Neurocomputing

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Neurocomputing uses living neuronal networks to explore principles of biological information processing, learning, and adaptation. In vitro neurocomputing experiments involve defined recording and stimulation paradigms to perform computational tasks. In these studies, biological neuronal networks can be entrained, perturbed, and monitored to study core mechanisms of plasticity, memory formation, pattern recognition, and signal integration, bridging neuroscience with bioinspired computing and neuromorphic technologies.

MaxWell Biosystems’ High-Density Microelectrode Array (HD-MEA) platforms are ideally suited for neurocomputing applications. With thousands of addressable electrodes and flexible stimulation capabilities, researchers can deliver precise input patterns and record high-resolution responses across entire networks. The HD-MEA’s ultra-high sensitivity enables detection of individual spikes, bursts, and dynamic activity flows, allowing real-time tracking of how networks compute, adapt, and evolve. This makes MaxWell Biosystems’ HD-MEA technology a powerful tool for understanding biological computation and building next-generation biohybrid systems.  

Pioneering the future of computing with neuronal models

At the intersection of biology and technology, researchers are investigating how in vitro brain models, such as neural organoids, can form complex, functional circuits that resemble the human brain. By using the MaxOne HD-MEA Chip to precisely record and stimulate activity within these networks, new frontiers in biohybrid computing are emerging. One example is the concept of a Brain Processing Unit (BPU), a living system capable of learning, adapting, and processing information in ways inspired by the human brain.

Empowering bioinspired computing with
HD-MEAs

Our Technology

Precisely interact with every cell using high-resolution recording and stimulation

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To decode how biological circuits compute, it is essential to interact with each cell in the network. MaxWell Biosystems’ dense electrode layout and flexible stimulation deliver precise inputs and enable real-time observation of dynamic activity, supporting experiments that map, entrain, and perturb functional connectivity at cellular resolution.

Achieve real-time, closed-loop experimental control via API

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The API empowers researchers to define stimulation paradigms, adapt HD-MEA commands in real time, and build feedback-based control loops. This capability supports cutting-edge neurocomputing use cases, such as real-time training, reinforcement learning, and AI-biology interface experiments.

Reveal learning and adaptation by tracking plasticity across the network

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Uncover how networks change over time with ultra-sensitive recordings that detect subtle shifts in activity patterns. HD-MEAs support detailed studies of synaptic plasticity, memory encoding, and network-level learning.

Bridge biohybrid systems through seamless integration

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Our HD-MEA systems can be interfaced directly with digital processors and neuromorphic hardware, enabling real-time communication between living neuronal networks and external devices. This provides a flexible foundation for connecting with neuromorphic systems and exploring hybrid models of intelligence.

Adaptive reservoir computing with cortical organoids on HD-MEAs

This publication demonstrates how cortical organoids plated on HD-MEAs can act as a living reservoir for neurocomputing tasks called Brainoware. Spatiotemporal electrical inputs are delivered to the organoid, generating complex responses recorded at high resolution across thousands of electrodes. By decoding evoked activity using machine learning, the system achieves classification, prediction, and adaptive computation in vitro. This approach leverages precise interaction with every cell in the network, enabling detailed studies of information processing and unsupervised learning in brain-inspired computing frameworks. In one application of the Brainoware framework, it was trained on speech recognition tasks, successfully identifying the correct speaker from different audio clips.
Read more on Cai et al., Nature Electronics, 2024.  

Real-time closed-loop biohybrid computing

This case study describes how a closed-loop system links cortical organoids (connectoids) on the MaxOne HD-MEA with the hardware spiking network emulated on BioemuS. Neural bursts detected in one system trigger stimulation in the other, with all detection and control steps automated through a custom Python interface. This experiment showcases the MaxLab Live API-based, real-time feedback capabilities enabling real-time, bi-directional communication between living and silicon-based networks for biohybrid neurocomputing.
Read more on Beaubois et al., Nature Communications, 2024

Synthetic biological intelligence: self-organized, goal-directed activity in cultures

This study showcases DishBrain, which integrates in vitro neural networks on the MaxOne HD-MEA with a simulated Pong game, creating a hybrid bio-digital system. Through closed-loop stimulation and feedback, the neurons adapt their activity in real time, demonstrating learning and self-organization in response to sensory consequences. Our HD-MEA platform enables precise tracking and modulation of these complex adaptive behaviors in living networks, bridging the gap between biological computation and artificial intelligence. By showing that in vitro neural circuits can adapt within a simulated environment, this work opens avenues for functional assays in toxicology, drug discovery, and high-throughput screening of cognitive performance.
Read more on Kagan et al., Neuron, 2022.

Relevant
Applications

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