Pith. sign in

REVIEW 5 major objections 7 minor 48 references

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

T0 review · 5 major / 7 minor · reviewed 2026-08-14 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read The paper claims that a matrix-based formulation of LIF dynamics makes its Python SNN simulator faster and more memory-efficient than four established simulators on CPUs, while also running ML and general-purpose workloads.

desk verdict A genuinely useful open-source CPU SNN simulator, but the headline speed claim rests on a degenerate benchmark and the STDP rule is mis-specified. read the letter →

arxiv 2608.08479 v1 pith:MMK35QW5 submitted 2026-08-09 cs.NE cs.AIcs.CEcs.ETcs.LG

Prasanna Date , Kevin Zhu , Shruti Kulkarni , Ashish Gautam , Chathika Gunaratne , Robert Patton , Tyler Nitzsche , Ian Mulet
show 7 more authors
Zachary Johnson-Scott Addison Helms Duncan Rowden Simon Weston Maryam Parsa Catherine Schuman Thomas Potok
This is my paper · ORCID
classification cs.NEcs.AIcs.CEcs.ETcs.LG
keywords spikingneuralnetworksleakyintegrate-and-firematrix-basedsimulationspike-timing-dependentplasticityneuromorphiccomputingsparsematrixcomputationevent-basedvisionCPU
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

SuperNeuroMAT is a Python simulator that models leaky integrate-and-fire (LIF) neuron dynamics entirely as matrix and vector updates, letting one CPU compute an entire network's state in a few array operations. The paper's central claim is that this matrix-based formulation makes the simulator consistently faster and more memory-efficient than four established SNN simulators across network sizes and connection probabilities, without special hardware. A sympathetic reader would care because it suggests that ordinary laptops can simulate roughly 10,000 neurons densely and 100,000 neurons sparsely, which lowers the barrier to experimenting with spiking networks. The same simulator is also used for conventional machine learning benchmarks, neuromorphic event-based vision, and general-purpose computations such as shortest paths and binary arithmetic, arguing that one framework can span neuroscience, ML, and general workloads.

What carries the argument

The central object is the vectorized LIF recurrence $v[t]=v[t-1]-\lambda+x[t]+W^T[t-1]s[t-1]$, where $v[t]$ is the membrane-potential vector, $\lambda$ the leak vector, $x[t]$ external input, $W[t-1]$ the synaptic weight matrix, and $s[t-1]$ the previous spike vector. Around it sit the spike test $s_i[t]=1$ if $v_i[t]>\nu_i$ and the refractory counter update $\tau'_i[t]=\tau_i$ on a spike, otherwise $\max\{0,\tau'_i[t-1]-1\}$. STDP is handled by forming spike-vector outer products over the learning window and adding them to the weight matrix. What this machinery does is replace per-neuron and per-synapse loops with a small number of highly optimized array operations, so each simulation time step costs $O(N^2)$ time and space in dense mode and $O(N+S)$ in sparse mode, making the CPU the bottleneck rather than Python-level bookkeeping.

What would settle it

Re-run the Section 6.1.2 comparison at 10,000 neurons and full connectivity using finite membrane time constants, nonzero thresholds, and randomly drawn synaptic delays; if the reported runtime ranking or memory ordering changes materially, the claim that the matrix-based approach consistently outperforms the four simulators fails.

Watch

Extended reading notes

Core claim

The discovery is that the full LIF update, including leak, input spikes, and recurrent network spikes, collapses into the vector recurrence $v[t]=v[t-1]-\lambda+x[t]+W^T[t-1]s[t-1]$, with firing decided by the threshold test in Equation 10 and refractory periods by Equation 11. Spikes are binary vectors, so the synaptic-weight matrix multiplied by the previous spike vector delivers all incoming contributions at once. STDP learning is likewise recast as outer products $s[t-t_s]s^T[t]$ summed over the learning window, so weight updates become matrix operations rather than synapse-by-synapse loops. The authors report that this array-oriented formulation, backed by dense and sparse array libraries, simulates up to about 10,000 neurons in dense mode and 100,000 in sparse mode on standard CPUs, and that in their comparisons it finishes 100-timestep simulations of 10,000 fully connected neurons in 7.18 seconds while the slowest baseline takes over 1,500 seconds. They further claim the same codebase trains a two-layer STDP network on Digits, classifies citation-graph nodes from topology alone, learns features on event-based N-CARS and ASL-DVS data, and reproduces shortest paths and 8-bit arithmetic exactly.

Load-bearing premise

The load-bearing premise is that the equalized benchmark configuration—zero threshold, infinite-leak (memoryless) LIF neurons with unit-delay delta synapses—is representative of real SNN workloads, so that the measured speed and memory differences reflect implementation efficiency rather than the artificial simplicity of the test.

Editorial extensions

If this is right

  • At the scales tested, a single CPU node can simulate 10,000-neuron dense networks and 100,000-neuron sparse networks, so off-chip training and experimentation no longer require a GPU or an HPC allocation.
  • Because the 10,000-neuron, fully connected 100-timestep simulation runs in seconds rather than minutes to hours, parameter sweeps over network size, connectivity, and STDP settings become practical on a laptop.
  • The same update equations support both dense and sparse execution, and the simulator auto-selects the mode, so users get the faster representation without manually choosing data structures.
  • STDP as outer products makes learning a native, vectorized operation, which the paper demonstrates by training a two-layer network on Digits and by unsupervised feature learning on event-camera datasets.
  • General-purpose computations, including shortest path and 8-bit addition and multiplication, can be encoded as spike timing in the same simulator, supporting the paper's claim that one framework covers ML and non-ML workloads.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the matrix formulation, not the specific benchmark, is the source of the speed, the same equations should port almost directly to GPU array libraries; the paper only reports CPU results, so a GPU port is a natural test.
  • The benchmark equalizes models using zero-threshold, infinite-leak, unit-delay neurons; that degenerate configuration may be the least favorable regime for event-driven simulators, so a sweep over finite time constants and heterogeneous delays would test whether the reported speedups generalize to biologically realistic settings.
  • The graph and arithmetic demonstrations are small (8 nodes, 8-bit operands); trusting the general-purpose claim would be easier after stress-testing these constructions at the 100,000-neuron scale advertised for sparse mode.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 7 minor

Summary. SuperNeuroMAT is a Python/NumPy/SciPy SNN simulator that expresses LIF membrane updates (Eq. 9), spike generation (Eq. 10), refractory dynamics (Eq. 11), and STDP learning (Eqs. 12-19) as vectorized dense or sparse operations, with synaptic delays realized as chains of delay neurons. The paper claims (i) a novel matrix-based formulation that enables roughly 10,000-neuron dense and 100,000-neuron sparse simulations on commodity CPUs; (ii) consistent outperformance of NEST, Brian2, BindsNET, and snnTorch in execution speed and peak resident memory across network sizes and connection probabilities; and (iii) versatility across conventional ML (Digits, citation networks), neuromorphic vision (N-CARS, ASL-DVS), and general-purpose workloads (shortest path, 8-bit addition and multiplication). The efficiency study consists of an internal dense-versus-sparse crossover analysis (Section 6.1.1) and a cross-simulator benchmark on synthetic Erdos-Renyi graphs (Section 6.1.2) using zero-threshold, infinite-leak LIF neurons with unit-delay delta synapses. The application sections verify the shortest-path decoding against Dijkstra's algorithm and the arithmetic circuits against exact integer arithmetic.

Significance. If the claims are sustained, SuperNeuroMAT would be a genuinely useful contribution: a lightweight, open-source, PyPI-installable CPU simulator that combines neuroscience-style features (delay chains, refractory period, STDP) with the throughput of vectorized linear algebra, and it validates non-ML use (shortest path, arithmetic) against external ground truth with exact matches. The dense/sparse crossover analysis in Section 6.1.1 is careful and honestly reports regimes in which each mode wins. However, the headline efficiency claim rests on a boundary-case benchmark whose semantics are not verified across frameworks, the benchmark excludes the STDP operation the paper itself identifies as the dominant cost, and the STDP depression update of Eq. (17) is internally inconsistent with the stated rule. The significance is therefore conditional: it depends on re-scoping the efficiency claims to the measured regimes and correcting or verifying the learning formulation.

major comments (5)
  1. [6.1.2 (with 5.8, 6.1.1, 6.2.3)] The cross-simulator benchmark that supports the headline efficiency claim matches all frameworks on a degenerate configuration: zero threshold, memoryless (infinite-leak) LIF neurons, unit-delay delta synapses, and, to judge from the two-orders-of-magnitude gap between the ~508 s dense runtime with STDP enabled in Section 6.1.1 and the 7.18 s dense runtime at the same size and connectivity in Section 6.1.2, STDP disabled. In SuperNeuroMAT this configuration reduces the update of Eq. (9) to a thresholded matrix-vector product with no temporal integration, no finite-leak memory, and no refractory interaction, which is precisely the operation its dense NumPy and sparse SciPy backends are specialized for; event-driven simulators such as NEST and Brian2 carry queue and per-spike object overhead that is largest in exactly this high-activity saturated regime. The assertion that matching semantics makes runtime differences attributable to implementation efficiency is not backed by a reported spike-train equivalence check across the five frameworks, and the parameterizations used (BindsNET LIFNodes with 'near-zero decay', snnTorch Leaky with 'zero decay', NEST iaf_psc_delta, and a custom Brian2 equation set) are not demonstrated to reproduce SuperNeuroMAT's clamp-to-reset leak behavior. The representativeness problem is internal to the paper: Section 5.8 identifies STDP as the dominant per-step cost (O(N^2 T_S)), and the paper's own application examples use finite leak (Section 6.2.3), refractory periods (Section 6.2.2), and 2 ms steps with fast and slow traces (Section 6.2.4), none of which is covered by the Section 6.1.2 configuration. As it stands, the 10-215x speedups support only this boundary configuration, not 'consistent' outperformance across realistic workloads; please (i) verify spike-train equivalence across frameworks, (ii) add benchmarks with finite leak, refractory periods, and STDP enabled, and (iii) scope the abstract claims to the measured regimes.
  2. [Abstract and Section 6.1.2] The abstract and Section 7 state that SuperNeuroMAT 'consistently outperforms' NEST, Brian2, BindsNET, and snnTorch on execution speed and peak resident memory 'across various network sizes and connection probabilities.' The paper's own measurements contradict this at two points. At the smallest network size, Section 6.1.2 reports that at 10 neurons NEST is 'marginally faster than SuperNeuroMAT.' At the largest scale, the same section reports that at 10,000 neurons with full connectivity SuperNeuroMAT consumes approximately the same peak memory as Brian2 and slightly more than the remaining three simulators, with the text arguing that the speed advantage 'more than compensates' for the memory shortfall. These are not minor footnotes: they fall inside the claimed domain and on the second of the two claimed metrics. The abstract and conclusion should be reworded to state the measured exceptions and the regimes in which each claim holds (for example, speed advantage for N >= 100, and memory advantage at large scale only at sparse-to-moderate connectivity).
  3. [Section 5.6, Eqs. (13), (15), (17)] The negative STDP update is internally inconsistent with the rule stated in Section 5.6. The text specifies that synapse (i,j) is depressed when the post-synaptic neuron j spiked before the pre-synaptic neuron i; the coincidence indicator at entry (i,j) is then s_i[t] s_j[t - t_s], i.e., an outer product of the current spike vector with the lagged spike vector (equivalently, the transpose of the potentiation outer product in Eq. (12)). Instead, Eqs. (13), (15), and (17) use the complement 1_{N x N} - s[t - t_s] s^T[t], which is equal to 1 at every entry except those exhibiting the potentiation pattern. Since the depression coefficients in Table 5 satisfy alpha^-_{ts} <= 0, this complement applies a negative update at essentially every STDP-enabled synapse at every time step, including synapses whose pre- and post-synaptic neurons have no anti-causal spiking coincidence at all. As written, Eq. (17) therefore implements a global weight decay coupled with a potentiation mask, not the causal anti-correlation rule described in the text. Because STDP is the learning mechanism behind the Digits (Section 6.2.1), citation-network (Section 6.2.2), N-CARS (Section 6.2.3), and ASL-DVS (Section 6.2.4) results, the authors should correct the derivation and report a unit-level verification (for example, analytic weight-change traces on a small two-neuron example) that the implemented update matches the stated rule.
  4. [Section 6.2.3, Approach 1] The reported gain of 4.3 percentage points (60.7% to 65.0%) is obtained by deactivating two excitatory neurons and increasing the leak of six others from 0.05 to 0.15, where the identity of these neurons was chosen after examining spike activity associated with false positives and false negatives, and the post-intervention accuracy is then measured on the same data partition. No independent validation split is described for the selection and tuning step, so the procedure amounts to post-hoc model selection on the test set, and the 4.3-point improvement is not a valid estimate of generalization. Please perform the selection on a training subset (or use a nested validation procedure) and report accuracy on an untouched test partition, or present the intervention solely as a descriptive analysis without a generalization claim.
  5. [Section 6.2.2, Table 6, and Section 6.2.1] The assertion that the citation-network accuracies in Table 6 (Cora 62.7%, CiteSeer 42.4%, PubMed 54.7%) are 'state-of-the-art results for spiking graph neural network models that do not incorporate features' is unsupported: no numbers from prior feature-free spiking graph methods are cited, and no topology-only baselines (for example, label propagation, degree or structural baselines, or standard GNNs such as GCN or GAT under the same feature-free constraint) are reported. Likewise, the Digits accuracy of 93% is described as 'at par with the state-of-the-art for such a two-layer SNN' without citing a comparison. Please add concrete baseline values with sources and state the experimental protocol (Planetoid splits, hyperparameters, number of runs) so that each 'state-of-the-art' or 'at par' claim is checkable.
minor comments (7)
  1. [Figure 4 caption] The Figure 4 caption describes the Y-axis as 'the time in seconds,' but the figure and the surrounding text plot peak resident memory in MB; please correct the caption.
  2. [Table 4, Delay row] The notation column of the Delay row reads 'W in N^{N x N}' where the delay matrix D is intended; the symbol should be D in N^{N x N}.
  3. [Section 6.1.2] No version numbers are reported for NEST, Brian2, BindsNET, snnTorch, or for NumPy/SciPy/BLAS; runtime and memory comparisons are not reproducible without these details, although the hardware description is otherwise commendably complete.
  4. [Section 5.5 and Abstract] The claim that the matrix-based formulation is 'unprecedented in the literature' and 'novel' is stronger than the evidence supports; tensor-based SNN libraries, including BindsNET and snnTorch which are benchmarked in this paper, already express LIF updates as vectorized matrix operations. Please soften the priority claim and attribute novelty to the specific design choices (dense/sparse auto-selection, delay chains, and the CPU-oriented engineering).
  5. [Section 2] The statement that BindsNET and snnTorch 'lack certain neuron or synapse parameters, for instance, synaptic delay' is not substantiated, and recent snnTorch releases support synaptic delays; please verify the claim against the current versions of both libraries.
  6. [Section 5.8] The space complexity of Eq. (19) is stated as O(S) for the sparse implementation, yet Eqs. (16)-(17) form dense N x N outer products at every time step; the total sparse space complexity does include the O(N^2 T_S) term, but the O(S) claim for the STDP update itself should be reconciled with that dense computation.
  7. [Section 6, first paragraph] The phrase 'as detailed in in Section 6.1' contains a duplicated word ('in in'); please correct it.

Circularity Check

1 steps flagged · score 4.0 of 10

Partial circularity in the N-CARS fine-tuning step; the LIF derivation and cross-simulator efficiency claims are independent.

  1. fitted input called prediction [Section 6.2.3, Approach 1 (N-CARS), paragraph following Figure 7]
    "SuperNeuroMAT’s spike train was then used to compare activity associated with true positive, true negative, false positive, and false negative predictions. As shown in Figure 7, two neurons exhibited firing rates at least 30% higher during misclassifications, whereas six additional neurons displayed slowly decaying error-associated activity. The two neurons were therefore deactivated, and the leak values of the six remaining neurons were increased from 0.05 to 0.15. These targeted interventions improved the excitatory neuron accuracy by 4.3 percentage points, from 60.7% to 65.0%."

    The intervention parameters (which two neurons to deactivate and which six leak values to raise) are selected by inspecting error-associated activity on the test partition, and the reported accuracy improvement is measured on that same partition. The deactivation and leak increases therefore encode information from the test labels, making the 65.0% result an in-sample fit rather than an out-of-sample prediction. This is a fitted-input-called-prediction step. It is localized to Approach 1 of Section 6.2.3 and does not affect the LIF discretization, the STDP formulation, or the cross-simulator runtime and memory benchmarks, which rest on external measurements.

full rationale

The core derivation is self-contained: Equation 8 and its vectorized form Equation 9 are a direct Euler discretization of the LIF differential equation (Equation 1) rewritten in the paper's constant-leak notation, and no target result is fitted to obtain these equations. The STDP update rules in Equations 12-19 are a matrix restatement of the standard spike-timing-dependent plasticity rule, not a claimed derived prediction. The central efficiency claim is supported by external measurements against NEST, Brian2, BindsNET, and snnTorch in Section 6.1.2; no SuperNeuroMAT parameter is fitted to those runtimes or memory values, and the benchmark configuration is an experimental-control choice rather than a circular derivation. The shortest-path and arithmetic examples are validated against Dijkstra's algorithm and exact integer arithmetic, respectively. The paper contains self-citations (e.g., [8], [9], [34]), but none is load-bearing for the simulator's equations or its measured performance. The one genuinely circular element is the N-CARS Approach 1 enhancement, where the model is hand-fitted to the test set and then evaluated on the same test set; this is a local soundness and data-leakage issue, not a flaw in the central derivation.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The central efficiency claim does not rest on fitted parameters. Free parameters appear only in the application examples, most notably the N-CARS fine-tuning adjustments and the shortest-path delay scaling. The strongest unverified premise is the benchmark equality assumption in Section 6.1.2, which the paper treats as enabling fair cross-simulator comparison.

free parameters (4)
  • N-CARS fine-tuning leak values and deactivated neurons = Leak raised from 0.05 to 0.15 for six neurons; two neurons deactivated
    Neuron-level corrections chosen after observing error-associated firing rates on the test partition; they directly raise the reported accuracy from 60.7% to 65.0% in Section 6.2.3, Approach 1.
  • Synaptic delay scale factor for shortest path = 100
    Edge weights are multiplied by 100 to preserve two decimal digits as integer spike delays in Section 6.2.5; a hand-chosen encoding constant with no sensitivity analysis.
  • STDP coefficients (alpha+, alpha-) = Not specified numerically
    Potentiation and depression amplitudes are user-selected hyperparameters in Section 5.4; no values or sensitivity analysis are provided for the learning examples.
  • Refractory period for citation graph neurons = Described only as sufficiently long
    Set in Section 6.2.2 so each neuron emits at most one spike; the actual value is not reported, which limits replication of the citation experiments.
assumptions (4)
  • domain assumption The discretized LIF update with Δt=1 (Eq. 9) faithfully represents the continuous LIF dynamics for the workloads tested.
    Section 5.5 sets Δt=1 and normalizes leak and input terms without convergence or error analysis; the physical meaning of the time step is delegated to the application.
  • ad hoc to paper The matched benchmark configuration (zero threshold, infinite-leak LIF, unit-delay delta synapses) is representative enough for cross-simulator speed and memory comparisons.
    Section 6.1.2 states that after matching, runtime and memory differences can be attributed to implementation efficiency; the paper does not test standard finite-leak LIF dynamics.
  • ad hoc to paper Equation 17 implements the stated causal STDP depression rule.
    The prose rule requires post-before-pre ordering, which is captured by the transpose outer product, but Eq. 17 depresses all non-coincident synapse pairs instead; an internal inconsistency.
  • standard math NumPy and SciPy BLAS operations execute with SIMD parallelism on target CPUs.
    Section 5.8 relies on this for practical speedups; a standard and reasonable background assumption for modern CPU hardware.

how reviews work

0 comments
Cite this review

Pith. "Pith review of SuperNeuroMAT: An Efficient Matrix-based Simulator for Spiking Neural Networks." pith.science (2026). https://pith.science/paper/MMK35QW5

@misc{pith2026260808479,
  author       = {Pith},
  title        = {Pith review of: SuperNeuroMAT: An Efficient Matrix-based Simulator for Spiking Neural Networks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MMK35QW5}},
  note         = {Machine review of arXiv:2608.08479}
}
read the original abstract

Spiking neural networks (SNNs) offer a promising pathway to energy-efficient AI and brain-inspired computing. However, their widespread adoption is hindered by a lack of fast, accessible, and versatile simulation frameworks. In this paper, we introduce SuperNeuroMAT, an open-source, scalable, and highly efficient Python-based SNN simulator. We devise a novel matrix-based approach to model the leaky integrate-and-fire (LIF) neuron dynamics and natively support dense and sparse execution modes. This enables fast simulation of approximately 10,000 neurons in dense mode and 100,000 neurons in sparse mode on standard laptops and desktops without requiring specialized hardware. We demonstrate that SuperNeuroMAT consistently outperforms four established SNN simulators---NEST, Brian2, BindsNET, and snnTorch---on two performance metrics (execution speed and peak resident memory) and across various network sizes and connection probabilities. Furthermore, we demonstrate SuperNeuroMAT's applicability across a diverse set of problems. SuperNeuroMAT can efficiently handle conventional machine learning benchmarks such as the Digits and citation network datasets as well as neuromorphic event-based vision tasks such as N-CARS and ASL-DVS. Moreover, it can be extended beyond machine learning workloads and facilitate general-purpose workloads. We validated this by implementing the neuromorphic shortest path algorithm and two arithmetic primitives (addition and multiplication). SuperNeuroMAT can be installed via the Python Package Index (PyPI), thereby lowering the barrier to entry into the field of neuromorphic computing and accelerating the broader development of neuromorphic algorithms.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

48 extracted references · 40 canonical work pages

  1. [1]

    Nature Computational Science2(1), 10–19 (2022)

    Schuman, C.D., Kulkarni, S.R., Parsa, M., Mitchell, J.P., Date, P., Kay, B.: Opportunities for neuromorphic computing algorithms and applications. Nature Computational Science2(1), 10–19 (2022)

    work page 2022

  2. [2]

    Neuromorphic Computing and Engineering2(3), 032003 (2022)

    Aimone, J.B., Date, P., Fonseca-Guerra, G.A., Hamilton, K.E., Henke, K., Kay, B., Kenyon, G.T., Kulkarni, S.R., Mniszewski, S.M., Parsa, M.,et al.: A review of non-cognitive applications for neuromorphic computing. Neuromorphic Computing and Engineering2(3), 032003 (2022)

    work page 2022

  3. [3]

    In: 2021 IEEE Workshop on Signal Processing Systems (SiPS), pp

    Orchard, G., Frady, E.P., Rubin, D.B.D., Sanborn, S., Shrestha, S.B., Sommer, F.T., Davies, M.: Efficient neuromorphic signal processing with loihi 2. In: 2021 IEEE Workshop on Signal Processing Systems (SiPS), pp. 254–259 (2021). IEEE

    work page 2021

  4. [4]

    In: 2024 IEEE International Solid-State Circuits Conference (ISSCC), vol

    Cassidy, A.S., Arthur, J.V., Akopyan, F., Andreopoulos, A., Appuswamy, R., Datta, P., Debole, M.V., Esser, S.K., Otero, C.O., Sawada, J.,et al.: 11.4 ibm northpole: An architecture for neural network inference with a 12nm chip. In: 2024 IEEE International Solid-State Circuits Conference (ISSCC), vol. 67, pp. 214–215 (2024). IEEE 31

    work page 2024

  5. [5]

    arXiv preprint arXiv:2506.14138 (2025)

    Gautam, A., Date, P., Kulkarni, S., Patton, R., Potok, T.: NeuroCoreX: An open- source fpga-based spiking neural network emulator with on-chip learning. arXiv preprint arXiv:2506.14138 (2025)

    arXiv 2025

  6. [6]

    In: 2023 IEEE International Conference on Rebooting Computing (ICRC), pp

    Maheshwari, D., Young, A., Date, P., Kulkarni, S., Witherspoon, B., Miniskar, N.R.: An fpga-based neuromorphic processor with all-to-all connectivity. In: 2023 IEEE International Conference on Rebooting Computing (ICRC), pp. 1–5 (2023). IEEE

    work page 2023

  7. [7]

    In: 2024 International Conference on Neuromorphic Systems (ICONS), pp

    Miniskar, N.R., Young, A.R., Asifuzzaman, K., Kulkarni, S., Date, P., Bean, A., Vetter, J.S.: Neuro-spark: A submicrosecond spiking neural networks architec- ture for in-sensor filtering. In: 2024 International Conference on Neuromorphic Systems (ICONS), pp. 63–70 (2024). IEEE

    work page 2024

  8. [8]

    In: Proceedings of the International Conference on Neuromorphic Systems 2022, pp

    Date, P., Potok, T., Schuman, C., Kay, B.: Neuromorphic computing is Turing- complete. In: Proceedings of the International Conference on Neuromorphic Systems 2022, pp. 1–10 (2022)

    work page 2022

Show all 48 references
  1. [9]

    In: International Conference on Neuromorphic Systems 2021, pp

    Date, P., Kay, B., Schuman, C., Patton, R., Potok, T.: Computational complex- ity of neuromorphic algorithms. In: International Conference on Neuromorphic Systems 2021, pp. 1–7 (2021)

    2021

  2. [10]

    In: Proceedings of the International Conference on Neuromorphic Systems 2022, pp

    Cong, G., Lim, S.-H., Kulkarni, S., Date, P., Potok, T., Snyder, S., Parsa, M., Schuman, C.: Semi-supervised graph structure learning on neuromorphic comput- ers. In: Proceedings of the International Conference on Neuromorphic Systems 2022, pp. 1–4 (2022)

    2022

  3. [11]

    In: 2023 International Conference on Machine Learning and Applications (ICMLA), pp

    Cong, G., Kulkarni, S., Lim, S.-H., Date, P., Snyder, S., Parsa, M., Kennedy, D., Schuman, C.: Hyperparameter optimization and feature inclusion in graph neural networks for spiking implementation. In: 2023 International Conference on Machine Learning and Applications (ICMLA),...

    2023

  4. [12]

    In: International Conference on Neuromorphic Systems 2021, pp

    Patton, R., Schuman, C., Kulkarni, S., Parsa, M., Mitchell, J.P., Haas, N.Q., Stahl, C., Paulissen, S., Date, P., Potok, T.,et al.: Neuromorphic computing for autonomous racing. In: International Conference on Neuromorphic Systems 2021, pp. 1–5 (2021)

    2021

  5. [13]

    R. Kulkarni, S., Young, A., Date, P., Rao Miniskar, N., Vetter, J., Fahim, F., Parpillon, B., Dickinson, J., Tran, N., Yoo, J.,et al.: On-sensor data filtering using neuromorphic computing for high energy physics experiments. In: Proceedings of the 2023 International Conferenc...

    2023

  6. [14]

    In: 2018 IEEE Symposium Series on Computational Intelligence (SSCI), pp

    Date, P., Carothers, C.D., Hendler, J.A., Magdon-Ismail, M.: Efficient classifi- cation of supercomputer failures using neuromorphic computing. In: 2018 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 242–249 (2018). IEEE 32

    2018

  7. [15]

    In: Proceedings of the 2020 Annual Neuro-inspired Computational Elements Workshop, pp

    Kay, B., Date, P., Schuman, C.: Neuromorphic graph algorithms: Extracting longest shortest paths and minimum spanning trees. In: Proceedings of the 2020 Annual Neuro-inspired Computational Elements Workshop, pp. 1–6 (2020)

    2020

  8. [16]

    In: International Conference on Neuromorphic Systems 2021, pp

    Kay, B., Schuman, C., O’connor, J., Date, P., Potok, T.: Neuromorphic graph algorithms: Cycle detection, odd cycle detection, and max flow. In: International Conference on Neuromorphic Systems 2021, pp. 1–7 (2021)

    2021

  9. [17]

    In: International Conference on Neuromorphic Systems 2020, pp

    Hamilton, K., Mintz, T., Date, P., Schuman, C.D.: Spike-based graph centrality measures. In: International Conference on Neuromorphic Systems 2020, pp. 1–8 (2020)

    2020

  10. [18]

    Scientific Reports13(1), 10975 (2023)

    Date, P., Kulkarni, S., Young, A., Schuman, C., Potok, T., Vetter, J.: Encoding integers and rationals on neuromorphic computers using virtual neuron. Scientific Reports13(1), 10975 (2023)

    2023

  11. [19]

    In: 2022 IEEE International Conference on Rebooting Computing (ICRC), pp

    Date, P., Kulkarni, S., Young, A., Schuman, C., Potok, T., Vetter, J.S.: Vir- tual neuron: A neuromorphic approach for encoding numbers. In: 2022 IEEE International Conference on Rebooting Computing (ICRC), pp. 100–105 (2022). IEEE

    2022

  12. [20]

    In: 2023 IEEE International Conference on Rebooting Computing (ICRC), pp

    Wurm, A., Seay, R., Date, P., Kulkarni, S., Young, A., Vetter, J.: Arithmetic primitives for efficient neuromorphic computing. In: 2023 IEEE International Conference on Rebooting Computing (ICRC), pp. 1–5 (2023). IEEE

    2023

  13. [21]

    In: 2021 IEEE Inter- national Parallel and Distributed Processing Symposium Workshops (IPDPSW), pp

    Schuman, C.D., Kay, B., Date, P., Kannan, R., Sao, P., Potok, T.E.: Sparse binary matrix-vector multiplication on neuromorphic computers. In: 2021 IEEE Inter- national Parallel and Distributed Processing Symposium Workshops (IPDPSW), pp. 308–311 (2021). IEEE

    2021

  14. [22]

    In: International Conference on Neuromorphic Systems 2020, pp

    Hamilton, K., Date, P., Kay, B., Schuman D, C.: Modeling epidemic spread with spike-based models. In: International Conference on Neuromorphic Systems 2020, pp. 1–5 (2020)

    2020

  15. [23]

    In: 2022 IEEE/ACM Redefining Scalability for Diversely Heterogeneous Architectures Workshop (RSDHA), pp

    Patton, R., Date, P., Kulkarni, S., Gunaratne, C., Lim, S.-H., Cong, G., Young, S.R., Coletti, M., Potok, T.E., Schuman, C.D.: Neuromorphic computing for scientific applications. In: 2022 IEEE/ACM Redefining Scalability for Diversely Heterogeneous Architectures Workshop (RSDHA...

    2022

  16. [24]

    Rensselaer Polytechnic Institute, ??? (2019)

    Date, P.: Combinatorial Neural Network Training Algorithm for Neuromorphic Computing. Rensselaer Polytechnic Institute, ??? (2019)

    2019

  17. [25]

    In: 2020 International Conference on Rebooting Computing (ICRC), pp

    Date, P., Carothers, C.D., Mitchell, J.E., Hendler, J.A., Magdon-Ismail, M.: Training deep neural networks with constrained learning parameters. In: 2020 International Conference on Rebooting Computing (ICRC), pp. 107–115 (2020). IEEE 33

    2020

  18. [26]

    elife8, 47314 (2019)

    Stimberg, M., Brette, R., Goodman, D.F.: Brian 2, an intuitive and efficient neural simulator. elife8, 47314 (2019)

    2019

  19. [27]

    Scholarpedia2(4), 1430 (2007)

    Gewaltig, M.-O., Diesmann, M.: Nest (neural simulation tool). Scholarpedia2(4), 1430 (2007)

    2007

  20. [28]

    In: Proceedings of the International Conference on Neuromorphic Systems, pp

    Aimone, J.B., Severa, W., Vineyard, C.M.: Composing neural algorithms with fugu. In: Proceedings of the International Conference on Neuromorphic Systems, pp. 1–8 (2019)

    2019

  21. [29]

    Neuromorphic Computing and Engineering4(2), 024002 (2024)

    Wang, F., Kulkarni, S., Theilman, B., Rothganger, F., Schuman, C., Lim, S.-H., Aimone, J.B.: Scaling neural simulations in stacs. Neuromorphic Computing and Engineering4(2), 024002 (2024)

    2024

  22. [30]

    Procedia Computer Science61, 322–327 (2015)

    Wang, F.: Simulation tool for asynchronous cortical streams (stacs): interfacing with spiking neural networks. Procedia Computer Science61, 322–327 (2015)

    2015

  23. [31]

    Frontiers in neuroinformatics12, 89 (2018)

    Hazan, H., Saunders, D.J., Khan, H., Patel, D., Sanghavi, D.T., Siegelmann, H.T., Kozma, R.: Bindsnet: A machine learning-oriented spiking neural networks library in python. Frontiers in neuroinformatics12, 89 (2018)

    2018

  24. [32]

    Proceedings of the IEEE111(9), 1016–1054 (2023)

    Eshraghian, J.K., Ward, M., Neftci, E., Wang, X., Lenz, G., Dwivedi, G., Ben- namoun, M., Jeong, D.S., Lu, W.D.: Training spiking neural networks using lessons from deep learning. Proceedings of the IEEE111(9), 1016–1054 (2023)

    2023

  25. [33]

    Science Advances9(40), 1480 (2023)

    Fang, W., Chen, Y., Ding, J., Yu, Z., Masquelier, T., Chen, D., Huang, L., Zhou, H., Li, G., Tian, Y.: Spikingjelly: An open-source machine learning infrastructure platform for spike-based intelligence. Science Advances9(40), 1480 (2023)

    2023

  26. [34]

    Kulkarni, S., Patton, R., Coletti, M., Potok, T.: SuperNeuro: A fast and scalable simulator for neuromorphic computing

    Date, P., Gunaratne, C., R. Kulkarni, S., Patton, R., Coletti, M., Potok, T.: SuperNeuro: A fast and scalable simulator for neuromorphic computing. In: Pro- ceedings of the 2023 International Conference on Neuromorphic Systems, pp. 1–4 (2023)

    2023

  27. [35]

    Journal de Physiologie et de Pathologie G´ en´ erale9, 620–635 (1907)

    Lapicque, L.: Recherches quantitatives sur l’excitation´ electrique des nerfs trait´ ee comme une polarisation. Journal de Physiologie et de Pathologie G´ en´ erale9, 620–635 (1907)

    1907

  28. [36]

    Cambridge University Press, Cambridge, UK (2002)

    Gerstner, W., Kistler, W.M.: Spiking Neuron Models: Single Neurons, Popula- tions, Plasticity. Cambridge University Press, Cambridge, UK (2002)

    2002

  29. [37]

    Nature585(7825), 357–362 (2020) https://doi.org/10.1038/s41586-020-2649-2 34

    Harris, C.R., Millman, K.J., Walt, S.J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N.J., Kern, R., Picus, M., Hoyer, S., Kerkwijk, M.H., Brett, M., Haldane, A., R ´ ıo, J.F., Wiebe, M., Peterson, P., G´ erard-Marchant, P., Sheppard, K....

    2020 doi

  30. [38]

    Nature Methods17, 261–272 (2020) https://doi.org/10.1038/ s41592-019-0686-2

    Virtanen, P., Gommers, R., Oliphant, T.E., Haberland, M., Reddy, T., Courna- peau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S.J., Brett, M., Wilson, J., Millman, K.J., Mayorov, N., Nelson, A.R.J., Jones, E., Kern, R., Larson, E., Carey, C.J., Po...

    2020

  31. [39]

    ACM Transactions on Mathematical Software (TOMS)5(3), 308–323 (1979)

    Lawson, C.L., Hanson, R.J., Kincaid, D.R., Krogh, F.T.: Basic linear algebra subprograms for fortran usage. ACM Transactions on Mathematical Software (TOMS)5(3), 308–323 (1979)

    1979

  32. [40]

    In: AI Magazine, vol

    Sen, P., Namata, G., Bilgic, M., Getoor, L., Gallagher, B., Eliassi-Rad, T.: Collective classification in network data. In: AI Magazine, vol. 29, pp. 93–106 (2008)

    2008

  33. [41]

    In: 10th International Workshop on Mining and Learning with Graphs (MLG) (2012)

    Namata, G., London, B., Getoor, L., Huang, B., EDU, U.: Query-driven active surveying for collective classification. In: 10th International Workshop on Mining and Learning with Graphs (MLG) (2012)

    2012

  34. [42]

    In: International Conference on Learning Representations (ICLR) (2017)

    Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: International Conference on Learning Representations (ICLR) (2017)

    2017

  35. [43]

    In: Proceedings of the 33rd International Conference on Machine Learning (ICML), pp

    Yang, Z., Cohen, W.W., Salakhutdinov, R.: Revisiting semi-supervised learning with graph embeddings. In: Proceedings of the 33rd International Conference on Machine Learning (ICML), pp. 40–48 (2016)

    2016

  36. [44]

    In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recog- nition, pp

    Sironi, A., Brambilla, M., Bourdis, N., Lagorce, X., Benosman, R.: Hats: His- tograms of averaged time surfaces for robust event-based object classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recog- nition, pp. 1731–1740. IEEE, ??? (2018). h...

    2018 doi

  37. [45]

    Frontiers in Computational Neuroscience9, 99 (2015) https://doi.org/10.3389/fncom.2015.00099

    Diehl, P.U., Cook, M.: Unsupervised learning of digit recognition using spike- timing-dependent plasticity. Frontiers in Computational Neuroscience9, 99 (2015) https://doi.org/10.3389/fncom.2015.00099

    2015

  38. [46]

    IEEE Transac- tions on Pattern Analysis and Machine Intelligence39(7), 1346–1359 (2017) https://doi.org/10.1109/TPAMI.2016.2574707

    Lagorce, X., Orchard, G., Galluppi, F., Shi, B.E., Benosman, R.B.: Hots: A hierarchy of event-based time-surfaces for pattern recognition. IEEE Transac- tions on Pattern Analysis and Machine Intelligence39(7), 1346–1359 (2017) https://doi.org/10.1109/TPAMI.2016.2574707

    2017

  39. [47]

    In: Proceedings of the 35 IEEE/CVF International Conference on Computer Vision, pp

    Bi, Y., Chadha, A., Abbas, A., Bourtsoulatze, E., Andreopoulos, Y.: Graph- based object classification for neuromorphic vision sensing. In: Proceedings of the 35 IEEE/CVF International Conference on Computer Vision, pp. 491–501 (2019). https://doi.org/10.1109/ICCV.2019.00058

    2019

  40. [48]

    https://algs4.cs.princeton.edu/code/edu/princeton/cs/algs4/DijkstraSP

    Sedgewick, R., Wayne, K.: DijkstraSP.java: Dijkstra’s Shortest Path Algo- rithm. https://algs4.cs.princeton.edu/code/edu/princeton/cs/algs4/DijkstraSP. java.html. Part of theAlgorithms, 4th Editioncodebase. Accessed: 2026-08-02 36

    2026

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.