Endocannabinoid-dependent neocortical layer-5 LTD in the absence of postsynaptic spiking

Lipids shape brain function through ion channel and receptor modulations: physiological mechanisms and clinical perspectives

Lipids represent the most abundant molecular type in the brain, with a fat content of ∼60% of the dry brain weight in humans. Despite this fact, little attention has been paid to circumscribe the dynamic role of lipids in brain function and disease. Membrane lipids such as cholesterol, phosphoinositide, sphingolipids, arachidonic acid, and endocannabinoids finely regulate both synaptic receptors and ion channels that ensure critical neural functions. After a brief introduction on brain lipids and their respective properties, we review here their role in regulating synaptic function and ion channel activity, action potential propagation, neuronal development, and functional plasticity and their contribution in the development of neurological and neuropsychiatric diseases. We also provide possible directions for future research on lipid function in brain plasticity and diseases.

Neural spiking for causal inference and learning

When a neuron is driven beyond its threshold, it spikes. The fact that it does not communicate its continuous membrane potential is usually seen as a computational liability. Here we show that this spiking mechanism allows neurons to produce an unbiased estimate of their causal influence, and a way of approximating gradient descent-based learning. Importantly, neither activity of upstream neurons, which act as confounders, nor downstream non-linearities bias the results. We show how spiking enables neurons to solve causal estimation problems and that local plasticity can approximate gradient descent using spike discontinuity learning.

The plasticitome of cortical interneurons

Hebb postulated that, to store information in the brain, assemblies of excitatory neurons coding for a percept are bound together via associative long-term synaptic plasticity. In this view, it is unclear what role, if any, is carried out by inhibitory interneurons. Indeed, some have argued that inhibitory interneurons are not plastic. Yet numerous recent studies have demonstrated that, similar to excitatory neurons, inhibitory interneurons also undergo long-term plasticity. Here, we discuss the many diverse forms of long-term plasticity that are found at inputs to and outputs from several types of cortical inhibitory interneuron, including their plasticity of intrinsic excitability and their homeostatic plasticity. We explain key plasticity terminology, highlight key interneuron plasticity mechanisms, extract overarching principles and point out implications for healthy brain functionality as well as for neuropathology. We introduce the concept of the plasticitome - the synaptic plasticity counterpart to the genome or the connectome - as well as nomenclature and definitions for dealing with this rich diversity of plasticity. We argue that the great diversity of interneuron plasticity rules is best understood at the circuit level, for example as a way of elucidating how the credit-assignment problem is solved in deep biological neural networks.

Functional Relevance of Endocannabinoid-Dependent Synaptic Plasticity in the Central Nervous System

The endocannabinoid (eCB) signaling system plays a key role in short-term and long-term synaptic plasticity in brain regions involved in various neural functions ranging from action selection to appetite control. This review will explore the role of eCBs in shaping neural circuit function to regulate behaviors. In particular, we will discuss the behavioral consequences of eCB mediated long-term synaptic plasticity in different brain regions. This review brings together evidence from in vitro and ex vivo studies and points out the need for more in vivo studies.

A memristive plasticity model of voltage-based STDP suitable for recurrent bidirectional neural networks in the hippocampus

Memristive systems have gained considerable attention in the field of neuromorphic engineering, because they allow the emulation of synaptic functionality in solid state nano-physical systems. In this study, we show that memristive behavior provides a broad working framework for the phenomenological modelling of cellular synaptic mechanisms. In particular, we seek to understand how close a memristive system can account for the biological realism. The basic characteristics of memristive systems, i.e. voltage and memory behavior, are used to derive a voltage-based plasticity rule. We show that this model is suitable to account for a variety of electrophysiology plasticity data. Furthermore, we incorporate the plasticity model into an all-to-all connecting network scheme. Motivated by the auto-associative CA3 network of the hippocampus, we show that the implemented network allows the discrimination and processing of mnemonic pattern information, i.e. the formation of functional bidirectional connections resulting in the formation of local receptive fields. Since the presented plasticity model can be applied to real memristive devices as well, the presented theoretical framework can support both, the design of appropriate memristive devices for neuromorphic computing and the development of complex neuromorphic networks, which account for the specific advantage of memristive devices.

Robustness of STDP to spike timing jitter

In Hebbian plasticity, neural circuits adjust their synaptic weights depending on patterned firing. Spike-timing-dependent plasticity (STDP), a synaptic Hebbian learning rule, relies on the order and timing of the paired activities in pre- and postsynaptic neurons. Classically, in ex vivo experiments, STDP is assessed with deterministic (constant) spike timings and time intervals between successive pairings, thus exhibiting a regularity that differs from biological variability. Hence, STDP emergence from noisy inputs as occurring in in vivo-like firing remains unresolved. Here, we used noisy STDP pairings where the spike timing and/or interval between pairings were jittered. We explored with electrophysiology and mathematical modeling, the impact of jitter on three forms of STDP at corticostriatal synapses: NMDAR-LTP, endocannabinoid-LTD and endocannabinoid-LTP. We found that NMDAR-LTP was highly fragile to jitter, whereas endocannabinoid-plasticity appeared more resistant. When the frequency or number of pairings was increased, NMDAR-LTP became more robust and could be expressed despite strong jittering. Our results identify endocannabinoid-plasticity as a robust form of STDP, whereas the sensitivity to jitter of NMDAR-LTP varies with activity frequency. This provides new insights into the mechanisms at play during the different phases of learning and memory and the emergence of Hebbian plasticity in in vivo-like activity.

Activity-Dependent Downscaling of Subthreshold Synaptic Inputs during Slow-Wave-Sleep-like Activity In Vivo

Activity-dependent synaptic plasticity is critical for cortical circuit refinement. The synaptic homeostasis hypothesis suggests that synaptic connections are strengthened during wake and downscaled during sleep; however, it is not obvious how the same plasticity rules could explain both outcomes. Using whole-cell recordings and optogenetic stimulation of presynaptic input in urethane-anesthetized mice, which exhibit slow-wave-sleep (SWS)-like activity, we show that synaptic plasticity rules are gated by cortical dynamics in vivo. While Down states support conventional spike timing-dependent plasticity, Up states are biased toward depression such that presynaptic stimulation alone leads to synaptic depression, while connections contributing to postsynaptic spiking are protected against this synaptic weakening. We find that this novel activity-dependent and input-specific downscaling mechanism has two important computational advantages: (1) improved signal-to-noise ratio, and (2) preservation of previously stored information. Thus, these synaptic plasticity rules provide an attractive mechanism for SWS-related synaptic downscaling and circuit refinement.

Voltage dependence of synaptic plasticity is essential for rate based learning with short stimuli

In computational neuroscience, synaptic plasticity rules are often formulated in terms of firing rates. The predominant description of in vivo neuronal activity, however, is the instantaneous rate (or spiking probability). In this article we resolve this discrepancy by showing that fluctuations of the membrane potential carry enough information to permit a precise estimate of the instantaneous rate in balanced networks. As a consequence, we find that rate based plasticity rules are not restricted to neuronal activity that is stable for hundreds of milliseconds to seconds, but can be carried over to situations in which it changes every few milliseconds. We illustrate this, by showing that a voltage-dependent realization of the classical BCM rule achieves input selectivity, even if stimulus duration is reduced to a few milliseconds each.

Cortical Up states induce the selective weakening of subthreshold synaptic inputs

Slow-wave sleep is thought to be important for retuning cortical synapses, but the cellular mechanisms remain unresolved. During slow-wave activity, cortical neurons display synchronized transitions between depolarized Up states and hyperpolarized Down states. Here, using recordings from LIII pyramidal neurons from acute slices of mouse medial entorhinal cortex, we find that subthreshold inputs arriving during the Up state undergo synaptic weakening. This does not reflect a process of global synaptic downscaling, as it is dependent on presynaptic spiking, with network state encoded in the synaptically evoked spine Ca²⁺ responses. Our data indicate that the induction of synaptic weakening is under postsynaptic control, as it can be prevented by correlated postsynaptic spiking activity, and depends on postsynaptic NMDA receptors and GSK3β activity. This provides a mechanism by which slow-wave activity might bias synapses towards weakening, while preserving the synaptic connections within active neuronal assemblies.

Slow oscillations between cortical Up and Down states are a defining feature of deep sleep, but their function is not well understood. Here the authors study Up/Down states in acute slices of entorhinal cortex, and find that Up states promote the weakening of subthreshold synaptic inputs, while suprathreshold inputs are preserved or strengthened.

Demonstrating Hybrid Learning in a Flexible Neuromorphic Hardware System

We present results from a new approach to learning and plasticity in neuromorphic hardware systems: to enable flexibility in implementable learning mechanisms while keeping high efficiency associated with neuromorphic implementations, we combine a general-purpose processor with full-custom analog elements. This processor is operating in parallel with a fully parallel neuromorphic system consisting of an array of synapses connected to analog, continuous time neuron circuits. Novel analog correlation sensor circuits process spike events for each synapse in parallel and in real-time. The processor uses this pre-processing to compute new weights possibly using additional information following its program. Therefore, to a certain extent, learning rules can be defined in software giving a large degree of flexibility. Synapses realize correlation detection geared towards Spike-Timing Dependent Plasticity (STDP) as central computational primitive in the analog domain. Operating at a speed-up factor of 1000 compared to biological time-scale, we measure time-constants from tens to hundreds of micro-seconds. We analyze variability across multiple chips and demonstrate learning using a multiplicative STDP rule. We conclude that the presented approach will enable flexible and efficient learning as a platform for neuroscientific research and technological applications.

Adenosine Shifts Plasticity Regimes between Associative and Homeostatic by Modulating Heterosynaptic Changes

Endogenous extracellular adenosine level fluctuates in an activity-dependent manner and with sleep-wake cycle, modulating synaptic transmission and short-term plasticity. Hebbian-type long-term plasticity introduces intrinsic positive feedback on synaptic weight changes, making them prone to runaway dynamics. We previously demonstrated that co-occurring, weight-dependent heterosynaptic plasticity can robustly prevent runaway dynamics. Here we show that at neocortical synapses in slices from rat visual cortex, adenosine modulates the weight dependence of heterosynaptic plasticity: blockade of adenosine A₁ receptors abolished weight dependence, while increased adenosine level strengthened it. Using model simulations, we found that the strength of weight dependence determines the ability of heterosynaptic plasticity to prevent runaway dynamics of synaptic weights imposed by Hebbian-type learning. Changing the weight dependence of heterosynaptic plasticity within an experimentally observed range gradually shifted the operating point of neurons between an unbalancing regime dominated by associative plasticity and a homeostatic regime of tightly constrained synaptic changes. Because adenosine tone is a natural correlate of activity level (activity increases adenosine tone) and brain state (elevated adenosine tone increases sleep pressure), modulation of heterosynaptic plasticity by adenosine represents an endogenous mechanism that translates changes of the brain state into a shift of the regime of synaptic plasticity and learning. We speculate that adenosine modulation may provide a mechanism for fine-tuning of plasticity and learning according to brain state and activity.SIGNIFICANCE STATEMENT Associative learning depends on brain state and is impaired when the subject is sleepy or tired. However, the link between changes of brain state and modulation of synaptic plasticity and learning remains elusive. Here we show that adenosine regulates weight dependence of heterosynaptic plasticity: adenosine strengthened weight dependence of heterosynaptic plasticity; blockade of adenosine A1 receptors abolished it. In model neurons, such changes of the weight dependence of heterosynaptic plasticity shifted their operating point between regimes dominated by associative plasticity or by synaptic homeostasis. Because adenosine tone is a natural correlate of activity level and brain state, modulation of plasticity by adenosine represents an endogenous mechanism for translation of brain state changes into a shift of the regime of synaptic plasticity and learning.

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.

Unified pre- and postsynaptic long-term plasticity enables reliable and flexible learning

Although it is well known that long-term synaptic plasticity can be expressed both pre- and postsynaptically, the functional consequences of this arrangement have remained elusive. We show that spike-timing-dependent plasticity with both pre- and postsynaptic expression develops receptive fields with reduced variability and improved discriminability compared to postsynaptic plasticity alone. These long-term modifications in receptive field statistics match recent sensory perception experiments. Moreover, learning with this form of plasticity leaves a hidden postsynaptic memory trace that enables fast relearning of previously stored information, providing a cellular substrate for memory savings. Our results reveal essential roles for presynaptic plasticity that are missed when only postsynaptic expression of long-term plasticity is considered, and suggest an experience-dependent distribution of pre- and postsynaptic strength changes.

Layer-specific endocannabinoid-mediated long-term depression of GABAergic neurotransmission onto principal neurons in mouse visual cortex

Visually induced endocannabinoid-mediated long-term depression of GABAergic neurotransmission (iLTD) mediates the maturation of GABAergic release in layer 2/3 of visual cortex. Here we examined whether the maturation of GABAergic transmission in other layers of visual cortex also requires endocannabinoids. The developmental plasticity of GABAergic neurotransmission onto the principal neurons in different layers of mouse visual cortex was examined in cortical slices by whole-cell recordings of inhibitory postsynaptic currents evoked by presynaptic inhibitory inputs. Theta burst stimulation of GABAergic inputs induced an endocannabinoid-mediated long-term depression of GABAergic neurotransmission onto pyramidal cells in layer 2/3 from postnatal day (P)10 to 30 and in layer 5 from P10 to 40, whereas that of GABAergic inputs did not induce iLTD onto star pyramidal neurons in layer 4 at any time postnatally, indicating that this plasticity is laminar-specific. The developmental loss of iLTD paralleled the maturation of GABAergic inhibition in both layer 2/3 and layer 5. Visual deprivation delayed the developmental loss of iLTD in layers 3 and 5 during a critical period, while 2 days of light exposure eliminated iLTD in both layers. Furthermore, the GABAergic synapses in layers 2/3 and 5 did not normally mature in the type 1 cannabinoid receptor knock-out mice, whereas those in layer 4 did not require endocannabinoid receptor for maturation. These results suggest that visually induced endocannabinoid-dependent iLTD mediates the maturation of GABAergic release in extragranular layer rather than in granular layer of mouse visual cortex.

Memory labilization in reconsolidation and extinction--evidence for a common plasticity system?

Reconsolidation and extinction are two processes occurring upon memory retrieval that have received great attention in memory research over the last decade, partly due to their purported potential in the treatment of anxiety disorders. Due to their opposite behavioral effects, the two phenomena have usually been considered as separate entities, with few attempts to build a unified view of how both could be produced by similar mechanisms. Based on computational modeling, we have previously proposed that reconsolidation and extinction are behavioral outcomes of the same set of plasticity systems, albeit working at different synapses. One of these systems seems to be pharmacologically similar to the one involved in initial memory consolidation, and likely involves traditional Hebbian plasticity, while the second seems to be more involved with the labilization of existing memories and/or synaptic changes. In this article, we review the evidence for the existence of a plasticity system specifically involved in memory labilization, as well as its possible molecular requirements, anatomical substrates, synaptic mechanisms and physiological roles. Based on these data, we propose that the field of memory updating might ultimately benefit from a paradigm shift in which reconsolidation and extinction are viewed not as separate processes but as different instantiations of plasticity systems responsible for reinforcement and labilization of synaptic changes.

Presynaptic long-term depression mediated by Gi/o-coupled receptors

Long-term depression (LTD) of the efficacy of synaptic transmission is now recognized as an important mechanism for the regulation of information storage and the control of actions, as well as for synapse, neuron, and circuit development. Studies of LTD mechanisms have focused mainly on postsynaptic AMPA-type glutamate receptor trafficking. However, the focus has now expanded to include presynaptically expressed plasticity, the predominant form being initiated by presynaptically expressed Gi/o-coupled metabotropic receptor (Gi/o-GPCR) activation. Several forms of LTD involving activation of different presynaptic Gi/o-GPCRs as a 'common pathway' are described. We review here the literature on presynaptic Gi/o-GPCR-mediated LTD, discuss known mechanisms, gaps in our knowledge, and evaluate whether all Gi/o-GPCRs are capable of inducing presynaptic LTD.

Mossy fiber-evoked subthreshold responses induce timing-dependent plasticity at hippocampal CA3 recurrent synapses

Dentate granule cells exhibit exceptionally low levels of activity and rarely elicit action potentials in targeted CA3 pyramidal cells. It is thus unclear how such weak input from the granule cells sustains adequate levels of synaptic plasticity in the targeted CA3 network. We report that subthreshold potentials evoked by mossy fibers are sufficient to induce synaptic plasticity between CA3 pyramidal cells, thereby complementing the sparse action potential discharge. Repetitive pairing of a CA3-CA3 recurrent synaptic response with a subsequent subthreshold mossy fiber response induced long-term potentiation at CA3 recurrent synapses in rat hippocampus in vitro. Reversing the timing of the inputs induced long-term depression. The underlying mechanism depends on a passively conducted giant excitatory postsynaptic potential evoked by a mossy fiber that enhances NMDA receptor-mediated current at active CA3 recurrent synapses by relieving magnesium block. The resulting NMDA spike generates a supralinear depolarization that contributes to synaptic plasticity in hippocampal neuronal ensembles implicated in memory.

Input statistics and Hebbian cross-talk effects

As an extension of prior work, we studied inspecific Hebbian learning using the classical Oja model. We used a combination of analytical tools and numerical simulations to investigate how the effects of synaptic cross talk (which we also refer to as synaptic inspecificity) depend on the input statistics. We investigated a variety of patterns that appear in dimensions higher than two (and classified them based on covariance type and input bias). We found that the effects of cross talk on learning dynamics and outcome is highly dependent on the input statistics and that cross talk may lead in some cases to catastrophic effects on learning or development. Arbitrarily small levels of cross talk are able to trigger bifurcations in learning dynamics, or bring the system in close enough proximity to a critical state, to make the effects indistinguishable from a real bifurcation. We also investigated how cross talk behaves toward unbiased ("competitive") inputs and in which circumstances it can help the system productively resolve the competition. Finally, we discuss the idea that sophisticated neocortical learning requires accurate synaptic updates (similar to polynucleotide copying, which requires highly accurate replication). Since it is unlikely that the brain can completely eliminate cross talk, we support the proposal that is uses a neural mechanism that "proofreads" the accuracy of the updates, much as DNA proofreading lowers copying error rate.

The Convallis rule for unsupervised learning in cortical networks

The phenomenology and cellular mechanisms of cortical synaptic plasticity are becoming known in increasing detail, but the computational principles by which cortical plasticity enables the development of sensory representations are unclear. Here we describe a framework for cortical synaptic plasticity termed the "Convallis rule", mathematically derived from a principle of unsupervised learning via constrained optimization. Implementation of the rule caused a recurrent cortex-like network of simulated spiking neurons to develop rate representations of real-world speech stimuli, enabling classification by a downstream linear decoder. Applied to spike patterns used in in vitro plasticity experiments, the rule reproduced multiple results including and beyond STDP. However STDP alone produced poorer learning performance. The mathematical form of the rule is consistent with a dual coincidence detector mechanism that has been suggested by experiments in several synaptic classes of juvenile neocortex. Based on this confluence of normative, phenomenological, and mechanistic evidence, we suggest that the rule may approximate a fundamental computational principle of the neocortex.

Presynaptic long-term plasticity

Long-term synaptic plasticity is a major cellular substrate for learning, memory, and behavioral adaptation. Although early examples of long-term synaptic plasticity described a mechanism by which postsynaptic signal transduction was potentiated, it is now apparent that there is a vast array of mechanisms for long-term synaptic plasticity that involve modifications to either or both the presynaptic terminal and postsynaptic site. In this article, we discuss current and evolving approaches to identify presynaptic mechanisms as well as discuss their limitations. We next provide examples of the diverse circuits in which presynaptic forms of long-term synaptic plasticity have been described and discuss the potential contribution this form of plasticity might add to circuit function. Finally, we examine the present evidence for the molecular pathways and cellular events underlying presynaptic long-term synaptic plasticity.

A calcium-based simple model of multiple spike interactions in spike-timing-dependent plasticity

Spike-timing-dependent plasticity (STDP) is a form of synaptic modification that depends on the relative timings of presynaptic and postsynaptic spikes. In this letter, we proposed a calcium-based simple STDP model, described by an ordinary differential equation having only three state variables: one represents the density of intracellular calcium, one represents a fraction of open state NMDARs, and one represents the synaptic weight. We shown that in spite of its simplicity, the model can reproduce the properties of the plasticity that have been experimentally measured in various brain areas (e.g., layer 2/3 and 5 visual cortical slices, hippocampal cultures, and layer 2/3 somatosensory cortical slices) with respect to various patterns of presynaptic and postsynaptic spikes. In addition, comparisons with other STDP models are made, and the significance and advantages of the proposed model are discussed.

Presynaptic self-depression at developing neocortical synapses

A central tenet of most theories of synaptic modification during cortical development is that correlated activity drives plasticity in synaptically connected neurons. Unexpectedly, however, using sensory-evoked activity patterns recorded from the developing mouse cortex in vivo, the synaptic learning rule that we uncover here relies solely on the presynaptic neuron. A burst of three presynaptic spikes followed, within a restricted time window, by a single presynaptic spike induces robust long-term depression (LTD) at developing layer 4 to layer 2/3 synapses. This presynaptic spike pattern-dependent LTD (p-LTD) can be induced by individual presynaptic layer 4 cells, requires presynaptic NMDA receptors and calcineurin, and is expressed presynaptically. However, in contrast to spike timing-dependent LTD, p-LTD is independent of postsynaptic and astroglial signaling. This spike pattern-dependent learning rule complements timing-based rules and is likely to play a role in the pruning of synaptic input during cortical development.

Presynaptic NMDA receptors: are they dendritic receptors in disguise?

The N-methyl-D-aspartate (NMDA) receptor plays an essential role in excitatory transmission, synaptic integration, and learning and memory. In the classical view, postsynaptic NMDA receptors act as canonical coincidence detectors providing a 'molecular switch' for the induction of various forms of short- and long-term synaptic plasticity. Over the past twenty years there has been accumulating evidence to suggest that NMDA receptors are also expressed presynaptically and are involved in the regulation of synaptic transmission and specific forms of activity-dependent plasticity in developing neural circuits. However, the existence of presynaptic NMDA receptors remains a contentious issue. In this review, I will discuss the criteria required for identifying functional presynaptic receptors, novel methods for probing NMDA receptor function, and recent evidence to suggest that NMDA receptors are expressed at presynaptic sites in a target-specific manner.

New rules governing synaptic plasticity in core nucleus accumbens medium spiny neurons

The nucleus accumbens is a forebrain region responsible for drug reward and goal-directed behaviors. It has long been believed that drugs of abuse exert their addictive properties on behavior by altering the strength of synaptic communication over long periods of time. To date, attempts at understanding the relationship between drugs of abuse and synaptic plasticity have relied on the high-frequency long-term potentiation model of T.V. Bliss & T. Lømo [(1973) Journal of Physiology, 232, 331-356]. We examined synaptic plasticity using spike-timing-dependent plasticity, a stimulation paradigm that reflects more closely the in vivo firing patterns of mouse core nucleus accumbens medium spiny neurons and their afferents. In contrast to other brain regions, the same stimulation paradigm evoked bidirectional long-term plasticity. The magnitude of spike-timing-dependent long-term potentiation (tLTP) changed with the delay between action potentials and excitatory post-synaptic potentials, and frequency, whereas that of spike-timing-dependent long-term depression (tLTD) remained unchanged. We showed that tLTP depended on N-methyl-d-aspartate receptors, whereas tLTD relied on action potentials. Importantly, the intracellular calcium signaling pathways mobilised during tLTP and tLTD were different. Thus, calcium-induced calcium release underlies tLTD but not tLTP. Finally, we found that the firing pattern of a subset of medium spiny neurons was strongly inhibited by dopamine receptor agonists. Surprisingly, these neurons were exclusively associated with tLTP but not with tLTD. Taken together, these data point to the existence of two subgroups of medium spiny neurons with distinct properties, each displaying unique abilities to undergo synaptic plasticity.

A history of spike-timing-dependent plasticity

How learning and memory is achieved in the brain is a central question in neuroscience. Key to today's research into information storage in the brain is the concept of synaptic plasticity, a notion that has been heavily influenced by Hebb's (1949) postulate. Hebb conjectured that repeatedly and persistently co-active cells should increase connective strength among populations of interconnected neurons as a means of storing a memory trace, also known as an engram. Hebb certainly was not the first to make such a conjecture, as we show in this history. Nevertheless, literally thousands of studies into the classical frequency-dependent paradigm of cellular learning rules were directly inspired by the Hebbian postulate. But in more recent years, a novel concept in cellular learning has emerged, where temporal order instead of frequency is emphasized. This new learning paradigm - known as spike-timing-dependent plasticity (STDP) - has rapidly gained tremendous interest, perhaps because of its combination of elegant simplicity, biological plausibility, and computational power. But what are the roots of today's STDP concept? Here, we discuss several centuries of diverse thinking, beginning with philosophers such as Aristotle, Locke, and Ribot, traversing, e.g., Lugaro's plasticità and Rosenblatt's perceptron, and culminating with the discovery of STDP. We highlight interactions between theoretical and experimental fields, showing how discoveries sometimes occurred in parallel, seemingly without much knowledge of the other field, and sometimes via concrete back-and-forth communication. We point out where the future directions may lie, which includes interneuron STDP, the functional impact of STDP, its mechanisms and its neuromodulatory regulation, and the linking of STDP to the developmental formation and continuous plasticity of neuronal networks.

Endocannabinoid signaling in neural plasticity

Plasticity refers to a physiologically measured change that may last for short or long periods of time. Endocannabinoids (ECBs) are prevalent throughout most of the brain, and modulate synaptic transmission in many ways. This chapter will focus on the roles of ECBs in neural plasticity in the mammalian brain. The topics covered can be divided loosely into two themes: how ECBs regulate synaptic plasticity, and how ECBs' actions themselves are regulated by neuronal activity. Because ECBs regulate synaptic plasticity, the modifiability of ECB mobilization constitutes a form of "metaplasticity" (as reported by Abraham and Bear (Trends Neurosci 19:126-130, 1996)), i.e., an upstream process that determines the nature and extent of synaptic plasticity. Many of their basic functions are still being discovered, and while there is consensus on large issues, many points of divergence exist as well. This chapter concentrates on developments in the roles of ECBs in synaptic plasticity that have come to light since the major review by Chevaleyre et al. (Annu Rev Neurosci 29:37-76, 2006).

Pyramidal neuron conductance state gates spike-timing-dependent plasticity

Neocortical neurons in vivo process each of their individual inputs in the context of ongoing synaptic background activity, produced by the thousands of presynaptic partners a typical neuron has. Previous work has shown that background activity affects multiple aspects of neuronal and network function. However, its effect on the induction of spike-timing dependent plasticity (STDP) is not clear. Here we report that injections of simulated background conductances (produced by a dynamic-clamp system) into pyramidal cells in rat brain slices selectively reduced the magnitude of timing-dependent synaptic potentiation while leaving the magnitude of timing-dependent synaptic depression unchanged. The conductance-dependent suppression also sharpened the STDP curve, with reliable synaptic potentiation induced only when EPSPs and action potentials (APs) were paired within 8 ms of each other. Dual somatic and dendritic patch recordings suggested that the deficit in synaptic potentiation arose from shunting of dendritic EPSPs and APs. Using a biophysically detailed computational model, we were not only able to replicate the conductance-dependent shunting of dendritic potentials, but show that synaptic background can truncate calcium dynamics within dendritic spines in a way that affects potentiation more strongly than depression. This conductance-dependent regulation of synaptic plasticity may constitute a novel homeostatic mechanism that can prevent the runaway synaptic potentiation to which Hebbian networks are vulnerable.

A Re-Examination of Hebbian-Covariance Rules and Spike Timing-Dependent Plasticity in Cat Visual Cortex in vivo

Spike timing-dependent plasticity (STDP) is considered as an ubiquitous rule for associative plasticity in cortical networks in vitro. However, limited supporting evidence for its functional role has been provided in vivo. In particular, there are very few studies demonstrating the co-occurrence of synaptic efficiency changes and alteration of sensory responses in adult cortex during Hebbian or STDP protocols. We addressed this issue by reviewing and comparing the functional effects of two types of cellular conditioning in cat visual cortex. The first one, referred to as the “covariance” protocol, obeys a generalized Hebbian framework, by imposing, for different stimuli, supervised positive and negative changes in covariance between postsynaptic and presynaptic activity rates. The second protocol, based on intracellular recordings, replicated in vivo variants of the theta-burst paradigm (TBS), proven successful in inducing long-term potentiation in vitro. Since it was shown to impose a precise correlation delay between the electrically activated thalamic input and the TBS-induced postsynaptic spike, this protocol can be seen as a probe of causal (“pre-before-post”) STDP. By choosing a thalamic region where the visual field representation was in retinotopic overlap with the intracellularly recorded cortical receptive field as the afferent site for supervised electrical stimulation, this protocol allowed to look for possible correlates between STDP and functional reorganization of the conditioned cortical receptive field. The rate-based “covariance protocol” induced significant and large amplitude changes in receptive field properties, in both kitten and adult V1 cortex. The TBS STDP-like protocol produced in the adult significant changes in the synaptic gain of the electrically activated thalamic pathway, but the statistical significance of the functional correlates was detectable mostly at the population level. Comparison of our observations with the literature leads us to re-examine the experimental status of spike timing-dependent potentiation in adult cortex. We propose the existence of a correlation-based threshold in vivo, limiting the expression of STDP-induced changes outside the critical period, and which accounts for the stability of synaptic weights during sensory cortical processing in the absence of attention or reward-gated supervision.

Mechanisms of induction and maintenance of spike-timing dependent plasticity in biophysical synapse models

We review biophysical models of synaptic plasticity, with a focus on spike-timing dependent plasticity (STDP). The common property of the discussed models is that synaptic changes depend on the dynamics of the intracellular calcium concentration, which itself depends on pre- and postsynaptic activity. We start by discussing simple models in which plasticity changes are based directly on calcium amplitude and dynamics. We then consider models in which dynamic intracellular signaling cascades form the link between the calcium dynamics and the plasticity changes. Both mechanisms of induction of STDP (through the ability of pre/postsynaptic spikes to evoke changes in the state of the synapse) and of maintenance of the evoked changes (through bistability) are discussed.

Voltage and Spike Timing Interact in STDP - A Unified Model

A phenomenological model of synaptic plasticity is able to account for a large body of experimental data on spike-timing-dependent plasticity (STDP). The basic ingredient of the model is the correlation of presynaptic spike arrival with postsynaptic voltage. The local membrane voltage is used twice: a first term accounts for the instantaneous voltage and the second one for a low-pass filtered voltage trace. Spike-timing effects emerge as a special case. We hypothesize that the voltage dependence can explain differential effects of STDP in dendrites, since the amplitude and time course of backpropagating action potentials or dendritic spikes influences the plasticity results in the model. The dendritic effects are simulated by variable choices of voltage time course at the site of the synapse, i.e., without an explicit model of the spatial structure of the neuron.

Dendritic synapse location and neocortical spike-timing-dependent plasticity

While it has been appreciated for decades that synapse location in the dendritic tree has a powerful influence on signal processing in neurons, the role of dendritic synapse location on the induction of long-term synaptic plasticity has only recently been explored. Here, we review recent work revealing how learning rules for spike-timing-dependent plasticity (STDP) in cortical neurons vary with the spatial location of synaptic input. A common principle appears to be that proximal synapses show conventional STDP, whereas distal inputs undergo plasticity according to novel learning rules. One crucial factor determining location-dependent STDP is the backpropagating action potential, which tends to decrease in amplitude and increase in width as it propagates into the dendritic tree of cortical neurons. We discuss additional location-dependent mechanisms as well as the functional implications of heterogeneous learning rules at different dendritic locations for the organization of synaptic inputs.

Distinct coincidence detectors govern the corticostriatal spike timing-dependent plasticity

Corticostriatal projections constitute the main input to the basal ganglia, an ensemble of interconnected subcortical nuclei involved in procedural learning. Thus, long-term plasticity at corticostriatal synapses would provide a basic mechanism for the function of basal ganglia in learning and memory. We had previously reported the existence of a corticostriatal anti-Hebbian spike timing-dependent plasticity (STDP) at synapses onto striatal output neurons, the medium-sized spiny neurons. Here, we show that the blockade of GABAergic transmission reversed the time dependence of corticostriatal STDP. We explored the receptors and signalling mechanisms involved in the corticostriatal STDP. Although classical models for STDP propose NMDA receptors as the unique coincidence detector, the involvement of multiple coincidence detectors has also been demonstrated. Here, we show that corticostriatal STDP depends on distinct coincidence detectors. Specifically, long-term potentiation is dependent on NMDA receptor activation, while long-term depression requires distinct coincidence detectors: the phospholipase Cbeta (PLCbeta) and the inositol-trisphosphate receptor (IP3R)-gated calcium stores. Furthermore, we found that PLCbeta activation is controlled by group-I metabotropic glutamate receptors, type-1 muscarinic receptors and voltage-sensitive calcium channel activities. Activation of PLCbeta and IP3Rs leads to robust retrograde endocannabinoid signalling mediated by 2-arachidonoyl-glycerol and cannabinoid CB1 receptors. Interestingly, the same coincidence detectors govern the corticostriatal anti-Hebbian STDP and the Hebbian STDP reported at cortical synapses. Therefore, LTP and LTD induced by STDP at corticostriatal synapses are mediated by independent signalling mechanisms, each one being controlled by distinct coincidence detectors.

Temporal modulation of spike-timing-dependent plasticity

Spike-timing-dependent plasticity (STDP) has attracted considerable experimental and theoretical attention over the last decade. In the most basic formulation, STDP provides a fundamental unit – a spike pair – for quantifying the induction of long-term changes in synaptic strength. However, many factors, both pre- and postsynaptic, can affect synaptic transmission and integration, especially when multiple spikes are considered. Here we review the experimental evidence for multiple types of nonlinear temporal interactions in STDP, focusing on the contributions of individual spike pairs, overall spike rate, and precise spike timing for modification of cortical and hippocampal excitatory synapses. We discuss the underlying processes that determine the specific learning rules at different synapses, such as postsynaptic excitability and short-term depression. Finally, we describe the success of efforts toward building predictive, quantitative models of how complex and natural spike trains induce long-term synaptic modifications.

Spike-timing dependent plasticity in the striatum

The striatum is the major input nucleus of basal ganglia, an ensemble of interconnected sub-cortical nuclei associated with fundamental processes of action-selection and procedural learning and memory. The striatum receives afferents from the cerebral cortex and the thalamus. In turn, it relays the integrated information towards the basal ganglia output nuclei through which it operates a selected activation of behavioral effectors. The striatal output neurons, the GABAergic medium-sized spiny neurons (MSNs), are in charge of the detection and integration of behaviorally relevant information. This property confers to the striatum the ability to extract relevant information from the background noise and select cognitive-motor sequences adapted to environmental stimuli. As long-term synaptic efficacy changes are believed to underlie learning and memory, the corticostriatal long-term plasticity provides a fundamental mechanism for the function of the basal ganglia in procedural learning. Here, we reviewed the different forms of spike-timing dependent plasticity (STDP) occurring at corticostriatal synapses. Most of the studies have focused on MSNs and their ability to develop long-term plasticity. Nevertheless, the striatal interneurons (the fast-spiking GABAergic, NO-synthase and cholinergic interneurons) also receive monosynaptic afferents from the cortex and tightly regulated corticostriatal information processing. Therefore, it is important to take into account the variety of striatal neurons to fully understand the ability of striatum to develop long-term plasticity. Corticostriatal STDP with various spike-timing dependence have been observed depending on the neuronal sub-populations and experimental conditions. This complexity highlights the extraordinary potentiality in term of plasticity of the corticostriatal pathway.

Endocannabinoid signaling and long-term synaptic plasticity

Endocannabinoids (eCBs) are key activity-dependent signals regulating synaptic transmission throughout the central nervous system. Accordingly, eCBs are involved in neural functions ranging from feeding homeostasis to cognition. There is great interest in understanding how exogenous (e.g., cannabis) and endogenous cannabinoids affect behavior. Because behavioral adaptations are widely considered to rely on changes in synaptic strength, the prevalence of eCB-mediated long-term depression (eCB-LTD) at synapses throughout the brain merits close attention. The induction and expression of eCB-LTD, although remarkably similar at various synapses, are controlled by an array of regulatory influences that we are just beginning to uncover. This complexity endows eCB-LTD with important computational properties, such as coincidence detection and input specificity, critical for higher CNS functions like learning and memory. In this article, we review the major molecular and cellular mechanisms underlying eCB-LTD, as well as the potential physiological relevance of this widespread form of synaptic plasticity.

Brief subthreshold events can act as Hebbian signals for long-term plasticity

BACKGROUND

Action potentials are thought to be determinant for the induction of long-term synaptic plasticity, the cellular basis of learning and memory. However, neuronal activity does not lead systematically to an action potential but also, in many cases, to synaptic depolarizing subthreshold events. This is particularly exemplified in corticostriatal information processing. Indeed, the striatum integrates information from the whole cerebral cortex and, due to the membrane properties of striatal medium spiny neurons, cortical inputs do not systematically trigger an action potential but a wide range of subthreshold postsynaptic depolarizations. Accordingly, we have addressed the following question: does a brief subthreshold event act as a Hebbian signal and induce long-term synaptic efficacy changes?

METHODOLOGY/PRINCIPAL FINDINGS

Here, using perforated patch-clamp recordings on rat brain corticostriatal slices, we demonstrate, that brief (30 ms) subthreshold depolarizing events in quasi-coincidence with presynaptic activity can act as Hebbian signals and are sufficient to induce long-term synaptic plasticity at corticostriatal synapses. This "subthreshold-depolarization dependent plasticity" (SDDP) induces strong, significant and bidirectional long-term synaptic efficacy changes at a very high occurrence (81%) for time intervals between pre- and postsynaptic stimulations (Deltat) of -110<Deltat<+110 ms. Such subthreshold depolarizations are able to induce robust long-term depression (cannabinoid type-1 receptor-activation dependent) as well as long-term potentiation (NMDA receptor-activation dependent).

CONCLUSION/SIGNIFICANCE

Our data show the existence of a robust, reliable and timing-dependent bidirectional long-term plasticity induced by brief subthreshold events paired with presynaptic activity. The existence of a subthreshold-depolarization dependent plasticity extends considerably, beyond the action potential, the neuron's capabilities to express long-term synaptic efficacy changes.

Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks. I. Input selectivity--strengthening correlated input pathways

Spike-timing-dependent plasticity (STDP) determines the evolution of the synaptic weights according to their pre- and post-synaptic activity, which in turn changes the neuronal activity. In this paper, we extend previous studies of input selectivity induced by (STDP) for single neurons to the biologically interesting case of a neuronal network with fixed recurrent connections and plastic connections from external pools of input neurons. We use a theoretical framework based on the Poisson neuron model to analytically describe the network dynamics (firing rates and spike-time correlations) and thus the evolution of the synaptic weights. This framework incorporates the time course of the post-synaptic potentials and synaptic delays. Our analysis focuses on the asymptotic states of a network stimulated by two homogeneous pools of "steady" inputs, namely Poisson spike trains which have fixed firing rates and spike-time correlations. The (STDP) model extends rate-based learning in that it can implement, at the same time, both a stabilization of the individual neuron firing rates and a slower weight specialization depending on the input spike-time correlations. When one input pathway has stronger within-pool correlations, the resulting synaptic dynamics induced by (STDP) are shown to be similar to those arising in the case of a purely feed-forward network: the weights from the more correlated inputs are potentiated at the expense of the remaining input connections.

Identification of the hippocampal input to medial prefrontal cortex in vitro

To delineate the cellular mechanisms underlying the function of medial prefrontal cortex (mPFC) networks, it is critical to understand how synaptic inputs from various afferents are integrated and drive neuronal activity in this region. Using a newly developed slice preparation, we were able to identify a bundle of axons that contain extraneocortical fibers projecting to neurons in the prelimbic cortex. The anatomical origin and functional connectivity of the identified fiber bundle were probed by in vivo track tracing in combination with optic and whole-cell recordings of neurons in layers 2/3 and 5/6. We demonstrate that the identified bundle contains afferent fibers primarily from the ventral hippocampus but does not include contributions from the mediodorsal nucleus of the thalamus, amygdala, or lateral hypothalamus/medial forebrain bundle. Further, we provide evidence that activation of this fiber bundle results in patterned activity of neurons in the mPFC, which is distinct from that of laminar stimulation of either the deep layers 5/6 or the superficial layer 1. Evoked excitatory postsynaptic potentials are monosynaptic and glutamatergic and exhibit bidirectional changes in synaptic efficacy in response to physiologically relevant induction protocols. These data provide the necessary groundwork for the characterization of the hippocampal pathway projecting to the mPFC.

Cannabinoid CB1 receptor-dependent long-term depression in autaptic excitatory neurons

Long-term depression (LTD) of synaptic signaling-lasting from tens of minutes to hours or longer-is a widespread form of synaptic plasticity in the brain. Neurons express diverse forms of LTD, including autaptic LTD (autLTD) observed in cultured hippocampal neurons, the mechanism of which remains unknown. We have recently reported that autaptic neurons express both endocannabinoid-mediated depolarization-induced suppression of excitation (DSE) and metabotropic suppression of excitation (MSE). We now report that activating cannabinoid CB(1) receptors is necessary for the induction of autLTD. Most surprisingly, CB(1) does not induce autLTD via the G(i/o) proteins typically activated by this receptor nor with G(s). Rather, the requirements of presynaptic phospholipase C and filled calcium stores suggest G(q). In autLTD, a 3- to 4-min activation of the receptor by the endocannabinoid 2-arachidonoyl glycerol leads to prolonged inhibition while leaving short-term inhibition (e.g., DSE) intact. autLTD requires activation of both metabo- and ionotropic glutamate receptors. autLTD also requires MEK/ERK activation. Under certain conditions, one or more DSE stimuli will elicit autLTD. It is becoming evident that cannabinoids mediate multiple forms of plasticity at a single synapse, stretching temporally from tens of seconds (DSE/MSE) to tens of minutes (autLTD) to hours (CB(1) desensitization). Our findings imply a remarkable flexibility for the cannabinoid signaling system whereby discrete mechanisms of CB(1) activation within a single neuron yield temporally and mechanistically distinct forms of plasticity.

Experimental and computational aspects of signaling mechanisms of spike-timing-dependent plasticity

STDP (spike-timing-dependent synaptic plasticity) is thought to be a synaptic learning rule that embeds spike-timing information into a specific pattern of synaptic strengths in neuronal circuits, resulting in a memory. STDP consists of bidirectional long-term changes in synaptic strengths. This process includes long-term potentiation and long-term depression, which are dependent on the timing of presynaptic and postsynaptic spikings. In this review, we focus on computational aspects of signaling mechanisms that induce and maintain STDP as a key step toward the definition of a general synaptic learning rule. In addition, we discuss the temporal and spatial aspects of STDP, and the requirement of a homeostatic mechanism of STDP in vivo.

Endocannabinoid-mediated control of synaptic transmission

The discovery of cannabinoid receptors and subsequent identification of their endogenous ligands (endocannabinoids) in early 1990s have greatly accelerated research on cannabinoid actions in the brain. Then, the discovery in 2001 that endocannabinoids mediate retrograde synaptic signaling has opened up a new era for cannabinoid research and also established a new concept how diffusible messengers modulate synaptic efficacy and neural activity. The last 7 years have witnessed remarkable advances in our understanding of the endocannabinoid system. It is now well accepted that endocannabinoids are released from postsynaptic neurons, activate presynaptic cannabinoid CB(1) receptors, and cause transient and long-lasting reduction of neurotransmitter release. In this review, we aim to integrate our current understanding of functions of the endocannabinoid system, especially focusing on the control of synaptic transmission in the brain. We summarize recent electrophysiological studies carried out on synapses of various brain regions and discuss how synaptic transmission is regulated by endocannabinoid signaling. Then we refer to recent anatomical studies on subcellular distribution of the molecules involved in endocannabinoid signaling and discuss how these signaling molecules are arranged around synapses. In addition, we make a brief overview of studies on cannabinoid receptors and their intracellular signaling, biochemical studies on endocannabinoid metabolism, and behavioral studies on the roles of the endocannabinoid system in various aspects of neural functions.

Input specificity and dependence of spike timing-dependent plasticity on preceding postsynaptic activity at unitary connections between neocortical layer 2/3 pyramidal cells

Layer 2/3 (L2/3) pyramidal cells receive excitatory afferent input both from neighbouring pyramidal cells and from cortical and subcortical regions. The efficacy of these excitatory synaptic inputs is modulated by spike timing-dependent plasticity (STDP). Here we report that synaptic connections between L2/3 pyramidal cell pairs are located proximal to the soma, at sites overlapping those of excitatory inputs from other cortical layers. Nevertheless, STDP at L2/3 pyramidal to pyramidal cell connections showed fundamental differences from known STDP rules at these neighbouring contacts. Coincident low-frequency pre- and postsynaptic activation evoked only LTD, independent of the order of the pre- and postsynaptic cell firing. This symmetric anti-Hebbian STDP switched to a typical Hebbian learning rule if a postsynaptic action potential train occurred prior to the presynaptic stimulation. Receptor dependence of LTD and LTP induction and their pre- or postsynaptic loci also differed from those at other L2/3 pyramidal cell excitatory inputs. Overall, we demonstrate a novel means to switch between STDP rules dependent on the history of postsynaptic activity. We also highlight differences in STDP at excitatory synapses onto L2/3 pyramidal cells which allow for input specific modulation of synaptic gain.

Sensory deprivation unmasks a PKA-dependent synaptic plasticity mechanism that operates in parallel with CaMKII

Calcium/calmodulin kinase II (CaMKII) is required for LTP and experience-dependent potentiation in the barrel cortex. Here, we find that whisker deprivation increases LTP in the layer IV to II/III pathway and that PKA antagonists block the additional LTP. No LTP was seen in undeprived CaMKII-T286A mice, but whisker deprivation again unmasked PKA-sensitive LTP. Infusion of a PKA agonist potentiated EPSPs in deprived wild-types and deprived CaMKII-T286A point mutants but not in undeprived animals of either genotype. The PKA-dependent potentiation mechanism was not present in GluR1 knockouts. Infusion of a PKA antagonist caused depression of EPSPs in undeprived but not deprived cortex. LTD was occluded by whisker deprivation and blocked by PKA manipulation, but not blocked by cannabinoid antagonists. NMDA receptor currents were unaffected by sensory deprivation. These results suggest that sensory deprivation causes synaptic depression by reversing a PKA-dependent process that may act via GluR1.

Dendritic excitability and synaptic plasticity

Most synaptic inputs are made onto the dendritic tree. Recent work has shown that dendrites play an active role in transforming synaptic input into neuronal output and in defining the relationships between active synapses. In this review, we discuss how these dendritic properties influence the rules governing the induction of synaptic plasticity. We argue that the location of synapses in the dendritic tree, and the type of dendritic excitability associated with each synapse, play decisive roles in determining the plastic properties of that synapse. Furthermore, since the electrical properties of the dendritic tree are not static, but can be altered by neuromodulators and by synaptic activity itself, we discuss how learning rules may be dynamically shaped by tuning dendritic function. We conclude by describing how this reciprocal relationship between plasticity of dendritic excitability and synaptic plasticity has changed our view of information processing and memory storage in neuronal networks.

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.

Signaling via CNS cannabinoid receptors

Because of the prominent psychoactive effects of cannabis and its preparations, much research has focused on the actions of cannabinoids, the primary psychoactive components of cannabis, on neuronal function. A convergence of research has identified (1) cannabinoid receptors, (2) endogenous compounds that activate these receptors (endocannabinoids), and (3) drugs that interact with these receptors and the proteins that synthesize and degrade the endocannabinoids. This review will first consider how endogenous cannabinoids signal through cannabinoid receptors and the various forms of synaptic plasticity mediated by endocannabinoids. Next the interactions between exogenous cannabinoids such as Delta9-tetrahydrocannabinol and endocannabinoids and endocannabinoid-mediated plasticity will be examined. Finally, a model will be presented that can explain the prominent psychoactivity of these plant-derived cannabinoids.

Neuromodulators control the polarity of spike-timing-dependent synaptic plasticity

Near coincidental pre- and postsynaptic action potentials induce associative long-term potentiation (LTP) or long-term depression (LTD), depending on the order of their timing. Here, we show that in visual cortex the rules of this spike-timing-dependent plasticity are not rigid, but shaped by neuromodulator receptors coupled to adenylyl cyclase (AC) and phospholipase C (PLC) signaling cascades. Activation of the AC and PLC cascades results in phosphorylation of postsynaptic glutamate receptors at sites that serve as specific "tags" for LTP and LTD. As a consequence, the outcome (i.e., whether LTP or LTD) of a given pattern of pre- and postsynaptic firing depends not only on the order of the timing, but also on the relative activation of neuromodulator receptors coupled to AC and PLC. These findings indicate that cholinergic and adrenergic neuromodulation associated with the behavioral state of the animal can control the gating and the polarity of cortical plasticity.

Reinforcement learning with modulated spike timing dependent synaptic plasticity

Spike timing-dependent synaptic plasticity (STDP) has emerged as the preferred framework linking patterns of pre- and postsynaptic activity to changes in synaptic strength. Although synaptic plasticity is widely believed to be a major component of learning, it is unclear how STDP itself could serve as a mechanism for general purpose learning. On the other hand, algorithms for reinforcement learning work on a wide variety of problems, but lack an experimentally established neural implementation. Here, we combine these paradigms in a novel model in which a modified version of STDP achieves reinforcement learning. We build this model in stages, identifying a minimal set of conditions needed to make it work. Using a performance-modulated modification of STDP in a two-layer feedforward network, we can train output neurons to generate arbitrarily selected spike trains or population responses. Furthermore, a given network can learn distinct responses to several different input patterns. We also describe in detail how this model might be implemented biologically. Thus our model offers a novel and biologically plausible implementation of reinforcement learning that is capable of training a neural population to produce a very wide range of possible mappings between synaptic input and spiking output.