Microcontroller Action Potential Generator

Here I demonstrate how to use a single microcontroller pin to generate action-potential-like waveforms. The output is similar my fully analog action potential generator circuit, but the waveform here is created in an entirely different way. A microcontroller is at the core of this project and determines when to fire action potentials. Taking advantage of the pseudo-random number generator (rand() in AVR-GCC’s stdlib.h), I am able to easily produce unevenly-spaced action potentials which more accurately reflect those observed in nature. This circuit has a potentiometer to adjust the action potential frequency (probability) and another to adjust the amount of overshoot (afterhyperpolarization, AHP). I created this project because I wanted to practice designing various types of action potential measurement circuits, so creating an action potential generating circuit was an obvious perquisite.

 

The core of this circuit is a capacitor which is charged and discharged by toggling a microcontroller pin between high, low, and high-Z states. In the high state (pin configured as output, clamped at 5V) the capacitor charges through a series resistor as the pin sources current. In the low state (pin configured as output, clamped at 0V) the capacitor discharges through a series resistor as the pin sinks current. In the high-Z / high impedance state (pin configured as an input and little current flows through it), the capacitor rests. By spending most of the time in high-Z then rapidly cycling through high/low states, triangular waveforms can be created with rapid rise/fall times. Amplifying this transient and applying a low-pass filter using a single operational amplifier stage of an LM-358 shapes this transient into something which resembles an action potential. Wikipedia has a section describing how to use an op-amp to design an active low-pass filter like the one used here.

The code to generate the digital waveform is very straightforward. I’m using PB4 to charge/discharge the capacitor, so the code which actually fires an action potential is as follows:

// rising part = charging the capacitor
DDRB|=(1<<PB4); // make output (low Z)
PORTB|=(1<<PB4); // make high (5v, source current)
_delay_ms(2); // 2ms rise time
	
// falling part
DDRB|=(1<<PB4); // make output (low Z)
PORTB&=~(1<<PB4); // make low (0V, sink current)
_delay_ms(2); // 2ms fall time
_delay_us(150); // extra fall time for AHP

// return to rest state
DDRB&=~(1<<PB4); // make input (high Z)

Programming the microcontroller was accomplished after it was soldered into the device using test clips attached to my ICSP (USBtinyISP). I only recently started using test clips, and for one-off projects like this it’s so much easier than adding header sockets or even wiring up header pins.

I am very pleased with how well this project turned out! I now have an easy way to make irregularly-spaced action potentials, and have a great starting point for future projects aimed at measuring action potential features using analog circuitry.

Project code (GitHub)

Notes

  • Action potential half-width (relating to the speed of the action potential) could be adjusted in software by reducing the time to charge and discharge the capacitor. A user control was not built in to the circuit shown here, however it would be very easy to allow a user to switch between regular and fast-spiking action potential waveforms.
  • I am happy that using the 1n4148 diode on the positive input of the op-amp works, but using two 100k resistors (forming a voltage divider floating around 2.5V) at the input and reducing the gain of this stage may have produced a more reliable result.
  • Action potential frequency (probability) is currently detected by sensing the analog voltage output by a rail-to-rail potentiometer. However, if you sensed a noisy line (simulating random excitatory and inhibitory synaptic input), you could easily make an integrate-and-fire model neuron which fires in response to excitatory input.
  • Discussion related to the nature of this “model neuron” with respect to other models (i.e., Hodgkin–Huxley) are on the previous post.
  • Something like this would make an interesting science fair project

     

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