Short-term Synaptic Depression Research Articles

Breaking the burst: Unveiling mechanisms behind fragmented network bursts in patient-derived neurons

Fragmented network bursts (NBs) are observed as a phenotypic driver in many patient-derived neuronal networks on multi-electrode arrays (MEAs), but the pathophysiological mechanisms underlying this phenomenon are unknown. Here, we used our previously developed biophysically detailed in silico model to investigate these mechanisms. Fragmentation of NBs in our model simulations occurred only when the level of short-term synaptic depression (STD) was enhanced, suggesting that STD is a key player. Experimental validation with Dynasore, an STD enhancer, induced fragmented NBs in healthy neuronal networks invitro. Additionally, we showed that strong asynchronous neurotransmitter release, NMDA currents, or short-term facilitation (STF) can support the emergence of multiple fragments in NBs by producing excitation that persists after high-frequency firing stops. Our results provide important insights into disease mechanisms and potential pharmaceutical targets for neurological disorders modeled using human induced pluripotent stem cell (hiPSC)-derived neurons.

Criticality and universality in neuronal cultures during "up" and "down" states.

The brain can be seen as a self-organized dynamical system that optimizes information processing and storage capabilities. This is supported by studies across scales, from small neuronal assemblies to the whole brain, where neuronal activity exhibits features typically associated with phase transitions in statistical physics. Such a critical state is characterized by the emergence of scale-free statistics as captured, for example, by the sizes and durations of activity avalanches corresponding to a cascading process of information flow. Another phenomenon observed during sleep, under anesthesia, and in in vitro cultures, is that cortical and hippocampal neuronal networks alternate between "up" and "down" states characterized by very distinct firing rates. Previous theoretical work has been able to relate these two concepts and proposed that only up states are critical whereas down states are subcritical, also indicating that the brain spontaneously transitions between the two. Using high-speed high-resolution calcium imaging recordings of neuronal cultures, we test this hypothesis here by analyzing the neuronal avalanche statistics in populations of thousands of neurons during "up" and "down" states separately. We find that both "up" and "down" states can exhibit scale-free behavior when taking into account their intrinsic time scales. In particular, the statistical signature of "down" states is indistinguishable from those observed previously in cultures without "up" states. We show that such behavior can not be explained by network models of non-conservative leaky integrate-and-fire neurons with short-term synaptic depression, even when realistic noise levels, spatial network embeddings, and heterogeneous populations are taken into account, which instead exhibits behavior consistent with previous theoretical models. Similar differences were also observed when taking into consideration finite-size scaling effects, suggesting that the intrinsic dynamics and self-organization mechanisms of these cultures might be more complex than previously thought. In particular, our findings point to the existence of different mechanisms of neuronal communication, with different time scales, acting during either high-activity or low-activity states, potentially requiring different plasticity mechanisms.

Synaptotagmin 7 Sculpts Short-Term Plasticity at a High Probability Synapse.

Synapses with high release probability (Pr ) tend to exhibit short-term synaptic depression. According to the prevailing model, this reflects the temporary depletion of release-ready vesicles after an initial action potential (AP). At the high-Pr layer 4 to layer 2/3 (L4-L2/3) synapse in rodent somatosensory cortex, short-term plasticity appears to contradict the depletion model: depression is absent at interstimulus intervals (ISIs) <50 ms and develops to a maximum at ∼200 ms. To understand the mechanism(s) underlying the biphasic time course of short-term plasticity at this synapse, we used whole-cell electrophysiology and two-photon calcium imaging in acute slices from male and female juvenile mice. We tested several candidate mechanisms including neuromodulation, postsynaptic receptor desensitization, and use-dependent changes in presynaptic AP-evoked calcium. We found that, at single L4-L2/3 synapses, Pr varies as a function of ISI, giving rise to the distinctive short-term plasticity time course. Furthermore, the higher-than-expected Pr at short ISIs depends on expression of synaptotagmin 7 (Syt7). Our results show that two distinct vesicle release processes summate to give rise to short-term plasticity at this synapse: (1) a basal, high-Pr release mechanism that undergoes rapid depression and recovers slowly (τ = ∼3 s) and (2) a Syt7-dependent mechanism that leads to a transient increase in Pr (τ = ∼100 ms) after the initial AP. We thus reveal how these synapses can maintain a very high probability of neurotransmission for multiple APs within a short time frame. Key words : depression; facilitation; short-term plasticity; synaptotagmin 7.

Fully-primed slowly-recovering vesicles mediate presynaptic LTP at neocortical neurons.

Pre- and postsynaptic forms of long-term potentiation (LTP) are candidate synaptic mechanisms underlying learning and memory. At layer 5 pyramidal neurons, LTP increases the initial synaptic strength but also short-term depression during high-frequency transmission. This classical form of presynaptic LTP has been referred to as redistribution of synaptic efficacy. However, the underlying mechanisms remain unclear. We therefore performed whole-cell recordings from layer 5 pyramidal neurons in acute cortical slices of rats and analyzed presynaptic function before and after LTP induction by paired pre- and postsynaptic neuronal activity. LTP was successfully induced in about half of the synaptic connections tested and resulted in increased synaptic short-term depression during high-frequency transmission and a decelerated recovery from short-term depression due to an increased fraction of a slow recovery component. Analysis with a recently established sequential two-step vesicle priming model indicates an increase in the abundance of fully-primed and slowly-recovering vesicles. A systematic analysis of short-term plasticity and synapse-to-synapse variability of synaptic strength at various types of synapses revealed that stronger synapses generally recover more slowly from synaptic short-term depression. Finally, pharmacological stimulation of the cyclic adenosine monophosphate and diacylglycerol signaling pathways, which are both known to promote synaptic vesicle priming, mimicked LTP and slowed the recovery from short-term depression. Our data thus demonstrate that LTP at layer 5 pyramidal neurons increases synaptic strength primarily by enlarging a subpool of fully-primed slowly-recovering vesicles.

Short-term neuronal and synaptic plasticity act in synergy for deviance detection in spiking networks.

Sensory areas of cortex respond more strongly to infrequent stimuli when these violate previously established regularities, a phenomenon known as deviance detection (DD). Previous modeling work has mainly attempted to explain DD on the basis of synaptic plasticity. However, a large fraction of cortical neurons also exhibit firing rate adaptation, an underexplored potential mechanism. Here, we investigate DD in a spiking neuronal network model with two types of short-term plasticity, fast synaptic short-term depression (STD) and slower threshold adaptation (TA). We probe the model with an oddball stimulation paradigm and assess DD by evaluating the network responses. We find that TA is sufficient to elicit DD. It achieves this by habituating neurons near the stimulation site that respond earliest to the frequently presented standard stimulus (local fatigue), which diminishes the response and promotes the recovery (global fatigue) of the wider network. Further, we find a synergy effect between STD and TA, where they interact with each other to achieve greater DD than the sum of their individual effects. We show that this synergy is caused by the local fatigue added by STD, which inhibits the global response to the frequently presented stimulus, allowing greater recovery of TA-mediated global fatigue and making the network more responsive to the deviant stimulus. Finally, we show that the magnitude of DD strongly depends on the timescale of stimulation. We conclude that highly predictable information can be encoded in strong local fatigue, which allows greater global recovery and subsequent heightened sensitivity for DD.

Artificial Neural Network Model with Astrocyte-Driven Short-Term Memory.

In this study, we introduce an innovative hybrid artificial neural network model incorporating astrocyte-driven short-term memory. The model combines a convolutional neural network with dynamic models of short-term synaptic plasticity and astrocytic modulation of synaptic transmission. The model's performance was evaluated using simulated data from visual change detection experiments conducted on mice. Comparisons were made between the proposed model, a recurrent neural network simulating short-term memory based on sustained neural activity, and a feedforward neural network with short-term synaptic depression (STPNet) trained to achieve the same performance level as the mice. The results revealed that incorporating astrocytic modulation of synaptic transmission enhanced the model's performance.

Nano-crystalline ZnO memristor for neuromorphic computing: Resistive switching and conductance modulation

In this work, a nano-crystalline (NC) ZnO-based memristor was fabricated to investigate the short-term memory characteristics for reservoir computing systems. The crystalline structure of the ZnO film was confirmed through transmission electron microscopy (TEM) and X-ray diffraction pattern (XRD), while X-ray photoelectron spectroscopy (XPS) confirmed the chemical and bonding states of each element. The NC-ZnO-based memristor exhibited remarkable endurance, enduring more than 200 DC cycles, and had a high to low resistance (RH/RL) ratio of 102. Furthermore, it displayed long data retention of 104 s and consistent resistive switching (RS) with restricted variation in the set and reset voltage, showing its excellent performance characteristics. By controlling the pulse amplitude and the time interval between pulses, it was possible to effectively replicate the key features of short-term synaptic plasticity, including potentiation, depression, and paired-pulse depression, through conductance modulation. An artificial neural network (ANN) simulation achieved a pattern recognition accuracy of approximately 90.1% for a 28 × 28-pixel image after 100 training epochs. Based on this extensive study, NC-ZnO-based memristor exhibits immense potential as a crucial element in constructing high-performance neuromorphic computing systems.

Multivesicular release favors short term synaptic depression in hippocampal autapses.

Presynaptic terminals of the central nervous system can support univesicular and multivesicular synchronous release of neurotransmitters, however, the functional implications of the prevalence of one mechanism over the other are yet unresolved. Here, we took advantage of the expression of SF-iGluSnFR.S72A in the astrocytic feeder layer of autaptic hippocampal neuronal cultures to associate the liberation of glutamate to excitatory postsynaptic currents. The presence of the glutamate sensor in glial cells avoided any interference with the function of endogenous postsynaptic receptors. It was possible to optically detect changes in neurotransmitter release probability, which was heterogeneous among synaptic boutons studied. For each neuron investigated, the liberation of neurotransmitters occurred through a predominant mechanism. The prevalence of multivesicular over univesicular release increased synaptic strength and enhanced short-term synaptic depression. These results show that the preference of hippocampal boutons to synchronously release one or more vesicles determines the strength and low pass filtering properties of the synapses established.

Learning to predict future locations with internally generated theta sequences.

Representing past, present and future locations is key for spatial navigation. Indeed, within each cycle of the theta oscillation, the population of hippocampal place cells appears to represent trajectories starting behind the current position of the animal and sweeping ahead of it. In particular, we reported recently that the position represented by CA1 place cells at a given theta phase corresponds to the location where animals were or will be located at a fixed time interval into the past or future assuming the animal ran at its typical, not the current, speed through that part of the environment. This coding scheme leads to longer theta trajectories, larger place fields and shallower phase precession in areas where animals typically run faster. Here we present a mechanistic computational model that accounts for these experimental observations. The model consists of a continuous attractor network with short-term synaptic facilitation and depression that internally generates theta sequences that advance at a fixed pace. Spatial locations are then mapped onto the active units via modified Hebbian plasticity. As a result, neighboring units become associated with spatial locations further apart where animals run faster, reproducing our earlier experimental results. The model also accounts for the higher density of place fields generally observed where animals slow down, such as around rewards. Furthermore, our modeling results reveal that an artifact of the decoding analysis might be partly responsible for the observation that theta trajectories start behind the animal's current position. Overall, our results shed light on how the hippocampal code might arise from the interplay between behavior, sensory input and predefined network dynamics.

Mesoscopic description of hippocampal replay and metastability in spiking neural networks with short-term plasticity.

Bottom-up models of functionally relevant patterns of neural activity provide an explicit link between neuronal dynamics and computation. A prime example of functional activity patterns are propagating bursts of place-cell activities called hippocampal replay, which is critical for memory consolidation. The sudden and repeated occurrences of these burst states during ongoing neural activity suggest metastable neural circuit dynamics. As metastability has been attributed to noise and/or slow fatigue mechanisms, we propose a concise mesoscopic model which accounts for both. Crucially, our model is bottom-up: it is analytically derived from the dynamics of finite-size networks of Linear-Nonlinear Poisson neurons with short-term synaptic depression. As such, noise is explicitly linked to stochastic spiking and network size, and fatigue is explicitly linked to synaptic dynamics. To derive the mesoscopic model, we first consider a homogeneous spiking neural network and follow the temporal coarse-graining approach of Gillespie to obtain a "chemical Langevin equation", which can be naturally interpreted as a stochastic neural mass model. The Langevin equation is computationally inexpensive to simulate and enables a thorough study of metastable dynamics in classical setups (population spikes and Up-Down-states dynamics) by means of phase-plane analysis. An extension of the Langevin equation for small network sizes is also presented. The stochastic neural mass model constitutes the basic component of our mesoscopic model for replay. We show that the mesoscopic model faithfully captures the statistical structure of individual replayed trajectories in microscopic simulations and in previously reported experimental data. Moreover, compared to the deterministic Romani-Tsodyks model of place-cell dynamics, it exhibits a higher level of variability regarding order, direction and timing of replayed trajectories, which seems biologically more plausible and could be functionally desirable. This variability is the product of a new dynamical regime where metastability emerges from a complex interplay between finite-size fluctuations and local fatigue.

Self-organized criticality in a mesoscopic model of excitatory-inhibitory neuronal populations by short-term and long-term synaptic plasticity.

Dynamics of an interconnected population of excitatory and inhibitory spiking neurons wandering around a Bogdanov-Takens (BT) bifurcation point can generate the observed scale-free avalanches at the population level and the highly variable spike patterns of individual neurons. These characteristics match experimental findings for spontaneous intrinsic activity in the brain. In this paper, we address the mechanisms causing the system to get and remain near this BT point. We propose an effective stochastic neural field model which captures the dynamics of the mean-field model. We show how the network tunes itself through local long-term synaptic plasticity by STDP and short-term synaptic depression to be close to this bifurcation point. The mesoscopic model that we derive matches the directed percolation model at the absorbing state phase transition.

Dynamic branching in a neural network model for probabilistic prediction of sequences.

An important function of the brain is to predict which stimulus is likely to occur based on the perceived cues. The present research studied the branching behavior of a computational network model of populations of excitatory and inhibitory neurons, both analytically and through simulations. Results show how synaptic efficacy, retroactive inhibition and short-term synaptic depression determine the dynamics of selection between different branches predicting sequences of stimuli of different probabilities. Further results show that changes in the probability of the different predictions depend on variations of neuronal gain. Such variations allow the network to optimize the probability of its predictions to changing probabilities of the sequences without changing synaptic efficacy.

Long Time Behavior of an Age- and Leaky Memory-Structured Neuronal Population Equation

We study the asymptotic stability of a two-dimensional mean-field equation, which takes the form of a nonlocal transport equation and generalizes the time-elapsed neuron network model by the inclusion of a leaky memory variable. This additional variable can represent a slow fatigue mechanism, such as spike-frequency adaptation or short-term synaptic depression. Even though two-dimensional models are known to have emergent behaviors, such as population bursts, which are not observed in standard one-dimensional models, we show that in the weak connectivity regime, two-dimensional models behave like one-dimensional models, i.e., they relax to a unique stationary state. The proof is based on an application of Harris's ergodic theorem and a perturbation argument, both adapted to the case of a multidimensional equation with delays.

Enhanced Feedback Inhibition Due to Increased Recruitment of Somatostatin-Expressing Interneurons and Enhanced Cortical Recurrent Excitation in a Genetic Mouse Model of Migraine.

Migraine is a complex brain disorder, characterized by attacks of unilateral headache and global dysfunction in multisensory information processing, whose underlying cellular and circuit mechanisms remain unknown. The finding of enhanced excitatory, but unaltered inhibitory, neurotransmission at cortical synapses between pyramidal cells (PCs) and fast-spiking interneurons (FS INs) in mouse models of familial hemiplegic migraine (FHM) suggested the hypothesis that dysregulation of the excitatory-inhibitory (E/I) balance in specific circuits is a key pathogenic mechanism. Here, we investigated the cortical layer 2/3 (L2/3) feedback inhibition microcircuit involving somatostatin-expressing (SOM) INs in FHM1 mice of both sexes carrying a gain-of-function mutation in CaV2.1. Unitary inhibitory neurotransmission at SOM IN-PC synapses was unaltered while excitatory neurotransmission at both PC-SOM IN and PC-PC synapses was enhanced, because of increased probability of glutamate release, in FHM1 mice. Short-term synaptic depression was enhanced at PC-PC synapses while short-term synaptic facilitation was unaltered at PC-SOM IN synapses during 25-Hz repetitive activity. The frequency-dependent disynaptic inhibition (FDDI) mediated by SOM INs was enhanced, lasted longer and required shorter high-frequency bursts to be initiated in FHM1 mice. These findings, together with previous evidence of enhanced disynaptic feedforward inhibition by FS INs, suggest that the increased inhibition may effectively counteract the increased recurrent excitation in FHM1 mice and may even prevail in certain conditions. Considering the involvement of SOM INs in γ oscillations, surround suppression and context-dependent sensory perception, the facilitated recruitment of SOM INs, together with the enhanced recurrent excitation, may contribute to dysfunctional sensory processing in FHM1 and possibly migraine.SIGNIFICANCE STATEMENT Migraine is a complex brain disorder, characterized by attacks of unilateral headache and global dysfunction in multisensory information processing, whose underlying cellular and circuit mechanisms remain unknown, although dysregulation of the excitatory-inhibitory (E/I) balance in specific circuits could be a key pathogenic mechanism. Here, we provide insights into these mechanisms by investigating the cortical feedback inhibition microcircuit involving somatostatin-expressing interneurons (SOM INs) in a mouse model of a rare monogenic migraine. Despite unaltered inhibitory synaptic transmission, the disynaptic feedback inhibition mediated by SOM INs was enhanced in the migraine model because of enhanced recruitment of the INs. Recurrent cortical excitation was also enhanced. These alterations may contribute to context-dependent sensory processing dysfunctions in migraine.

CKAMP44 controls synaptic function and strength of relay neurons during early development of the dorsal lateral geniculate nucleus.

Relay neurons of the dorsal lateral geniculate nucleus (dLGN) receive inputs from retinal ganglion cells via retinogeniculate synapses. These connections undergo pruning in the first 2weeks after eye opening. The remaining connections are strengthened several-fold by the insertion of AMPA receptors (AMPARs) into weak or silent synapses. In this study, we found that the AMPAR auxiliary subunit CKAMP44 is required for receptor insertion and function of retinogeniculate synapses during development. Genetic deletion of CKAMP44 resulted in decreased synaptic strength and a higher number of silent synapses in young (P9-11) mice. Recovery from desensitisation of AMPARs was faster in CKAMP44 knockout (CKAMP44-/- ) than in wild-type mice. Moreover, loss of CKAMP44 increased the probability of inducing plateau potentials, which are known to be important for eye-specific input segregation and retinogeniculate synapse maturation. The anatomy of relay neurons in the dLGN was changed in young CKAMP44-/- mice showing a transient increase in dendritic branching that normalised during later development (P26-33). Interestingly, input segregation in young CKAMP44-/- mice was not affected when compared to wild-type mice. These results demonstrate that CKAMP44 promotes maturation and modulates function of retinogeniculate synapses during early development of the visual system without affecting input segregation. KEY POINTS: Expression of CKAMP44starts early during development of the dorsal lateral geniculate nucleus (dLGN) and remains stable in relay neurons and interneurons. Genetic deletion of CKAMP44 decreases synaptic strength and increases silent synapse number in dLGN relay neurons; increases the rate of recovery from desensitisation of AMPA receptors in dLGN relay neurons; and reduces synaptic short-term depression in retinogeniculate synapses. The probability of inducing plateau potentials is elevated in relay neurons of CKAMP44-/- mice. Eye-specific input segregation is unaffected in the dLGN of CKAMP44-/- mice. Deletion of CKAMP44mildly affects dendritic arborisation of relay neurons in the dLGN.

Auditory cortex modelled as a dynamical network of oscillators: understanding event-related fields and their adaptation

Adaptation, the reduction of neuronal responses by repetitive stimulation, is a ubiquitous feature of auditory cortex (AC). It is not clear what causes adaptation, but short-term synaptic depression (STSD) is a potential candidate for the underlying mechanism. In such a case, adaptation can be directly linked with the way AC produces context-sensitive responses such as mismatch negativity and stimulus-specific adaptation observed on the single-unit level. We examined this hypothesis via a computational model based on AC anatomy, which includes serially connected core, belt, and parabelt areas. The model replicates the event-related field (ERF) of the magnetoencephalogram as well as ERF adaptation. The model dynamics are described by excitatory and inhibitory state variables of cell populations, with the excitatory connections modulated by STSD. We analysed the system dynamics by linearising the firing rates and solving the STSD equation using time-scale separation. This allows for characterisation of AC dynamics as a superposition of damped harmonic oscillators, so-called normal modes. We show that repetition suppression of the N1m is due to a mixture of causes, with stimulus repetition modifying both the amplitudes and the frequencies of the normal modes. In this view, adaptation results from a complete reorganisation of AC dynamics rather than a reduction of activity in discrete sources. Further, both the network structure and the balance between excitation and inhibition contribute significantly to the rate with which AC recovers from adaptation. This lifetime of adaptation is longer in the belt and parabelt than in the core area, despite the time constants of STSD being spatially homogeneous. Finally, we critically evaluate the use of a single exponential function to describe recovery from adaptation.

Mesodiencephalic junction GABAergic inputs are processed separately from motor cortical inputs in the basilar pons

SummaryThe basilar pontine nuclei (bPN) are known to receive excitatory input from the entire neocortex and constitute the main source of mossy fibers to the cerebellum. Various potential inhibitory afferents have been described, but their origin, synaptic plasticity, and network function have remained elusive. Here we identify the mesodiencephalic junction (MDJ) as a prominent source of monosynaptic GABAergic inputs to the bPN. We found no evidence that these inputs converge with motor cortex (M1) inputs at the single neuron or at the local network level. Tracing the inputs to GABAergic MDJ neurons revealed inputs to these neurons from neocortical areas. Additionally, we observed little short-term synaptic facilitation or depression in afferents from the MDJ, enabling MDJ inputs to carry sign-inversed neocortical inputs. Thus, our results show a prominent source of GABAergic inhibition to the bPN that could enrich input to the cerebellar granule cell layer.

Retina‐Inspired Two‐Terminal Optoelectronic Neuromorphic Devices with Light‐Tunable Short‐Term Plasticity for Self‐Adjusting Sensing

In current optical imaging devices, complex peripheral circuits are required to obtain clear and true images, which leads to limited power and area efficiency. Inspired by the visual processing in retina, an optoelectronic neuromorphic device with short‐term plasticity (STP) can realize self‐adjustment of the visual system to real time in response to the external stimuli, including light controlled short‐term facilitation (STF) and short‐term depression (STD). Herein, an optoelectronic synaptic device based on a simple Au/ZnS/Pt structure is found to show both synaptic STD and STF under light stimulation. STD originates from photoexcited carriers trapped by defects in the ZnS film, whereas STF results from photocurrent rising due to trap filling via previous electrical stimulation. Moreover, the STP of the device exhibits excellent stability and repeatability. In addition, this optoelectronic device can be used to adjust the sharpness of the view according to different brightness levels through STD and STF. The realization of self‐adjustment through STP of the emerging optoelectronic neuromorphic device may pave the way for bionic systems facing complex environments.

Paired Feed-Forward Excitation With Delayed Inhibition Allows High Frequency Computations Across Brain Regions.

The transmission of high frequency temporal information across brain regions is critical to perception, but the mechanisms underlying such transmission remain unclear. Long-range projection patterns across brain areas are often comprised of paired feed-forward excitation followed closely by delayed inhibition, including the thalamic triad synapse, thalamic projections to cortex, and projections within the hippocampus. Previous studies have shown that these joint projections produce a shortened period of depolarization, sharpening the timing window over which the postsynaptic neuron can fire. Here we show that these projections can facilitate the transmission of high frequency computations even at frequencies that are highly filtered by neuronal membranes. This temporal facilitation occurred over a range of synaptic parameter values, including variations in synaptic strength, synaptic time constants, short-term synaptic depression, and the delay between excitation and inhibition. Further, these projections can coordinate computations across multiple network levels, even amid ongoing local activity. We suggest that paired feed-forward excitation and inhibition provide a hybrid signal—carrying both a value and a clock-like trigger—to allow circuits to be responsive to input whenever it arrives.

The Adaptation Model Offers a Challenge for the Predictive Coding Account of Mismatch Negativity.

An unpredictable stimulus elicits a stronger event-related response than a high-probability stimulus. This differential in response magnitude is termed the mismatch negativity (MMN). Over the past decade, it has become increasingly popular to explain the MMN terms of predictive coding, a proposed general principle for the way the brain realizes Bayesian inference when it interprets sensory information. This perspective article is a reminder that the issue of MMN generation is far from settled, and that an alternative model in terms of adaptation continues to lurk in the wings. The adaptation model has been discounted because of the unrealistic and simplistic fashion in which it tends to be set up. Here, simulations of auditory cortex incorporating a modern version of the adaptation model are presented. These show that locally operating short-term synaptic depression accounts both for adaptation due to stimulus repetition and for MMN responses. This happens even in cases where adaptation has been ruled out as an explanation of the MMN (e.g., in the stimulus omission paradigm and the multi-standard control paradigm). Simulation models that would demonstrate the viability of predictive coding in a similarly multifaceted way are currently missing from the literature, and the reason for this is discussed in light of the current results.