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Real Time Multiplicative Memory Amplification Mediated by Whole-Cell Scaling of Synaptic Response in Key Neurons

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  • Iris Reuveni
  • Sourav Ghosh
  • Edi Barkai

Abstract

Intense spiking response of a memory-pattern is believed to play a crucial role both in normal learning and pathology, where it can create biased behavior. We recently proposed a novel model for memory amplification where the simultaneous two-fold increase of all excitatory (AMPAR-mediated) and inhibitory (GABAAR-mediated) synapses in a sub-group of cells that constitutes a memory-pattern selectively amplifies this memory. Here we confirm the cellular basis of this model by validating its major predictions in four sets of experiments, and demonstrate its induction via a whole-cell transduction mechanism. Subsequently, using theory and simulations, we show that this whole-cell two-fold increase of all inhibitory and excitatory synapses functions as an instantaneous and multiplicative amplifier of the neurons’ spiking. The amplification mechanism acts through multiplication of the net synaptic current, where it scales both the average and the standard deviation of the current. In the excitation-inhibition balance regime, this scaling creates a linear multiplicative amplifier of the cell’s spiking response. Moreover, the direct scaling of the synaptic input enables the amplification of the spiking response to be synchronized with rapid changes in synaptic input, and to be independent of previous spiking activity. These traits enable instantaneous real-time amplification during brief elevations of excitatory synaptic input. Furthermore, the multiplicative nature of the amplifier ensures that the net effect of the amplification is large mainly when the synaptic input is mostly excitatory. When induced on all cells that comprise a memory-pattern, these whole-cell modifications enable a substantial instantaneous amplification of the memory-pattern when the memory is activated. The amplification mechanism is induced by CaMKII dependent phosphorylation that doubles the conductance of all GABAA and AMPA receptors in a subset of neurons. This whole-cell transduction mechanism enables both long-term induction of memory amplification when necessary and extinction when not further required.Author Summary: Amplifying the strength of a neuronal assembly that underlies a behavioral choice can lead to a particularly long lasting dominant memory. We report experimental and theoretical evidence for a long-term mechanism that amplifies the response of a neuronal assembly which we termed “memory amplification mechanism”. The amplification mechanism is mediated by doubling the strength of all inhibitory and all excitatory synapses in the cell and is induced by whole-cell phosphorylation of all inhibitory and excitatory synaptic receptors in a subset of cells, via a process that is distinct from memory formation. Computationally, the inherent scaling of both excitation and inhibition yields a robust and stable amplifier of the neuron’s response. When such an amplifier is induced in a set of cells that compose a memory-pattern, it can selectively amplify the response of this memory. The memory amplification mechanism is independent from associative learning. Thus, while associative learning forms a memory that encodes new associations, the amplification mechanism can promote an already formed memory to a dominant memory.

Suggested Citation

  • Iris Reuveni & Sourav Ghosh & Edi Barkai, 2017. "Real Time Multiplicative Memory Amplification Mediated by Whole-Cell Scaling of Synaptic Response in Key Neurons," PLOS Computational Biology, Public Library of Science, vol. 13(1), pages 1-31, January.
  • Handle: RePEc:plo:pcbi00:1005306
    DOI: 10.1371/journal.pcbi.1005306
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    References listed on IDEAS

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    1. Gina G. Turrigiano & Kenneth R. Leslie & Niraj S. Desai & Lana C. Rutherford & Sacha B. Nelson, 1998. "Activity-dependent scaling of quantal amplitude in neocortical neurons," Nature, Nature, vol. 391(6670), pages 892-896, February.
    2. Andrew Y. Y. Tan & Yuzhi Chen & Benjamin Scholl & Eyal Seidemann & Nicholas J. Priebe, 2014. "Sensory stimulation shifts visual cortex from synchronous to asynchronous states," Nature, Nature, vol. 509(7499), pages 226-229, May.
    3. Iris Reuveni & Drorit Saar & Edi Barkai, 2013. "A Novel Whole-Cell Mechanism for Long-Term Memory Enhancement," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-14, July.
    4. James F. A. Poulet & Carl C. H. Petersen, 2008. "Internal brain state regulates membrane potential synchrony in barrel cortex of behaving mice," Nature, Nature, vol. 454(7206), pages 881-885, August.
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