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Benchmarking the Connectomes of Caenorhabditis elegans within the Reservoir Computing Framework
Felix S. Reimers, Ola Huse Ramstad, Aliaksandr Hubin, Stefano Nichele
TL;DR
The paper asks whether C. elegans connectomes have useful computational performance and whether wiring, biological input/output choices, or age matter. It benchmarks connectome-derived echo state network reservoirs against randomized null models across tasks and configurations. Null models often outperform original models, while outcomes vary with connectome type, task, and configuration, without a consistent age trend.
Problem
The paper asks whether C. elegans connectomes show beneficial computational performance and whether biological wiring, input/output choices, or age affect benchmark outcomes.
Source: §1 · p. 2Method
Results
In most cases null models outperform original models, and whether biological wiring and input/output choices help depends on connectome type, reservoir configuration, task, and performance measure.
Source: §5 · p. 12Takeaways & Limitations
The results reflect differences among connectome types, but aging shows no consistent performance increase or decrease across tasks or series.
Source: §5 · p. 12Statistical significance was identified using a 0.05 threshold.
Source: §3 · p. 7
Abstract
from arXiv · showhide
The aim of this work is to examine the connectomes of Caenorhabditis elegans through a computational lens using the reservoir computing framework. Connectomes are mappings of biological neural networks; C. elegans is the first organism for which physical connectomes covering the whole nervous system have been published. The connectomes of C. elegans used in this paper have been derived at different ages of the organism and are based on three different ways of measuring inter-cellular connections. They have, with minimal preprocessing, been implemented as reservoirs in the form of echo state networks, which are recurrent neural networks. In reservoir computing, the reservoir itself is not trained, rather the output of the reservoir is passed to a comparatively small read-out module in which training takes place. Training and testing is conducted in different neuro-inspired tasks, with the aim of using these tasks as a benchmark for the connectomes. This process has been repeated with different configurations of the reservoir and equally sized but randomized null models have been used for comparison. The results show that the biological wiring and a bio-informed configuration of input and output nodes of the reservoirs do not necessarily lead to better performance. Contrarily, the randomized null models are often outperforming the original connectomes on the chosen benchmarks. At the same time it becomes clear that the results depend a lot on the configuration of the reservoir and the way the connectome has been derived from the organism. Connectomes from different ages may produce varying outcome, without a clear trend becoming visible.
1 Introduction
The paper treats the C. elegans connectome as a computational substrate rather than analyzing it only with graph-theoretical measures. It benchmarks connectome-derived reservoirs against randomized null models across tasks, configurations, and ages.
1 Introduction
- C. elegans is the first animal with a published physical connectome covering its whole nervous system.The connectome can be interpreted as network topology with nodes labeled by biological function.Source: §1 · p. 2
- Reservoir computing tests connectomes as computational substrates by projecting inputs through untrained recurrent networks and training only a comparatively small read-out module.The framework is comparatively agnostic to the system used and requires relatively little training.Source: §1 · p. 2
- The study asks whether C. elegans connectomes show beneficial benchmark performance, whether biological wiring and input/output choices improve it, and whether performance varies with age.Connectome-derived reservoirs are compared with randomized null models across neuro-inspired tasks.Source: §1 · p. 2
2 Related Work
The paper draws on prior work using network performance to examine network state and on biological cell-culture reservoirs. It applies a computational-reservoir perspective to digital snapshots of an organism’s connectome.
2 Related Work
- Prior work found that iteratively dropping nodes from Norway’s mobile communication network measurably decreased performance in a similar framework [4].This was described as a proof-of-concept examination of whether network state can be inferred from computational performance.Source: §2 · p. 3
- The conn2res toolbox implements connectomes as reservoirs, supports biological node labels and multiple architectures, and natively supports tasks from NeuroGym and ReservoirPy.It provides the technical basis for the paper’s implementation.Source: §2 · p. 3
- Prior studies used in-vitro cell cultures as reservoirs or actively trained substrates [8], whereas this work examines digital connectome snapshots from an organism.The authors suggest in-silico benchmarking might help set targets for biological computers and allow early testing.Source: §2 · p. 3
3 Framework
The framework implements physical C. elegans connectomes as echo state network reservoirs and tests them across tasks, reservoir configurations, and randomized null models. Inputs and outputs can be selected using biological labels, while only the read-out is trained.
3 Framework
- The 22 examined connectomes represent 180 brain neurons plus muscles, glia, and canal-associated neurons, across larval and adult stages and three physical connection measures.Synapse-count and synapse-size networks are directed; physical-contact networks are bidirectional and describe structure rather than information flow.Source: §3 · pp. 3–4
- Echo state network reservoirs use normalized, scaled connectome weights that remain frozen during training, while only the read-out module is trained.Fixed input weights project signals to selected reservoir nodes, and the read-out interprets states sampled from selected output nodes; sensory neurons and body-wall muscles can be selected biologically.Source: §3 · pp. 4–5, Figure 2 · p. 5
- The framework compares original connectomes with three null models that separately randomize wiring, input/output nodes, or both.Each original-model run is compared with null models sampled 15 times; randomized input/output nodes are not used in the first reservoir configuration.Source: §3 · p. 5
- Five configurations vary the number and selection of input/output nodes, including age-wide biological node availability and sensory or muscle-group proportions of 30%, 50%, or 80%.The first two configurations present inputs without alteration; the remaining configurations randomly sample input weights from a uniform distribution over [−1, 1].Source: §3 · pp. 5–6
- Five benchmarks test memory, chaotic time-series prediction, and input integration for classification, using shared sampled instances and a 70%/30% train/test split.Regression uses squared Pearson correlation for later statistical analysis and RMSE; classification uses filtered accuracy measured during decision time steps.Source: §3 · p. 6
- Statistical analysis models performance differences between original and null models using beta regression with fixed effects, with additional Gaussian or gamma regressions for three prediction and memory tasks.The pooled model includes network, connectome, age, and reservoir series with two-way terms; analyses are also repeated separately by connectome type.Source: §3 · pp. 6–7
- Statistical significance is identified using a 0.05 threshold.The stated significance level is an analysis criterion.Source: §3 · p. 7
4 Results and Analysis
Across tasks, connectome performance varies with age, reservoir configuration, and connectome type, without a clear age trend. Randomized input/output nodes often outperform original wiring, and conclusions also depend on task and performance measure.
4 Results and Analysis
- Null models two and three often perform similarly, while synaptic-size and synapse-number connectomes also perform quite similarly.These similarities motivated focusing subsequent discussion on the first and second null models and on physical-contact versus synaptic connectomes.Source: §4 · p. 7
- Performance often varies significantly across ages, reservoir configurations, and connectome types, although Perceptual Decision Making often shows no significant variation.Visual inspection shows no clear overall increase or decrease in performance with organism age.Source: §4 · p. 7, Figure 3 · p. 9, Figure 4 · p. 10
- Memory Capacity varies strongly by connectome type, and signs can flip between physical-contact and synaptic connectomes, making age margins sensitive to averaging.The analysis repeated by connectome type confirmed differing benchmark results for connectomes derived from the same organism.Source: §4 · p. 7
- Against the first null model, originals often match it on Perceptual Decision Making, do better on Go/No-go, and fare worse on the two time-series prediction tasks.Memory Capacity results depend on the connectome type.Source: §4 · pp. 7, 9
- Randomized-I/O null models outperform originals especially in configurations allowing many I/O nodes, and randomizing I/O appears to matter more than rewiring.The pattern is reported for Perceptual Decision Making and the Hénon and Mackey-Glass prediction tasks.Source: §4 · p. 9, Figure 5 · p. 11, Figure 6 · p. 11
- For Hénon and Mackey-Glass prediction, RMSE results largely agree with correlation-based results, but RMSE no longer shows synaptic connectomes outperforming null models on Memory Capacity.The Memory Capacity contrasts were relatively small, and mean age contrasts for synapse-based connectomes flip under RMSE, favoring null models.Source: §4 · pp. 9–10
5 Conclusion
The study benchmarks C. elegans connectomes as reservoirs across five tasks, using three null models to examine the effects of biologically informed wiring and input/output choices. Performance varies with connectome type, reservoir configuration, task, and measure; null models outperform originals in most cases, while age shows no consistent trend.
5 Conclusion
- The study tests connectomes derived using different measurements and ages as reservoirs with varied I/O configurations on five neuro-inspired tasks, comparing them with three null models.The null models are used to examine the impact of biologically informed wiring and I/O choices.Source: §5 · p. 12
- Performance differences reflect the three connectome measurement types, while aging shows no consistent increase or decrease across tasks or series.The authors propose testing node or edge deletions as a follow-up, noting their effect on the mobile communication networks examined in [4].Source: §5 · p. 12
- In most cases, null models outperform original models, but whether biologically informed wiring and I/O helps depends on connectome type, reservoir configuration, task, and performance measure.The advantage is especially true for random I/O nodes; synaptic networks are an exception on Memory Capacity, motivating further investigation.Source: §5 · p. 12
Appendix
The appendix includes network heatmaps, task time-series plots, and pooled-analysis contrasts comparing original connectomes with the first null model.
Appendix
- Figures 7 and 8 show the L1.1 synapse-count network before and after rewiring, respectively.The captions identify the networks but do not state a performance outcome.Source: Figure 7 · p. 13, Figure 8 · p. 13
- Figure 9 plots the time-series data associated with each task, with the Go/No-go series identified in panel (e).Source: Figure 9 · p. 14, Appendix · p. 14
- Figures 10 and 11 plot pooled-analysis original-minus-null1 contrasts on the logit scale, by connectome type and age, respectively.Both show confidence intervals and use color to indicate statistical significance; Figure 11 merges the two adult states.Source: Figure 10 · p. 15, Figure 11 · p. 15