Lu.i – educational neuron PCB

Lu.i is an electronic neuron circuit mimicking and illustrating the basic dynamics of real, biological neurons.

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Lu.i (phonetic notation of »Louis«, an homage to Louis Lapicque, who first formulated the leaky integrate-and-fire model) is an electronic neuron circuit mimicking and illustrating the basic dynamics of real, biological neurons. The printed circuit board (PCB) features a configurable, fully analog implementation of the leaky integrate-and-fire model and visualizes the internal state, the membrane potential, through a VU-meter-style chain of LEDs. The neuron emits a short pulse whenever the membrane potential crosses a predefined threshold voltage. Neurons communicate by exchanging these spikes. Multiple boards can be connected via jumper wires to form networks.

- configurable leak potential and membrane time constant
- three synapses with tunable weight (excitatory or inhibitory)
- three output terminals to forward spikes to other neurons
- VU-meter-style visualization of the membrane potential and spike LED
- optimized for low-cost production (~ 3 $)

Example Networks

Very much resembling the structure of the nervous system, multiple lu.i boards can be connected to form even complex neural networks. The latter may simply serve illustrative purposes, mimick biological behavior, or even solve functional tasks. The following hierarchical network, e.g., implements an exclusive OR (XOR).

For a more extensive list of example networks please refer to lu.i's documentation.

Neuron Model

The PCB implements the leaky integrate-and-fire (LIF) model. For this model, the membrane voltage follows the dynamics described by the differential equation This equation describes the rate of change of the membrane voltage (denoted by the temporal derivative on the left-hand side), which can be decomposed in two currents: The membrane always decays back to the leak potential with a leak current proportional to the deflection from that resting state. This yields an exponential decay to the baseline. The second contribution results from synaptic stimulation, pulling the membrane to either positive or negative potentials.

These dynamics are accompanied by a spike condition: Whenever the membrane potential reaches a certain threshold, the neuron emits a spike – here realized as a short voltage pulse – which is then relayed to other neurons. After spiking, the neuron is then reset to a low potential.


The PCB implements the model dynamics based on a low-pass filter mimicking the membrane, a spike threshold comparator, and three separate input circuits. The latter gate a configurable conductance (representing the synaptic weights) based on voltage pulses from presynaptic neurons.

Educational purpose

Lu.i visualizes the basic time-continuous dynamics and sparse, event-based communication of biological neurons and introduces the concept of physical and neuromorphic computation. Small neural networks can be formed in a playful and hands-on way to illustrate the interaction of neurons.

The PCBs can be easily interfaced with Arduinos or other microcontrollers, e.g., to mimic sensors or other inputs to a neural network. It may, furthermore, allow a soft introduction into electronics and the handling of oscilloscopes by providing access to the neuron-internal membrane potential.

Lu.i has been used in multiple outreach projects to communicate the foundations of brain-inspired and physical computation (,

  • New revision: exponentially decaying synaptic currents

    Sebastian Billaudelle04/25/2023 at 13:24 0 comments

    So far, lu.i implemented a rather rudimentary synaptic interaction: While implementing a conductance-based stimulation (, the impact of a presynaptic neuron was realized as a short, delta-like pulse, leading to almost step-like increments of the postsynaptic membrane potential.

    More realistic synapse models feature more complex interaction kernels to realize, e.g., exponentially decaying synaptic currents

    A direct comparison (taken from Billaudelle, 2022) of different interaction kernels showcases these more complex temporal dynamics and their impact on the shape of postsynaptic membrane potentials.

    On April 14th, we submitted a new revision of lu.i, now incorporating such exponential synapses. The exponential current is generated by an additional leaky integrator stage with a tunable synaptic timeconstant. The revised version, furthermore, includes switches to configure a synapse to be excitatory or inhibitory (previously realized by swapping the jumper wires).

    The updated fabrication files can be found on GitHub. We are currently awaiting delivery of the first batch of the new revision!

  • Arrival of the first batch!

    Sebastian Billaudelle04/25/2023 at 12:56 0 comments

    When the first batch of Lu.i PCBs was delivered in Juli 2022, we were funnily enough spread across the globe and could thus get together only virtually. The PCBs arrived almost fully populated, just the coin cell holder had to be soldered manually.

    We had gone all in: Time constraints did not allow for an initial smaller prototype batch, and we had ordered a total of 100 little neurons! Our pre-fabrication verification was thus limited to SPICE simulations. Our limited confidence in those was further reduced by the fact that – with the design being powered by a coin cell – we could only barely honor the specified voltage limits of the used components.

    We were super excited when the LEDs immediately lit up – but the circuit appeared somehow broken and reacted unpredictably to us turning the little potentiometers. After some tinkering, we found the first (and only "significant") bug: We had messed up the silkscreen! The markings for the two potentiometers for the membrane timeconstant and the leak potential were swapped🤦 Nothing that we could not fix with a few stickers. One hundred stickers. Well, could have been worse!

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opera2390 wrote 02/27/2024 at 06:51 point

Hello. I think this is a great idea! I've been following the Lui Educational Neuron PCB project and it's awesome! The level of detail in the circuits and the potential for educational purposes is impressive. If anyone needs more help with electronics homework while working on similar projects, I found this website really helpful They have excellent resources that can be useful for this type of work. Happy crafting everyone!

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Allportmail wrote 12/21/2023 at 17:30 point

This is an exciting field! I believe such studies can enhance our broad understanding of artificial intelligence and machine learning. I'm also interested in this article and read them with enthusiasm. Education plays a crucial role in our development, and research on its significance emphasizes how learning and knowledge impact our intellectual and cultural growth.

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John wrote 05/22/2023 at 18:42 point

I hope you submit to Op-amp design challenge! This is super cool.

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the_3d6 wrote 05/19/2023 at 10:10 point

They look awesome! Would be very interesting to make some network from them (like maybe 5-10 nodes) with pattern generation ability - I guess such design allows that

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Sebastian Billaudelle wrote 05/25/2023 at 14:14 point

Indeed! We have brought a hundred to the CapoCaccia Cognitive Neuromorphic Engineering Workshop and saw quite a few cool networks emerge! We'll try to update our GitHub and pages with some further information and instructions.

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