibers. Using these models, the stochastic information transfer from cochlear implant electrodes to auditory nerve fibers is estimated from the mutual information between channel inputs (the locations of electrodes) and channel outputs (the set of electrode-activated nerve fibers). Here we describe a revised model of the channel output in the framework that avoids the side effects caused by an "ambiguity state" in the original model and also makes fewer assumptions about perceptual processing in the brain. A detailed comparison of how different assumptions on fibers and current spread modes impact on the information transfer in the original model and in the revised model is presented. We also mathematically derive an upper bound on the mutual information in the revised model, which becomes tighter as the number of electrodes increases. We found that the revised model leads to a significantly larger maximum mutual information and corresponding number of electrodes compared with the original model and conclude that the assumptions made in this part of the modeling framework are crucial to the model's overall utility.

Error correction and fast detectors implemented by ultrafast neuronal plasticity

We experimentally show that the neuron functions as a precise time integrator, where the accumulated changes in neuronal response latencies, under complex and random stimulation patterns, are solely a function of a global quantity, the average time lag between stimulations. In contrast, momentary leaps in the neuronal response latency follow trends of consecutive stimulations, indicating ultrafast neuronal plasticity. On a circuit level, this ultrafast neuronal plasticity phenomenon implements error-correction mechanisms and fast detectors for misplaced stimulations. Additionally, at moderate (high) stimulation rates this phenomenon destabilizes (stabilizes) a periodic neuronal activity disrupted by misplaced stimulations.