A jolt to the field: a self‐generating and self‐propagating ephaptically mediated slow spontaneous network activity pattern in the hippocampus
Bibliographic record
Abstract
Neural activity has been traditionally studied in terms of a sensory-motor (or input-output) function. That is to say, the operation of the nervous system has been typically viewed as an intermediary between actively sensing the environment and then actively producing a response. This framework unfortunately ignores the propensity of the brain to generate its own patterned and synchronized activity in the absence of any active input or output – most notably during states of sleep, or even anaesthesia. A major pattern in this regard is the slow oscillation, a ≤1 Hz network rhythm that appears across vast expanses of the forebrain and which entrains other local patterns of population activity (Steriade et al. 1993). The functional relevance of this input- and output-decoupled slow network rhythm remains a mystery, but one that will probably be solved by an elucidation of both the cellular and the inter-cellular mechanisms giving rise to it in the first place. While the study of spontaneous network neural rhythms is probably best done in situ (Steriade, 2001), certain ex vivo preparations, like brain slices, better lend themselves to experimental probing. Even more compelling for this field of study is that these types of preparations, even with the minimal amount of dissociated neural circuitry contained within, are capable of generating emergent forms of slow spontaneous network activity that bear more than a passing resemblance to the slow-wave patterns observed during sleep (Sanchez-Vives & McCormick, 2000; Dickson et al. 2003). Advantageously, ex vivo preparations are also highly useful to experimentally evaluate both the cellular and the network mechanisms giving rise to spontaneous population neural activity. Traditionally, it had been thought that spontaneous brain rhythms were a result of the interaction between the intrinsic properties of individual neurons and their extrinsic interactions via classical chemical or electrical synaptic transmission. While non-synaptic influences via exogenous or even endogenous electric fields (i.e. ephaptic mechanisms) have been suggested to play a role in modulating ongoing activity, these effects were thought to be reasonably limited, at least during physiologically relevant activity (Anastassiou & Koch, 2015). In this issue of The Journal of Physiology, Chiang, Shivacharan, Wei, Gonzalez-Reyes and Durand (Chiang et al. 2019) show that slow periodic activity in a horizontal hippocampal slice preparation occurs through dendritic NMDA receptor-dependent Ca2+ spiking, which is itself self-generating and self-propagating, via ephaptic interactions across neurons. Consistent with purely ephaptic transmission, this activity and its active propagation across the slice were resistant to pharmacological blockers of fast ionotropic chemical neurotransmission, as well as pharmacological blockade of electrical transmission via gap junctions. What is particularly compelling is that the activity could be not only modulated, but also eliminated or even regenerated by imposed electrical fields. Most shockingly, this activity could be transmitted from one side of a surgically severed slice to the other when the two cut edges were simply placed in close proximity. These surprising findings were further supported by a computer model of hippocampal circuitry. The role of endogenous electric fields generated by the brain (as well as those exogenously imposed on the brain) in neural synchronization is still a matter of investigation (Anastassiou & Koch, 2015). However, the implications of ephaptic mechanisms are certainly relevant to both physiological and pathological states, not to mention therapeutic possibilities. While it remains to be seen if the findings of Chiang et al (2019) are relevant to spontaneous slow rhythms that occur in both cortical and hippocampal tissue in situ during sleep and sleep-like states (Wolansky et al. 2006), they should probably (and quite literally) electrify the field. None declared. None declared. I would like to acknowledge Drs Silvia Pagliardini and Claire Scavuzzo, as well as Dr-to-be Brandon Hauer, for reviewing, editing, and commenting on a previous version of this Perspectives article.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".