Nanoconnectomic upper bound on the variability of synaptic plasticity

By T. Bartol, Cailey Bromer, J. Kinney, Michael A. Chirillo, Jennifer N. Bourne, Kristen M. Harris, Terrence J. Sejnowski. 2015

279 citations recorded by the source corpus.

Information in a computer is quantified by the number of bits that can be stored and recovered. An important question about the brain is how much information can be stored at a synapse through synaptic plasticity, which depends on the history of probabilistic synaptic activity. The strong correlation between size and efficacy of a synapse allowed us to estimate the variability of synaptic plasticity. In an EM reconstruction of hippocampal neuropil we found single axons making two or more synaptic contacts onto the same dendrites, having shared histories of presynaptic and postsynaptic activity. The spine heads and neck diameters, but not neck lengths, of these pairs were nearly identical in size. We found that there is a minimum of 26 distinguishable synaptic strengths, corresponding to storing 4.7 bits of information at each synapse. Because of stochastic variability of synaptic activation the observed precision requires averaging activity over several minutes. DOI: http://dx.doi.org/10.7554/eLife.10778.001

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering, Neuroscience

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Citations in the corpus

Recurrent World Models Facilitate Policy Evolution (2018, background): “While the human brain can hold decades and even centuries of memories to some resolution [2], our neural networks trained with backpropagation have more limited capacity and suffer from issues such as catastrophic forgetting [16, 43, 69].”
World Models (2018, background): “While the human brain can hold decades and even centuries of memories to some resolution (Bartol et al., 2015), our neural networks trained with backpropagation have more limited capacity and suffer from issues such as catastrophic forgetting (Ratcliff, 1990; French, 1994; Kirkpatrick et al.”
Learned Step Size Quantization (2019, background): “…between artiﬁcial neural networks and biological neural networks, which themselves employ synapses represented by perhaps a few bits of information (Bartol Jr et al., 2015) and single bit spikes that may be employed in small spatial and/or temporal ensembles to provide low bit width data…”
A Growing Long-term Episodic & Semantic Memory (2016, background): “7 bits per synapse [2], yielding roughly 700 trillion bits.”
Convolutional networks for fast, energy-efficient neuromorphic computing (2016, background): “This approach draws inspiration from the spiking neurons and lowprecision synapses of the brain (8) and builds on work showing that deep learning can create networks with constrained connectivity (9), low-precision synapses (10, 11), low-precision neurons (12–14), or both low-precision synapses and ”