Towards a unified understanding of synaptic plasticity

Understanding how learning and memory formation work in the brain is a major challenge in neuroscience, with important implications for many other fields, including medicine and industry. It is nowadays widely accepted that synaptic plasticity is the biological foundation of these higher order brain functions. So far, many different plastic behaviors have been intensively studied and characterized, leading to the definition of several forms of plasticity: structural, functional, homeostatic, inhibitory, and many others. Unfortunately, despite all the interest and efforts of the scientific community, a complete and consistent understanding of synaptic plasticity is still lacking. The main goal of this study is to unify data and theories on synaptic plasticity in a comprehensive model, suitable for studying learning and memory down to the synapse level. To reach our objective, we identified a minimal set of biological mechanisms responsible for plastic dynamics and integrated them into a single synapse model, relying whenever possible on well accepted sub-models from literature. We designed a data-driven fitting and generalization strategy to parameterize all excitatory-to-excitatory synapses in a large scale reconstruction of neocortical tissue [Markram et al., 2015]. Finally, we tested the effects of functional synaptic plasticity on neural circuits in simulations. Our model was able to capture not only the outcome of Spike Timing Dependent Plasticity (STDP) protocols used in the training phase, but also the one of all others available in the same experimental dataset [Markram et al., 1997b]. Moreover, it correctly reproduced results on distance-dependent synaptic plasticity [Sjöström and Häusser, 2006], even though this dataset was never used during fitting. In network simulations, we observed the emergence of a self-regulatory homeostatic mechanism, preventing runaway excitation. Furthermore, we noticed the strengthening of connections between neurons that are similarly innervated, as previously shown in vitro [Perin et al., 2011].

Connectivity reflects coding: a model of voltage-based STDP with homeostasis

Claudia Clopath, Wulfram Gerstner, Eleni Vasilaki

Electrophysiological connectivity patterns in cortex often have a few strong connections, which are sometimes bidirectional, among a lot of weak connections. To explain these connectivity patterns, we created a model of spike timing–dependent plasticity (STDP) in which synaptic changes depend on presynaptic spike arrival and the postsynaptic membrane potential, filtered with two different time constants. Our model describes several nonlinear effects that are observed in STDP experiments, as well as the voltage dependence of plasticity. We found that, in a simulated recurrent network of spiking neurons, our plasticity rule led not only to development of localized receptive fields but also to connectivity patterns that reflect the neural code. For temporal coding procedures with spatio-temporal input correlations, strong connections were predominantly unidirectional, whereas they were bidirectional under rate-coded input with spatial correlations only. Thus, variable connectivity patterns in the brain could reflect different coding principles across brain areas; moreover, our simulations suggested that plasticity is fast.

Nature Publishing Group2010

Network Activity and Plasticity

Vincent Delattre

The cortex must maintain balanced levels of neural activity to correctly integrate inputs and to provide contextually meaningful outputs. Neuronal excitation is counterbalanced by various forms of inhibition such as spike frequency adaptation, short- and long-term depression at excitatory synapses, and inhibitory cell coupling. When excitatory activity is low, homeostatic mechanisms enhance network excitability. When excitation is stronger than inhibition, network bursts of activity arise that also need to be dampened lest they run out of control. A disruption of the fine balance between excitatory and inhibitory systems is observed in an increasing numbers of brain disorders such as epilepsy, mental retardation and autism. This thesis aims to study aspects of the regulation of excitation in cortical networks at a molecular, cellular, network and disease level of investigation. We present in the first chapter an investigation of the impact of network bursts on standard spike-timing dependent plasticity (STDP) events in acute cortical brain slices. We discovered that these two kinds of events, which are the most well-established ways to induce synaptic plasticity in vitro, can block each other as well as cooperate to change the amplitude and direction of plasticity. Because the outcome of the interaction depends on the precise timing of network bursts relative to a STDP event in a single synaptic pathway, the observed network-timing dependent plasticity provides a novel mechanism for the modulation of local synaptic plasticity by the network level of activity, a form of global control of local synaptic plasticity. We hypothesized that network bursts consume extracellular resources needed to potentiate, restricting potentiation to active synapses at the burst onset while yielding depression of synapses activated at later times. As we demonstrate with simulation, this simple mechanism supports paradigm-shifting implications for models of learning, solving a persistent and fundamental problem since BCM (Bienenstock Cooper Munro, 1981) to incorporate synaptic learning into realistic neuronal networks while simultaneously establishing and maintaining excitatory-inhibitory balance. The proposed mechanism also links experimental evidence for interactions between STDP and network activity to observations of self-organized criticality in neural systems, a regime known to be optimal for information coding. In the second chapter, we investigated the effect of the loss of NLGN4 protein on synapse function and on network response to electrical stimulation. The neuroligin family of genes encodes for trans-synaptic proteins involved in synaptic differentiation and maturation. Former studies have reported that both NLGN1-KO and NLGN2-KO give rise to pathologic deregulations of the balance between excitation and inhibition in neural networks, and the presence of loss-of-function mutations in NLGN4 in a small number of autistic patients indicates a possible link between Neuroligin-4 gene and autism. Using extra-cellular stimulation and intra-cellular recordings in acute brain slices, we observed a decreased response of both excitatory and inhibitory response in NLGN4-KO animals, suggesting that NLGN4 is expressed at both excitatory and inhibitory synapses. Furthermore, we observed that the NLGN4-KO resulted in a significantly larger decrease of the excitatory response than of the inhibitory response, thus yielding a hypo-excitable network. The final chapter consists of the characterization of excitatory pathways in acute slices of the somato-sensory cortex. In the first study, we used chemical stimulation to promote the emergence of spontaneous recurrent activity and observed waves of activity originating from the cortical layer five. In the second study, we performed whole-cell patch-clamping of layer five pyramidal cells along with recording local field potentials (LFP). With the systematic layer-wise application of electrical stimulation at various frequencies coupled with LFP recordings, we obtained a frequency-dependent mapping of cortical inputs at all cortical layers at both the cellular and the network levels. Using this data, we developed a virtual framework for performing an analogous mapping in the virtual cortical column of the Blue Brain Project (BBP). This helped us understand the contribution of each cell population in the network response to stimulation. The framework also served as a validation test for the BBP cortical models.

EPFL2013

Synaptic plasticity across different time scales and its functional implications

Claudia Clopath

Humans and animals learn by modifying the synaptic strength between neurons, a phenomenon known as synaptic plasticity. These changes can be induced by rather short stimuli (lasting, for instance, only a few seconds), yet, in order to be useful for long-term memory, they should remain stable for months or years. Experimentalists study synaptic plasticity by applying a vast variety of protocols. In the present thesis we focus on protocols that fall under two main categories: (i) Those that induce synaptic modifications that last for only a few hours ("early phase" of plasticity) (ii) Those that allow synapses to undergo a sequence of steps that transforms the rapid changes occurring during the "early phase" into a stable memory trace ("late phase" of plasticity). The goal of this thesis is to better understand synaptic plasticity across these different phases, early and late, by creating compact mathematic models to describe the plasticity mechanisms. Our approach allows for a synthetic view of the field as well as the exploration of functional consequences of learning. In this direction, we propose a model for the induction of synaptic plasticity that depends on the presynaptic spike time and nonlinearly on the postsynaptic voltage. The model is able to reproduce a broad range of experimental protocols such as voltage-clamp experiments and spike-timing experiments. Since the voltage is a key element in the model, we describe the neuronal activity by using a compact neuron model that faithfully reproduces the voltage time course of pyramidal neurons. In addition, this model of the induction of synaptic plasticity is combined with a trigger process for protein synthesis, and the final stabilization mechanism in order to describe the "late phase". In this combinatory form, it is able to explain experimental phenomena known as tagging experiments and to make testable predictions. A study of functional consequences of the induction model reveals selectivity in the inputs, independent component analysis computation and a tight relation between connectivity and coding. In parallel a top-down approach finding independent components is used to derive a rate-based learning rule which shows structural correlations with the induction model. This unified model across different time scales allowing the stabilization of synapses is crucial to understand learning and memory processes in animals and humans, and a necessary ingredient for any large-scale model of the brain.