Memory: Overwriting the past with supervised plasticity
Our ability to retain and remember information has fascinated humans as far back as classical antiquity. Already Cicero mused about the formation of memory, “For, what is it that enables us to remember?” (…) Do we think there is (...) a sort of roominess into which the things we remember can be poured as if in a kind of vessel? (...) Or do we think that memory consists of the traces of things registered in the mind?” (Stewart, 2009). Two thousand and eighty years later, having a tangible substrate for the physical basis of memory and unprecedented access to neurons during learning, these questions remain relevant.
When the brain forms memories or learns a new task, it encodes the new input by tuning the connections between neurons. These synaptic connections can be strengthened or weakened in a process called plasticity, which is a key part of learning new skills. It is thought that synaptic signaling can be amped up or dialed down to change the output of the connection between two neurons, but exactly how this happens remains unclear. Is it directed by a supervisory signal, or does it solely rely on the affected neurons?
Certain factors modulating synaptic plasticity have been well established (Roelfsema and Holtmaat, 2018; Magee and Grienberger, 2020). Recent studies have uncovered a potentially new mechanism that regulates synaptic plasticity in the hippocampus (Bittner et al., 2015; Bittner et al., 2017). There, complex bursts of activity called plateau potentials increase the strength of synapses that have been stimulated in the past few seconds. Crucially, the nature of these potentials indicates that they are triggered by signals from other parts of the brain.
Plateau potentials act on so called place cells in the hippocampus, which are important for navigation and spatial memory. Plateau potentials can increase the strength of recently stimulated synapses and can abruptly activate otherwise silent place cells when the body is in a specific location. However, it was not clear if they would also be able to decrease the strength of synapses, which would enable the brain to fine-tune the connectivity of synapses and the animal’s behavior. This way, the brain could update its map of the environment by causing active neurons to stop responding at one location and instead respond at a new position.
Now, in eLife, Jeffrey Magee, Sandro Romani and colleagues at Stanford University School of Medicine, Rutgers University, Baylor College of Medicine and Janelia Research Campus – including Aaron Milstein as first author – report new insights into plateau potentials (Milstein et al., 2021). The team recorded place cells in the hippocampus of mice while the animals navigated a virtual environment. Then, they experimentally induced plateau potentials in these cells. This revealed that plateau potentials enable place cells to change which location in the environment they respond to by either increasing or decreasing synaptic strength.
Similar to previous studies, inducing a plateau potential in cells that did not previously encode a particular place caused that cell to encode the location of an animal at the moment of stimulation (Bittner et al., 2017). However, Milstein et al. go on to show that a plateau potential can cause a place cell to respond less at the location it previously encoded, and more at the new location at the moment of stimulation. This indicates that it is possible to overwrite what had been previously learned and that plateau potentials may serve as some ‘supervisory’ signal to instruct synapses.
Further experiments led the team to propose and then test a computational model in which a synapse being strengthened or weakened depends on how strong that connection was to start with (Figure 1). According to the simulations, a weak synapse exposed to a plateau potential can only become stronger. However, a strong synapse can also weaken – whether it does depends on the time between receiving the plateau potential and the synapse being active.
Milstein et al. successfully demonstrate an additional mechanism of synaptic plasticity that enables rapid and reversible learning. It remains to be seen if this type of ‘supervised’ plasticity is consistent with other forms of plasticity observed in different regions of the brain, such as the cerebellum or cortex (Albus, 1971; Marr and Thach, 1991; Ito, 2008; Magee and Grienberger, 2020). Also, precisely how does the supervisory signal originate? And is supervised plasticity sufficient to explain the learning of complex tasks (Payeur et al., 2021), as well as the formation of episodic memory?
We may see a parallel between Cicero’s mechanism of pouring and today’s supervised plasticity, and between his “trace of things registered” and today’s connection strengths. The synapses (“trace of things registered”) are created by previous learning events, which form a network of varying strength (the “vessel”) on which the plateau potentials (and through them, the new life events) can act (be “poured”). It may well be that both processes are interlinked, and that learning – and memory – might both arise from these processes.
References
-
A theory of cerebellar functionMathematical Biosciences 10:25–61.https://doi.org/10.1016/0025-5564(71)90051-4
-
Conjunctive input processing drives feature selectivity in hippocampal CA1 neuronsNature Neuroscience 18:1133–1142.https://doi.org/10.1038/nn.4062
-
Control of mental activities by internal models in the cerebellumNature Reviews Neuroscience 9:304–313.https://doi.org/10.1038/nrn2332
-
Synaptic plasticity forms and functionsAnnual Review of Neuroscience 43:95–117.https://doi.org/10.1146/annurev-neuro-090919-022842
-
BookA theory of cerebellar cortexIn: Vaina L, editors. In From the Retina to the Neocortex. Birkhäuser Boston: Springer. pp. 11–50.https://doi.org/10.1007/978-1-4684-6775-8_3
-
Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuitsNature Neuroscience 24:1010–1019.https://doi.org/10.1038/s41593-021-00857-x
-
Control of synaptic plasticity in deep cortical networksNature Reviews. Neuroscience 19:166–180.https://doi.org/10.1038/nrn.2018.6
Article and author information
Author details
Publication history
Copyright
© 2022, Wang and Naud
This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 1,521
- views
-
- 134
- downloads
-
- 0
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Cell Biology
- Neuroscience
Overactivity of the sympathetic nervous system is a hallmark of aging. The cellular mechanisms behind this overactivity remain poorly understood, with most attention paid to likely central nervous system components. In this work, we hypothesized that aging also affects the function of motor neurons in the peripheral sympathetic ganglia. To test this hypothesis, we compared the electrophysiological responses and ion-channel activity of neurons isolated from the superior cervical ganglia of young (12 weeks), middle-aged (64 weeks), and old (115 weeks) mice. These approaches showed that aging does impact the intrinsic properties of sympathetic motor neurons, increasing spontaneous and evoked firing responses. A reduction of M current emerged as a major contributor to age-related hyperexcitability. Thus, it is essential to consider the effect of aging on motor components of the sympathetic reflex as a crucial part of the mechanism involved in sympathetic overactivity.
-
- Neuroscience
In amniotes, head motions and tilt are detected by two types of vestibular hair cells (HCs) with strikingly different morphology and physiology. Mature type I HCs express a large and very unusual potassium conductance, gK,L, which activates negative to resting potential, confers very negative resting potentials and low input resistances, and enhances an unusual non-quantal transmission from type I cells onto their calyceal afferent terminals. Following clues pointing to KV1.8 (Kcna10) in the Shaker K channel family as a candidate gK,L subunit, we compared whole-cell voltage-dependent currents from utricular HCs of KV1.8-null mice and littermate controls. We found that KV1.8 is necessary not just for gK,L but also for fast-inactivating and delayed rectifier currents in type II HCs, which activate positive to resting potential. The distinct properties of the three KV1.8-dependent conductances may reflect different mixing with other KV subunits that are reported to be differentially expressed in type I and II HCs. In KV1.8-null HCs of both types, residual outwardly rectifying conductances include KV7 (Knq) channels. Current clamp records show that in both HC types, KV1.8-dependent conductances increase the speed and damping of voltage responses. Features that speed up vestibular receptor potentials and non-quantal afferent transmission may have helped stabilize locomotion as tetrapods moved from water to land.