Brain-inspired computers built from spiking neurons and analogue circuits. Carver Mead's 1980s vision, Loihi and TrueNorth, BrainScaleS and SpiNNaker, the ten-thousand-fold energy claim, and the unanswered question: what is this machine actually for?
A human brain runs on twenty watts. A GPU cluster training a frontier language model draws several megawatts. The gap is six orders of magnitude. Neuromorphic computing is the proposition that closing it is possible — and that the architectural recipe is brain-like rather than von-Neumann.
The technical idea: replace the synchronous, separated CPU/memory architecture with massively parallel arrays of spiking neurons that fire only when stimulated, communicating via sparse asynchronous events. Co-locate compute and memory. Use analogue or mixed-signal circuits where possible. Trade exact arithmetic for biological plausibility.
The promise has been thirty years in the making and is still mostly promise. This deck covers what works (event-based vision, ultra-low-power inference at the edge), what doesn't (training methods that match deep learning), and where the field actually is in 2026.
Carver Mead, then at Caltech, coined "neuromorphic" in the late 1980s. Mead's argument, laid out in Analog VLSI and Neural Systems (1989), was that biological neural circuits achieve their efficiency through subthreshold analogue operation — transistors operating in the regime where current depends exponentially on gate voltage, the same regime as ion channels.
Mead built silicon retinas and cochleas in the late 1980s with Misha Mahowald (the silicon retina, 1991, in Nature) and Lloyd Watts. The chips were small, low-power, and architecturally astonishing. They were also commercially marginal — too analogue for industrial design flows, too specialised to compete with general-purpose digital.
Mead's 1990 paper Neuromorphic Electronic Systems in Proceedings of the IEEE remains the founding manifesto. The core claim: physical computation in analogue hardware can be vastly more efficient than digital simulation, if you are willing to accept the noise and the design complexity. Three decades later the field is still arguing whether Mead was right.
A spiking neural network (SNN) communicates with discrete events — spikes — rather than continuous activations. The dominant neuron model is the Leaky Integrate-and-Fire (LIF) equation: a membrane potential V integrates input current, decays exponentially, and emits a spike when V exceeds threshold, after which V resets and a refractory period begins.
More biologically faithful variants — Hodgkin-Huxley (1952, four coupled differential equations modelling sodium and potassium channels), Izhikevich (2003, two-equation model that reproduces 20+ firing patterns), adaptive exponential — are used where richer dynamics are needed. SNN simulation crowds toward the LIF and Izhikevich endpoints because they are cheap.
The conceptual shift from rate-coded artificial neurons (sigmoid activations of summed weighted inputs) to spike-timing models is large. Information is carried by when spikes occur, not just how often. The field is divided on whether timing-precision matters operationally; most working SNN inference systems treat spike rate as the dominant signal.
Spike-Timing-Dependent Plasticity (STDP) is the classical learning rule. If a presynaptic spike arrives just before a postsynaptic spike, the connection strengthens (long-term potentiation, LTP). If after, it weakens (long-term depression, LTD). The biological evidence is from Bi and Poo (1998) on cultured hippocampal neurons.
STDP is local and unsupervised. It is also weak — the learning rate is slow, and it is not obvious how to use it for the deep representational tasks at which backpropagation excels. The neuromorphic field's central technical problem for thirty years has been: how do you train a deep SNN to compete with a deep ANN?
Three answers in current use. Surrogate gradient methods (Neftci et al. 2019) treat the non-differentiable spike function as if it had a smooth surrogate during backprop, allowing standard deep learning to train SNNs. ANN-to-SNN conversion trains a conventional ANN, then converts activations to firing rates. e-prop (Bellec et al. 2020), a biologically-plausible online approximation to backpropagation through time, brings online local learning into reach.
IBM TrueNorth, announced August 2014 in Science, was the first major industrial neuromorphic chip. The specifications were striking.
TrueNorth was funded by DARPA SyNAPSE. Architecturally it was a network of digital cores, each implementing a small spiking-neuron crossbar. The energy efficiency claim — roughly 46 billion synaptic operations per second per watt — was orders of magnitude better than CPU/GPU equivalents on the sparse event-driven workloads it was designed for.
The legacy is mixed. TrueNorth proved the architecture was buildable at industrial scale. It also proved that programming such a system is hard: the training story was weak, the toolchain idiosyncratic, and the chip was not on-chip-trainable. IBM has largely moved on; the lessons informed everyone else's designs.
Intel's Loihi (2017) was the strategic answer to TrueNorth: digital, asynchronous, on-chip-learning enabled. Loihi 1 had 128 neuromorphic cores, 130,000 neurons, 130 million synapses on Intel's 14 nm process. Crucially it supported on-chip learning rules — STDP variants and reinforcement signals — and was the first major neuromorphic chip explicitly designed for adaptive operation rather than trained-and-frozen inference.
Loihi 2 (2021) moved to Intel 4 process, reaching 1 million neurons per chip and improved programmability with microcoded neuron behaviours. The Hala Point system unveiled by Intel in April 2024 packed 1,152 Loihi 2 chips into a 6U server delivering 1.15 billion neurons and 128 billion synapses — comparable in neuron count to an owl brain. Power: roughly 2,600 watts at peak.
The Loihi software stack — Lava, open-sourced 2021 — is the major investment. It uses a process-based programming model (channels and processes communicating via event streams) that maps naturally to neuromorphic execution and also runs in CPU simulation. Without Lava, Loihi would be inaccessible; with it, hundreds of academic groups have actually written code that runs on the hardware.
Steve Furber's group at the University of Manchester took a different bet: rather than analogue or specialised digital, simulate spiking networks on a massive array of conventional ARM cores with custom interconnect. SpiNNaker (Spiking Neural Network Architecture) was completed in 2018 with 1 million cores arranged across 1,200 boards and is part of the EU Human Brain Project.
SpiNNaker's elegance is in the interconnect. Each core can transmit a spike packet (essentially "neuron N just fired") and the routing fabric delivers it to all subscribed targets — a multicast event-routing network optimised for the sparse, high-fanout traffic of biological neural simulation. The architecture sacrifices analogue efficiency for digital programmability and reproducibility.
SpiNNaker 2 (Dresden, 2024) — co-developed with TU Dresden's CHIPS group — moves to GlobalFoundries 22 nm and adds machine-learning acceleration. The chip targets both biological-scale neural simulation and edge-AI workloads. The dual-target strategy reflects the field's pragmatism: pure neuromorphic markets are small; embedded AI markets are huge.
BrainScaleS, at the Kirchhoff Institute (Heidelberg University), is the most aggressive surviving analogue-mixed-signal neuromorphic system. It is a wafer-scale platform — entire silicon wafers operated as a single device — with neurons running on accelerated time, roughly 10,000× biological speed. A second of biological simulation completes in about 100 microseconds.
The architecture (HICANN-X, current generation): adaptive exponential integrate-and-fire neurons in analogue circuits, digital STDP, configurable synaptic plasticity. The acceleration factor matters scientifically — long-timescale phenomena like sleep cycles or developmental plasticity become tractable to simulate experimentally.
The trade-off: BrainScaleS chips are noisy. Process variation between transistors produces neuron-by-neuron differences in dynamics; calibration is a substantial step in any experiment. The Heidelberg group argues this is biologically realistic — real neurons are not identical either — and have built training and learning methods that exploit rather than fight the variability.
The Dynamic Vision Sensor — invented by Tobi Delbrück's group at the University of Zurich and ETH Zurich, descended from Mead's silicon retina — is the most commercially successful neuromorphic technology to date. A DVS pixel emits an event only when its log-intensity changes by a threshold; the sensor produces an asynchronous stream of (x, y, t, polarity) events rather than frames.
The advantages over conventional cameras are stark for high-dynamic-range, low-latency, low-power applications. Latency under 1 millisecond. Effective dynamic range over 120 dB. Power as low as 5 mW. No motion blur. The disadvantages: events are sparse and unfamiliar; the standard computer vision toolkit doesn't apply directly; processing pipelines must be rewritten event-driven.
iniVation (the Zurich spinout) and Prophesee (Paris, with Sony partnership) sell production DVS sensors. Sony's IMX636 (2020) put a Prophesee sensor in commercial production at automotive volume. Use cases: automotive driver-assistance, industrial high-speed inspection, gesture recognition in AR/VR, satellite imaging. Event-based vision is the place where neuromorphic ideas have crossed into commercial reality.
Conventional digital architecture pays an enormous energy tax shuttling data between memory and compute units — the von Neumann bottleneck. Neuromorphic systems try to defeat this by co-locating memory and compute. Memristors are the most discussed substrate.
The memristor (memory + resistor) was theoretically predicted by Leon Chua in 1971 and physically realised by HP Labs (Strukov, Williams) in 2008 in Nature. The key property: a passive two-terminal device whose resistance depends on the history of current that flowed through it. Stack many in a crossbar and you get an analogue matrix-vector multiplier that performs the operation in O(1) time and very little energy — the multiply-accumulate is the physics of Ohm's law and Kirchhoff's current law.
The challenges are real: device-to-device variability, drift, endurance limits, and the precision-floor for neural-network weights. Knowm, Crossbar, and IBM's analog AI research group have demonstrated working chips. Mythic AI shipped an analog matrix processor for edge inference, then pivoted in 2023 — illustrating the hard commercial path. The technology is closer to practical than it has ever been; "almost" is still not "yes."
The headline neuromorphic claim is enormous energy advantage over GPU/CPU baselines on certain workloads. The number depends heavily on what is being measured.
Caveats. The advantage depends on workload sparsity — if every neuron in the network must spike on every input, the gap collapses to roughly an order of magnitude. The advantage assumes the model fits the architecture; ANN-to-SNN conversion typically loses accuracy and the conversion overhead is real. The advantage requires sparse event-driven I/O — feeding a neuromorphic chip with conventional dense camera frames eats most of the power budget at the interface.
The defensible summary: for a narrow class of always-on, sensor-driven, event-sparse workloads, neuromorphic hardware is genuinely 100×–1000× more efficient than a GPU. For deep learning training, it is not competitive. For most things in between, it depends.
Application areas where neuromorphic systems have demonstrated real, not promised, advantage:
Always-on keyword spotting. "Hey Siri" / "OK Google" wake-word detection at <1 mW. GrAI Matter Labs' GrAI VIP chip does keyword spotting at sub-microjoule energy per inference — orders of magnitude below DSP baselines.
Sensor-fused robotics. Loihi-based controllers for prosthetic limbs (Applied Brain Research, 2022) and drone obstacle-avoidance (IBM/Air Force Research Lab, 2023) demonstrate adaptive control under tight latency and power budgets.
Optimisation. Surprisingly, mapping certain combinatorial optimisation problems (graph colouring, satisfiability, constraint satisfaction) onto Hopfield-like spiking networks gives energy advantages and competitive solution quality. Intel's Loihi has demonstrated this on classical NP-hard benchmarks.
Smell and chemical sensing. The Cornell-Intel collaboration on neuromorphic olfaction (Imam & Cleland 2020, Nature Machine Intelligence) showed Loihi could learn and identify chemical signatures from electronic-nose arrays with far fewer training samples than deep-learning baselines.
The honest list of neuromorphic limitations:
Training large models. Backpropagation through time on SNNs is approximately as expensive as ANN training plus an unbiased-spike-gradient overhead. Surrogate-gradient SNNs trained on conventional GPUs lose 1–3% accuracy on ImageNet vs equivalent ANNs and don't gain efficiency until deployment on neuromorphic hardware.
The ImageNet of SNNs. There is no canonical benchmark suite. The N-MNIST, DVS-CIFAR10, and SHD datasets exist but are small. Without a standard hard problem, claims of architectural advantage are hard to compare.
Tooling. Lava (Intel), PyNN, Nengo, Norse, snnTorch — the ecosystem is fractured. A practitioner targeting Loihi cannot easily port to BrainScaleS or SpiNNaker without a substantial rewrite.
Models that are simply not spiking-natural. Transformer attention has no obvious efficient SNN implementation. Most large-language-model architectures are not currently candidates for neuromorphic deployment.
The killer app. The field has been waiting for fifteen years for an unambiguous commercial use case that only neuromorphic can solve. Edge AI on conventional silicon keeps eating the application space first.
A heterodox neuromorphic-adjacent paradigm: leave a recurrent network's weights random, train only a linear readout. Herbert Jaeger's Echo State Networks (2001) and Wolfgang Maass's Liquid State Machines (2002) independently developed the framework. The "reservoir" is a fixed dynamical system whose rich internal state can be linearly projected to compute many things.
The appeal for hardware is obvious: physical dynamical systems make natural reservoirs. Photonic reservoirs (delay-coupled lasers, MZI meshes), spintronic reservoirs (skyrmion lattices, 2017+), memristive reservoirs, even bucket-of-water reservoirs (the famous Fernando-Sojakka demonstration, 2003) have all been built. Output training is a single matrix solve.
Reservoir computing's practical niche is time-series tasks where latency matters and accuracy is moderate: speech recognition, chaotic-system prediction, signal classification. It is rarely state-of-the-art on benchmarks but is often state-of-the-art at energy per inference, which is the brief.
Neuromorphic engineers borrow from neuroscience selectively. The cortex is the obvious target — six layers, columnar organisation, hierarchical receptive fields — and most SNN models are at some level cortical-inspired. The cerebellum is a less-discussed but architecturally tighter source of inspiration: a regular three-layer feed-forward fan-in/fan-out structure that performs rapid sensorimotor prediction.
The basal ganglia have inspired reinforcement-learning architectures (Frank, Doya). The hippocampus has inspired episodic-memory systems (the Numenta HTM lineage). The retina, repeatedly, has inspired event-based vision. Across these the pattern is the same: a circuit motif identified in biology is engineered into silicon, often with substantial liberty.
The honest scientific situation: we do not understand the cortex well enough to engineer it. Neuromorphic systems borrow what looks useful and abandon what looks fussy. Whether this leaves the most important features behind is the field's recurring nightmare.
If electrons are too slow and copper too lossy, photons are an alternative carrier. Photonic neuromorphic systems use waveguides, modulators, and detectors to perform analogue matrix-vector multiplication at the speed of light. MIT's Lightmatter and Lightelligence (both founded around 2017–2018) productised silicon-photonic AI accelerators. PsiQuantum's photonic quantum work draws on adjacent fabrication technology.
The advantages are real: bandwidth is enormous, intrinsic propagation is fast, certain operations are nearly free. The challenges are also real: optical-to-electrical conversion is energy-expensive, photonic memory is hard, integrated nonlinearities (needed for activation functions) are an active research problem.
The 2024 state of the art has photonic accelerators at TOPS-class performance with single-digit-watt power for inference of moderate-sized models. They are not yet outperforming top-end GPUs on standard benchmarks; they are differentiated on data-centre rack-density and on specific high-bandwidth applications. The pure-photonic neuromorphic dream is still further out than electronic neuromorphic.
The largest threat to neuromorphic computing's commercial relevance is not failure of the technology — it is success of the alternatives. ARM's Ethos-U series, Google's Edge TPU, NVIDIA's Jetson family, Hailo-8/15, Ambarella, Apple's Neural Engine: conventional digital edge-AI accelerators have driven inference power down by orders of magnitude in seven years.
The 2024 baseline: a Hailo-8 does YOLO-class object detection at ~26 TOPS at 2.5 W. A Jetson Orin Nano hits 40 TOPS at 7 W. Apple's Neural Engine is integrated in every iPhone and runs continuously at sub-100mW. Whatever the always-on neuromorphic chip's advantage, the conventional silicon is already good enough for most applications, with vastly better tooling and a vastly larger developer base.
The neuromorphic argument is that this works only down to a certain power floor; below microwatts, conventional architectures cannot compete. The bet is that energy-harvesting sensors, smart-dust applications, implantable medical devices, and similar ultra-low-power niches form a real-enough market.
One application area where neuromorphic logic is structurally appropriate: brain-computer interfaces. The signals are spikes; the data rate is high; the latency budget is tight; the power budget is constrained by what an implant can dissipate without damaging tissue (~mW/cm²).
Neuralink's N1 implant (first human in January 2024) uses conventional digital signal processing on-die. Synchron's Stentrode uses an external processor. Paradromics, Blackrock Neurotech, and others are at varying stages. None are publicly committed to neuromorphic on-implant logic; the engineering inertia of conventional ASICs is strong.
The structural argument for neuromorphic BCI: the signals are events, the conditioning and feature-extraction can be done in spiking primitives, the on-chip learning matters because each user's neural recordings drift over months. The University of Zurich's group (Indiveri, Liu) has shown working SNN-based seizure-detection processors. Whether neuromorphic wins in commercial BCI by 2030 is a tractable open question.
What the leading groups say their next decade looks like.
Intel Loihi line: continued integration with classical compute, focus on the optimisation and edge-control workloads where Hala Point and Loihi 2 have shown advantage. Lava ecosystem expansion.
BrainScaleS / Heidelberg: third-generation wafer with denser integration and improved on-chip plasticity, deepened collaboration with the Human Brain Project successor (EBRAINS). Use case: brain-scale simulation for neuroscience.
SpiNNaker 2: shift toward dual-use machine-learning + biological-simulation chip, partnership with edge-AI customers. Manchester continues to anchor the basic-research side.
Memristor / IBM analog AI: HERMES chips and successors target large-language-model inference with substantial per-chip TOPS/W advantage. Whether this scales to data-centre relevance is the bet.
Innatera, GrAI Matter Labs, BrainChip: edge-AI startups with shipping or near-shipping spiking processors, betting on the always-on consumer-electronics market.
BrainChip Holdings (Australian, listed ASX) shipped the Akida AKD1000 in 2021 — among the first commercially available neuromorphic processors aimed at OEM integration. The AKD1500 (2023) and AKD2000 (announced 2024) raise capability for vision and time-series workloads. Customers include Renesas (microcontroller integration via licensing), Mercedes (Vision EQXX concept-car research), and Edge Impulse for the developer toolchain.
Akida's selling proposition: drop-in replacement for conventional edge-AI processors with 5–10× energy advantage on event-sparse workloads, and on-chip learning for personalisation without cloud round-trip. Whether it captures share against Hailo, NVIDIA, and Apple silicon is unclear; the licensing model with Renesas may matter more than direct chip sales.
BrainChip and the other neuromorphic startups are the answer to the question "is this only a research toy?" The financial answer is hesitant — small revenues, measured customer wins, slower growth than the pure deep-learning accelerator field — but the answer is no longer "only a research toy."
The case against neuromorphic, made fairly:
1. The empirical advantages are narrow. Outside event-sparse workloads, neuromorphic is comparable to or worse than digital, and the workload niche is small.
2. The training problem is unsolved. Backprop with surrogate gradients works but loses the on-chip-learning advantage; STDP is biologically plausible but does not scale to deep representational learning.
3. The tooling moat is the wrong way. CUDA + PyTorch has fifteen years of compounding advantage; Lava and Nengo are tiny ecosystems by comparison.
4. Conventional silicon keeps closing the energy gap. Each new generation of edge-AI accelerator narrows the niche neuromorphic was supposed to fill.
5. The biological-plausibility argument is overrated. Aircraft do not flap. Computers do not need to spike to be efficient; they may just need to be designed properly.
The skeptic's conclusion: neuromorphic is a beautiful research programme whose commercial moment may already have passed. The believer's response: real architectural shifts take 30–40 years to mature, and we are perhaps two-thirds of the way through.
Behind the engineering is a deeper question. If physical dynamical systems can compute, the brain is a particular instantiation of a more general principle. Mead believed this. The early reservoir-computing demonstrations — even a bucket of water can compute under appropriate readout — push the same direction. The field of physical computing takes the position that all matter computes; the question is what we know how to read.
The implications, if you take this seriously: the right machine for a given task is not necessarily a digital simulation of an abstract algorithm; it is a physical system whose dynamics natively perform the computation. This is also a research programme — done well, it is what neuromorphic engineering is trying to do; done badly, it is hand-wave-and-hope.
Tom Kohonen, Herbert Jaeger, Wolfgang Maass, and on the more philosophical side, Daniel Hillis and Stephen Wolfram have all worked in adjacent veins. The unifying claim — that information processing in nature follows architectural principles distinct from von Neumann's — is older than neuromorphic computing and survives independent of it.
The intellectual ancestry of contemporary neuromorphic computing is small enough to name. Carver Mead (Caltech) — the founder. Misha Mahowald (Caltech, then Zurich; died 1996) — silicon retina, a haunting early career. Rodney Douglas & Kevan Martin (Zurich INI) — institutional anchors of the European programme. Giacomo Indiveri (Zurich) — current INI director, primary force in mixed-signal SNN ASIC design.
Steve Furber (Manchester) — co-designer of the original ARM, then SpiNNaker. Karlheinz Meier (Heidelberg, died 2018) — BrainScaleS architect, Human Brain Project co-founder. Wolfgang Maass (TU Graz) — Liquid State Machines, theoretical foundations. Tobi Delbrück (Zurich) — DVS sensor.
Dharmendra Modha (IBM) — TrueNorth lead. Mike Davies (Intel) — Loihi lead. Jeff Hawkins (Numenta, formerly Palm) — Hierarchical Temporal Memory; eccentric, vocal, parallel. The field is small, communicates at a few key conferences (NICE, telluride workshop, ISCAS), and is worth following directly via these names rather than via summary articles.
The standard SNN benchmark suite is small. N-MNIST and N-Caltech101 (Orchard et al. 2015) — saccaded versions of MNIST and Caltech101 captured with a DVS, the field's MNIST. DVS-Gesture (IBM 2017) — 11-class gesture recognition. SHD / SSC (Spiking Heidelberg Digits / Spiking Speech Commands, 2020) — auditory benchmark from the BrainScaleS group.
For real-world driving and robotics: MVSEC and DSEC stereo event datasets, 1Mpx Detection Dataset from Prophesee, and the ROAD-event dataset (Oxford VGG, 2024). For neuromorphic optimisation benchmarks: QUBO on the Loihi platform.
The honest reading: SNN benchmarks are far less mature than ANN benchmarks. The field would benefit enormously from a "neuromorphic ImageNet" — a large, hard task that everyone agrees on and competes on. The Tellurde Neuromorphic workshop's annual challenges and the IEEE NICE conference's open competitions are the closest current attempts.
Practical entry points for someone wanting to actually build something.
Nengo (Applied Brain Research, Waterloo). High-level Python framework for biologically-plausible neural simulation. Compiles to multiple backends including Loihi. The most teachable starting point.
snnTorch (Eshraghian et al., 2021). PyTorch-native SNN library with surrogate gradients. The right choice for someone coming from deep learning.
Lava (Intel, 2021). Process-based programming model for Loihi 2 and Hala Point. Open-source, runs in CPU simulation if you don't have hardware.
PyNN. Older, simulator-agnostic Python frontend (NEST, NEURON, BrainScaleS, SpiNNaker backends). The neuroscience-research lingua franca.
Norse (Heidelberg, GitHub, 2020+). PyTorch SNN library focused on biologically faithful simulation.
Tonic. Dataset library for event-based vision. The PyTorch-Vision equivalent for DVS data.
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An honest summary of neuromorphic computing in 2026.
It works. Loihi, BrainScaleS, SpiNNaker, TrueNorth, Akida — all real chips, all running real workloads, all delivering measured energy advantages on the workloads they were designed for.
It hasn't won. The applications where neuromorphic is unambiguously the right choice are narrow. The training story is improving but not solved. The tooling and developer ecosystem are far smaller than mainstream deep learning.
It is becoming a niche, not a revolution. The "ten thousand times more efficient than a GPU" headline applies to specific event-sparse always-on workloads — keyword spotting, edge sensor fusion, certain optimisation problems. Outside that, conventional silicon catches up faster than neuromorphic moves.
The long-bet case persists. If brain-scale, low-power, online-adaptive computation is going to matter — for ubiquitous sensing, for implantable devices, for autonomous systems at the edge — neuromorphic architecture is the most plausible path. Major architectural transitions take generations. We are roughly thirty-five years in.
1. The brain runs on twenty watts. Closing the gap to silicon is the long ambition.
2. Neuromorphic chips trade exact computation for sparse, asynchronous, event-driven processing. The trade pays off where workloads match.
3. Event-based vision is the place where neuromorphic ideas have crossed into commercial reality.
4. The training problem is the field's central technical bottleneck — surrogate gradients are the current best answer; it is not yet biologically plausible.
5. The honest near-term niche is ultra-low-power edge inference. The honest long-term ambition is brain-scale adaptive computation. Both are alive.
Neuromorphic Computing — Volume XIII, Deck 15 of The Deck Catalog. Set in JetBrains Mono and Space Grotesk. Phosphor green and amber on black-grid.
Thirty-one leaves on the architecture problem that has been thirty-five years in development and may be ten more from clear answer.
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