Rui Ponte Costa

Target-Specific Expression of Presynaptic NMDA Receptors in Neocortical Microcircuits

Summary Traditionally, NMDA receptors are located postsynaptically; yet, putatively presynaptic NMDA receptors (preNMDARs) have been reported. Although implicated in controlling synaptic plasticity, their function is not well understood and their expression patterns are debated. We demonstrate that, in layer 5 of developing mouse visual cortex, preNMDARs specifically control synaptic transmission at pyramidal cell inputs to other pyramidal cells and to Martinotti cells, while leaving those to basket cells unaffected. We also reveal a type of interneuron that mediates ascending inhibition. In agreement with synapse-specific expression, we find preNMDAR-mediated calcium signals in a subset of pyramidal cell terminals. A tuned network model predicts that preNMDARs specifically reroute information flow in local circuits during high-frequency firing, in particular by impacting frequency-dependent disynaptic inhibition mediated by Martinotti cells, a finding that we experimentally verify. We conclude that postsynaptic cell type determines presynaptic terminal molecular identity and that preNMDARs govern information processing in neocortical columns.

Target-cell-specific short-term plasticity in local circuits

Short-term plasticity (STP) denotes changes in synaptic strength that last up to tens of seconds. It is generally thought that STP impacts information transfer across synaptic connections and may thereby provide neurons with, for example, the ability to detect input coherence, to maintain stability and to promote synchronization. STP is due to a combination of mechanisms, including vesicle depletion and calcium accumulation in synaptic terminals. Different forms of STP exist, depending on many factors, including synapse type. Recent evidence shows that synapse dependence holds true even for connections that originate from a single presynaptic cell, which implies that postsynaptic target cell type can determine synaptic short-term dynamics. This arrangement is surprising, since STP itself is chiefly due to presynaptic mechanisms. Target-specific synaptic dynamics in addition imply that STP is not a bug resulting from synapses fatiguing when driven too hard, but rather a feature that is selectively implemented in the brain for specific functional purposes. As an example, target-specific STP results in sequential somatic and dendritic inhibition in neocortical and hippocampal excitatory cells during high-frequency firing. Recent studies also show that the Elfn1 gene specifically controls STP at some synapse types. In addition, presynaptic NMDA receptors have been implicated in synapse-specific control of synaptic dynamics during high-frequency activity. We argue that synapse-specific STP deserves considerable further study, both experimentally and theoretically, since its function is not well known. We propose that synapse-specific STP has to be understood in the context of the local circuit, which requires combining different scientific disciplines ranging from molecular biology through electrophysiology to computer modeling.

Probabilistic inference of short-term synaptic plasticity in neocortical microcircuits.

Short-term synaptic plasticity is highly diverse across brain area, cortical layer, cell type, and developmental stage. Since short-term plasticity (STP) strongly shapes neural dynamics, this diversity suggests a specific and essential role in neural information processing. Therefore, a correct characterization of short-term synaptic plasticity is an important step towards understanding and modeling neural systems. Phenomenological models have been developed, but they are usually fitted to experimental data using least-mean-square methods. We demonstrate that for typical synaptic dynamics such fitting may give unreliable results. As a solution, we introduce a Bayesian formulation, which yields the posterior distribution over the model parameters given the data. First, we show that common STP protocols yield broad distributions over some model parameters. Using our result we propose a experimental protocol to more accurately determine synaptic dynamics parameters. Next, we infer the model parameters using experimental data from three different neocortical excitatory connection types. This reveals connection-specific distributions, which we use to classify synaptic dynamics. Our approach to demarcate connection-specific synaptic dynamics is an important improvement on the state of the art and reveals novel features from existing data.

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. DOI: http://dx.doi.org/10.7554/eLife.09457.001

Differential Regulation of Evoked and Spontaneous Release by Presynaptic NMDA Receptors

Presynaptic NMDA receptors (preNMDARs) control synaptic release, but it is not well understood how. Rab3-interacting molecules (RIMs) provide scaffolding at presynaptic active zones and are involved in vesicle priming. Moreover, c-Jun N-terminal kinase (JNK) has been implicated in regulation of spontaneous release. We demonstrate that, at connected layer 5 pyramidal cell pairs of developing mouse visual cortex, Mg2+-sensitive preNMDAR signaling upregulates replenishment of the readily releasable vesicle pool during high-frequency firing. In conditional RIM1αβ deletion mice, preNMDAR upregulation of vesicle replenishment was abolished, yet preNMDAR control of spontaneous release was unaffected. Conversely, JNK2 blockade prevented Mg2+-insensitive preNMDAR signaling from regulating spontaneous release, but preNMDAR control of evoked release remained intact. We thus discovered that preNMDARs signal differentially to control evoked and spontaneous release by independent and non-overlapping mechanisms. Our findings suggest that preNMDARs may sometimes signal metabotropically and support the emerging principle that evoked and spontaneous release are distinct processes. VIDEO ABSTRACT.

One cell to rule them all, and in the dendrites bind them

More than 60 years ago, the McGill University professor Donald Hebb published his famous postulate stating that to store a memory trace, the connection from a neuron that persistently helps activate another one should be strengthened (Hebb, 1949). Inspired by Hebbs postulate, Rosenblatt (1958) a decade later introduced the perceptron learning machine as a simplified model of information storage and retrieval in the brain. This model was able to perform binary classification through a learning rule that altered synaptic weights, and thereby created big expectations in the field of artificial neural networks: Here was a simple neural-network-like machine that could learn to recognize patterns and to tell them apart. The excitement was not long-lived, however. Minsky and Papert (1969) proved that a single-layer perceptron is only capable of learning linearly separable patterns, which means it cannot learn a XOR function, as this would require it to respond when one, or the other input is active but not both. Individual perceptrons were thus inherently flawed, it seemed. Minsky and Paperts findings were therefore widely but erroneously interpreted to mean that all perceptrons suffered from the same problem, even though they had in actuality shown that multi-layer perceptrons had the capacity for non-linear computations. Nevertheless, the winter of connectionism research had arrived, and it required around a decade for interest in the field to be revived, after developments by pioneers such as Stephen Grossberg, John Hopfield, and David Rumelhart (Abbott, 2008). Yet even after this revival it has remained unclear what types of non-linear computations are possible to execute in individual neurons of the actual brain. In more recent years, synaptic plasticity theory has been extended to include the precise timing of spikes in pre and postsynaptic neurons, based on theoretical as well as experimental studies (Gerstner et al., 1996; Markram et al., 1997). This has led to the development of the spike-timing-dependent plasticity (STDP) paradigm, which has caused great interest as a biologically plausible neuronal basis for information storage in the brain, in particular for the learning of causal relationships, as it is temporally sensitive (Markram et al., 2011). As was the case in Rosenblatts (1958) perceptron paper, the vast majority of theoretical synaptic plasticity studies treat neurons as points in space, entirely devoid of dendritic arborizations. There has been an ongoing debate in the field regarding the extent to which dendrites are important for computations in the brain; perhaps they are merely an epiphenomenal bug rather than a feature (Hausser and Mel, 2003)? Hebb (1972) took an interestingly extreme view and surmised that dendrites are merely there to connect and therefore serve no purpose in plasticity. But dendrites are key to distinguishing neuronal types – the fan-shaped dendritic tree typifies the Purkinje cells, while the ascending thick-tufted dendritic arbor defines the neocortical layer-5 pyramidal cell – so it would seem strange if dendrites did nothing more than to hook cells up to each other (Sjostrom et al., 2008). Indeed, recent studies have shown that synaptic plasticity depends on the location of a synapse in the dendritic tree (Sjostrom and Hausser, 2006) and that dendritic branches themselves are plastic (Losonczy et al., 2008). By measuring the coupling between local dendritic spikes and the soma before and after a synaptic plasticity induction protocol in the hippocampus, Losonczy et al. (2008) discovered that dendrites too are plasticity. Based on their findings, they proposed the existence of a branch-strength potentiation (BSP) cellular learning rule, which is input-specific to a degree, suggesting that individual dendritic compartments could be involved in storing spatio-temporal features. But why is BSP needed? After all, it would seem that Hebbian learning in general and STDP in particular provide sufficient means for information storage in the brain. In a recently published study, Legenstein and Maass (2011) attacked this key issue using an entirely theoretical approach. They introduced a new experimentally based phenomenological model that brought together the STDP and BSP learning rules. They applied their model to a simple feature-binding problem, in which cell assemblies coding for different features (e.g., yellow, star, black, and disk) were randomly connected to the branches of the postsynaptic cell. The neuron was then trained on pairs of features, such as yellow + star and black + disk, after which the neuron responded correctly to pairs of trained features, but not to other combinations such as yellow + disk. This feature was due to the emergence of synaptic clustering and competition between dendritic branches that resulted from the interplay between STDP and BSP, allowing a single neuron to bind input features in a self-organized manner. Despite the interesting features emerging from this model, and as happened with the perceptron, the Legenstein–Maass model was not able to solve the XOR problem (i.e., responding to either pair of features, but not to both pairs together). Indeed, the XOR problem might only be solvable at the network level, requiring inhibitory interneurons to do so. Nevertheless, whether a single neuron of the brain can or cannot perform non-linear pattern separation remains an open question (Sjostrom et al., 2008). It would also be interesting to know the information storage capacity of such Legenstein–Maass neurons. Finally, although STDP is necessary in their model, Hebbian learning together with synaptic scaling (Turrigiano et al., 1998) are likely to yield similar results. The take-home message of the study of Legenstein and Maass (2011) is that individual neurons can potentially operate as small networks in their own right, binding features at the single-cell level. This suggests a form of dendritic homunculus, which can dendritically bind specific feature combinations via a combination of STDP and BSP, thus acting as a substrate for the correlation theory of brain function (von der Malsburg, 1981) as well as for the binding problem (Treisman, 1996). The Legenstein–Maass study is therefore relevant to several disciplines, including experimental and theoretical neuroscience as well as psychology.

Synaptic Transmission Optimization Predicts Expression Loci of Long-Term Plasticity

Summary Long-term modifications of neuronal connections are critical for reliable memory storage in the brain. However, their locus of expression—pre- or postsynaptic—is highly variable. Here we introduce a theoretical framework in which long-term plasticity performs an optimization of the postsynaptic response statistics toward a given mean with minimal variance. Consequently, the state of the synapse at the time of plasticity induction determines the ratio of pre- and postsynaptic modifications. Our theory explains the experimentally observed expression loci of the hippocampal and neocortical synaptic potentiation studies we examined. Moreover, the theory predicts presynaptic expression of long-term depression, consistent with experimental observations. At inhibitory synapses, the theory suggests a statistically efficient excitatory-inhibitory balance in which changes in inhibitory postsynaptic response statistics specifically target the mean excitation. Our results provide a unifying theory for understanding the expression mechanisms and functions of long-term synaptic transmission plasticity.

A computational environment for long-term multi-feature and multi-algorithm seizure prediction

The daily life of epilepsy patients is constrained by the possibility of occurrence of seizures. Until now, seizures cannot be predicted with sufficient sensitivity and specificity. Most of the seizure prediction studies have been focused on a small number of patients, and frequently assuming unrealistic hypothesis. This paper adopts the view that for an appropriate development of reliable predictors one should consider long-term recordings and several features and algorithms integrated in one software tool. A computational environment, based on Matlab ®, is presented, aiming to be an innovative tool for seizure prediction. It results from the need of a powerful and flexible tool for long-term EEG/ECG analysis by multiple features and algorithms. After being extracted, features can be subjected to several reduction and selection methods, and then used for prediction. The predictions can be conducted based on optimized thresholds or by applying computational intelligence methods. One important aspect is the integrated evaluation of the seizure prediction characteristic of the developed predictors.

How to train a neuron

A cellular learning rule known as spike-timing-dependent plasticity can form, reshape and erase the response preferences of visual cortex neurons.

Probabilistic inference of synaptic dynamics in neocortical microcircuits

Short-term synaptic plasticity (STP) is highly varied across brain area, cortical layer, cell type, and developmental stage (Reyes & Sakmann 1999). This variability is probably not coincidental and since synaptic dynamics shape neural computations, it suggests an important role of STP in neural information processing (Abbott & Regehr 2004). Therefore, an accurate description of STP is a key step towards a comprehensive understanding of neural systems. Many phenomenological STP models have been developed (Markram et al. 1998), but they have typically been fitted to experimental data using least-mean-square methods. With the Tsodyks-Markram model, we find that for typical synaptic dynamics such fitting procedures may give erratic outcomes. A Bayesian formulation based on a Markov Chain Monte Carlo method was introduced as a solution. This formulation provides the posterior distribution over the model parameters given the data statistics. We discovered that standard STP electrophysiology protocols yielded wide distributions over some model parameters. Based on this result we propose experimental protocols to more accurately determine model parameters. Next, the model parameters were inferred using experimental data from three different neocortical excitatory connection types: Pyramidal Cell-Pyramidal Cell (PC-PC), Pyramidal Cell-Basket Cell (PC-BC) and Pyramidal Cell-Martinotti Cell (PC-MC), (see Figure ​Figure1).1). This disclosed connection-specific distributions, which we used to classify synapses. This approach to determining connection-specific synaptic dynamics provides a more comprehensive representation of STP and unveils novel features from existing data. Figure 1 Posterior distributions of STP parameters from experimental data from visual cortex layer-5. (A) Sample experimental STP traces are shown for PC-PC (red), PC-BC (green), and for PC-MC (blue) connections. (B) Marginalized posterior distributions obtained ...