Presynaptic LTP and LTD of Excitatory and Inhibitory Synapses

Ubiquitous forms of long-term potentiation (LTP) and depression (LTD) are caused by enduring increases or decreases in neurotransmitter release. Such forms or presynaptic plasticity are equally observed at excitatory and inhibitory synapses and the list of locations expressing presynaptic LTP and LTD continues to grow. In addition to the mechanistically distinct forms of postsynaptic plasticity, presynaptic plasticity offers a powerful means to modify neural circuits. A wide range of induction mechanisms has been identified, some of which occur entirely in the presynaptic terminal, whereas others require retrograde signaling from the postsynaptic to presynaptic terminals. In spite of this diversity of induction mechanisms, some common induction rules can be identified across synapses. Although the precise molecular mechanism underlying long-term changes in transmitter release in most cases remains unclear, increasing evidence indicates that presynaptic LTP and LTD can occur in vivo and likely mediate some forms of learning.At several excitatory and inhibitory synapses, neuronal activity can trigger enduring increases or decreases in neurotransmitter release, thereby producing long-term potentiation (LTP) or long-term depression (LTD) of synaptic strength, respectively. In the last decade, many studies have revealed that these forms of plasticity are ubiquitously expressed in the mammalian brain, and accumulating evidence indicates that they may underlie behavioral adaptations occurring in vivo. These studies have also uncovered a wide range of induction mechanisms, which converge on the presynaptic terminal where an enduring modification in the neurotransmitter release process takes place. Interestingly, presynaptic forms of LTP/LTD can coexist with classical forms of postsynaptic plasticity. Such diversity expands the dynamic range and repertoire by which neurons modify their synaptic connections. This review discusses mechanistic aspects of presynaptic LTP and LTD at both excitatory and inhibitory synapses in the mammalian brain, with an emphasis on recent findings.     

Inhibition as a Binary Switch for Excitatory Plasticity in Pyramidal Neurons

Synaptic plasticity is thought to induce memory traces in the brain that are the foundation of learning. To ensure the stability of these traces in the presence of further learning, however, a regulation of plasticity appears beneficial. Here, we take up the recent suggestion that dendritic inhibition can switch plasticity of excitatory synapses on and off by gating backpropagating action potentials (bAPs) and calcium spikes, i.e., by gating the coincidence signals required for Hebbian forms of plasticity. We analyze temporal and spatial constraints of such a gating and investigate whether it is possible to suppress bAPs without a simultaneous annihilation of the forward-directed information flow via excitatory postsynaptic potentials (EPSPs). In a computational analysis of conductance-based multi-compartmental models, we demonstrate that a robust control of bAPs and calcium spikes is possible in an all-or-none manner, enabling a binary switch of coincidence signals and plasticity. The position of inhibitory synapses on the dendritic tree determines the spatial extent of the effect and allows a pathway-specific regulation of plasticity. With appropriate timing, EPSPs can still trigger somatic action potentials, although backpropagating signals are abolished. An annihilation of bAPs requires precisely timed inhibition, while the timing constraints are less stringent for distal calcium spikes. We further show that a wide-spread motif of local circuits—feedforward inhibition—is well suited to provide the temporal precision needed for the control of bAPs. Altogether, our model provides experimentally testable predictions and demonstrates that the inhibitory switch of plasticity can be a robust and attractive mechanism, hence assigning an additional function to the inhibitory elements of neuronal microcircuits beyond modulation of excitability.     

Somato-dendritic Synaptic Plasticity and Error-backpropagation in Active Dendrites

In the last decade dendrites of cortical neurons have been shown to nonlinearly combine synaptic inputs by evoking local dendritic spikes. It has been suggested that these nonlinearities raise the computational power of a single neuron, making it comparable to a 2-layer network of point neurons. But how these nonlinearities can be incorporated into the synaptic plasticity to optimally support learning remains unclear. We present a theoretically derived synaptic plasticity rule for supervised and reinforcement learning that depends on the timing of the presynaptic, the dendritic and the postsynaptic spikes. For supervised learning, the rule can be seen as a biological version of the classical error-backpropagation algorithm applied to the dendritic case. When modulated by a delayed reward signal, the same plasticity is shown to maximize the expected reward in reinforcement learning for various coding scenarios. Our framework makes specific experimental predictions and highlights the unique advantage of active dendrites for implementing powerful synaptic plasticity rules that have access to downstream information via backpropagation of action potentials.     

Energy Efficient Sparse Connectivity from Imbalanced Synaptic Plasticity Rules

It is believed that energy efficiency is an important constraint in brain evolution. As synaptic transmission dominates energy consumption, energy can be saved by ensuring that only a few synapses are active. It is therefore likely that the formation of sparse codes and sparse connectivity are fundamental objectives of synaptic plasticity. In this work we study how sparse connectivity can result from a synaptic learning rule of excitatory synapses. Information is maximised when potentiation and depression are balanced according to the mean presynaptic activity level and the resulting fraction of zero-weight synapses is around 50%. However, an imbalance towards depression increases the fraction of zero-weight synapses without significantly affecting performance. We show that imbalanced plasticity corresponds to imposing a regularising constraint on the L ₁-norm of the synaptic weight vector, a procedure that is well-known to induce sparseness. Imbalanced plasticity is biophysically plausible and leads to more efficient synaptic configurations than a previously suggested approach that prunes synapses after learning. Our framework gives a novel interpretation to the high fraction of silent synapses found in brain regions like the cerebellum.     

Computational Consequences of Temporally Asymmetric Learning Rules: II. Sensory Image Cancellation

The electrosensory lateral line lobe (ELL) of mormyrid electric fish is a cerebellum-like structure that receives primary afferent input from electroreceptors in the skin. Purkinje-like cells in ELL store and retrieve a temporally precise negative image of prior sensory input. The stored image is derived from the association of centrally originating predictive signals with peripherally originating sensory input. The predictive signals are probably conveyed by parallel fibers. Recent in vitro experiments have demonstrated that pairing parallel fiber-evoked excitatory postsynaptic potentials (epsps) with postsynaptic spikes in Purkinje-like cells depresses the strength of these synapses. The depression has a tight dependence on the temporal order of pre- and postsynaptic events. The postsynaptic spike must follow the onset of the epsp within a window of about 60 msec for the depression to occur and pairings at other delays yield a nonassociative enhancement of the epsp. Mathematical analyses and computer simulations are used here to test the hypothesis that synaptic plasticity of the type established in vitro could be responsible for the storage of temporal patterns that is observed in vivo. This hypothesis is confirmed. The temporally asymmetric learning rule established in vitro results in the storage of activity patterns as observed in vivo and does so with significantly greater fidelity than other types of learning rules. The results demonstrate the importance of precise timing in pre- and postsynaptic activity for accurate storage of temporal information.     

Vesicular zinc promotes presynaptic and inhibits postsynaptic long-term potentiation of mossy fiber-CA3 synapse

The presence of zinc in glutamatergic synaptic vesicles of excitatory neurons of mammalian cerebral cortex suggests that zinc might regulate plasticity of synapses formed by these neurons. Long-term potentiation (LTP) is a form of synaptic plasticity that may underlie learning and memory. We tested the hypothesis that zinc within vesicles of mossy fibers (mf) contributes to mf-LTP, a classical form of presynaptic LTP. We synthesized an extracellular zinc chelator with selectivity and kinetic properties suitable for study of the large transient of zinc in the synaptic cleft induced by mf stimulation. We found that vesicular zinc is required for presynaptic mf-LTP. Unexpectedly, vesicular zinc also inhibits?a form of postsynaptic mf-LTP. Because the mf-CA3 synapse provides a major source of excitatory input to the hippocampus, regulating its efficacy by these dual actions, vesicular zinc is critical to proper function of hippocampal circuitry in health and disease.     

Synapses identifiable in the parietal ganglia of the snail Helix lucorum

The synaptic plasticity is a background for learning and memory. Identifiable synapses that are the synapses between individually identifiable neurons are a very convenient model for studying plasticity. Synapses between the interoceptive mechanosensory neurons and the command neurons of the withdrawal behavior were identified in the Helix lucorum brain. It was shown that synaptic plasticity estimated by the dynamics of the elementary postsynaptic potentials elicited by single presynaptic spikes differed from the synaptic plasticity estimated by the dynamics of compound synaptic responses of the same neurons to sensory stimulation. Habituation and heterosynaptic facilitation phenomena are discussed in terms of the dynamics of the elementary postsynaptic potentials.     

Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity

Precise spatio-temporal patterns of neuronal action potentials underly e.g. sensory representations and control of muscle activities. However, it is not known how the synaptic efficacies in the neuronal networks of the brain adapt such that they can reliably generate spikes at specific points in time. Existing activity-dependent plasticity rules like Spike-Timing-Dependent Plasticity are agnostic to the goal of learning spike times. On the other hand, the existing formal and supervised learning algorithms perform a temporally precise comparison of projected activity with the target, but there is no known biologically plausible implementation of this comparison. Here, we propose a simple and local unsupervised synaptic plasticity mechanism that is derived from the requirement of a balanced membrane potential. Since the relevant signal for synaptic change is the postsynaptic voltage rather than spike times, we call the plasticity rule Membrane Potential Dependent Plasticity (MPDP). Combining our plasticity mechanism with spike after-hyperpolarization causes a sensitivity of synaptic change to pre- and postsynaptic spike times which can reproduce Hebbian spike timing dependent plasticity for inhibitory synapses as was found in experiments. In addition, the sensitivity of MPDP to the time course of the voltage when generating a spike allows MPDP to distinguish between weak (spurious) and strong (teacher) spikes, which therefore provides a neuronal basis for the comparison of actual and target activity. For spatio-temporal input spike patterns our conceptually simple plasticity rule achieves a surprisingly high storage capacity for spike associations. The sensitivity of the MPDP to the subthreshold membrane potential during training allows robust memory retrieval after learning even in the presence of activity corrupted by noise. We propose that MPDP represents a biophysically plausible mechanism to learn temporal target activity patterns.     

LTD windows of the STDP learning rule and synaptic connections having a large transmission delay enable robust sequence learning amid background noise

Spike-timing-dependent synaptic plasticity (STDP) is a simple and effective learning rule for sequence learning. However, synapses being subject to STDP rules are readily influenced in noisy circumstances because synaptic conductances are modified by pre- and postsynaptic spikes elicited within a few tens of milliseconds, regardless of whether those spikes convey information or not. Noisy firing existing everywhere in the brain may induce irrelevant enhancement of synaptic connections through STDP rules and would result in uncertain memory encoding and obscure memory patterns. We will here show that the LTD windows of the STDP rules enable robust sequence learning amid background noise in cooperation with a large signal transmission delay between neurons and a theta rhythm, using a network model of the entorhinal cortex layer II with entorhinal-hippocampal loop connections. The important element of the present model for robust sequence learning amid background noise is the symmetric STDP rule having LTD windows on both sides of the LTP window, in addition to the loop connections having a large signal transmission delay and the theta rhythm pacing activities of stellate cells. Above all, the LTD window in the range of positive spike-timing is important to prevent influences of noise with the progress of sequence learning.     

Spike-timing dependent synaptic plasticity: a phenomenological framework

 In this paper a phenomenological model of spike-timing dependent synaptic plasticity (STDP) is developed that is based on a Volterra series-like expansion. Synaptic weight changes as a function of the relative timing of pre- and postsynaptic spikes are described by integral kernels that can easily be inferred from experimental data. The resulting weight dynamics can be stated in terms of statistical properties of pre- and postsynaptic spike trains. Generalizations to neurons that fire two different types of action potentials, such as cerebellar Purkinje cells where synaptic plasticity depends on correlations in two distinct presynaptic fibers, are discussed. We show that synaptic plasticity, together with strictly local bounds for the weights, can result in synaptic competition that is required for any form of pattern formation. This is illustrated by a concrete example where a single neuron equipped with STDP can selectively strengthen those synapses with presynaptic neurons that reliably deliver precisely timed spikes at the expense of other synapses which transmit spikes with a broad temporal distribution. Such a mechanism may be of vital importance for any neuronal system where information is coded in the timing of individual action potentials. Received: 23 January 2002 / Accepted: 28 March 2002 Correspondence to: W.M. Kistler (e-mail: kistler@anat.fgg.eur.nl Fax: +31 10 408 5459)     

Noise-tolerant stimulus discrimination by synchronization with depressing synapses

 Some synapses between cortical pyramidal neurons exhibit a rapid depression of excitatory postsynaptic potentials for successive presynaptic spikes. Since depressing synapses do not transmit information on sustained presynaptic firing rates, it has been speculated that they are favorable for temporal coding. In this paper, we study the dynamical effects of depressing synapses on stimulus-induced transient synchronization in a simple network of inhibitory interneurons and excitatory neurons, assuming that the recurrent excitation is mediated by depressing synapses. This synchronization occurs in a temporal pattern which depends on a given stimulus. Since the presence of noise is always a potential hazard in temporal coding, we investigate the extent to which noise in stimuli influences the synchronization phenomena. It is demonstrated that depressing synapses greatly contribute to suppressing the influences of noise on the stimulus-specific temporal patterns of synchronous firing. The timing-based Hebbian learning revealed by physiological experiments is shown to stabilize the temporal patterns in cooperation with synaptic depression. Thus, the times at which synchronous firing occurs provides a reliable information representation in the presence of synaptic depression. Received: 5 July 2000 / Accepted in revised form: 12 January 2001     

Neurotransmission: emerging roles of endocannabinoids

Postsynaptic release of endocannabinoids can inhibit presynaptic neurotransmitter release on short and long timescales. This retrograde inhibition occurs at both excitatory and inhibitory synapses and may provide a mechanism for synaptic gain control, short-term associative plasticity, reduction of synaptic crosstalk, and metaplasticity.     

Spike timing-dependent plasticity and memory

Spike timing-dependent plasticity (STDP) is a bidirectional form of synaptic plasticity discovered about 30 years ago and based on the relative timing of pre- and post-synaptic spiking activity with a millisecond precision. STDP is thought to be involved in the formation of memory but the millisecond-precision spike-timing required for STDP is difficult to reconcile with the much slower timescales of behavioral learning. This review therefore aims to expose and discuss recent findings about i) the multiple STDP learning rules at both excitatory and inhibitory synapses in vitro, ii) the contribution of STDP-like synaptic plasticity in the formation of memory in vivo and iii) the implementation of STDP rules in artificial neural networks and memristive devices.     

GABA and neuroligin signaling: linking synaptic activity and adhesion in inhibitory synapse development

GABA-mediated synaptic inhibition is crucial in neural circuit operations. In mammalian brains, the development of inhibitory synapses and innervation patterns is often a prolonged postnatal process, regulated by neural activity. Emerging evidence indicates that gamma-aminobutyric acid (GABA) acts beyond inhibitory transmission and regulates inhibitory synapse development. Indeed, GABA(A) receptors not only function as chloride channels that regulate membrane voltage and conductance but also play structural roles in synapse maturation and stabilization. The link from GABA(A) receptors to postsynaptic and presynaptic adhesion is probably mediated, partly by neuroligin-reurexin interactions, which are potent in promoting GABAergic synapse formation. Therefore, similar to glutamate signaling at excitatory synapse, GABA signaling may coordinate maturation of presynaptic and postsynaptic sites at inhibitory synapses. Defining the many steps from GABA signaling to receptor trafficking/stability and neuroligin function will provide further mechanistic insights into activity-dependent development and possibly plasticity of inhibitory synapses.     

A neural network model of the cerebellar cortex performing dynamic associations

The present paper proposes a model which applies formal neural network modeling techniques to construct a theoretical representation of the cerebellar cortex and its performances in motor control. A schema that makes explicit use of propagation delays of neural signals, is introduced to describe the ability to store temporal sequences of patterns in the Golgi-granule cell system. A perceptron association is then performed on these sequences of patterns by the Purkinje cell layer. The model conforms with important biological constraints, such as the known excitatory or inhibitory nature of the various synapses. Also, as suggested by experimental evidence, the synaptic plasticity underlying the learning ability of the model, is confined to the parallel fiber — Purkinje cell synapses, and takes place under the control of the climbing fibers. The result is a neural network model, constructed according to the anatomy of the cerebellar cortex, and capable of learning and retrieval of temporal sequences of patterns. It provides a framework to represent and interpret properties of learning and control of movements by the cerebellum, and to assess the capacity of formal neural network techniques for modeling of real neural systems.     

Neuromuscular synapses on the dactyl opener muscle of the lobster Homarus americanus

The crustacean dactyl opener neuromuscular system has been studied extensively as a model system that exhibits several forms of synaptic plasticity. We report the ultrastructural features of the synapses on dactyl opener of the lobster (Homarus americanus) as determined by examination of serial thin sections. Several innervation sites supplied by an inhibitory motoneuron have been observed without nearby excitatory innervation, indicating that excitatory and inhibitory inputs to the muscle are not always closely matched. The ultrastructural features of the lobster synapses are generally similar to those described previously for the homologous crayfish muscle, with one major distinction: few dense bars are seen at the presynaptic membranes of these lobster synapses. The majority of the lobster neuromuscular synapses lack dense bars altogether, and the mean number of dense bars per synapse is relatively low. In view of the finding that the physiology of the lobster dactyl opener synapses is similar to that reported for crayfish, these ultrastructural observations suggest that the structural complexity of the synapses may not be a critical factor determining synaptic plasticity.This work was supported by funds from the University of Virginia Research and Development Committee.     

Excitatory and feed-forward inhibitory hippocampal synapses work synergistically as an adaptive filter of natural spike trains

Short-term synaptic plasticity (STP) is an important mechanism for modifying neural circuits during computation. Although STP is much studied, its role in the processing of complex natural spike patterns is unknown. Here we analyze the responses of excitatory and inhibitory hippocampal synapses to natural spike trains at near-physiological temperatures. Our results show that excitatory and inhibitory synapses express complementary sets of STP components that selectively change synaptic strength during epochs of high-frequency discharge associated with hippocampal place fields. In both types of synapses, synaptic strength rapidly alternates between a near-constant level during low activity and another near-constant, but elevated (for excitatory synapses) or reduced (for inhibitory synapses) level during high-frequency epochs. These history-dependent changes in synaptic strength are largely independent of the particular temporal pattern within the discharges, and occur concomitantly in the two types of synapses. When excitatory and feed-forward inhibitory synapses are co-activated within the hippocampal feed-forward circuit unit, the net effect of their complementary STP is an additional increase in the gain of excitatory synapses during high-frequency discharges via selective disinhibition. Thus, excitatory and feed-forward inhibitory hippocampal synapses in vitro act synergistically as an adaptive filter that operates in a switch-like manner and is selective for high-frequency epochs.     

Computational Consequences of Temporally Asymmetric Learning Rules: I. Differential Hebbian Learning

Temporally asymetric learning rules governing plastic changes in synaptic efficacy have recently been identified in physiological studies. In these rules, the exact timing of pre- and postsynaptic spikes is critical to the induced change of synaptic efficacy. The temporal learning rules treated in this article are approximately antisymmetric; the synaptic efficacy is enhanced if the postsynaptic spike follows the presynaptic spike by a few milliseconds, but the efficacy is depressed if the postsynaptic spike precedes the presynaptic spike. The learning dynamics of this rule are studied using a stochastic model neuron receiving a set of serially delayed inputs. The average change of synaptic efficacy due to the temporally antisymmetric learning rule is shown to yield differential Hebbian learning. These results are demonstrated with both mathematical analyses and computer simulations, and connections with theories of classical conditioning are discussed.     

Synaptic destabilization by neuronal Nogo-A

Formation and maintenance of a neuronal network is based on a balance between plasticity and stability of synaptic connections. Several molecules have been found to regulate the maintenance of excitatory synapses but nothing is known about the molecular mechanisms involved in synaptic stabilization versus disassembly at inhibitory synapses. Here, we demonstrate that Nogo-A, which is well known to be present in myelin and inhibit growth in the adult CNS, is present in inhibitory presynaptic terminals in cerebellar Purkinje cells at the time of Purkinje cell-Deep Cerebellar Nuclei (DCN) inhibitory synapse formation and is then downregulated during synapse maturation. We addressed the role of neuronal Nogo-A in synapse maturation by generating several mouse lines overexpressing Nogo-A, starting at postnatal ages and throughout adult life, specifically in cerebellar Purkinje cells and their terminals. The overexpression of Nogo-A induced a progressive disassembly, retraction and loss of the inhibitory Purkinje cell terminals. This led to deficits in motor learning and coordination in the transgenic mice. Prior to synapse disassembly, the overexpression of neuronal Nogo-A led to the downregulation of the synaptic scaffold proteins spectrin, spectrin-E and β-catenin in the postsynaptic neurons. Our data suggest that neuronal Nogo-A might play a role in the maintenance of inhibitory synapses by modulating the expression of synaptic anchoring molecules. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Long-term plasticity at inhibitory synapses

Experience-dependent modifications of neural circuits and function are believed to heavily depend on changes in synaptic efficacy such as LTP/LTD. Hence, much effort has been devoted to elucidating the mechanisms underlying these forms of synaptic plasticity. Although most of this work has focused on excitatory synapses, it is now clear that diverse mechanisms of long-term inhibitory plasticity have evolved to provide additional flexibility to neural circuits. By changing the excitatory/inhibitory balance, GABAergic plasticity can regulate excitability, neural circuit function and ultimately, contribute to learning and memory, and neural circuit refinement. Here we discuss recent advancements in our understanding of the mechanisms and functional relevance of GABAergic inhibitory synaptic plasticity.