Small-scale neural simulation of Caenorhabditis elegans on Loihi-2 using NeuroML2Loihi

Danny Rosen; Ramashish Gaurav; Yang Yi

cs.NEOct 18, 2026

Small-scale neural simulation of Caenorhabditis elegans on Loihi-2 using NeuroML2Loihi

Authors: Danny Rosen, Ramashish Gaurav, Yang Yi

Organizations: Virginia Tech, Institute for Advanced Computing

Abstract

Neural simulation is an essential methodology for studying biological nervous systems, but it remains limited by the computational costs of repeatedly updating neural and synaptic states over many timesteps. Neuromorphic hardware is a promising substrate for this class of workloads because such devices combine event-driven execution, localized memory, and spike-based communication. However, neuromorphic software remains comparatively fragmented and difficult to integrate with the model-description standards used by the computational neuroscience community. This disconnect limits reproducible experimentation and makes direct comparison between conventional and neuromorphic simulation workflows difficult. This paper introduces ``NeuroML2Loihi'', an open-source framework for translating NeuroML model descriptions into Loihi~~2-deployable Lava primitives, enabling standardized biological models to be executed on Intel's Loihi~~2 neuromorphic chip. We evaluate our framework using c302-derived C. elegans neural models across three connectome-scale configurations. Cross-platform similarity is assessed using spike-train and voltage-trace metrics, while latency, power, and energy consumption are benchmarked against the conventional reference workflow. Across the evaluated models, Loihi2 execution enabled through NeuroML2Loihi achieves 514--1211 $\times$ lower latency and a 1.8--1.9 $\times$ reduction in mean wattage relative to the NEURON reference, producing substantially lower total energy consumption primarily through reduced execution time. These results demonstrate that standards-based neuromorphic neural simulation is feasible on Loihi2 and can support meaningful comparison against established computational neuroscience workflows.

Figures & tables

Panel of Figure 1 — Figure 1. The NeuroML2Loihi framework. A NeuroML model ( .nml ) and its LEMS simulation description ( .xml ) are read by separate parsers into a Network (a NetworkX graph) and a Configuration. Guided by the Mapping module, NeuroML components are assembled into Lava primitives (LIF/Dense) within the Network. The Experiment couples the Network and Configuration with an externally selected Backend—Loihi 2 hardware or the LoihiSim simulator—executes the run, and records outputs for Analysis.

Panel of Figure 2 — Figure 2. Vectorized Lava process network. All neurons are represented by a single LIF process connected recurrently to a Dense synaptic process through the adjacency matrix $W$ derived from the parsed NeuroML model.

Panel of Figure 3 — Figure 3. Hardware input and recording pipeline. Input spikes drive the vectorized LIF process, which carries a recurrent Dense synapse. Spike outputs are routed through a Dense process to Receiver and StateProbe processes, while membrane voltage is recorded directly by a StateProbe on the neuron’s $v$ property.

Panel of Figure 4 — Figure 4. Visualization of the Full ( A ) and Active ( B ) neuron subsets in the c302-derived NeuroML2 networks used in this study. Figures were generated using from the corresponding NeuroML model files used for simulation using pyNeuroML ( Vella et al., 2014 ) .

Parameter	Value
$d_{u}$	$49$
$d_{v}$	$8$
$v_{\mathrm{th}}$	$131,071$
Recurrent scale	$10\cdot 2^{5}=320$
Input scale	$4$
Biological duration	$300\text{\,}\mathrm{ms}$

Table 1. Model parameters.

metric	N2L vs NEURON	N2L vs Poisson	Ratio
VP@1	1.0914	3.8133	0.2862
VP@50	5.5700	12.2079	0.4563
VR	3.4194	4.7154	0.7252
$\lvert\Delta\mathrm{spikes}\rvert/\mathrm{neuron}$	1.0000	3.5000	0.2857

Table 2. NeuroML2Loihi vs NEURON

metric	PLM	Active	Full
Active Neurons Loihi 2	2	18	18
Active Neurons NEURON	2	13	13
voltage r	-0.1441	0.4334	0.4334
voltage RMSE ( $mV$ )	8.8322	3.9428	0.2350
voltage MAE ( $mV$ )	7.2744	3.3601	0.2003

Table 3. Voltage (NeuroML2Loihi affine-mapped to NEURON voltage scales)

Figure 5. Superimposed PLML voltage traces from the NeuroML2Loihi and NEURON simulations. The comparison illustrates that the platforms produce similar structured voltage evolution while differing in the exact subthreshold trajectory due to the use of Lava LIF and Dense primitives on Loihi versus the NeuroML/c302 neuron and synapse dynamics used by the reference simulator.

Model	N2L latency (s)	NEURON run time (s)	Speedup	N2L energy (J)	NEURON energy (J)	Energy (N2L / NEURON)
Minimal	$0.0243\pm 0.0001$	$12.47\pm 0.11$	$514\times$	$0.0122\pm 0.0001$	$11.65\pm 0.89$	0.105%
Active	$0.0242\pm 0.0001$	$18.62\pm 0.09$	$768\times$	$0.0123\pm 0.0001$	$18.11\pm 0.36$	0.0676%
Full	$0.0258\pm 0.0001$	$31.19\pm 0.12$	$1211\times$	$0.0131\pm 0.0001$	$28.52\pm 0.55$	0.0458%

Table 4. N2L on Loihi 2 vs NEURON on Jetson – Latency and Energy

Model	N2L power (W)	NEURON power (W)	Power ratio (NEURON / N2L)
Minimal	$0.5047\pm 0.0018$	$0.9393\pm 0.0706$	1.9x
Active	$0.5052\pm 0.0020$	$0.9778\pm 0.0199$	1.9x
Full	$0.5067\pm 0.0020$	$0.9189\pm 0.0178$	1.8x

Table 5. Power comparison between N2L simulation on Loihi-2 vs NEURON on Jetson

Model	Cores	Footprint (MB)	Total % mean
Minimal	3	0.001686	0.29966
Active	3	0.005424	0.96418
Full	9	0.6571	38.93860

Table 6. Loihi on-chip memory footprint

Neuromorphic Computing

Neuromorphic computing uses brain-inspired, event-driven algorithms and hardware to process information with low power and latency.

See all 186 papers

Sigma-Delta Neural Network Conversion on Loihi 2
May 9, 2025Matthew Brehove, Sadia Anjum Tumpa, Espoir Kyubwa +2Efficient Neural Network Inference Spiking Neural Networks
ChromoLogic LLC Monrovia, California, USA · Pennsylvania State University University Park, Pennsylvania, USA
SuperNeuroMAT: An Efficient Matrix-based Simulator for Spiking Neural Networks
Aug 9, 2026Prasanna Date, Kevin Zhu, Shruti Kulkarni +12LIF Neurons Spiking Neural Networks
Oak Ridge National Laboratory, Oak Ridge, 37830, Tennessee, United States. · George Mason University, Fairfax, 22030, Virginia, United States. · University of Tennessee, Knoxville, Knoxville, 37996, Tennessee, United States. +1
Convolutional Sparse Coding via the Locally Competitive Algorithm on Loihi 2
Jun 7, 2026Geoffrey Kasenbacher, Daniel Ruepp, Gerrit A. EckeRepresentation Learning Neuromorphic Computing
Mercedes-Benz AG · Böblingen, Germany · Institut für Robotik und Kognitive Systeme, Universität zu Lübeck

May 9, 2025J/K move · Enter open · S save

Sigma-Delta Neural Network Conversion on Loihi 2

Matthew Brehove, Sadia Anjum Tumpa, Espoir Kyubwa, Naresh Menon+1

ChromoLogic LLC Monrovia, California, USA · Pennsylvania State University University Park, Pennsylvania, USA

Efficient Neural Network Inference Spiking Neural Networks Neuromorphic Computing

Open paper AskPDF arXiv

Neuromorphic computing aims to improve the efficiency of artificial neural networks by taking inspiration from biological neurons and leveraging temporal sparsity, spatial sparsity, and compute near/in memory. Although these approaches have shown efficiency gains, training these spiking neural networks (SNN) remains difficult. The original attempts at converting trained conventional analog neural networks (ANN) to SNNs used the rate of binary spikes to represent neuron activations. This required many simulation time steps per inference, which degraded efficiency. Intel's Loihi 2 is a neuromorphic platform that supports graded spikes which can be used to represent changes in neuron activation. In this work, we use Loihi 2's graded spikes to develop a method for converting ANN networks to spiking networks, which exploits temporal and spatial sparsity. We evaluated the performance of this network on Loihi 2 and compared it to NVIDIA's Jetson Xavier edge AI platform. The results show that neuromorphic approaches achieve significant improvements in efficiency and latency (energy-delay product) over existing solutions.

Abstract

Neuromorphic Computing

Explore similar work

Sigma-Delta Neural Network Conversion on Loihi 2

SuperNeuroMAT: An Efficient Matrix-based Simulator for Spiking Neural Networks

Convolutional Sparse Coding via the Locally Competitive Algorithm on Loihi 2