A calcium-based plasticity model for predicting long-term potentiation and depression in the neocortex.

Pyramidal cells (PCs) form the backbone of the layered structure of the neocortex, and plasticity of their synapses is thought to underlie learning in the brain. However, such long-term synaptic changes have been experimentally characterized between only a few types of PCs, posing a significant barrier for studying neocortical learning mechanisms. Here we introduce a model of synaptic plasticity based on data-constrained postsynaptic calcium dynamics, and show in a neocortical microcircuit model that a single parameter set is sufficient to unify the available experimental findings on long-term potentiation (LTP) and long-term depression (LTD) of PC connections. In particular, we find that the diverse plasticity outcomes across the different PC types can be explained by cell-type-specific synaptic physiology, cell morphology and innervation patterns, without requiring type-specific plasticity. Generalizing the model to in vivo extracellular calcium concentrations, we predict qualitatively different plasticity dynamics from those observed in vitro. This work provides a first comprehensive null model for LTP/LTD between neocortical PC types in vivo, and an open framework for further developing models of cortical synaptic plasticity.

Short-term depression and long-term plasticity together tune sensitive range of synaptic plasticity.

Synaptic efficacy is subjected to activity-dependent changes on short- and long time scales. While short-term changes decay over minutes, long-term modifications last from hours up to a lifetime and are thought to constitute the basis of learning and memory. Both plasticity mechanisms have been studied extensively but how their interaction shapes synaptic dynamics is little known. To investigate how both short- and long-term plasticity together control the induction of synaptic depression and potentiation, we used numerical simulations and mathematical analysis of a calcium-based model, where pre- and postsynaptic activity induces calcium transients driving synaptic long-term plasticity. We found that the model implementing known synaptic short-term dynamics in the calcium transients can be successfully fitted to long-term plasticity data obtained in visual- and somatosensory cortex. Interestingly, the impact of spike-timing and firing rate changes on plasticity occurs in the prevalent firing rate range, which is different in both cortical areas considered here. Our findings suggest that short- and long-term plasticity are together tuned to adapt plasticity to area-specific activity statistics such as firing rates.

Synergism of type 1 metabotropic and ionotropic glutamate receptors in cerebellar molecular layer interneurons in vivo.

Type 1 metabotropic glutamate receptors (mGluR1s) are key elements in neuronal signaling. While their function is well documented in slices, requirements for their activation in vivo are poorly understood. We examine this question in adult mice in vivo using 2-photon imaging of cerebellar molecular layer interneurons (MLIs) expressing GCaMP. In anesthetized mice, parallel fiber activation evokes beam-like Cai rises in postsynaptic MLIs which depend on co-activation of mGluR1s and ionotropic glutamate receptors (iGluRs). In awake mice, blocking mGluR1 decreases Cai rises associated with locomotion. In vitro studies and freeze-fracture electron microscopy show that the iGluR-mGluR1 interaction is synergistic and favored by close association of the two classes of receptors. Altogether our results suggest that mGluR1s, acting in synergy with iGluRs, potently contribute to processing cerebellar neuronal signaling under physiological conditions.

Beyond Freezing: Temporal Expectancy of an Aversive Event Engages the Amygdalo-Prefronto-Dorsostriatal Network.

During Pavlovian aversive conditioning, a neutral conditioned stimulus (CS) becomes predictive of the time of arrival of an aversive unconditioned stimulus (US). Using a paradigm where animals had to discriminate between a CS+ (associated with a footshock) and a CS- (never associated with a footshock), we show that, early in training, dynamics of neuronal oscillations in an amygdalo-prefronto-striatal network are modified during the CS+ in a manner related to the CS-US time interval (30 or 10 s). This is the case despite a generalized high level of freezing to both CS+ and CS-. The local field potential oscillatory power was decreased between 12 and 30 Hz in the dorsomedial striatum (DMS) and increased between 55 and 95 Hz in the prelimbic cortex (PL), while the coherence between DMS, PL, and the basolateral amygdala was increased in the 3-6 Hz frequency range up to the expected time of US arrival only for the CS+ and not for the CS-. Changing the CS-US interval from 30 to 10 s shifted these changes in activity toward the newly learned duration. The results suggest a functional role of the amygdalo-prefronto-dorsostriatal network in encoding temporal information of Pavlovian associations independently of the behavioral output.

Updating temporal expectancy of an aversive event engages striatal plasticity under amygdala control.

Pavlovian aversive conditioning requires learning of the association between a conditioned stimulus (CS) and an unconditioned, aversive stimulus (US) but also involves encoding the time interval between the two stimuli. The neurobiological bases of this time interval learning are unknown. Here, we show that in rats, the dorsal striatum and basal amygdala belong to a common functional network underlying temporal expectancy and learning of a CS-US interval. Importantly, changes in coherence between striatum and amygdala local field potentials (LFPs) were found to couple these structures during interval estimation within the lower range of the theta rhythm (3-6 Hz). Strikingly, we also show that a change to the CS-US time interval results in long-term changes in cortico-striatal synaptic efficacy under the control of the amygdala. Collectively, this study reveals physiological correlates of plasticity mechanisms of interval timing that take place in the striatum and are regulated by the amygdala.

Natural Firing Patterns Imply Low Sensitivity of Synaptic Plasticity to Spike Timing Compared with Firing Rate.

Synaptic plasticity is sensitive to the rate and the timing of presynaptic and postsynaptic action potentials. In experimental protocols inducing plasticity, the imposed spike trains are typically regular and the relative timing between every presynaptic and postsynaptic spike is fixed. This is at odds with firing patterns observed in the cortex of intact animals, where cells fire irregularly and the timing between presynaptic and postsynaptic spikes varies. To investigate synaptic changes elicited by in vivo-like firing, we used numerical simulations and mathematical analysis of synaptic plasticity models. We found that the influence of spike timing on plasticity is weaker than expected from regular stimulation protocols. Moreover, when neurons fire irregularly, synaptic changes induced by precise spike timing can be equivalently induced by a modest firing rate variation. Our findings bridge the gap between existing results on synaptic plasticity and plasticity occurring in vivo, and challenge the dominant role of spike timing in plasticity. SIGNIFICANCE STATEMENT: Synaptic plasticity, the change in efficacy of connections between neurons, is thought to underlie learning and memory. The dominant paradigm posits that the precise timing of neural action potentials (APs) is central for plasticity induction. This concept is based on experiments using highly regular and stereotyped patterns of APs, in stark contrast with natural neuronal activity. Using synaptic plasticity models, we investigated how irregular, in vivo-like activity shapes synaptic plasticity. We found that synaptic changes induced by precise timing of APs are much weaker than suggested by regular stimulation protocols, and can be equivalently induced by modest variations of the AP rate alone. Our results call into question the dominant role of precise AP timing for plasticity in natural conditions.

Memory maintenance in synapses with calcium-based plasticity in the presence of background activity.

Most models of learning and memory assume that memories are maintained in neuronal circuits by persistent synaptic modifications induced by specific patterns of pre- and postsynaptic activity. For this scenario to be viable, synaptic modifications must survive the ubiquitous ongoing activity present in neural circuits in vivo. In this paper, we investigate the time scales of memory maintenance in a calcium-based synaptic plasticity model that has been shown recently to be able to fit different experimental data-sets from hippocampal and neocortical preparations. We find that in the presence of background activity on the order of 1 Hz parameters that fit pyramidal layer 5 neocortical data lead to a very fast decay of synaptic efficacy, with time scales of minutes. We then identify two ways in which this memory time scale can be extended: (i) the extracellular calcium concentration in the experiments used to fit the model are larger than estimated concentrations in vivo. Lowering extracellular calcium concentration to in vivo levels leads to an increase in memory time scales of several orders of magnitude; (ii) adding a bistability mechanism so that each synapse has two stable states at sufficiently low background activity leads to a further boost in memory time scale, since memory decay is no longer described by an exponential decay from an initial state, but by an escape from a potential well. We argue that both features are expected to be present in synapses in vivo. These results are obtained first in a single synapse connecting two independent Poisson neurons, and then in simulations of a large network of excitatory and inhibitory integrate-and-fire neurons. Our results emphasise the need for studying plasticity at physiological extracellular calcium concentration, and highlight the role of synaptic bi- or multistability in the stability of learned synaptic structures.

Synaptic input correlations leading to membrane potential decorrelation of spontaneous activity in cortex.

Correlations in the spiking activity of neurons have been found in many regions of the cortex under multiple experimental conditions and are postulated to have important consequences for neural population coding. While there is a large body of extracellular data reporting correlations of various strengths, the subthreshold events underlying the origin and magnitude of signal-independent correlations (called noise or spike count correlations) are unknown. Here we investigate, using intracellular recordings, how synaptic input correlations from shared presynaptic neurons translate into membrane potential and spike-output correlations. Using a pharmacologically activated thalamocortical slice preparation, we perform simultaneous recordings from pairs of layer IV neurons in the auditory cortex of mice and measure synaptic potentials/currents, membrane potentials, and spiking outputs. We calculate cross-correlations between excitatory and inhibitory inputs to investigate correlations emerging from the network. We furthermore evaluate membrane potential correlations near resting potential to study how excitation and inhibition combine and affect spike-output correlations. We demonstrate directly that excitation is correlated with inhibition thereby partially canceling each other and resulting in weak membrane potential and spiking correlations between neurons. Our data suggest that cortical networks are set up to partially cancel correlations emerging from the connections between neurons. This active decorrelation is achieved because excitation and inhibition closely track each other. Our results suggest that the numerous shared presynaptic inputs do not automatically lead to increased spiking correlations.

Endogenous cholinergic inputs and local circuit mechanisms govern the phasic mesolimbic dopamine response to nicotine.

Nicotine exerts its reinforcing action by stimulating nicotinic acetylcholine receptors (nAChRs) and boosting dopamine (DA) output from the ventral tegmental area (VTA). Recent data have led to a debate about the principal pathway of nicotine action: direct stimulation of the DAergic cells through nAChR activation, or disinhibition mediated through desensitization of nAChRs on GABAergic interneurons. We use a computational model of the VTA circuitry and nAChR function to shed light on this issue. Our model illustrates that the α4ß2-containing nAChRs either on DA or GABA cells can mediate the acute effects of nicotine. We account for in vitro as well as in vivo data, and predict the conditions necessary for either direct stimulation or disinhibition to be at the origin of DA activity increases. We propose key experiments to disentangle the contribution of both mechanisms. We show that the rate of endogenous acetylcholine input crucially determines the evoked DA response for both mechanisms. Together our results delineate the mechanisms by which the VTA mediates the acute rewarding properties of nicotine and suggest an acetylcholine dependence hypothesis for nicotine reinforcement.

Calcium-based plasticity model explains sensitivity of synaptic changes to spike pattern, rate, and dendritic location.

Multiple stimulation protocols have been found to be effective in changing synaptic efficacy by inducing long-term potentiation or depression. In many of those protocols, increases in postsynaptic calcium concentration have been shown to play a crucial role. However, it is still unclear whether and how the dynamics of the postsynaptic calcium alone determine the outcome of synaptic plasticity. Here, we propose a calcium-based model of a synapse in which potentiation and depression are activated above calcium thresholds. We show that this model gives rise to a large diversity of spike timing-dependent plasticity curves, most of which have been observed experimentally in different systems. It accounts quantitatively for plasticity outcomes evoked by protocols involving patterns with variable spike timing and firing rate in hippocampus and neocortex. Furthermore, it allows us to predict that differences in plasticity outcomes in different studies are due to differences in parameters defining the calcium dynamics. The model provides a mechanistic understanding of how various stimulation protocols provoke specific synaptic changes through the dynamics of calcium concentration and thresholds implementing in simplified fashion protein signaling cascades, leading to long-term potentiation and long-term depression. The combination of biophysical realism and analytical tractability makes it the ideal candidate to study plasticity at the synapse, neuron, and network levels.

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.

Modeling nicotinic neuromodulation from global functional and network levels to nAChR based mechanisms.

Neuromodulator action has received increasing attention in theoretical neuroscience. Yet models involving both neuronal populations dynamics at the circuit level and detailed receptor properties are only now being developed. Here we review recent computational approaches to neuromodulation, focusing specifically on acetylcholine (ACh) and nicotine. We discuss illustrative examples of models ranging from functional top-down to neurodynamical bottom-up. In the top-down approach, a computational theory views ACh as encoding the uncertainty expected in an environment. A different line of models accounts for neural population dynamics treating ACh as toggling neuronal networks between read-in of information and recall of memory. Building on the neurodynamics idea we discuss two models of nicotine's action with increasing degree of biological realism. Both consider explicitly receptor-level mechanisms but with different scales of detail. The first is a large-scale model of nicotine-dependent modulation of dopaminergic signaling that is capable of simulating nicotine self-administration. The second is a novel approach where circuit-level neurodynamics of the ventral tegmental area (VTA) are combined with explicit models of the dynamics of specific nicotinic ACh receptor subtypes. We show how the model is constructed based on local anatomy, electrophysiology and receptor properties and provide an illustration of its potential. In particular, we show how the model can shed light on the specific mechanisms by which nicotine controls dopaminergic neurotransmission in the VTA. This model serves us to conclude that detailed accounts for neuromodulator action at the basis of behavioral and cognitive models are crucial to understand how neuromodulators mediate their functional properties.

STDP in a bistable synapse model based on CaMKII and associated signaling pathways.

The calcium/calmodulin-dependent protein kinase II (CaMKII) plays a key role in the induction of long-term postsynaptic modifications following calcium entry. Experiments suggest that these long-term synaptic changes are all-or-none switch-like events between discrete states. The biochemical network involving CaMKII and its regulating protein signaling cascade has been hypothesized to durably maintain the evoked synaptic state in the form of a bistable switch. However, it is still unclear whether experimental LTP/LTD protocols lead to corresponding transitions between the two states in realistic models of such a network. We present a detailed biochemical model of the CaMKII autophosphorylation and the protein signaling cascade governing the CaMKII dephosphorylation. As previously shown, two stable states of the CaMKII phosphorylation level exist at resting intracellular calcium concentration, and high calcium transients can switch the system from the weakly phosphorylated (DOWN) to the highly phosphorylated (UP) state of the CaMKII (similar to a LTP event). We show here that increased CaMKII dephosphorylation activity at intermediate Ca(2+) concentrations can lead to switching from the UP to the DOWN state (similar to a LTD event). This can be achieved if protein phosphatase activity promoting CaMKII dephosphorylation activates at lower Ca(2+) levels than kinase activity. Finally, it is shown that the CaMKII system can qualitatively reproduce results of plasticity outcomes in response to spike-timing dependent plasticity (STDP) and presynaptic stimulation protocols. This shows that the CaMKII protein network can account for both induction, through LTP/LTD-like transitions, and storage, due to its bistability, of synaptic changes.

A theory of plasma membrane calcium pump stimulation and activity.

The ATP-driven Plasma Membrane Calcium pump or Ca(2+)-ATPase (PMCA) is characterized by a high affinity for calcium and a low transport rate compared to other transmembrane calcium transport proteins. It plays a crucial role for calcium extrusion from cells. Calmodulin is an intracellular calcium buffering protein which is capable in its Ca(2+) liganded form of stimulating the PMCA by increasing both the affinity to calcium and the maximum calcium transport rate. We introduce a new model of this stimulation process and derive analytical expressions for experimental observables in order to determine the model parameters on the basis of specific experiments. We furthermore develop a model for the pumping activity. The pumping description resolves the seeming contradiction of the Ca(2+):ATP stoichiometry of 1:1 during a translocation step and the observation that the pump binds two calcium ions at the intracellular site. The combination of the calcium pumping and the stimulation model correctly describes PMCA function. We find that the processes of calmodulin-calcium complex attachment to the pump and of stimulation have to be separated. Other PMCA properties are discussed in the framework of the model. The presented model can serve as a tool for calcium dynamics simulations and provides the possibility to characterize different pump isoforms by different type-specific parameter sets.