Efficient information coding and degeneracy in the nervous system

Seenivasan, Pavithraa; Narayanan, Rishikesh

doi:10.1016/j.conb.2022.102620

Cited by 11 publications

(9 citation statements)

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“…Biological complex systems manifest pronounced heterogeneities and exhibit degeneracy in the realization of precise functional outcomes. The nervous system in general, and the mammalian hippocampal formation with specific reference to this study, is no exception to this observation (Tononi et al, 1994; Sporns et al, 2000; Goldman et al, 2001; Price and Friston, 2002; Prinz et al, 2004; Leonardo, 2005; Gjorgjieva et al, 2016; Rathour and Narayanan, 2019; Goaillard and Marder, 2021; Mishra and Narayanan, 2021d; Seenivasan and Narayanan, 2022; Marom and Marder, 2023). Within the mammalian hippocampal formation, degeneracy in the emergence of characteristic physiological properties has been observed in pyramidal neurons of the CA1 (Rathour and Narayanan, 2012; 2014; Srikanth and Narayanan, 2015; Rathour et al, 2016; Migliore et al, 2018; Jain and Narayanan, 2020; Srikanth and Narayanan, 2023), pyramidal neurons of the CA3 (Roy and Narayanan, 2023), basket (Mishra and Narayanan, 2019) and granule (Beining et al, 2017b; Mishra and Narayanan, 2019; 2021c; b; Schneider et al, 2023) cells of the dentate gyrus, and stellate cells of the entorhinal cortex (Mittal and Narayanan, 2018; 2022).…”

Section: Discussionsupporting

confidence: 52%

“…The overall approach employed here could be used to build different heterogeneous populations of granule cells built with distinct morphologies and disparate sets of ion channels that emerge from respective experimental measurements that span a large set of characteristics from the same set of cells. Such analyses would enable a fundamental understanding of differences in heterogeneities, composition, extent of degeneracy in each state, and how they contribute to the physiology of the neurons and their networks (Ratté et al, 2014; Ratte and Prescott, 2016; Goaillard and Marder, 2021; Mishra and Narayanan, 2021d; Seenivasan and Narayanan, 2022; Marom and Marder, 2023; Mittag et al, 2023; Stober et al, 2023; Yang and Prescott, 2023).…”

Section: Discussionmentioning

confidence: 99%

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Ion-channel degeneracy and heterogeneities in the emergence of signature physiological characteristics of dentate gyrus granule cells

Kumari,

Narayanan

2024

Preprint

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Complex systems are neither fully determined nor completely random. Biological complex systems, including single neurons, manifest intermediate regimes of randomness that recruit integration of specific combinations of functionally segregated subsystems. Such emergence of biological function provides the substrate for the expression of degeneracy, the ability of disparate combinations of subsystems to yield similar function. Here, we present evidence for the expression of degeneracy in morphologically realistic models of dentate gyrus granule cells (GC) through functional integration of disparate ion-channel combinations. We performed a 45-parameter randomized search spanning 16 active and passive ion channels, each biophysically constrained by their gating kinetics and localization profiles, to search for valid GC models. Valid models were those that satisfied 17 sub- and supra-threshold cellular-scale electrophysiological measurements from rat GCs. A vast majority (>99%) of the 15,000 random models were not electrophysiologically valid, demonstrating that arbitrarily random ion-channel combinations wouldn't yield GC functions. The 141 valid models (0.94% of 15,000) manifested heterogeneities in and cross-dependencies across local and propagating electrophysiological measurements, which matched with their respective biological counterparts. Importantly, these valid models were widespread throughout the parametric space and manifested weak cross-dependencies across different parameters. These observations together showed that GC physiology could neither be obtained by entirely random ion-channel combinations nor is there an entirely determined single parametric combination that satisfied all constraints. The complexity, the heterogeneities in measurement and parametric spaces, and degeneracy associated with GC physiology should be rigorously accounted for, while assessing GCs and their robustness under physiological and pathological conditions.

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Section: Discussionsupporting

confidence: 52%

Section: Discussionmentioning

confidence: 99%

Ion-channel degeneracy and heterogeneities in the emergence of signature physiological characteristics of dentate gyrus granule cells

Kumari,

Narayanan

2024

Preprint

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“…Hence both extrinsic (synaptic) as well as intrinsic properties belong to important degenerate parameters of the circuit 112 . Therefore, in principle, synaptic mechanisms can compensate for the impaired function of intrinsic ion channels and vice versa 31 , 111 , thus increasing the robustness of the nervous system 23 by counterbalancing changes in overall synaptic drive and intrinsic excitability 113 .…”

Section: Degeneracy In Epilepsy At the Network Levelmentioning

confidence: 99%

Degeneracy in epilepsy: multiple routes to hyperexcitable brain circuits and their repair

et al. 2023

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Due to its complex and multifaceted nature, developing effective treatments for epilepsy is still a major challenge. To deal with this complexity we introduce the concept of degeneracy to the field of epilepsy research: the ability of disparate elements to cause an analogous function or malfunction. Here, we review examples of epilepsy-related degeneracy at multiple levels of brain organisation, ranging from the cellular to the network and systems level. Based on these insights, we outline new multiscale and population modelling approaches to disentangle the complex web of interactions underlying epilepsy and to design personalised multitarget therapies.

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“…3) as well as the constant current required to elicit action potentials in E neurons towards achieving patterned activity propagation. Thus, the intrinsic excitability ( IE ) of the neuron needed to be in balance with the overall synaptic drive that is defined by the E–I balance of the circuit for robust function, further emphasizing the need to assess E–I–IE balance in networks rather than focusing on E–I balance (Seenivasan and Narayanan, 2020, 2022). We tested the robustness of these 9 hand-tuned homogeneous ring networks to noise introduced by high background activity.…”

Section: Resultsmentioning

confidence: 99%

“…First, many CAN models assume idealized scenarios where the simpler elements (the neurons) are repeating homogeneous replicates with precisely defined spatially replicating interactions (synaptic connections) between them, manifesting no heterogeneities. However, heterogeneities are ubiquitous across scales of analysis in biological systems (Renart et al, 2003; Gjorgjieva et al, 2016; Cembrowski and Spruston, 2019; Mishra and Narayanan, 2019; Rathour and Narayanan, 2019; Mittal and Narayanan, 2021; Perez-Nieves et al, 2021; Mittal and Narayanan, 2022; Seenivasan and Narayanan, 2022; Shridhar et al, 2022; Segal et al, 2023). Hence, one of the fundamental requirements for extending the CAN framework to biological substrates is to assess the impact of neural-circuit heterogeneities on CAN model performance.…”

Section: Introductionmentioning

confidence: 99%

HCN channels enhance robustness of patterned activity propagation in heterogeneous conductance-based ring networks

Mittal,

Narayanan

2023

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Continuous attractor network (CAN) models lend a powerful framework that has provided deep insights about several aspects of brain physiology. However, most CAN models employ homogeneous, rate-based or artificially spiking neurons with precisely structured synaptic connectivity, precluding detailed analyses of the impact of specific neural-circuit components and associated heterogeneities on CAN dynamics. To address this caveat, we built populations of tunable and scalable conductance-based, physiologically constrained, ring network models consisting of distinct rings of excitatory and inhibitory neurons. We assessed the network for its ability to sustain robust propagation of patterned activity across the rings. First, in homogeneous ring networks, we found that robust activity propagation could be sustained through several different combinations of synaptic weights, demonstrating synaptic degeneracy in the emergence of robust activity propagation. We incorporated intrinsic heterogeneity through randomized perturbations to ion channel parameters of all neurons and synaptic heterogeneity by adding jitter to the Mexican-hat connectivity between inhibitory neurons. We found the number of networks exhibiting robust propagation of patterned activity to reduce with increase in the degree of synaptic or intrinsic heterogeneities. Motivated by the ability of intrinsic neuronal resonance to stabilize heterogeneous rate-based CAN models, we hypothesized that increasing HCN-channel (a resonating conductance) density would stabilize activity propagation in heterogeneous ring networks. Strikingly, we observed that increases in HCN-channel density resulted in a pronounced increase in the proportion of heterogeneous networks that exhibited robust activity propagation, across multiple trials and across three degrees of either form of heterogeneity. Together, heterogeneous networks made of neurons with disparate intrinsic properties and variable HCN channel densities yielded robust activity propagation, demonstrating intrinsic degeneracy in the emergence of robust activity propagation. Finally, as HCN channels also contribute to changes in excitability, we performed excitability-matched controls with fast HCN channels that do not introduce resonance. We found that fast HCN channels did not stabilize heterogeneous network dynamics over a wide range of conductance values, suggesting that the slow negative feedback loop introduced by HCN channels is a critical requirement for network stabilization. Together, our results unveil a cascade of degeneracy in ring-network physiology, spanning the molecular-cellular-network scales. These results also demonstrate a critical role for the widely expressed HCN channels in enhancing the robustness of heterogeneous neural circuits by implementing a slow negative feedback loop at the cellular scale.

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Efficient information coding and degeneracy in the nervous system

Cited by 11 publications

References 100 publications

Ion-channel degeneracy and heterogeneities in the emergence of signature physiological characteristics of dentate gyrus granule cells

Ion-channel degeneracy and heterogeneities in the emergence of signature physiological characteristics of dentate gyrus granule cells

Degeneracy in epilepsy: multiple routes to hyperexcitable brain circuits and their repair

HCN channels enhance robustness of patterned activity propagation in heterogeneous conductance-based ring networks

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