Dominant role of adult neurogenesis‐induced structural heterogeneities in driving plasticity heterogeneity in dentate gyrus granule cells

Abstract

Neurons and synapses manifest pronounced variability in the amount of plasticity induced by identical activity patterns. The mechanisms underlying such plasticity heterogeneity, which have been implicated in context-specific resource allocation during encoding, have remained unexplored. Here, we employed a systematic physiologically constrained parametric search to identify the cellular mechanisms behind plasticity heterogeneity in dentate gyrus granule cells. We used heterogeneous model populations to ensure that our conclusions were not biased by parametric choices in a single hand-tuned model. We found that each of intrinsic, synaptic, and structural heterogeneities independently yielded heterogeneities in synaptic plasticity profiles obtained with two different induction protocols. However, among the disparate forms of neural-circuit heterogeneities, our analyses demonstrated the dominance of neurogenesis-induced structural heterogeneities in driving plasticity heterogeneity in granule cells. We found that strong relationships between neuronal intrinsic excitability and plasticity emerged only when adult neurogenesis-induced heterogeneities in neural structure were accounted for. Importantly, our analyses showed that it was not imperative that the manifestation of neural-circuit heterogeneities must translate to heterogeneities in plasticity profiles. Specifically, despite the expression of heterogeneities in structural, synaptic, and intrinsic neuronal properties, similar plasticity profiles were attainable across all models through synergistic interactions among these heterogeneities. We assessed the parametric combinations required for the manifestation of such degeneracy in the expression of plasticity profiles. We found that immature cells showed physiological plasticity profiles despite receiving afferent inputs with weak synaptic strengths. Thus, the high intrinsic excitability of immature granule cells was sufficient to counterbalance their low excitatory drive in the expression of plasticity profile degeneracy. Together, our analyses demonstrate that disparate forms of neural-circuit heterogeneities could mechanistically drive plasticity heterogeneity, but also caution against treating neural-circuit heterogeneities as proxies for plasticity heterogeneity. Our study emphasizes the need for quantitatively characterizing the relationship between neural-circuit and plasticity heterogeneities across brain regions.Sameera Shridhar and Poonam Mishra contributed equally to this study.

Ion-channel degeneracy and heterogeneities in the emergence of complex spike bursts in CA3 pyramidal neurons

“…2 A ). Such a population of models, apart from avoiding the obvious biases and disadvantages associated with using a single model for all analyses, also provides a mechanism for assessing heterogeneities and degeneracy in the system under consideration (Prinz et al ., 2004; Rathour & Narayanan, 2014; Anirudhan & Narayanan, 2015; Srikanth & Narayanan, 2015; Mukunda & Narayanan, 2017; Basak & Narayanan, 2018; Mittal & Narayanan, 2018; Mishra & Narayanan, 2019; Rathour & Narayanan, 2019; Basak & Narayanan, 2020; Jain & Narayanan, 2020; Seenivasan & Narayanan, 2020; Goaillard & Marder, 2021; Roy & Narayanan, 2021; Shridhar et al ., 2022). To generate a population of models, we employed a multi-parametric multi-objective stochastic search algorithm (Fig.…”

“…Thus, the lack of structure in the underlying parameters should not be considered as evidence that any parametric combination would yield valid CA3 pyramidal neurons. Instead, the lack of structure in the valid parametric space should be interpreted as evidence for the manifestation of degeneracy, whereby disparate (yet specific) combinations of parameters yield similar physiological properties (Edelman & Gally, 2001; Prinz et al ., 2004; Taylor et al ., 2009; Rathour & Narayanan, 2012, 2014; Anirudhan & Narayanan, 2015; Srikanth & Narayanan, 2015; Rathour et al ., 2016; Das et al ., 2017; Basak & Narayanan, 2018; Migliore et al ., 2018; Mittal & Narayanan, 2018; Mishra & Narayanan, 2019; Rathour & Narayanan, 2019; Basak & Narayanan, 2020; Jain & Narayanan, 2020; Seenivasan & Narayanan, 2020; Goaillard & Marder, 2021; Mishra & Narayanan, 2021a; Roy & Narayanan, 2021; Shridhar et al ., 2022).…”

“…2A). Such a population of models, apart from avoiding the obvious biases and disadvantages associated with using a single model for all analyses, also provides a mechanism for assessing heterogeneities and degeneracy in the system under consideration (Prinz et al, 2004 Shridhar et al, 2022). To generate a population of models, we employed a multi-parametric multi-objective stochastic search algorithm (Fig.…”

“…;Anirudhan & Narayanan, 2015;Srikanth & Narayanan, 2015;Rathour et al, 2016;Das et al, 2017;Basak & Narayanan, 2018;Migliore et al, 2018;Mittal & Narayanan, 2018;Rathour & Narayanan, 2019;Basak & Narayanan, 2020;Jain & Narayanan, 2020;Seenivasan & Narayanan, 2020;Goaillard & Marder, 2021;Mishra & Narayanan, 2021a;Roy & Narayanan, 2021;Shridhar et al, 2022).…”

Plasticity manifolds and ion-channel degeneracy govern circadian oscillations of neuronal intrinsic properties in the suprachiasmatic nucleus

“…If the parametric composition of the individual neuron changes across cycles, it stands to reason that the same magnitudes of changes that were employed in the previous cycle might not yield a valid physiological transition in this cycle. The plasticity degeneracy provides an elegant framework for achieving valid transitions not just across neurons in a population manifesting parametric heterogeneities (Anirudhan and Narayanan, 2015; Mukunda and Narayanan, 2017; Shridhar et al, 2022), but also to individual neurons transitioning across cycles through different combinations of ion-channel plasticity. Plasticity degeneracy implies that the set of ion channels and mechanisms mediating circadian oscillations could be very different in adjacent SCN neurons as well as in different cycles of the same neuron.…”