Dopamine-endocannabinoid interactions mediate spike-timing-dependent potentiation in the striatum.
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ABSTRACT:
SUBMITTER: Xu H
PROVIDER: S-EPMC6175920 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Synaptic plasticity is a cardinal cellular mechanism for learning and memory. The endocannabinoid (eCB) system has emerged as a pivotal pathway for synaptic plasticity because of its widely characterized ability to depress synaptic transmission on short- and long-term scales. Recent reports indicate that eCBs also mediate potentiation of the synapse. However, it is not known how eCB signaling may support bidirectionality. Here, we combined electrophysiology experiments with mathematical modeling to question the mechanisms of eCB bidirectionality in spike-timing dependent plasticity (STDP) at corticostriatal synapses. We demonstrate that STDP outcome is controlled by eCB levels and dynamics: prolonged and moderate levels of eCB lead to eCB-mediated long-term depression (eCB-tLTD) while short and large eCB transients produce eCB-mediated long-term potentiation (eCB-tLTP). Moreover, we show that eCB-tLTD requires active calcineurin whereas eCB-tLTP necessitates the activity of presynaptic PKA. Therefore, just like glutamate or GABA, eCB form a bidirectional system to encode learning and memory.
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Project description:Spike timing-dependent synaptic plasticity (STDP) serves as a key cellular correlate of associative learning, which is facilitated by elevated attentional and emotional states involving activation of adrenergic signaling. At cellular levels, adrenergic signaling increases dendrite excitability, but the underlying mechanisms remain elusive. Here we show that activation of ?2-adrenoceptors promoted STD long-term synaptic potentiation at mouse hippocampal excitatory synapses by inactivating dendritic Kv1.1-containing potassium channels, which increased dendrite excitability and facilitated dendritic propagation of postsynaptic depolarization, potentially improving coincidental activation of pre- and postsynaptic terminals. We further demonstrate that adrenergic modulation of Kv1.1 was mediated by the signaling scaffold SAP97, which, through direct protein-protein interactions, escorts ?2 signaling to remove Kv1.1 from the dendrite surface. These results reveal a mechanism through which the postsynaptic signaling scaffolds bridge the aroused brain state to promote induction of synaptic plasticity and potentially to enhance spike timing and memory encoding.
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Project description:Most reinforcement learning models assume that the reward signal arrives after the activity that led to the reward, placing constraints on the possible underlying cellular mechanisms. Here we show that dopamine, a positive reinforcement signal, can retroactively convert hippocampal timing-dependent synaptic depression into potentiation. This effect requires functional NMDA receptors and is mediated in part through the activation of the cAMP/PKA cascade. Collectively, our results support the idea that reward-related signaling can act on a pre-established synaptic eligibility trace, thereby associating specific experiences with behaviorally distant, rewarding outcomes. This finding identifies a biologically plausible mechanism for solving the 'distal reward problem'.
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Project description:Spike timing-dependent long-term potentiation (t-LTP) is the embodiment of Donald Hebb's postulated rule for associative memory formation. Pre- and postsynaptic action potentials need to be precisely correlated in time to induce this form of synaptic plasticity. NMDA receptors have been proposed to detect correlated activity and to trigger synaptic plasticity. However, the slow kinetic of NMDA receptor currents is at odds with the millisecond precision of coincidence detection. Here we show that AMPA receptors are responsible for the extremely narrow time window for t-LTP induction. Furthermore, we visualized synergistic interactions between AMPA and NMDA receptors and back-propagating action potentials on the level of individual spines. Supralinear calcium signals were observed for spike timings that induced t-LTP and were most pronounced in spines well isolated from the dendrite. We conclude that AMPA receptors gate the induction of associative synaptic plasticity by regulating the temporal precision of coincidence detection.
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Project description:Endocannabinoids are important mediators of short- and long-term synaptic plasticity, but the mechanisms of endocannabinoid release have not been studied extensively outside the hippocampus and cerebellum. Here, we examined the mechanisms of endocannabinoid-mediated long-term depression (eCB-LTD) in the dorsal striatum, a brain region critical for motor control and reinforcement learning. Unlike other cell types, strong depolarization of medium spiny neurons was not sufficient to yield detectable endocannabinoid release. However, when paired with postsynaptic depolarization sufficient to activate L-type calcium channels, activation of postsynaptic metabotropic glutamate receptors (mGluRs), either by high-frequency tetanic stimulation or an agonist, induced eCB-LTD. Pairing bursts of afferent stimulation with brief subthreshold membrane depolarizations that mimicked down-state to up-state transitions also induced eCB-LTD, which not only required activation of mGluRs and L-type calcium channels but also was bidirectionally modulated by dopamine D2 receptors. Consistent with network models, these results demonstrate that dopamine regulates the induction of a Hebbian form of long-term synaptic plasticity in the striatum. However, this gating of plasticity by dopamine is accomplished via an unexpected mechanism involving the regulation of mGluR-dependent endocannabinoid release.
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Project description:Changes in basal ganglia plasticity at the corticostriatal and thalamostriatal levels are required for motor learning. Endocannabinoid-dependent long-term depression (eCB-LTD) is known to be a dominant form of synaptic plasticity expressed at these glutamatergic inputs; however, whether eCB-LTD can be induced at all inputs on all striatal neurons is still debatable. Using region-specific Cre mouse lines combined with optogenetic techniques, we directly investigated and distinguished between corticostriatal and thalamostriatal projections. We found that eCB-LTD was successfully induced at corticostriatal synapses, independent of postsynaptic striatal spiny projection neuron (SPN) subtype. Conversely, eCB-LTD was only nominally present at thalamostriatal synapses. This dichotomy was attributable to the minimal expression of cannabinoid type 1 (CB1) receptors on thalamostriatal terminals. Furthermore, coactivation of dopamine receptors on SPNs during LTD induction re-established SPN-subtype-dependent eCB-LTD. Altogether, our findings lay the groundwork for understanding corticostriatal and thalamostriatal synaptic plasticity and for striatal eCB-LTD in motor learning.
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Project description:Spike timing-dependent plasticity (STDP) is a strong candidate for an N-methyl-D-aspartate (NMDA) receptor-dependent form of synaptic plasticity that could underlie the development of receptive field properties in sensory neocortices. Whilst induction of timing-dependent long-term potentiation (t-LTP) requires postsynaptic NMDA receptors, timing-dependent long-term depression (t-LTD) requires the activation of presynaptic NMDA receptors at layer 4-to-layer 2/3 synapses in barrel cortex. Here we investigated the developmental profile of t-LTD at layer 4-to-layer 2/3 synapses of mouse barrel cortex and studied their NMDA receptor subunit dependence. Timing-dependent LTD emerged in the first postnatal week, was present during the second week and disappeared in the adult, whereas t-LTP persisted in adulthood. An antagonist at GluN2C/D subunit-containing NMDA receptors blocked t-LTD but not t-LTP. Conversely, a GluN2A subunit-preferring antagonist blocked t-LTP but not t-LTD. The GluN2C/D subunit requirement for t-LTD appears to be synapse specific, as GluN2C/D antagonists did not block t-LTD at horizontal cross-columnar layer 2/3-to-layer 2/3 synapses, which was blocked by a GluN2B antagonist instead. These data demonstrate an NMDA receptor subunit-dependent double dissociation of t-LTD and t-LTP mechanisms at layer 4-to-layer 2/3 synapses, and suggest that t-LTD is mediated by distinct molecular mechanisms at different synapses on the same postsynaptic neuron.
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Project description:Spike-timing-dependent plasticity (STDP) has been observed in many brain areas such as sensory cortices, where it is hypothesized to structure synaptic connections between neurons. Previous studies have demonstrated how STDP can capture spiking information at short timescales using specific input configurations, such as coincident spiking, spike patterns and oscillatory spike trains. However, the corresponding computation in the case of arbitrary input signals is still unclear. This paper provides an overarching picture of the algorithm inherent to STDP, tying together many previous results for commonly used models of pairwise STDP. For a single neuron with plastic excitatory synapses, we show how STDP performs a spectral analysis on the temporal cross-correlograms between its afferent spike trains. The postsynaptic responses and STDP learning window determine kernel functions that specify how the neuron "sees" the input correlations. We thus denote this unsupervised learning scheme as 'kernel spectral component analysis' (kSCA). In particular, the whole input correlation structure must be considered since all plastic synapses compete with each other. We find that kSCA is enhanced when weight-dependent STDP induces gradual synaptic competition. For a spiking neuron with a "linear" response and pairwise STDP alone, we find that kSCA resembles principal component analysis (PCA). However, plain STDP does not isolate correlation sources in general, e.g., when they are mixed among the input spike trains. In other words, it does not perform independent component analysis (ICA). Tuning the neuron to a single correlation source can be achieved when STDP is paired with a homeostatic mechanism that reinforces the competition between synaptic inputs. Our results suggest that neuronal networks equipped with STDP can process signals encoded in the transient spiking activity at the timescales of tens of milliseconds for usual STDP.
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Project description:Rhythmic activity has been associated with a wide range of cognitive processes including the encoding of sensory information, navigation, the transfer of information and others. Rhythmic activity in the brain has also been suggested to be used for multiplexing information. Multiplexing is the ability to transmit more than one signal via the same channel. Here we focus on frequency division multiplexing, in which different signals are transmitted in different frequency bands. Recent work showed that spike-timing-dependent plasticity (STDP) can facilitate the transfer of rhythmic activity downstream the information processing pathway. However, STDP has also been known to generate strong winner-take-all like competition between subgroups of correlated synaptic inputs. This competition between different rhythmicity channels, induced by STDP, may prevent the multiplexing of information. Thus, raising doubts whether STDP is consistent with the idea of multiplexing. This study explores whether STDP can facilitate the multiplexing of information across multiple frequency channels, and if so, under what conditions. We address this question in a modelling study, investigating the STDP dynamics of two populations synapsing downstream onto the same neuron in a feed-forward manner. Each population was assumed to exhibit rhythmic activity, albeit in a different frequency band. Our theory reveals that the winner-take-all like competitions between the two populations is limited, in the sense that different rhythmic populations will not necessarily fully suppress each other. Furthermore, we found that for a wide range of parameters, the network converged to a solution in which the downstream neuron responded to both rhythms. Yet, the synaptic weights themselves did not converge to a fixed point, rather remained dynamic. These findings imply that STDP can support the multiplexing of rhythmic information, and demonstrate how functionality (multiplexing of information) can be retained in the face of continuous remodeling of all the synaptic weights. The constraints on the types of STDP rules that can support multiplexing provide a natural test for our theory.
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Project description:Spike-timing dependent plasticity (STDP) is a widespread plasticity mechanism in the nervous system. The simplest description of STDP only takes into account pairs of pre- and postsynaptic spikes, with potentiation of the synapse when a presynaptic spike precedes a postsynaptic spike and depression otherwise. In light of experiments that explored a variety of spike patterns, the pair-based STDP model has been augmented to account for multiple pre- and postsynaptic spike interactions. As a result, a number of different "multi-spike" STDP models have been proposed based on different experimental observations. The behavior of these models at the population level is crucial for understanding mechanisms of learning and memory. The challenging balance between the stability of a population of synapses and their competitive modification is well studied for pair-based models, but it has not yet been fully analyzed for multi-spike models. Here, we address this issue through numerical simulations of an integrate-and-fire model neuron with excitatory synapses subject to STDP described by three different proposed multi-spike models. We also analytically calculate average synaptic changes and fluctuations about these averages. Our results indicate that the different multi-spike models behave quite differently at the population level. Although each model can produce synaptic competition in certain parameter regions, none of them induces synaptic competition with its originally fitted parameters. The dichotomy between synaptic stability and Hebbian competition, which is well characterized for pair-based STDP models, persists in multi-spike models. However, anti-Hebbian competition can coexist with synaptic stability in some models. We propose that the collective behavior of synaptic plasticity models at the population level should be used as an additional guideline in applying phenomenological models based on observations of single synapses.
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