Can Neuroscience be Implemented into Robotics?

At the core are neurons: the brain’s basic units.¹ These cells fire electrical spikes and communicate through chemical signals, forming vast networks that can adapt, learn, and self-organize. That kind of flexible, low-power processing is something artificial neural networks still struggle to achieve.

To put it in perspective, the human brain manages everything from keeping you balanced on a bike to solving complex problems, all while using about 20 watts of power. Most AI models, by comparison, require massive computational resources to pull off even narrow tasks. We’re getting better at simulating intelligence, but we haven’t figured out how to do it with the brain’s elegance or efficiency.

That gap is driving interest in biological neural networks (BNNs). In labs around the world, researchers are growing living neurons in dishes and linking them to robotic systems.¹ These networks retain key features of brain function—like synaptic plasticity and nonlinear responses—which allow them to process sensory data and issue motor commands in real time. In effect, they’re acting as the robot’s nervous system. The result is a closed feedback loop that’s surprisingly similar to what happens in our own bodies: the robot senses its environment, the neural network interprets the input, and the robot responds accordingly.

It’s early days, but this work is already raising profound questions about what happens when you embed actual biological intelligence into machines—not just algorithms modeled on the brain, but living cells doing the computing.

Technical Approaches: From Spikes to Thought-Controlled Machines

There’s no single blueprint for bringing neuroscience into robotics, but a few technical approaches are starting to stand out.

One of the most active areas is spiking neural networks (SNNs). Unlike traditional neural networks that process continuous values, SNNs use discrete electrical spikes, much like real neurons. This makes them both more biologically realistic and potentially far more energy-efficient, especially when paired with neuromorphic chips designed to handle event-driven processing.

The results are promising. Researchers have used SNNs to control robotic arms and navigate unpredictable environments, producing behavior that feels less pre-programmed and more adaptive than what conventional systems typically allow.

Another emerging approach involves bidirectional neural interfaces. Here, living neural networks cultured in lab dishes communicate directly with robotic systems. Using microelectrode arrays, scientists can stimulate specific neurons, record their activity, and use those signals to guide robotic actions. It creates a two-way feedback loop, where artificial and biological components influence each other in real time. This is the core of neurorobotics: blending living tissue with machines to build systems that can learn, adapt, and respond dynamically.¹

Meanwhile, brain–computer interfaces (BCIs) are bringing humans directly into the loop. These systems translate neural activity captured through EEG or implanted electrodes into control signals for robotic devices. From wheelchairs and robotic arms to drones, the goal is intuitive, hands-free operation. Some labs are even experimenting with simulated neural assemblies based on EEG data to drive robots in tasks like picking and placing objects, pushing the boundary of what “thought-controlled” really means.^2,3

Sensory Processing and Motor Control: Letting Robots Move More Like Animals

Brain–computer interfaces tend to focus on intention, namely, decoding signals from the brain’s cortex to control machines. But a lot of what makes behavior feel intelligent actually happens at a lower level, where perception and movement are closely linked. In biological systems, sensing and acting aren’t two separate steps—they’re part of the same continuous loop. And that idea is starting to influence how robots are built.

Take central pattern generators (CPGs), for example, neural circuits in animals that handle rhythmic actions like walking, swimming, or breathing.³ These circuits can keep going on their own but also adjust based on sensory input, which makes them ideal for responding to a changing environment. Roboticists have used CPG-inspired designs in walking and swimming robots to create motion that’s not just coordinated, but flexible—less rigid than traditional, pre-scripted movement.

There’s also growing interest in how decisions and actions connect. In humans, movement often starts with a gradual buildup of brain activity, not just a simple on/off signal. This is seen in patterns like the lateralized readiness potential (LRP), which shows that the brain starts preparing for action even before we move. By mimicking that kind of buildup in robots, rather than triggering motion with a basic threshold, researchers can create machines that weigh input over time, respond more thoughtfully, and behave more like living systems.

It’s all part of a bigger shift to design robots that don’t just move, but move in ways that are responsive, adaptive, and grounded in how real organisms interact with the world.

Embodied Intelligence and Closed-Loop Adaptation

Designing natural movement is just one piece of the puzzle. What really sets biological systems apart is their ability to learn—to refine their behavior through experience. That’s where the concept of embodied intelligence comes in.

In robotics, embodied intelligence suggests that cognition doesn’t just happen in the brain, or its artificial equivalent, but emerges through ongoing interaction between a robot’s body and its environment. Neuroscience offers valuable blueprints for this, showing how perception, memory, inference, and action are all tightly linked. By using brain-inspired feedback loops, researchers are building robots that can learn from what they do, adjusting their behavior based on past success or failure. That ability to adapt is key to handling real-world complexity.^2,4

One technique gaining traction is reservoir computing. In this approach, a recurrent neural network or even a physical system acts as a kind of dynamic memory, transforming input into a high-dimensional space where it becomes easier to recognize patterns or make decisions. In recent experiments, researchers have used biological neural networks (BNNs) as living reservoirs, connected to robots in the lab. When paired with lightweight artificial neural "readers," these systems were able to learn quickly and generate strong, real-time motor responses—whether for obstacle avoidance, target tracking, or basic navigation.¹

This kind of setup doesn’t just mimic intelligence; it starts to feel like it, too, as the robot is learning from experience, adapting on the fly, and developing behavior that fits the world it’s in.

Learning, Memory, and Synaptic Plasticity

Embodied interaction can support fast adaptation, but lasting learning depends on deeper mechanisms, ones that support memory, stability, and long-term change. This is where neuroscience offers especially valuable insight.

Biological brains are designed for lifelong, cumulative learning. A core part of that capability is synaptic plasticity, the process by which synapses strengthen or weaken in response to activity. Models like Hebbian learning, critical periods, and homeostatic regulation help explain how systems can stay adaptable without losing their internal balance. Drawing from these principles, researchers are designing robots that can build new skills over time, retain what they’ve previously learned, and adjust to shifting environments without starting from scratch.^1,5

In the lab, in vitro neural cultures show how this kind of learning might work. When exposed to sensory input and feedback, these living networks demonstrate targeted learning and short-term memory. They can handle both supervised and unsupervised learning and even show signs of associative memory, linking inputs in meaningful ways based on experience. These behaviors provide a foundation for robotic systems that learn in richer, more stable ways, closer to how biological systems operate over time.¹

Biological Inspiration in Sensorimotor Systems

While neural learning offers powerful ways for robots to adapt over time, biological systems also provide inspiration at a physical level, particularly in how animals move, sense, and interact with their environment. 

Biomimetic robots take direct cues from animal physiology in everything from actuator design to control strategies. By studying how the motor cortex and spinal circuits work together in animals, researchers are developing robots that can grasp and manipulate objects with greater dexterity. Advances in soft robotics and neuromorphic engineering are also enabling the design of flexible, compliant systems that better mimic the coordination of muscles, sensory feedback, and reflexive responses found in nature.^6,7

Neuromorphic Computing: Bridging Energy and Complexity Gaps

Alongside more lifelike movement and sensing, there’s growing momentum behind mimicking the brain’s hardware—how it actually processes information.

Neuromorphic engineering draws from neuroscience to create specialized chips that process data in ways similar to the brain: in parallel, and triggered by events rather than continuous computation. This architecture significantly reduces energy use compared to traditional processors, making it well-suited for real-time control in robotics. Neuromorphic chips are already being used in adaptive prosthetics, autonomous vehicles, and embodied robotic systems. Their design continues to be shaped by neuroscientific insights into how the brain represents data, learns from input, and adapts under changing conditions.⁸

Human–Robot Integration and Cognitive Robotics

Biology’s influence on robotics doesn’t stop at movement or computation; it also shapes how machines interact with people, particularly in shared environments where communication and awareness matter.

Guided by neuroscience, robotic systems are becoming more intuitive, socially aware, and responsive to human behavior. Brain–robot interfaces (BRIs) are one example: they use neural signals not only to control robots, but also to provide real-time feedback that helps machines adapt to human users. This kind of interaction is already being explored in fields like healthcare, assistive tech, and education.

Progress in cognitive robotics also relies on tight collaboration between neuroscience and engineering, aligning theories of perception, attention, and learning with robotic architectures. Building truly integrated systems means developing better neural models, refining interfaces, and thinking carefully about the ethical implications of biohybrid machines.

Where Robotics Could Go From Here

Robots are starting to move beyond preprogrammed behavior. With neuroscience in the mix, they’re learning to sense, adapt, and respond in ways that reflect how real organisms operate.

The goal isn’t to replicate the brain; it’s to build systems that work in context, learn from experience, and handle uncertainty. As machines grow more capable, the hard questions shift from how they work to how they fit into the world around us.

That’s where neuroscience matters most; not just in shaping smarter robots, but in helping us decide what kind of intelligence we actually want to create.

References and Further Reading

1. Chen, Z. et al. (2023). An Overview of In Vitro Biological Neural Networks for Robot Intelligence. Cyborg Bionic Syst. DOI:10.34133/cbsystems.0001. https://spj.science.org/doi/10.34133/cbsystems.0001
2. Jones, A. et al. (2023). Bridging Neuroscience and Robotics: Spiking Neural Networks in Action. Sensors, 23(21), 8880. DOI:10.3390/s23218880. https://www.mdpi.com/1424-8220/23/21/8880
3. Valle, I. D. (2025). Nature’s Ultimate Recyclable Robot: Neuroscience as the Key to Next-Generation AI and Robotics. LinkedIn. https://www.linkedin.com/pulse/natures-ultimate-recyclable-robot-neuroscience-key-ai-del-valle-vpzhe/
4. Zhao, Z. et al. (2024). Exploring Embodied Intelligence in Soft Robotics: A Review. Biomimetics, 9(4), 248. DOI:10.3390/biomimetics9040248. https://www.mdpi.com/2313-7673/9/4/248
5. Wei, H., Bu, Y., & Zhu, Z. (2020). Robotic arm controlling based on a spiking neural circuit and synaptic plasticity. Biomedical Signal Processing and Control, 55, 101640. DOI:10.1016/j.bspc.2019.101640. https://www.sciencedirect.com/science/article/abs/pii/S1746809419302216
6. Alepuz, A. M. et al. (2024). Brain-inspired biomimetic robot control: A review. Frontiers in Neurorobotics, 18, 1395617. DOI:10.3389/fnbot.2024.1395617. https://www.frontiersin.org/journals/neurorobotics/articles/10.3389/fnbot.2024.1395617/full
7. Su, J. et al. (2025). Soft Materials and Devices Enabling Sensorimotor Functions in Soft Robots. Chemical Reviews. DOI:10.1021/acs.chemrev.4c00906. https://pubs.acs.org/doi/10.1021/acs.chemrev.4c00906
8. Wang, K. et al. (2025). Neuromorphic chips for biomedical engineering. Mechanobiology in Medicine, 3(3), 100133. DOI:10.1016/j.mbm.2025.100133. https://www.sciencedirect.com/science/article/pii/S294990702500021X

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