The informational content of the mind determines its underlying neural structure.

Neural ensembles: role of intrinsic excitability and its plasticity. 2024. https://doi.org/10.3389/fncel.2024.1440588

Synaptic connectivity defines groups of neurons that engage in correlated activity during specific functional tasks. These co-active groups of neurons form ensembles, the operational units involved in, for example, sensory perception, motor coordination and memory (then called an engram). Traditionally, ensemble formation has been thought to occur via strengthening of synaptic connections via long-term potentiation (LTP) as a plasticity mechanism. This synaptic theory of memory arises from the learning rules formulated by Hebb and is consistent with many experimental observations. Here, we propose, as an alternative, that the intrinsic excitability of neurons and its plasticity constitute a second, non-synaptic mechanism that could be important for the initial formation of ensembles. Indeed, enhanced neural excitability is widely observed in multiple brain areas subsequent to behavioral learning. In cortical structures and the amygdala, excitability changes are often reported as transient, even though they can last tens of minutes to a few days. Perhaps it is for this reason that they have been traditionally considered as modulatory, merely supporting ensemble formation by facilitating LTP induction, without further involvement in memory function (memory allocation hypothesis). We here suggest−based on two lines of evidence—that beyond modulating LTP allocation, enhanced excitability plays a more fundamental role in learning. First, enhanced excitability constitutes a signature of active ensembles and, due to it, subthreshold synaptic connections become suprathreshold in the absence of synaptic plasticity (iceberg model). Second, enhanced excitability promotes the propagation of dendritic potentials toward the soma and allows for enhanced coupling of EPSP amplitude (LTP) to the spike output (and thus ensemble participation). This permissive gate model describes a need for permanently increased excitability, which seems at odds with its traditional consideration as a Indeed, enhanced neural excitability is widely observed in multiple brain areas subsequent to behavioral learning. In cortical structures and the amygdala, excitability changes are often reported as transient, even though they can last tens of minutes to a few days. Perhaps it is for this reason that they have been traditionally considered as modulatory, merely supporting ensemble formation by facilitating LTP induction, without further involvement in memory function (memory allocation hypothesis). We here suggest−based on two lines of evidence—that beyond modulating LTP allocation, enhanced excitability plays a more fundamental role in learning. We propose that longer modifications in excitability are made possible by a low threshold for intrinsic plasticity induction, suggesting that excitability might be on/off-modulated at short intervals. Consistent with this, in cerebellar Purkinje cells, excitability lasts days to weeks, which shows that in some circuits the duration of the phenomenon is not a limiting factor in the first place. In our model, synaptic plasticity defines the information content received by neurons through the connectivity network that they are embedded in. Permanent role for enhanced excitability despite transient nature: role for an on/off modulation Let us assume for a moment that enhanced neuronal excitability is essential for ensemble function (in a later paragraph, we will present evidence to support this claim). Would not the transient nature of excitability potentiation make such essential contribution impossible? To begin with, it should be noted that intrinsic plasticity is not in all brain structures and neuron types short-lived. In cerebellar Purkinje cells, enhanced excitability was observed one month after eyeblink conditioning ( Schreurs et al., 1998 ). (C) When a mature network exists that is fully synaptically connected and encodes defined input, context-dependent activation of an ensemble based on this underlying network is possible by activity-driven enhancement of intrinsic excitability that may be transient or lasting, depending on the type of neuron and the activation conditions. This excitability enhancement requires synaptic drive but does not involve synaptic plasticity. Synaptic plasticity remains an active learning mechanism that is recruited to stabilize synapses with new information content and to adjust their input weights. The synaptic plasticity machinery for these processes is available from early postnatal development onwards and the molecular pathways involved in LTD and LTP remain similar across these developmental stages ( Piochon et al., 2016b ). When we emphasize the importance of intrinsic plasticity in ensemble and engram function, we do not suggest that synaptic plasticity is not important. Synaptic plasticity mechanisms may be the main mechanism that neurons use to detect and learn associative input relationships and establish neural circuits based on connectivity principles informed by such associative structures ( Hansel, 2024 ). Given the specific positioning of GABAergic interneuronal input on dendritic shafts ( Kwon et al., 2018 ), a role for this mechanism in ensemble integration needs to be further explored. However, in contrast to any synaptic mechanism, intrinsic plasticity is in principle not associative, but cell autonomous, and it is activity-dependent and reflects the activation history of a neuron. In this way, intrinsic excitability and its plasticity may act as a constant driver for drifts in neural ensemble composition and activity ( Delamore et al., 2023 ). Experimental and computational future work will be required to further test the model, in particular in cortical structures. This work on plasticity mechanisms needs to go hand-in-hand with attempts to better understand the nature and signaling consequences of ensembles themselves. Author contributions CH: Conceptualization, Funding acquisition, Investigation, Resources, Validation,