Mitochondrial activity is crucial for the plasticity of central synapses, but how the firing pattern of pre- and postsynaptic neurons affects the mitochondria remains elusive. We recorded changes in the fluorescence of cytosolic and mitochondrial Ca2+ indicators in cell bodies, axons, and dendrites of cortical pyramidal neurons in mouse brain slices while evoking pre- and postsynaptic spikes. Postsynaptic spike firing elicited fast mitochondrial Ca2+ responses that were about threefold larger in the somas and apical dendrites than in basal dendrites and axons. The amplitude of these responses and metabolic activity were extremely sensitive to the firing frequency. Furthermore, while an EPSP alone caused no detectable Ca2+ elevation in the dendritic mitochondria, the coincidence of EPSP with a backpropagating spike produced prominent, highly localized mitochondrial Ca2+ hotspots. Our results indicate that mitochondria decode the spike firing frequency and the Hebbian temporal coincidences into the Ca2+ signals, which are further translated into the metabolic output and most probably lead to long-term changes in synaptic efficacy.
A representative subset of the raw electrical recording and imaging data has been deposited to Dryad (https://doi.org/10.5061/dryad.sxksn0348). The dataset contains the Microcal Origin opj files of the electrical and optical recordings and quantitative analysis of the data. We are unable to make all raw electrical recording and imaging data publicly available as due to the large size of our raw dataset (>10TB). Interested researchers should contact the corresponding author to gain access to the raw data.
Frequency- and spike-timing-dependent mitochondrial Ca2+ signaling regulates the metabolic rate and synaptic efficacy in cortical neuronsDryad Digital Repository, doi:10.5061/dryad.sxksn0348.
- Ilya Fleidervish
- Daniel Gitler
- Israel Sekler
- Grace E. Stutzmann, Ph.D.
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Animal experimentation: All experiments were approved by the Animal Care and Use Committee of Ben Gurion University of the Negev (protocol #IL-68-09-2019).
- Sacha B Nelson, Brandeis University, United States
© 2022, Stoler et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Artificial neural networks could pave the way for efficiently simulating large-scale models of neuronal networks in the nervous system.
A key question in decision making is how humans arbitrate between competing learning and memory systems to maximize reward. We address this question by probing the balance between the effects, on choice, of incremental trial-and-error learning versus episodic memories of individual events. Although a rich literature has studied incremental learning in isolation, the role of episodic memory in decision making has only recently drawn focus, and little research disentangles their separate contributions. We hypothesized that the brain arbitrates rationally between these two systems, relying on each in circumstances to which it is most suited, as indicated by uncertainty. We tested this hypothesis by directly contrasting contributions of episodic and incremental influence to decisions, while manipulating the relative uncertainty of incremental learning using a well-established manipulation of reward volatility. Across two large, independent samples of young adults, participants traded these influences off rationally, depending more on episodic information when incremental summaries were more uncertain. These results support the proposal that the brain optimizes the balance between different forms of learning and memory according to their relative uncertainties and elucidate the circumstances under which episodic memory informs decisions.