Disparate forms of heterogeneities and interactions among them drive channel decorrelation in the dentate gyrus: Degeneracy and dominance

Abstract

The ability of a neuronal population to effectuate channel decorrelation, which is one form of response decorrelation, has been identified as an essential prelude to efficient neural encoding. To what extent are diverse forms of local and afferent heterogeneities essential in accomplishing channel decorrelation in the dentate gyrus (DG)? Here, we incrementally incorporated four distinct forms of biological heterogeneities into conductance‐based network models of the DG and systematically delineate their relative contributions to channel decorrelation. First, to effectively incorporate intrinsic heterogeneities, we built physiologically validated heterogeneous populations of granule (GC) and basket cells (BC) through independent stochastic search algorithms spanning exhaustive parametric spaces. These stochastic search algorithms, which were independently constrained by experimentally determined ion channels and by neurophysiological signatures, revealed cellular‐scale degeneracy in the DG. Specifically, in GC and BC populations, disparate parametric combinations yielded similar physiological signatures, with underlying parameters exhibiting significant variability and weak pair‐wise correlations. Second, we introduced synaptic heterogeneities through randomization of local synaptic strengths. Third, in including adult neurogenesis, we subjected the valid model populations to randomized structural plasticity and matched neuronal excitability to electrophysiological data. We assessed networks comprising different combinations of these three local heterogeneities with identical or heterogeneous afferent inputs from the entorhinal cortex. We found that the three forms of local heterogeneities were independently and synergistically capable of mediating significant channel decorrelation when the network was driven by identical afferent inputs. However, when we incorporated afferent heterogeneities into the network to account for the divergence in DG afferent connectivity, the impact of all three forms of local heterogeneities was significantly suppressed by the dominant role of afferent heterogeneities in mediating channel decorrelation. Our results unveil a unique convergence of cellular‐ and network‐scale degeneracy in the emergence of channel decorrelation in the DG, whereby disparate forms of local and afferent heterogeneities could synergistically drive input discriminability.

Degeneracy in hippocampal physiology and plasticity

“…In most encoding or homeostatic scenarios involving changes in constituent components, however, the functional outcome that is expected is a more quantitative readout of, say, firing rate or calcium concentration altered or returned to specific values . Therefore, a widely employed alternate interpretation (Basak & Narayanan, ; Foster et al, ; Goldman et al, ; Marder, ; Marder & Goaillard, ; Marder et al, ; Marder & Taylor, ; Migliore et al, ; Mishra & Narayanan, ; Mittal & Narayanan, ; Prinz et al, ; Rathour & Narayanan, ; Rathour & Narayanan, ; Srikanth & Narayanan, ; Taylor et al, ) is where degeneracy is assessed as the ability of different structural components to elicit quantitatively similar functional measurements. With reference to our chosen example, this would translate to assessing degeneracy as the ability to achieve a specific range of values of resonance frequency with disparate combinations of parameters (Figure b).…”

“…Within the purview of degeneracy, the emergence of specific combinations of higher-scale functions (within the limits of biological variability) could be achieved (Figure 1b) through interactions among disparate parametric combinations in a lower scale (Basak & Narayanan, 2018b;Edelman & Gally, 2001;Foster et al, 1993;Gjorgjieva et al, 2016;Goldman et al, 2001;Marder, 2011;Marder et al, 2015;Marder & Taylor, 2011;Mishra & Narayanan, 2019;Mittal & Narayanan, 2018;Prinz et al, 2004;Rathour & Narayanan, 2012a;Rathour & Narayanan, 2014;Srikanth & Narayanan, 2015;Stelling et al, 2004;Taylor et al, 2009). A straightforward corollary to this is that robust homeostasis in the maintenance of specific combinations of higherscale functions in the face of perturbations there would be achieved through very different routes involving disparate parametric combinations in a lower scale.…”

“…Such focus is especially important because of the seemingly contradictory requirements of the two processes, where one necessitates change and the other works to negate any change, resulting in the possibility where there could be detrimental cross-interference working towards negating each other. Therefore, for the framework of degeneracy to be relevant in learning systems, it is important that future studies assess the twin goals of encoding and homeostasis to be synergistically conjoined rather than treat them as isolated processes that independently achieve their respective goals (Basak & Narayanan, 2018b;Mishra & Narayanan, 2019). Without the recognition of such synergy between encoding and homeostatic systems, assessing the ability of these two processes to avoid cross-interference becomes intractable.…”

“…These observations provide specific insights about the relationship between channels and physiological properties (Section 2.1-2.3; Figure 2 and 3). First, there is degeneracy in the emergence of neurophysiological properties, where disparate combinations of channels could come together to elicit similar functional properties (Basak & Narayanan, 2018b;Drion et al, 2015;Foster et al, 1993;Goldman et al, 2001;Marder, 2011;Mishra & Narayanan, 2019;Mittal & Narayanan, 2018;Rathour et al, 2016;Rathour & Narayanan, 2012a;Rathour & Narayanan, 2014;Taylor et al, 2009). Second, the dependence of different physiological properties on distinct channels is variable even within the same neuronal subtype, and is a function of the variable expression profiles of these channels (Drion et al, 2015;Rathour & Narayanan, 2014;Taylor et al, 2009).…”

“…In contrast, A-type K + and HCN channels strongly overlap both in their voltage-dependence and localization, resulting in their ability to co-regulate the same form of resonance in hippocampal pyramidal neurons (Rathour et al, 2016;Rathour & Narayanan, 2012a;Rathour & Narayanan, 2014) These insights are driven by experimental observations coupled with physiologically relevant computational models that allowed greater flexibility in terms of understanding mechanistic basis, importance of ion channel interactions and the degree of contribution of each channel type in regulating neuronal properties. Multi parametric multi objective stochastic search algorithms are a class of algorithms that has been employed as an extremely effective method to explore cellular-level degeneracy in a systematic and rigorous manner through global sensitivity analysis (Anirudhan & Narayanan, 2015;Basak & Narayanan, 2018b;Drion et al, 2015;Foster et al, 1993;Goldman et al, 2001;Mishra & Narayanan, 2019;Mittal & Narayanan, 2018;Mukunda & Narayanan, 2017;Rathour & Narayanan, 2012a;Rathour & Narayanan, 2014;Taylor et al, 2009). These algorithms provide a quantitative route to understanding the structure of the global parametric space in any given model, without making explicit assumptions about co-variation of different parameters test the robustness of the system to parametric variability.…”