Degeneracy in hippocampal physiology and plasticity

Abstract

Degeneracy, defined as the ability of structurally disparate elements to perform analogous function, has largely been assessed from the perspective of maintaining robustness of physiology or plasticity. How does the framework of degeneracy assimilate into an encoding system where the ability to change is an essential ingredient for storing new incoming information? Could degeneracy maintain the balance between the apparently contradictory goals of the need to change for encoding and the need to resist change towards maintaining homeostasis? In this review, we explore these fundamental questions with the mammalian hippocampus as an example encoding system. We systematically catalog lines of evidence, spanning multiple scales of analysis that point to the expression of degeneracy in hippocampal physiology and plasticity. We assess the potential of degeneracy as a framework to achieve the conjoint goals of encoding and homeostasis without cross‐interferences. We postulate that biological complexity, involving interactions among the numerous parameters spanning different scales of analysis, could establish disparate routes towards accomplishing these conjoint goals. These disparate routes then provide several degrees of freedom to the encoding‐homeostasis system in accomplishing its tasks in an input‐ and state‐dependent manner. Finally, the expression of degeneracy spanning multiple scales offers an ideal reconciliation to several outstanding controversies, through the recognition that the seemingly contradictory disparate observations are merely alternate routes that the system might recruit towards accomplishment of its goals.

Ion-channel regulation of response decorrelation in a multi-scale model of the dentate gyrus

“…However, the contribution of BK ion channels to R in is high for model #36, whereas it is lower for model #50. This represents the variability in the dependence of different models on the same ion channel in regulating a given physiological measurement, and demonstrates that the contribution of a given structural component to a functional measurement is heterogeneous (Rathour and Narayanan, 2019).…”

“…An important difference between a hand-tuned model and this stochastic search approach is that the stochastic search model is unbiased, with no relationship in terms of which channel was introduced for matching what specific physiological property. Therefore, results arrived on the impact of individual channels emerge from the heterogeneous neuronal populations, without assignment of specific physiological purposes to individual channels (Foster et al, 1993;Goldman et al, 2001;Prinz et al, 2003;Taylor et al, 2009;Marder and Taylor, 2011;Narayanan, 2012a, 2014;Mishra and Narayanan, 2015;Srikanth and Narayanan, 2015;Basak and Narayanan, 2018;Mittal and Narayanan, 2018;Rathour and Narayanan, 2019).…”

“…Specifically, in a large proportion of GCs, knockout of CaL or BK ion channels introduced large increases in firing rate, elimination of CaN or CaT or HCN or SK ion channels resulted in relatively smaller increases in firing rate, and KA ion channel VKMs exhibited relatively small reduction in firing rate compared to their respective base models. Interpretations of changes, however, should not be drawn from the summary statistics, but should be driven by heterogeneities (Rathour and Narayanan, 2019). It should be noted that there are significant differences in different neurons in the same network during the same virtual traversal in terms of which ion channel plays a dominant role in altering firing rate.…”

“…properties. This provides neurons with considerable flexibility in stitching together their signature physiological characteristics and in maintaining robustness in these properties, without strong constraints on the specific expression profiles of individual ion channels (Goldman et al, 2001;Marder and Goaillard, 2006;Grashow et al, 2010;Marder and Taylor, 2011;Kispersky et al, 2012;Narayanan, 2012a, 2014;Drion et al, 2015;Basak and Narayanan, 2018;Mittal and Narayanan, 2018;Mishra and Narayanan, 2019;Rathour and Narayanan, 2019).…”

Plasticity manifolds: Conjunctive changes in multiple ion channels mediate activity-dependent plasticity in hippocampal granule cells

“…The concomitant roles of synaptic plasticity and neuron-specific intrinsic plasticity as putative cellular substrates of learning and memory are well established (Zhang and Linden, 2003;Kim and Linden, 2007;Johnston and Narayanan, 2008;Nelson and Turrigiano, 2008;Neves et al, 2008;Mozzachiodi and Byrne, 2010;Mayford et al, 2012;Narayanan and Johnston, 2012;Titley et al, 2017;Lisman et al, 2018;Rathour and Narayanan, 2019). The DG has been implicated in spatial navigation, response decorrelation, pattern separation, engram formation, learning and memory (Bliss and Lomo, 1973;Amaral et al, 2007;Leutgeb et al, 2007;McHugh et al, 2007;Bakker et al, 2008;Sahay et al, 2011;Aimone et al, 2014;Neunuebel and Knierim, 2014;Kropff et al, 2015;Diamantaki et al, 2016;Heigele et al, 2016;Danielson et al, 2017;GoodSmith et al, 2017;Li et al, 2017;Senzai and Buzsaki, 2017;Tonegawa et al, 2018;Mishra and Narayanan, 2019).…”