Third-generation neural networks usually means spiking neural networks (SNNs): models that represent activity as discrete spike events unfolding over time. Unlike conventional artificial neural networks, which typically pass continuous-valued activations from layer to layer, SNNs can use when spikes occur—and their patterns over time—to carry information. That difference can suit temporal, sparse workloads, but it does not automatically make an SNN more accurate or energy-efficient.
What “third generation” means
“Third generation” is a common way to classify neural-network models, not a formal guarantee about how biologically realistic, capable, or efficient a particular network is. In this taxonomy, early artificial neurons are often described as threshold-based units, later networks as using continuous-valued activations, and spiking networks as adding discrete events and time to the computation.
An SNN neuron accumulates incoming signals in an internal state. In a leaky integrate-and-fire model, for example, that state gradually decays; when it reaches a threshold, the neuron emits a spike and its state is reset or otherwise updated. The network’s activity therefore evolves over time rather than being represented only by a single pass of activation values. The MIT Press review “Advancements in Algorithms and Neuromorphic Hardware for Spiking Neural Networks” (2022) and a 2024 review in Communications Engineering describe the range of models and design choices involved.
How spikes differ from conventional activations
A conventional network commonly represents each unit’s output as a numerical activation, such as a real-valued score. An SNN communicates through events: a neuron either emits a spike at a particular time or does not. The neuron’s internal state can still contain numerical values, but its output communication is event-based.
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Information can be represented in several ways. A rate code uses the number or frequency of spikes over an interval; temporal codes can use the timing of individual spikes or patterns of spikes. These choices affect how much time a system needs to process an input, how robust the representation is to noise, and how much computation is required. “Spike” does not mean that every SNN uses precise timing, nor that all SNNs use the same neuron model or coding scheme.
How SNNs learn
There is no single standard training recipe for SNNs. The learning rule determines how synaptic connections change, and the choice often reflects a trade-off between local, biologically motivated updates and methods that make deep networks easier to optimize. Surveys in Neurocomputing (2023) and Frontiers in Neuroscience (2024) review these approaches.
Local plasticity
Some methods update a connection using activity at the connected neurons. Spike-timing-dependent plasticity (STDP), for example, adjusts synaptic strength based on the relative timing of spikes. Such rules can be local to a synapse, but that does not mean every SNN learns online or that a local rule will be the best choice for a given task.
Surrogate-gradient training
Deep SNNs are also trained with surrogate gradients. A spike-generation operation is not differentiable in the usual way, so training uses a smooth proxy for the gradient during optimization. A model trained this way may then run as an SNN; some workflows also convert or deploy a model onto event-based hardware. Training method, data requirements, and any conversion step should be part of a meaningful comparison with a conventional network.
Where SNNs may be useful
SNNs are most compelling when the input or task has meaningful timing or sparse activity and the implementation can exploit it. Reviews discuss event-based vision, audio processing, temporal-pattern processing, and optimization-related work. These are active areas of research, not evidence that SNNs broadly outperform conventional deep learning.
For example, an event-based camera can report changes in a scene rather than producing a full conventional image at every instant. A system designed to process those events may avoid repeatedly handling unchanged parts of the scene. Whether an SNN is a good fit still depends on the task, the accuracy and latency required, the encoding method, and how well the model maps to available hardware.
Are spiking neural networks more energy-efficient?
Sometimes, under suitable conditions—but not by virtue of the SNN label alone. Sparse spikes can avoid computation during inactive periods, while neuromorphic hardware may reduce data movement by placing memory and computation close together or communicating asynchronously. These features can help when a workload and implementation actually take advantage of them.
Energy comparisons can be misleading if they omit part of the work. Encoding ordinary data as spikes, converting a model, moving data, or keeping hardware underutilized can change the result. Simulating an SNN on a conventional CPU or GPU is not by itself evidence of neuromorphic energy savings. A fair comparison should identify:
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- the task and dataset, and the accuracy or robustness achieved;
- latency and the temporal resolution used;
- the hardware and software implementation;
- what the energy measurement includes, such as preprocessing, encoding, data movement, and inference;
- whether the reported result covers training, inference, or both; and
- how the model was trained and whether conversion or other deployment steps were required.
The 2024 review “A Review of Computing with Spiking Neural Networks” and the MIT Press review (2022) discuss the relationship between sparse event-driven computation and hardware. A platform or vendor claim should not be treated as a general SNN-versus-ANN benchmark unless the workload and measurement conditions support that comparison.
Ways to compare SNN design choices
These choices are related but distinct: the neuron model describes how a unit behaves; the coding scheme describes how information is represented; the learning rule describes how connections change; and the execution platform determines where computation runs.
| Choice | Examples | What to assess |
|---|---|---|
| Neuron model | Leaky integrate-and-fire or more biologically detailed models | Computational cost, temporal behavior, biological detail, and fit to the hardware |
| Spike coding | Rate-based or timing-based representations | Robustness, temporal resolution, and the number of spikes or duration needed to represent inputs |
| Learning rule | Local plasticity such as STDP, or surrogate-gradient training | Training data and compute, optimization needs, and whether updates are local or gradient-based |
| Execution | Software simulation on conventional processors or neuromorphic hardware | Hardware access, mapping effort, utilization, latency, and energy measured at a stated boundary |
No single option is best for every application. Compare complete systems on the same task where possible, and report the implementation details needed to reproduce the result. Without those details, a headline accuracy, speed, or energy figure may not answer whether the approach is useful for your workload.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Software and hardware for trying an SNN
Lava and Loihi
Lava is an open-source, Python-facing framework for developing neuromorphic applications. Its documentation describes processes that communicate through event-based messages and supports prototyping on conventional hardware. That makes it a software route for exploring neuromorphic-style programs without treating a specialized chip as a prerequisite.
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Running on conventional hardware is not the same as running on a neuromorphic processor. The Lava project repository says Loihi support uses an extension made available to members of Intel’s Neuromorphic Research Community. It also says Loihi research systems are not commercially available as ordinary retail development boards; access may be through the community, including cloud access or possible loan arrangements. Check the project’s current documentation and access terms before planning a hardware experiment.
Intel describes Loihi 2 as focused on sparse event-driven computation and presents Lava as its software framework. Intel’s overview says Loihi 2 offers “up to 10x faster processing capability” than its predecessor; that is a vendor claim about Loihi generations, not a general comparison between SNNs and conventional neural networks.
SpiNNaker as a research platform
SpiNNaker is a processor platform used for SNN simulation and research. A 2024 article in Nature, “Neuromorphic computing at scale,” describes a SpiNNaker platform at the “1 million core” scale; that is a platform description, not an SNN speed or energy result. The technical book SpiNNaker: A Spiking Neural Network Architecture is a focused reference on that architecture rather than a general textbook on the whole SNN field.
Quick Recap
A practical decision framework
- Start with the input and task. Determine whether timing, event sparsity, or temporal patterns matter to the problem, rather than choosing an SNN solely because it is labeled neuromorphic.
- Choose the representation and model. Decide whether rate or precise timing is useful, then select a neuron model and topology that meet the task’s computational and temporal requirements.
- Choose how to train. Evaluate whether a local rule, surrogate-gradient method, or a workflow involving conversion best matches the data, accuracy target, and deployment constraints.
- Prototype on accessible software first. Use a framework such as Lava on conventional hardware to explore the application. Treat those results as software-prototype results, not proof of specialized-hardware efficiency.
- Check hardware access before optimizing for it. Confirm platform availability, software support, and mapping requirements; research hardware may require community or institutional access.
- Compare complete, reproducible workloads. Record accuracy, robustness, latency, energy boundaries, preprocessing and encoding costs, training method, hardware, and software. Compare with a conventional baseline on the same task where feasible.
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