Tag-Trigger-Consolidation: A Model of Early and Late Long-Term-Potentiation and Depression

Changes in synaptic efficacies need to be long-lasting in order to serve as a substrate for memory. Experimentally, synaptic plasticity exhibits phases covering the induction of long-term potentiation and depression (LTP/LTD) during the early phase of synaptic plasticity, the setting of synaptic tags, a trigger process for protein synthesis, and a slow transition leading to synaptic consolidation during the late phase of synaptic plasticity. We present a mathematical model that describes these different phases of synaptic plasticity. The model explains a large body of experimental data on synaptic tagging and capture, cross-tagging, and the late phases of LTP and LTD. Moreover, the model accounts for the dependence of LTP and LTD induction on voltage and presynaptic stimulation frequency. The stabilization of potentiated synapses during the transition from early to late LTP occurs by protein synthesis dynamics that are shared by groups of synapses. The functional consequence of this shared process is that previously stabilized patterns of strong or weak synapses onto the same postsynaptic neuron are well protected against later changes induced by LTP/LTD protocols at individual synapses

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

Recruitment and Consolidation of Cell Assemblies for Words by Way of Hebbian Learning and Competition in a Multi-Layer Neural Network

Current cognitive theories postulate either localist representations of knowledge or fully overlapping, distributed ones. We use a connectionist model that closely replicates known anatomical properties of the cerebral cortex and neurophysiological principles to show that Hebbian learning in a multi-layer neural network leads to memory traces (cell assemblies) that are both distributed and anatomically distinct. Taking the example of word learning based on action-perception correlation, we document mechanisms underlying the emergence of these assemblies, especially (i) the recruitment of neurons and consolidation of connections defining the kernel of the assembly along with (ii) the pruning of the cell assembly’s halo (consisting of very weakly connected cells). We found that, whereas a learning rule mapping covariance led to significant overlap and merging of assemblies, a neurobiologically grounded synaptic plasticity rule with fixed LTP/LTD thresholds produced minimal overlap and prevented merging, exhibiting competitive learning behaviour. Our results are discussed in light of current theories of language and memory. As simulations with neurobiologically realistic neural networks demonstrate here spontaneous emergence of lexical representations that are both cortically dispersed and anatomically distinct, both localist and distributed cognitive accounts receive partial support

An experimental test of the role of postsynaptic calcium levels in determining synaptic strength using perirhinal cortex of rat

We have investigated the prediction of a relationship between the magnitude of activity-dependent increases in postsynaptic calcium and both the magnitude and direction of synaptic plastic change in the central nervous system. Activity-dependent increases in calcium were buffered to differing degrees using a range of concentrations of EGTA and the effects on synaptic plasticity were assessed.Activity-dependent synaptic plasticity was induced during whole-cell recording in rat perirhinal cortex in vitro. In control conditions (0.5 mm EGTA) low frequency stimulation (LFS; 200 stimuli) delivered to neurones held at -40 or -70 mV induced long-term depression (LTD) or, at -10 mV, induced long-term potentiation (LTP).The relationship between EGTA concentration (0.2 to 10 mm) and the magnitude of LTD was examined. This relationship described a U-shaped curve, as predicted by models of synaptic plasticity. This provides strong evidence that the magnitude of LTD is determined by the magnitude of the increase in intracellular calcium concentration.LFS paired with depolarisation to -10 mV induced LTD, no change or LTP as activity-dependent postsynaptic calcium levels were allowed to increase progressively by the use of progressively lower concentrations of buffer (10 to 0.2 mm EGTA).We investigated if the lack of plasticity that occurs at the transition between LTD and LTP is due to induction of both of these processes with zero net change, or is due to neither LTD nor LTP being induced. These experiments were possible as LTP but not LTD was blocked by the protein kinase inhibitor staurosporine while LTD but not LTP was blocked by the mGlu receptor antagonist MCPG. At the transition between LTD and LTP, blocking LTP mechanisms did not uncover LTD whilst blocking LTD mechanisms did not uncover LTP. This suggests that the transition between LTD and LTP is due to the lack of induction of both of these processes and also suggests that these two processes are induced independently of one another