Consequences of degeneracy in network function

The Brain's Best Kept Secret Is Its Degenerate Structure

Degeneracy is defined as multiple sets of solutions that can produce very similar system performance. Degeneracy is seen across phylogenetic scales, in all kinds of organisms. In neuroscience, degeneracy can be seen in the constellation of biophysical properties that produce a neuron's characteristic intrinsic properties and/or the constellation of mechanisms that determine circuit outputs or behavior. Here, we present examples of degeneracy at multiple levels of organization, from single-cell behavior, small circuits, large circuits, and, in cognition, drawing conclusions from work ranging from bacteria to human cognition. Degeneracy allows the individual-to-individual variability within a population that creates potential for evolution.

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

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 specialized 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 (GCs) 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 suprathreshold 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 would not 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.NEW & NOTEWORTHY A recent study from our laboratory had demonstrated pronounced heterogeneities in a set of 17 electrophysiological measurements obtained from a large population of rat hippocampal granule cells. Here, we demonstrate the manifestation of ion-channel degeneracy in a heterogeneous population of morphologically realistic conductance-based granule cell models that were validated against these measurements and their cross-dependencies. Our analyses show that single neurons are complex entities whose functions emerge through intricate interactions among several functionally specialized subsystems.

Applications of information geometry to spiking neural network activity

The space of possible behaviors that complex biological systems may exhibit is unimaginably vast, and these systems often appear to be stochastic, whether due to variable noisy environmental inputs or intrinsically generated chaos. The brain is a prominent example of a biological system with complex behaviors. The number of possible patterns of spikes emitted by a local brain circuit is combinatorially large, although the brain may not make use of all of them. Understanding which of these possible patterns are actually used by the brain, and how those sets of patterns change as properties of neural circuitry change is a major goal in neuroscience. Recently, tools from information geometry have been used to study embeddings of probabilistic models onto a hierarchy of model manifolds that encode how model outputs change as a function of their parameters, giving a quantitative notion of "distances" between outputs. We apply this method to a network model of excitatory and inhibitory neural populations to understand how the competition between membrane and synaptic response timescales shapes the network's information geometry. The hyperbolic embedding allows us to identify the statistical parameters to which the model behavior is most sensitive, and demonstrate how the ranking of these coordinates changes with the balance of excitation and inhibition in the network.

Persistent modulatory actions and task switching in the feeding network of Aplysia

The activity of multifunctional networks is configured by neuromodulators that exert persistent effects. This raises a question, does this impact the ability of a network to switch from one type of activity to another? We review studies that have addressed this question in the Aplysia feeding circuit. Task switching in this system occurs "asymmetrically." When there is a switch from egestion to ingestion neuromodulation impedes switching (creates a "negative bias"). When there is a switch from ingestion to egestion the biasing is "positive." Ingestion promotes subsequent egestion. We contrast mechanisms responsible for the two types of biasing and show that the observed asymmetry is a consequence of the fact that there is more than one set of egestive circuit parameters.

Variable task switching in the feeding network of Aplysia is a function of differential command input

Command systems integrate sensory information and then activate the interneurons and motor neurons that mediate behavior. Much research has established that the higher-order projection neurons that constitute these systems can play a key role in specifying the nature of the motor activity induced, or determining its parametric features. To a large extent, these insights have been obtained by contrasting activity induced by stimulating one neuron (or set of neurons) to activity induced by stimulating a different neuron (or set of neurons). The focus of our work differs. We study one type of motor program, ingestive feeding in the mollusc Aplysia californica, which can either be triggered when a single projection neuron (CBI-2) is repeatedly stimulated or can be triggered by projection neuron coactivation (e.g., activation of CBI-2 and CBI-3). We ask why this might be an advantageous arrangement. The cellular/molecular mechanisms that configure motor activity are different in the two situations because the released neurotransmitters differ. We focus on an important consequence of this arrangement, the fact that a persistent state can be induced with repeated CBI-2 stimulation that is not necessarily induced by CBI-2/3 coactivation. We show that this difference can have consequences for the ability of the system to switch from one type of activity to another.NEW & NOTEWORTHY We study a type of motor program that can be induced either by stimulating a higher-order projection neuron that induces a persistent state, or by coactivating projection neurons that configure activity but do not produce a state change. We show that when an activity is configured without a state change, it is possible to immediately return to an intermediate state that subsequently can be converted to any type of motor program.

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

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.

Degeneracy and stability in neural circuits of dopamine and serotonin neuromodulators: A theoretical consideration

Degenerate neural circuits perform the same function despite being structurally different. However, it is unclear whether neural circuits with interacting neuromodulator sources can themselves degenerate while maintaining the same neuromodulatory function. Here, we address this by computationally modeling the neural circuits of neuromodulators serotonin and dopamine, local glutamatergic and GABAergic interneurons, and their possible interactions, under reward/punishment-based conditioning tasks. The neural modeling is constrained by relevant experimental studies of the VTA or DRN system using, e.g., electrophysiology, optogenetics, and voltammetry. We first show that a single parsimonious, sparsely connected neural circuit model can recapitulate several separate experimental findings that indicated diverse, heterogeneous, distributed, and mixed DRNVTA neuronal signaling in reward and punishment tasks. The inability of this model to recapitulate all observed neuronal signaling suggests potentially multiple circuits acting in parallel. Then using computational simulations and dynamical systems analysis, we demonstrate that several different stable circuit architectures can produce the same observed network activity profile, hence demonstrating degeneracy. Due to the extensive D2-mediated connections in the investigated circuits, we simulate the D2 receptor agonist by increasing the connection strengths emanating from the VTA DA neurons. We found that the simulated D2 agonist can distinguish among sub-groups of the degenerate neural circuits based on substantial deviations in specific neural populations' activities in reward and punishment conditions. This forms a testable model prediction using pharmacological means. Overall, this theoretical work suggests the plausibility of degeneracy within neuromodulator circuitry and has important implications for the stable and robust maintenance of neuromodulatory functions.

Minimal requirements for a neuron to co-regulate many properties and the implications for ion channel correlations and robustness

Neurons regulate their excitability by adjusting their ion channel levels. Degeneracy – achieving equivalent outcomes (excitability) using different solutions (channel combinations) – facilitates this regulation by enabling a disruptive change in one channel to be offset by compensatory changes in other channels. But neurons must coregulate many properties. Pleiotropy – the impact of one channel on more than one property – complicates regulation because a compensatory ion channel change that restores one property to its target value often disrupts other properties. How then does a neuron simultaneously regulate multiple properties? Here, we demonstrate that of the many channel combinations producing the target value for one property (the single-output solution set), few combinations produce the target value for other properties. Combinations producing the target value for two or more properties (the multioutput solution set) correspond to the intersection between single-output solution sets. Properties can be effectively coregulated only if the number of adjustable channels (n_in) exceeds the number of regulated properties (n_out). Ion channel correlations emerge during homeostatic regulation when the dimensionality of solution space (n_in − n_out) is low. Even if each property can be regulated to its target value when considered in isolation, regulation as a whole fails if single-output solution sets do not intersect. Our results also highlight that ion channels must be coadjusted with different ratios to regulate different properties, which suggests that each error signal drives modulatory changes independently, despite those changes ultimately affecting the same ion channels.

Perturbation-specific responses by two neural circuits generating similar activity patterns

A fundamental question in neuroscience is whether neuronal circuits with variable circuit parameters that produce similar outputs respond comparably to equivalent perturbations.^1-4 Work on the pyloric rhythm of the crustacean stomatogastric ganglion (STG) showed that highly variable sets of intrinsic and synaptic conductances can generate similar circuit activity patterns.^5-9 Importantly, in response to physiologically relevant perturbations, these disparate circuit solutions can respond robustly and reliably,^10-12 but when exposed to extreme perturbations the underlying circuit parameter differences produce diverse patterns of disrupted activity.^7,12,13 In this example, the pyloric circuit is unchanged; only the conductance values vary. In contrast, the gastric mill rhythm in the STG can be generated by distinct circuits when activated by different modulatory neurons and/or neuropeptides.^14-21 Generally, these distinct circuits produce different gastric mill rhythms. However, the rhythms driven by stimulating modulatory commissural neuron 1 (MCN1) and bath-applying CabPK (Cancer borealis pyrokinin) peptide generate comparable output patterns, despite having distinct circuits that use separate cellular and synaptic mechanisms.^22-25 Here, we use these two gastric mill circuits to determine whether such circuits respond comparably when challenged with persisting (hormonal: CCAP) or acute (sensory: GPR neuron) metabotropic influences. Surprisingly, the hormone-mediated action separates these two rhythms despite activating the same ionic current in the same circuit neuron during both rhythms, whereas the sensory neuron evokes comparable responses despite acting via different synapses during each rhythm. These results highlight the need for caution when inferring the circuit response to a perturbation when that circuit is not well defined physiologically.

The Drosophila Larval Locomotor Circuit Provides a Model to Understand Neural Circuit Development and Function

It is difficult to answer important questions in neuroscience, such as: "how do neural circuits generate behaviour?," because research is limited by the complexity and inaccessibility of the mammalian nervous system. Invertebrate model organisms offer simpler networks that are easier to manipulate. As a result, much of what we know about the development of neural circuits is derived from work in crustaceans, nematode worms and arguably most of all, the fruit fly, Drosophila melanogaster. This review aims to demonstrate the utility of the Drosophila larval locomotor network as a model circuit, to those who do not usually use the fly in their work. This utility is explored first by discussion of the relatively complete connectome associated with one identified interneuron of the locomotor circuit, A27h, and relating it to similar circuits in mammals. Next, it is developed by examining its application to study two important areas of neuroscience research: critical periods of development and interindividual variability in neural circuits. In summary, this article highlights the potential to use the larval locomotor network as a "generic" model circuit, to provide insight into mammalian circuit development and function.

Ion Channel Degeneracy, Variability, and Covariation in Neuron and Circuit Resilience

The large number of ion channels found in all nervous systems poses fundamental questions concerning how the characteristic intrinsic properties of single neurons are determined by the specific subsets of channels they express. All neurons display many different ion channels with overlapping voltage- and time-dependent properties. We speculate that these overlapping properties promote resilience in neuronal function. Individual neurons of the same cell type show variability in ion channel conductance densities even though they can generate reliable and similar behavior. This complicates a simple assignment of function to any conductance and is associated with variable responses of neurons of the same cell type to perturbations, deletions, and pharmacological manipulation. Ion channel genes often show strong positively correlated expression, which may result from the molecular and developmental rules that determine which ion channels are expressed in a given cell type.

Chronic vs acute manipulations reveal degeneracy in a thermosensory neuron network

Degenerate networks can drive similar circuit outputs. Via acute manipulation of individual neurons, we previously identified circuit components that are necessary and sufficient to drive starvation-dependent plasticity in C. elegans thermotaxis behavior. Here we find that when these components are instead silenced chronically, degenerate mechanisms compensate to drive this behavior. Our results indicate that degeneracy in neuronal network function can be revealed under specific experimental conditions.

A Multicellular Network Mechanism for Temperature-Robust Food Sensing

Responsiveness to external cues is a hallmark of biological systems. In complex environments, it is crucial for organisms to remain responsive to specific inputs even as other internal or external factors fluctuate. Here, we show how the nematode Caenorhabditis elegans can discriminate between different food levels to modulate its lifespan despite temperature perturbations. This end-to-end robustness from environment to physiology is mediated by food-sensing neurons that communicate via transforming growth factor β (TGF-β) and serotonin signals to form a multicellular gene network. Specific regulations in this network change sign with temperature to maintain similar food responsiveness in the lifespan output. In contrast to robustness of stereotyped outputs, our findings uncover a more complex robustness process involving the higher order function of discrimination in food responsiveness. This process involves rewiring a multicellular network to compensate for temperature and provides a basis for understanding gene-environment interactions. Together, our findings unveil sensory computations that integrate environmental cues to govern physiology.

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C. elegans’ ability to modulate lifespan in response to food is robust to temperature

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Robustness requires TGF-β and serotonin signaling in a neuronal network

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Specific regulations in the neuronal network change sign with temperature

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Temperature-dependent regulations compensate for temperature

Feeding state functionally reconfigures a sensory circuit to drive thermosensory behavioral plasticity

Internal state alters sensory behaviors to optimize survival strategies. The neuronal mechanisms underlying hunger-dependent behavioral plasticity are not fully characterized. Here we show that feeding state alters C. elegans thermotaxis behavior by engaging a modulatory circuit whose activity gates the output of the core thermotaxis network. Feeding state does not alter the activity of the core thermotaxis circuit comprised of AFD thermosensory and AIY interneurons. Instead, prolonged food deprivation potentiates temperature responses in the AWC sensory neurons, which inhibit the postsynaptic AIA interneurons to override and disrupt AFD-driven thermotaxis behavior. Acute inhibition and activation of AWC and AIA, respectively, restores negative thermotaxis in starved animals. We find that state-dependent modulation of AWC-AIA temperature responses requires INS-1 insulin-like peptide signaling from the gut and DAF-16/FOXO function in AWC. Our results describe a mechanism by which functional reconfiguration of a sensory network via gut-brain signaling drives state-dependent behavioral flexibility.

Degeneracy and Redundancy in Active Inference

The notions of degeneracy and redundancy are important constructs in many areas, ranging from genomics through to network science. Degeneracy finds a powerful role in neuroscience, explaining key aspects of distributed processing and structure-function relationships in the brain. For example, degeneracy accounts for the superadditive effect of lesions on functional deficits in terms of a "many-to-one" structure-function mapping. In this paper, we offer a principled account of degeneracy and redundancy, when function is operationalized in terms of active inference, namely, a formulation of perception and action as belief updating under generative models of the world. In brief, "degeneracy" is quantified by the "entropy" of posterior beliefs about the causes of sensations, while "redundancy" is the "complexity" cost incurred by forming those beliefs. From this perspective, degeneracy and redundancy are complementary: Active inference tries to minimize redundancy while maintaining degeneracy. This formulation is substantiated using statistical and mathematical notions of degenerate mappings and statistical efficiency. We then illustrate changes in degeneracy and redundancy during the learning of a word repetition task. Finally, we characterize the effects of lesions-to intrinsic and extrinsic connections-using in silico disconnections. These numerical analyses highlight the fundamental difference between degeneracy and redundancy-and how they score distinct imperatives for perceptual inference and structure learning that are relevant to synthetic and biological intelligence.

Network architectures supporting learnability

Human learners acquire complex interconnected networks of relational knowledge. The capacity for such learning naturally depends on two factors: the architecture (or informational structure) of the knowledge network itself and the architecture of the computational unit-the brain-that encodes and processes the information. That is, learning is reliant on integrated network architectures at two levels: the epistemic and the computational, or the conceptual and the neural. Motivated by a wish to understand conventional human knowledge, here, we discuss emerging work assessing network constraints on the learnability of relational knowledge, and theories from statistical physics that instantiate the principles of thermodynamics and information theory to offer an explanatory model for such constraints. We then highlight similarities between those constraints on the learnability of relational networks, at one level, and the physical constraints on the development of interconnected patterns in neural systems, at another level, both leading to hierarchically modular networks. To support our discussion of these similarities, we employ an operational distinction between the modeller (e.g. the human brain), the model (e.g. a single human's knowledge) and the modelled (e.g. the information present in our experiences). We then turn to a philosophical discussion of whether and how we can extend our observations to a claim regarding explanation and mechanism for knowledge acquisition. What relation between hierarchical networks, at the conceptual and neural levels, best facilitate learning? Are the architectures of optimally learnable networks a topological reflection of the architectures of comparably developed neural networks? Finally, we contribute to a unified approach to hierarchies and levels in biological networks by proposing several epistemological norms for analysing the computational brain and social epistemes, and for developing pedagogical principles conducive to curious thought. This article is part of the theme issue 'Unifying the essential concepts of biological networks: biological insights and philosophical foundations'.

Pain revised - learning from anomalies

As professional health care personnel we are well educated in anatomy, physiology, clinical medicine and so forth. Our patients present with various symptoms and signs that we use this knowledge to diagnose and treat. But sometimes the patient case contradicts our knowledge. Since the patient is the terrain and our knowledge is the map, these patient cases are anomalies that give us the opportunity to update our maps. One such anomaly is how time restricted amnesia can improve or even eradicate an underlying chronic pain condition and eliminate the patient's dependence on daily opioid consumption. In this short communication I will use amnesia as a starting point to briefly review chronic pain from a learning and memory perspective. I will introduce, for many readers, new concepts like degeneracy and criticality, and together with more familiar concepts like habits and brain network activity, we will end with overarching principles for how chronic pain treatment in general can be crafted and individualized almost independently of the chronic pain condition at hand. This introductory article is followed by a review series that elaborates on the fundamental biological principles for chronic pain, treatment options, and testing the theory with real world data.

Multidimensional Neural Selectivity in the Primate Amygdala

The amygdala contributes to multiple functions including attention allocation, sensory processing, decision-making, and the elaboration of emotional behaviors. The diversity of functions attributed to the amygdala is reflected in the response selectivity of its component neurons. Previous work claimed that subsets of neurons differentiate between broad categories of stimuli (e.g., objects vs faces, rewards vs punishment), while other subsets are narrowly specialized to respond to individual faces or facial features (e.g., eyes). Here we explored the extent to which the same neurons contribute to more than one neural subpopulation in a task that activated multiple functions of the amygdala. The subjects (Macaca mulatta) watched videos depicting conspecifics or inanimate objects, and learned by trial and error to choose the individuals or objects associated with the highest rewards. We found that the same neurons responded selectively to two or more of the following task events or stimulus features: (1) alerting, task-related stimuli (fixation icon, video start, and video end); (2) reward magnitude; (3) stimulus categories (social vs nonsocial); and (4) stimulus-unique features (faces, eyes). A disproportionate number of neurons showed selectivity for all of the examined stimulus features and task events. These results suggest that neurons that appear specialized and uniquely tuned to specific stimuli (e.g., face cells, eye cells) are likely to respond to multiple other types of stimuli or behavioral events, if/when these become behaviorally relevant in the context of a complex task. This multidimensional selectivity supports a flexible, context-dependent evaluation of inputs and subsequent decision making based on the activity of the same neural ensemble.

Network Degeneracy and the Dynamics of Task Switching in the Feeding Circuit in Aplysia

The characteristics of a network are determined by parameters that describe the intrinsic properties of the component neurons and their synapses. Degeneracy occurs when more than one set of parameters produces the same (or very similar) output. It is not clear whether network degeneracy impacts network function or is simply a reflection of the fact that, although it is important for a network to be able to generate a particular output, it is not important how this is achieved. We address this issue in the feeding network of the mollusc Aplysia In this system, there are two stimulation paradigms that generate egestive motor programs: repetition priming and positive biasing. We demonstrate that circuit parameters differ in the 2 cases (e.g., egestive repetition priming requires activity in an interneuron, B20, which is not essential for positive biasing). We show that degeneracy has consequences for task switching. If egestive repetition priming is immediately followed by stimulation of an ingestive input to the feeding central pattern generator, the first few cycles of activity are egestive (not ingestive). In this situation, there is a task switch cost. This "cost" is in part due to the potentiating effect of egestive repetition priming on B20. In contrast, there is no switch cost after positive biasing. Stimulation of the ingestive central pattern generator input immediately triggers ingestive activity. Our results indicate that the mechanisms used to pattern activity can impact network function in that they can determine how readily a network can switch from one configuration to another.SIGNIFICANCE STATEMENT A particular pattern of neural activity can be generated by more than one set of circuit parameters. How or whether this impacts network function is unclear. We address this issue in the feeding network of Aplysia and demonstrate that degeneracy in network function can have consequences for task switching. Namely, we show that, when egestive activity is generated via one set of circuit modifications, an immediate switch to ingestive activity is not possible. In contrast, rapid transitions to ingestive activity are possible if egestive activity is generated via a different set of circuit modifications.

A central control circuit for encoding perceived food value

Hunger state can substantially alter the perceived value of a stimulus, even to the extent that the same sensory cue can trigger antagonistic behaviors. How the nervous system uses these graded perceptual shifts to select between opposed motor patterns remains enigmatic. Here, we challenged food-deprived and satiated Lymnaea to choose between two mutually exclusive behaviors, ingestion or egestion, produced by the same feeding central pattern generator. Decoding the underlying neural circuit reveals that the activity of central dopaminergic interneurons defines hunger state and drives network reconfiguration, biasing satiated animals toward the rejection of stimuli deemed palatable by food-deprived ones. By blocking the action of these neurons, satiated animals can be reconfigured to exhibit a hungry animal phenotype. This centralized mechanism occurs in the complete absence of sensory retuning and generalizes across different sensory modalities, allowing food-deprived animals to increase their perception of food value in a stimulus-independent manner to maximize potential calorific intake.

Newly Identified Aplysia SPTR-Gene Family-Derived Peptides: Localization and Function

When individual neurons in a circuit contain multiple neuropeptides, these peptides can target different sets of follower neurons. This endows the circuit with a certain degree of flexibility. Here we identified a novel family of peptides, the Aplysia SPTR-Gene Family-Derived peptides (apSPTR-GF-DPs). We demonstrated apSPTR-GF-DPs, particularly apSPTR-GF-DP2, are expressed in the Aplysia CNS using immunohistochemistry and MALDI-TOF MS. Furthermore, apSPTR-GF-DP2 is present in single projection neurons, e.g., in the cerebral-buccal interneuron-12 (CBI-12). Previous studies have demonstrated that CBI-12 contains two other peptides, FCAP/CP2. In addition, CBI-12 and CP2 promote shortening of the protraction phase of motor programs. Here, we demonstrate that FCAP shortens protraction. Moreover, we show that apSPTR-GF-DP2 also shortens protraction. Surprisingly, apSPTR-GF-DP2 does not increase the excitability of retraction interneuron B64. B64 terminates protraction and is modulated by FCAP/CP2 and CBI-12. Instead, we show that apSPTR-GF-DP2 and CBI-12 increase B20 excitability and B20 activity can shorten protraction. Taken together, these data indicate that different CBI-12 peptides target different sets of pattern-generating interneurons to exert similar modulatory actions. These findings provide the first definitive evidence for SPTR-GF's role in modulation of feeding, and a form of molecular degeneracy by multiple peptide cotransmitters in single identified neurons.

Four Principles Regarding an Effective Treatment of Aging

The question whether aging is a disease or not, has been asked by many professionals who are involved in the study of age-related degeneration. However, not only an agreement on this remains elusive, but also effective clinical treatments against human aging have not been forthcoming. In this Opinion paper I suggest that the complexity involved in aging is such that we need to remodel our thinking to involve a much more 'systems-oriented' approach. I explore four main principles which should be employed by those who are working on finding treatments against agerelated degeneration. First, I discuss the problems encountered in translating laboratory research into effective therapies for humans. Second, I propose that a 'systems-thinking' method needs to be more extensively employed, instead of relying exclusively on the current reductionist one. Third, it is submitted that we must learn from the history of life-extension research, and not blindly follow contemporary paradigms, which may lead us into yet more 'dead ends' with regards to therapies. Finally, I suggest that, we may need to employ certain universal notions and use these in order to gain insights into the mechanics of a possible therapy against age-related degeneration. Examples may be the principle of hormesis, those of degeneracy, exaptation, and others from cybernetic or systems science domains. By using this four-pronged approach we liberate our thinking from the shackles of existing common mistakes and fallacies, and we open the way for a fresh approach that may lead us towards entirely new paradigms for providing clinically effective therapies against agerelated degeneration.