AI trends
Artificial intelligence (AI) and deep learning are revolutionizing our digital world today, with neural networks at the very core of this technological transformation. Among the different types, two primary architectures stand out: Artificial Neural Networks (ANN) and Spiking Neural Networks (SNN).
Artificial Neural Networks (ANNs) process information continuously, while spiking neural networks (SNNs) introduce time‑dependent, event‑driven computation inspired by biological neurons.
Major tech companies like Google, Amazon, Apple, Intel, BrainChip, and Innatera are leveraging ANN and SNN technologies to power their most advanced products. These products range from image recognition (such as Google Photos), autonomous vehicles (Waymo, Tesla), and voice-activated assistants (Alexa, Siri) to neuromorphic chips, smart sensors, and solutions for industrial IoT and healthcare.
This trend is especially evident in biomedicine, where the rapid rise of synaptic AI is reshaping next‑generation healthcare solutions, as explored in our article The Rise of Synaptic AI in Biomedicine.
Digital twin brain
As SNNs are increasingly used to model biological neural processes, they naturally pave the way for more advanced concepts such as digital twin brains. Digital twin models are important in fields that rely on biologically realistic neural dynamics.
A digital twin brain is a large‑scale computational replica of the human brain that simulates how billions of neurons interact, fire and form activity patterns. A concept closely tied to neuromorphic computing and spiking neural networks. These models can reproduce functional signals such as BOLD activity seen in fMRI scans, allowing researchers to explore brain states, test surgical scenarios, and analyze patient‑specific neural behavior in a safe virtual environment. As SNN‑based simulations become more accurate and energy‑efficient, digital twin brain systems are emerging as a powerful bridge between neuroscience and new‑generation AI.
What Makes ANN Different from SNN? Key Features Infographic
For understanding of the key differences between Artificial Neural Networks (ANN) and Spiking Neural Networks (SNN), see the summarized table below.
Companies and World Products Using Artificial Neural Networks and Spiking Neural Networks
Basic technical concepts of artificial neural networks
An artificial neuron is inspired by biological neurons and consists of input signals each multiplied by weighting factors. The sum is processed through an activation function that determines the neuron's output. Neurons are connected in layers — input, hidden, and output through which information is processed. The connection weights are adjusted during the training phase by algorithms such as backpropagation, enabling the network to learn and generalize.
The following is an overview of leading technology companies and their products that leverage different types of neural networks to enhance their AI solutions:
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Google: Image recognition, autonomous driving, Google Assistant (ANN)
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Amazon: Alexa, supply chain management, product recommendations (ANN)
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Apple: FaceID, Siri, healthcare AI applications (ANN)
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Intel: Loihi neuromorphic computing architectures for robotics, wearable sensors, edge AI (SNN)
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BrainChip & Renesas: Akida chips for IoT devices, industrial automation, autonomous systems (SNN)
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Innatera: Ultra-low power microchips tailored for sensor processing in healthcare and industrial IoT (SNN)Some models were discussed in AI Trends 2025.
Neurorobotics
Neurorobotics is rapidly evolving as brain‑inspired AI models become more capable of real‑time, event‑driven processing. Biological principles like spiking activity, adaptive control, and dynamic neural states are used to guide robotic behavior and decision‑making. Adaptive control in neurorobotics mirrors how biological synapses strengthen or weaken through plasticity, a process explained in our article on brain plasticity.This brain‑inspired approach is shaping the future of neurorobotics, where neuromorphic chips and SNN‑based controllers enable robots to operate with greater autonomy, lower energy consumption, and more natural responsiveness to sensory input. As these systems mature, neurorobotics is expected to play a key role in healthcare, rehabilitation, industrial automation, and human‑machine interaction.ANN and SNN: Complementary Strengths
The key to innovation in artificial intelligence lies in embracing diversity.ANN is the reliable workhorse for handling massive datasets, providing fast, precise computation required in many machine learning applications, while SNN enables breakthroughs where biological fidelity, energy efficiency, and real-time dynamic processing are critical, such as smart robotics and IoT. Increasingly, hybrid approaches combining ANN and SNN technologies deliver the best of both worlds.When deciding on the right neural network architecture for your AI project, carefully evaluate your requirements and how you want your system to "think". The future of AI is at the intersection of bio-inspired computing and advanced machine intelligence.Want to dive deeper into how spiking neural networks work and why they're biologically inspired?
Read "What are Spikes and why are They Special?"Explore related pillars
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