A neuron resembles an electrical circuit with batteries and variable resistors. The resistors are played by ion channels — protein pores in the membrane that open and close, letting charged particles through. Whether a nerve impulse fires depends on how fast and at what voltage these pores operate. The trouble is that for many channels, such "instructions" are poorly known. An engineer would have to guess how a part works and then tweak the whole circuit to match real measurements, and still get it wrong.
The authors took a different approach. They took a standard neuron model, a set of equations describing currents through known channels, and inserted a trainable block into it. This block, like a meticulous craftsman, looks only at the voltage trace and infers how the unknown pores should open and close. No ready-made formulas are given: it learns on the fly, fitting curves that resemble real gating mechanisms. The method was tested on 2400 different virtual neurons, and it confidently recovered hidden rules even under strong electrical noise.
The main advantage of the approach is transparency. The trainable block does not become a black box: its output consists of the same smooth voltage-dependent curves that biologists use to describe real channels. You can take these curves and understand what was happening in the cell. Thus, a hybrid of physics and machine learning patches gaps in knowledge without sweeping them under the rug.
A single neuron can host dozens of types of ion channels, each opening at its own voltage — much like a lock that clicks only with a key of a specific shape.
In Peter Watts's novel Blindsight, the crew tries to decipher the workings of alien neurons without direct access — much like the algorithm reconstructs invisible channels from indirect data.