The Role of Proteases in Hippocampal Synaptic Plasticity: Putting Together Small Pieces of a Complex Puzzle

Long-term synaptic plasticity in the hippocampus is thought to underlie the formation of certain forms of memory, including spatial memory. The early phase of long-term synaptic potentiation and synaptic depression depends on post-translational modifications of synaptic proteins, while protein synthesis is also required for the late-phase of both forms of synaptic plasticity (L-LTP and L-LTD). Numerous pieces of evidence show a role for different types of proteases in synaptic plasticity, further increasing the diversity of mechanisms involved in the regulation of the intracellular and extracellular protein content. The cleavage of extracellular proteins is coupled to changes in postsynaptic intracellular mechanisms, and additional alterations in this compartment result from the protease-mediated targeting of intracellular proteins. Both mechanisms contribute to initiate signaling cascades that drive downstream pathways coupled to synaptic plasticity. In this review we summarize the evidence pointing to a role for extracellular and intracellular proteases, with distinct specificities, in synaptic plasticity. Where in the cells the proteases are located, and how they are regulated is also discussed. The combined actions of proteases and translation mechanisms contribute to a tight control of the synaptic proteome relevant for long-term synaptic potentiation and synaptic depression in the hippocampus. Additional studies are required to elucidate the mechanisms whereby these changes in the synaptic proteome are related with plasticity phenomena.     

Cholinergic input uncouples Ca2+ changes from K+ conductance activation and amplifies intradendritic Ca2+ changes in hippocampal neurons

Muscarinic synaptic activation is known to be involved in cortical arousal as well as learning. Although simple increases in the electrical responsiveness of neurons might be the basis of arousal, the linkage of muscarinic transmission to the synaptic plasticity that might underlie learning is lacking. Most models of synaptic plasticity involve postsynaptic Ca2+ changes as a trigger for subsequent processes. We imaged muscarinic effects on free Ca2+ accumulation during intracellular recordings from CA3 pyramidal neurons in the guinea pig hippocampal slice. Muscarinic activation, either by repetitive stimulation of cholinergic fibers or by bath-applied carbachol, strongly increased intradendritic Ca2+ accumulation during directly evoked repetitive firing, in part by blocking a Ca(2+)-dependent K+ conductance. The effects of repetitive stimulation of cholinergic fibers were enhanced by the acetylcholine-esterase blocker eserine and blocked by the muscarinic antagonist atropine. These findings demonstrate a novel muscarinic reinforcement of Ca2+ changes during excitation, which are probably significant for synapse modification.     

Calcium stores and synaptic plasticity

Chemical transmission at central synapses is known to be highly plastic; the strength of synaptic connections can be modified bi-directionally as a result of activity at individual synapses. Long-term changes in synaptic efficacy, both increases and decreases, are thought to be involved in the development of the nervous system, and in ongoing changes in response to external cues such as during learning and addiction. Other, shorter lasting changes in synaptic transmission are also likely to be important in normal functioning of the CNS. Calcium mobilisation is an important step in multiple forms of plasticity and, although entry into neurones from the extracellular space is often the initial trigger for plasticity changes, release of calcium from intracellular stores also has an important part to play in a variety of forms of synaptic plasticity.     

Protein kinase signaling in synaptic plasticity and memory

The relay of extracellular signals into changes in cellular physiology involves a Byzantine array of intracellular signaling pathways, of which cytoplasmic protein kinases are a crucial component. In the nervous system, a great deal of effort has focused on understanding the conversion of patterns of synaptic activity into long-lasting changes in synaptic efficacy that are thought to underlie memory. The goal is both to understand synaptic plasticity mechanisms, such as long-term potentiation, at a molecular level and to understand the relationship of these synaptic mechanisms to behavioral memory. Although both involve the activation of multiple signaling pathways, recent studies are beginning to define discrete roles and mechanisms for individual kinases in the different temporal phases of both synaptic and behavioral plasticity.     

Long-lasting increases in intrinsic excitability triggered by inhibition

Although experience-dependent changes in neural circuits are commonly assumed to be mediated by synaptic plasticity, modifications of intrinsic excitability may serve as a complementary mechanism. In whole-cell recordings from spontaneously firing vestibular nucleus neurons, brief periods of inhibitory synaptic stimulation or direct membrane hyperpolarization triggered long-lasting increases in spontaneous firing rates and firing responses to intracellular depolarization. These increases in excitability, termed firing rate potentiation, were induced by decreases in intracellular calcium and expressed as reductions in the sensitivity to the BK-type calcium-activated potassium channel blocker iberiotoxin. Firing rate potentiation is a novel form of cellular plasticity that could contribute to motor learning in the vestibulo-ocular reflex.     

Active hippocampal networks undergo spontaneous synaptic modification

The brain is self-writable; as the brain voluntarily adapts itself to a changing environment, the neural circuitry rearranges its functional connectivity by referring to its own activity. How the internal activity modifies synaptic weights is largely unknown, however. Here we report that spontaneous activity causes complex reorganization of synaptic connectivity without any external (or artificial) stimuli. Under physiologically relevant ionic conditions, CA3 pyramidal cells in hippocampal slices displayed spontaneous spikes with bistable slow oscillations of membrane potential, alternating between the so-called UP and DOWN states. The generation of slow oscillations did not require fast synaptic transmission, but their patterns were coordinated by local circuit activity. In the course of generating spontaneous activity, individual neurons acquired bidirectional long-lasting synaptic modification. The spontaneous synaptic plasticity depended on a rise in intracellular calcium concentrations of postsynaptic cells, but not on NMDA receptor activity. The direction and amount of the plasticity varied depending on slow oscillation patterns and synapse locations, and thus, they were diverse in a network. Once this global synaptic refinement occurred, the same neurons now displayed different patterns of spontaneous activity, which in turn exhibited different levels of synaptic plasticity. Thus, active networks continuously update their internal states through ongoing synaptic plasticity. With computational simulations, we suggest that with this slow oscillation-induced plasticity, a recurrent network converges on a more specific state, compared to that with spike timing-dependent plasticity alone.     

Robustness of learning that is based on covariance-driven synaptic plasticity

It is widely believed that learning is due, at least in part, to long-lasting modifications of the strengths of synapses in the brain. Theoretical studies have shown that a family of synaptic plasticity rules, in which synaptic changes are driven by covariance, is particularly useful for many forms of learning, including associative memory, gradient estimation, and operant conditioning. Covariance-based plasticity is inherently sensitive. Even a slight mistuning of the parameters of a covariance-based plasticity rule is likely to result in substantial changes in synaptic efficacies. Therefore, the biological relevance of covariance-based plasticity models is questionable. Here, we study the effects of mistuning parameters of the plasticity rule in a decision making model in which synaptic plasticity is driven by the covariance of reward and neural activity. An exact covariance plasticity rule yields Herrnstein's matching law. We show that although the effect of slight mistuning of the plasticity rule on the synaptic efficacies is large, the behavioral effect is small. Thus, matching behavior is robust to mistuning of the parameters of the covariance-based plasticity rule. Furthermore, the mistuned covariance rule results in undermatching, which is consistent with experimentally observed behavior. These results substantiate the hypothesis that approximate covariance-based synaptic plasticity underlies operant conditioning. However, we show that the mistuning of the mean subtraction makes behavior sensitive to the mistuning of the properties of the decision making network. Thus, there is a tradeoff between the robustness of matching behavior to changes in the plasticity rule and its robustness to changes in the properties of the decision making network.     

Modulation of hippocampal plasticity in learning and memory

Synaptic plasticity plays a central role in the study of neural mechanisms of learning and memory. Plasticity rules are not invariant over time but are under neuromodulatory control, enabling behavioral states to influence memory formation. Neuromodulation controls synaptic plasticity at network level by directing information flow, at circuit level through changes in excitation/inhibition balance, and at synaptic level through modulation of intracellular signaling cascades. Although most research has focused on modulation of principal neurons, recent progress has uncovered important roles for interneurons in not only routing information, but also setting conditions for synaptic plasticity. Moreover, astrocytes have been shown to both gate and mediate plasticity. These additional mechanisms must be considered for a comprehensive mechanistic understanding of learning and memory.     

Pre- and postsynaptically expressed spike-timing-dependent plasticity contribute differentially to neuronal learning

A plethora of experimental studies have shown that long-term synaptic plasticity can be expressed pre- or postsynaptically depending on a range of factors such as developmental stage, synapse type, and activity patterns. The functional consequences of this diversity are not clear, although it is understood that whereas postsynaptic expression of plasticity predominantly affects synaptic response amplitude, presynaptic expression alters both synaptic response amplitude and short-term dynamics. In most models of neuronal learning, long-term synaptic plasticity is implemented as changes in connective weights. The consideration of long-term plasticity as a fixed change in amplitude corresponds more closely to post- than to presynaptic expression, which means theoretical outcomes based on this choice of implementation may have a postsynaptic bias. To explore the functional implications of the diversity of expression of long-term synaptic plasticity, we adapted a model of long-term plasticity, more specifically spike-timing-dependent plasticity (STDP), such that it was expressed either independently pre- or postsynaptically, or in a mixture of both ways. We compared pair-based standard STDP models and a biologically tuned triplet STDP model, and investigated the outcomes in a minimal setting, using two different learning schemes: in the first, inputs were triggered at different latencies, and in the second a subset of inputs were temporally correlated. We found that presynaptic changes adjusted the speed of learning, while postsynaptic expression was more efficient at regulating spike timing and frequency. When combining both expression loci, postsynaptic changes amplified the response range, while presynaptic plasticity allowed control over postsynaptic firing rates, potentially providing a form of activity homeostasis. Our findings highlight how the seemingly innocuous choice of implementing synaptic plasticity by single weight modification may unwittingly introduce a postsynaptic bias in modelling outcomes. We conclude that pre- and postsynaptically expressed plasticity are not interchangeable, but enable complimentary functions.     

Pain Facilitation and Activity-Dependent Plasticity in Pain Modulatory Circuitry: Role of BDNF-TrkB Signaling and NMDA Receptors

Pain modulatory circuitry in the brainstem exhibits considerable synaptic plasticity. The increased peripheral neuronal barrage after injury activates spinal projection neurons that then activate multiple chemical mediators including glutamatergic neurons at the brainstem level, leading to an increased synaptic strength and facilitatory output. It is not surprising that a well-established regulator of synaptic plasticity, brain-derived neurotrophic factor (BDNF), contributes to the mechanisms of descending pain facilitation. After tissue injury, BDNF and TrkB signaling in the brainstem circuitry is rapidly activated. Through the intracellular signaling cascade that involves phospholipase C, inositol trisphosphate, protein kinase C, and nonreceptor protein tyrosine kinases; N-methyl-D-aspartate (NMDA) receptors are phosphorylated, descending facilitatory drive is initiated, and behavioral hyperalgesia follows. The synaptic plasticity observed in the pain pathways shares much similarity with more extensively studied forms of synaptic plasticity such as long-term potentiation (LTP) and long-term depression (LTD), which typically express NMDA receptor dependency and regulation by trophic factors. However, LTP and LTD are experimental phenomena whose relationship to functional states of learning and memory has been difficult to prove. Although mechanisms of synaptic plasticity in pain pathways have typically not been related to LTP and LTD, pain pathways have an advantage as a model system for synaptic modifications as there are many well-established models of persistent pain with clear measures of the behavioral phenotype. Further studies will elucidate cellular and molecular mechanisms of pain sensitization and further our understanding of principles of central nervous system plasticity and responsiveness to environmental challenge.     

Role for calcium in heat shock-mediated synaptic thermoprotection in Drosophila larvae

Chemical synaptic transmission is the mechanism for fast, excitation-coupled information transfer between neurons. Previous work in larval Drosophila has shown that transmission at synaptic boutons is protected by heat shock exposure from subsequent thermal stress through pre- and postsynaptic modifications. This protective effect has been, at least partially, ascribed to an up-regulation in the inducible heat shock protein, hsp70. Effects of hsp70 are correlated with changes to intracellular calcium handling, and the dynamics of intracellular calcium regulate synaptic transmission. Consistent with such a relationship, synaptic plasticity increases at locust neuromuscular junctions following heat shock, suggesting an effect of heat shock on residual presynaptic calcium. Intracellular recording from single abdominal muscle fibers of Drosophila larvae showed that prior heat shock imparts thermoprotection by increasing the upper temperature limit for synaptic transmission. Heat shock exposure enhances short-term synaptic plasticity and increases its thermosensitivity. Increasing extracellular calcium levels eliminates the physiological differences between control and heat shock preparations; excess calcium itself induces thermoprotection at elevated concentrations. These data support the hypothesis that stress-induced neuroprotection at the nerve terminal acts, at least partially, through an alteration to the physiological effects of residual presynaptic calcium.     

Mirrored STDP Implements Autoencoder Learning in a Network of Spiking Neurons

The autoencoder algorithm is a simple but powerful unsupervised method for training neural networks. Autoencoder networks can learn sparse distributed codes similar to those seen in cortical sensory areas such as visual area V1, but they can also be stacked to learn increasingly abstract representations. Several computational neuroscience models of sensory areas, including Olshausen & Field’s Sparse Coding algorithm, can be seen as autoencoder variants, and autoencoders have seen extensive use in the machine learning community. Despite their power and versatility, autoencoders have been difficult to implement in a biologically realistic fashion. The challenges include their need to calculate differences between two neuronal activities and their requirement for learning rules which lead to identical changes at feedforward and feedback connections. Here, we study a biologically realistic network of integrate-and-fire neurons with anatomical connectivity and synaptic plasticity that closely matches that observed in cortical sensory areas. Our choice of synaptic plasticity rules is inspired by recent experimental and theoretical results suggesting that learning at feedback connections may have a different form from learning at feedforward connections, and our results depend critically on this novel choice of plasticity rules. Specifically, we propose that plasticity rules at feedforward versus feedback connections are temporally opposed versions of spike-timing dependent plasticity (STDP), leading to a symmetric combined rule we call Mirrored STDP (mSTDP). We show that with mSTDP, our network follows a learning rule that approximately minimizes an autoencoder loss function. When trained with whitened natural image patches, the learned synaptic weights resemble the receptive fields seen in V1. Our results use realistic synaptic plasticity rules to show that the powerful autoencoder learning algorithm could be within the reach of real biological networks.     

Emergence of Functional Specificity in Balanced Networks with Synaptic Plasticity

In rodent visual cortex, synaptic connections between orientation-selective neurons are unspecific at the time of eye opening, and become to some degree functionally specific only later during development. An explanation for this two-stage process was proposed in terms of Hebbian plasticity based on visual experience that would eventually enhance connections between neurons with similar response features. For this to work, however, two conditions must be satisfied: First, orientation selective neuronal responses must exist before specific recurrent synaptic connections can be established. Second, Hebbian learning must be compatible with the recurrent network dynamics contributing to orientation selectivity, and the resulting specific connectivity must remain stable for unspecific background activity. Previous studies have mainly focused on very simple models, where the receptive fields of neurons were essentially determined by feedforward mechanisms, and where the recurrent network was small, lacking the complex recurrent dynamics of large-scale networks of excitatory and inhibitory neurons. Here we studied the emergence of functionally specific connectivity in large-scale recurrent networks with synaptic plasticity. Our results show that balanced random networks, which already exhibit highly selective responses at eye opening, can develop feature-specific connectivity if appropriate rules of synaptic plasticity are invoked within and between excitatory and inhibitory populations. If these conditions are met, the initial orientation selectivity guides the process of Hebbian learning and, as a result, functionally specific and a surplus of bidirectional connections emerge. Our results thus demonstrate the cooperation of synaptic plasticity and recurrent dynamics in large-scale functional networks with realistic receptive fields, highlight the role of inhibition as a critical element in this process, and paves the road for further computational studies of sensory processing in neocortical network models equipped with synaptic plasticity.     

Modeling of Substance P and 5-HT Induced Synaptic Plasticity in the Lamprey Spinal CPG: Consequences for Network Pattern Generation

Consequences of synaptic plasticity in the lamprey spinal CPG are analyzed by means of simulations. This is motivated by the effects substance P (a tachykinin) and serotonin (5-hydroxytryptamin; 5-HT) have on synaptic transmission in the locomotor network. Activity-dependent synaptic depression and potentiation have recently been shown experimentally using paired intracellular recordings. Although normally activity-dependent plasticity presumably does not contribute to the patterning of network activity, this changes in the presence of the neuromodulators substance P and 5-HT, which evoke significant plasticity. Substance P can induce a faster and larger depression of inhibitory connections but potentiation of excitatory inputs, whereas 5-HT induces facilitation of both inhibitory and excitatory inputs. Changes in the amplitude of the first postsynaptic potential are also seen. These changes could thus be a potential mechanism underlying the modulatory role these substances have on the rhythmic network activity.The aim of the present study has been to implement the activity dependent synaptic depression and facilitation induced by substance P and 5-HT into two alternative models of the lamprey spinal locomotor network, one relying on reciprocal inhibition for bursting and one in which each hemicord is capable of oscillations. The consequences of the plasticity of inhibitory and excitatory connections are then explored on the network level.In the intact spinal cord, tachykinins and 5-HT, which can be endogenously released, increase and decrease the frequency of the alternating left-right burst pattern, respectively. The frequency decreasing effect of 5-HT has previously been explained based on its conductance decreasing effect on K _Ca underlying the postspike afterhyperpolarization (AHP). The present simulations show that short-term synaptic plasticity may have strong effects on frequency regulation in the lamprey spinal CPG. In the network model relying on reciprocal inhibition, the observed effects substance P and 5-HT have on network behavior (i.e., a frequency increase and decrease respectively) can to a substantial part be explained by their effects on the total extent and time dynamics of synaptic depression and facilitation. The cellular effects of these substances will in the 5-HT case further contribute to its network effect.     

Regulation of AMPA receptor trafficking and synaptic plasticity

AMPA receptors (AMPARs) mediate the majority of fast excitatory synaptic transmission in the brain. Dynamic changes in neuronal synaptic efficacy, termed synaptic plasticity, are thought to underlie information coding and storage in learning and memory. One major mechanism that regulates synaptic strength involves the tightly regulated trafficking of AMPARs into and out of synapses. The life cycle of AMPARs from their biosynthesis, membrane trafficking, and synaptic targeting to their degradation are controlled by a series of orchestrated interactions with numerous intracellular regulatory proteins. Here we review recent progress made toward the understanding the regulation of AMPAR trafficking, focusing on the roles of several key intracellular AMPAR interacting proteins.     

Phenomenological models of synaptic plasticity based on spike timing

Synaptic plasticity is considered to be the biological substrate of learning and memory. In this document we review phenomenological models of short-term and long-term synaptic plasticity, in particular spike-timing dependent plasticity (STDP). The aim of the document is to provide a framework for classifying and evaluating different models of plasticity. We focus on phenomenological synaptic models that are compatible with integrate-and-fire type neuron models where each neuron is described by a small number of variables. This implies that synaptic update rules for short-term or long-term plasticity can only depend on spike timing and, potentially, on membrane potential, as well as on the value of the synaptic weight, or on low-pass filtered (temporally averaged) versions of the above variables. We examine the ability of the models to account for experimental data and to fulfill expectations derived from theoretical considerations. We further discuss their relations to teacher-based rules (supervised learning) and reward-based rules (reinforcement learning). All models discussed in this paper are suitable for large-scale network simulations.     

Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity

The principles by which networks of neurons compute, and how spike-timing dependent plasticity (STDP) of synaptic weights generates and maintains their computational function, are unknown. Preceding work has shown that soft winner-take-all (WTA) circuits, where pyramidal neurons inhibit each other via interneurons, are a common motif of cortical microcircuits. We show through theoretical analysis and computer simulations that Bayesian computation is induced in these network motifs through STDP in combination with activity-dependent changes in the excitability of neurons. The fundamental components of this emergent Bayesian computation are priors that result from adaptation of neuronal excitability and implicit generative models for hidden causes that are created in the synaptic weights through STDP. In fact, a surprising result is that STDP is able to approximate a powerful principle for fitting such implicit generative models to high-dimensional spike inputs: Expectation Maximization. Our results suggest that the experimentally observed spontaneous activity and trial-to-trial variability of cortical neurons are essential features of their information processing capability, since their functional role is to represent probability distributions rather than static neural codes. Furthermore it suggests networks of Bayesian computation modules as a new model for distributed information processing in the cortex.