33 research outputs found
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
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