Synaptic scaling enables dynamically distinct short- and long-term memory formation.
Bottom Line:
How time scale integration and synaptic differentiation is simultaneously achieved remains unclear.The interaction between plasticity and scaling provides also an explanation for an established paradox where memory consolidation critically depends on the exact order of learning and recall.These results indicate that scaling may be fundamental for stabilizing memories, providing a dynamic link between early and late memory formation processes.
View Article:
PubMed Central - PubMed
Affiliation: Faculty of Physics - Biophysics, Georg August University Friedrich-Hund Platz 1, Göttingen, Germany ; Network Dynamics Group, Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany ; Bernstein Center for Computational Neuroscience, Georg-August-University Friedrich-Hund Platz 1, Göttingen, Germany.
ABSTRACT
Show MeSH
Memory storage in the brain relies on mechanisms acting on time scales from minutes, for long-term synaptic potentiation, to days, for memory consolidation. During such processes, neural circuits distinguish synapses relevant for forming a long-term storage, which are consolidated, from synapses of short-term storage, which fade. How time scale integration and synaptic differentiation is simultaneously achieved remains unclear. Here we show that synaptic scaling - a slow process usually associated with the maintenance of activity homeostasis - combined with synaptic plasticity may simultaneously achieve both, thereby providing a natural separation of short- from long-term storage. The interaction between plasticity and scaling provides also an explanation for an established paradox where memory consolidation critically depends on the exact order of learning and recall. These results indicate that scaling may be fundamental for stabilizing memories, providing a dynamic link between early and late memory formation processes. Related in: MedlinePlus |
Related In:
Results -
Collection
License getmorefigures.php?uid=PMC3814677&req=5
Mentions: In the following we will show in an abbreviated form the analytical calculations (see Text S1 for more details). We assume that the long-range inhibition separates the circuit into two (or more) subnetworks: (i) the externally stimulated local patch(es) and (ii) the unaffected control units. This enables us to average Equation 1 over all units within such a subnetwork. To calculate the fixed point of the resulting mean field differential equation we set it equal to zero and solve it. As result we receive the weight-cline of the system (The weight-cline is a set of states where weights do not change under the given dynamics.):(2)with as averaged value of variable . Equation 2 describes the resulting strength of the synaptic weights within a subnetwork given the dynamic of plasticity and scaling and a mean neuronal activation . As the maximal activation of each unit can not exceed (given by the input-output function ), the maximal possible synaptic weight is given by . The resulting weight-activity function in the phase space is shown in Figure 3 B,C (blue line) for the parameters used in Figure 1. Of course, the course of the function depends on the used synaptic plasticity rule (the numerator in Eq. 2), but it also shows that the LTP-term () dominates and that additional plasticity mechanisms (e.g., LTD [34] or short-term plasticity [61]) do not alter the basic dynamic (see Figure S1 in Text S1). |
View Article: PubMed Central - PubMed
Affiliation: Faculty of Physics - Biophysics, Georg August University Friedrich-Hund Platz 1, Göttingen, Germany ; Network Dynamics Group, Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany ; Bernstein Center for Computational Neuroscience, Georg-August-University Friedrich-Hund Platz 1, Göttingen, Germany.