Uploaded May 2022 | Updated September 2026, 2 weeks ago
A single analog "neuron" in Eurorack processing audio live.
Recently, I've been hooked on the idea of neurons and electronic and digital models of them. As always, this interest is focused on how these models can help us make interesting music and sound design.
I wouldn't want to learn biology from a composer, so I'll keep this in the most simple terms possible (so I don't mess up): The concept here is that neuron is receives a bunch of signals into its dendrites, and, based off of these signals, send out its own signal through its axon.
Are you with me so far?
In the case of biological neurons these "signals" are chemical or electrical, and in these sonic explorations the signals are the continuous changing voltages of an analog audio signal. So, in audio, the way we combine multiple audio source is a mixer:
Now, the interesting thing here is that a neuron doesn't just combine the signals from its dendrites and send them to the output. It gives these inputs different weights (levels), and the inputs are combined in a nonlinear way. In our sonic models of neurons, this "nonlinearity" could be a number of things: waveshapers, rectifiers, etc.
In the case of our sonic explorations, different nonlinear transformations will lead to different sonic results, but there's no real "better" or "worse" choices (except driven by your aesthetic goals). Now, if I wanted to train an artificial neural net to identify pictures or compose algorithmic music, I'd think more about it (and there's lots of literature about these activation function choices).
Ok! A mixer with the ability to control the input levels and a nonlinear transformation! That's our neuron! That's it!
In this patch, our mixer receives three inputs: a sequenced sine wave, a chaotically-modulated triangle wave, and one more thing I'll get back to in a sec. That output is put through a hyperbolic tan function (soft-clipping, basically), then run into a comparator (if the input is high enough, fire the synapse!), then comparator is filtered, run to a spring reverb, and then the reverb is fed back into that third input of the mixer.
Now, as it stands, this neuron doesn't learn anything. That would require the neuron getting some feedback from it's output--is it delivering the result we want based on the inputs? If not, how can it change the weights of these inputs so that it does?
We'll save that discussion for another day, though, and, for now, just enjoy the sounds
Official Website - simonhutchinson.com
Subscribe: youtube.com/user/SimonHutc/?sub_confirmation=1
bandcamp: simonhutchinson.bandcamp.com
Buy me a coffee: ko-fi.com/simonhutchinson
Sign up for my mailing list: eepurl.com/hVs7bT
#SoundSynthesis #SoundDesign #ExperimentalMusic #shorts #analogneuron #eurorack
A single analog "neuron" in Eurorack processing audio live.
Recently, I've been hooked on the idea of neurons and electronic and digital models of them. As always, this interest is focused on how these models can help us make interesting music and sound design.
I wouldn't want to learn biology from a composer, so I'll keep this in the most simple terms possible (so I don't mess up): The concept here is that neuron is receives a bunch of signals into its dendrites, and, based off of these signals, send out its own signal through its axon.
Are you with me so far?
In the case of biological neurons these "signals" are chemical or electrical, and in these sonic explorations the signals are the continuous changing voltages of an analog audio signal. So, in audio, the way we combine multiple audio source is a mixer:
Now, the interesting thing here is that a neuron doesn't just combine the signals from its dendrites and send them to the output. It gives these inputs different weights (levels), and the inputs are combined in a nonlinear way. In our sonic models of neurons, this "nonlinearity" could be a number of things: waveshapers, rectifiers, etc.
In the case of our sonic explorations, different nonlinear transformations will lead to different sonic results, but there's no real "better" or "worse" choices (except driven by your aesthetic goals). Now, if I wanted to train an artificial neural net to identify pictures or compose algorithmic music, I'd think more about it (and there's lots of literature about these activation function choices).
Ok! A mixer with the ability to control the input levels and a nonlinear transformation! That's our neuron! That's it!
In this patch, our mixer receives three inputs: a sequenced sine wave, a chaotically-modulated triangle wave, and one more thing I'll get back to in a sec. That output is put through a hyperbolic tan function (soft-clipping, basically), then run into a comparator (if the input is high enough, fire the synapse!), then comparator is filtered, run to a spring reverb, and then the reverb is fed back into that third input of the mixer.
Now, as it stands, this neuron doesn't learn anything. That would require the neuron getting some feedback from it's output--is it delivering the result we want based on the inputs? If not, how can it change the weights of these inputs so that it does?
We'll save that discussion for another day, though, and, for now, just enjoy the sounds
Official Website - simonhutchinson.com
Subscribe: youtube.com/user/SimonHutc/?sub_confirmation=1
bandcamp: simonhutchinson.bandcamp.com
Buy me a coffee: ko-fi.com/simonhutchinson
Sign up for my mailing list: eepurl.com/hVs7bT
#SoundSynthesis #SoundDesign #ExperimentalMusic #shorts #analogneuron #eurorack
