Action Potential Generator Circuit

Few biological cells are as interesting to the electrical engineer as the neuron. Neurons are essentially capacitors (with a dielectric cell membrane separating conductive fluid on each side) with parallel charge pumps, leak currents, and nonlinear voltage-dependent currents. When massively parallelized, these individual functional electrical units yield complex behavior and underlie consciousness. The study of the electrical properties of neurons (neurophysiologically, a subset of electrophysiology) often involves the development and use of sensitive electrical equipment aimed at studying these small potentials produced by neurons and currents which travel through channels embedded in their membranes. It seems neurophysiology has gained an emerging interest from the hacker community, as evidenced by the success of Back Yard Brains, projects like the OpenEEG, and Hack-A-Day’s recent feature The Neuron – a Hacker’s Perspective.

In pondering designs for complex action potential detection and analysis circuitry, I realized that it would be beneficial to be able to generate action-potential-like waveforms on my workbench. The circuit I came up with to do this is a fully analog (technically mixed signal) action potential generator which produces lifelike action potentials.


Cellular Neurophysiology for Electrical Engineers (in 2 sentences): Neuron action potentials (self-propagating voltage-triggered depolarizations) in individual neurons are measured in scientific environments using single cell recording tools such as sharp microelectrodes and patch-clamp pipettes. Neurons typically rest around -70mV and when depolarized (typically by external excitatory input) above a threshold they engage in a self-propagating depolarization until they reach approximately +40mV, at which time a self-propagating repolarization occurs (often over-shooting the initial rest potential by several mV), then the cell slowly returns to the rest voltage so after about 50ms the neuron is prepared to fire another action potential. Impassioned budding electrophysiologists may enjoy further reading Active Behavior of the Cell Membrane and Introduction to Computational Neuroscience.

The circuit I describe here produces waveforms which visually mimic action potentials rather than serve to replicate the exact conductances real neurons employ to exhibit their complex behavior. It is worth noting that numerous scientists and engineers have designed more physiological electrical representations of neuronal circuitry using discrete components. In fact, the Hodgkin-Huxley model of the initiation and propagation of action potentials earned Alan Hodgkin and Andrew Huxley the Nobel Prize in Physiology and Medicine in 1936. Some resources on the internet describe how to design lifelike action potential generating circuits by mimicking the endogenous ionic conductances which underlie them, notably Analog and Digital Hardware Neural Models, Active Cell Model, and Neuromorphic Silicon Neuron Circuits. My goal for this project is to create waveforms which resemble action potentials, rather than waveforms which truly model them. I suspect it is highly unlikely I will earn a Nobel Prize for the work presented here.

The analog action potential simulator circuit I came up with creates a continuous series action potentials. This is achieved using a 555 timer (specifically the NE555) in an astable configuration to provide continuous square waves (about 6 Hz at about 50% duty). The rising edge of each square wave is isolated with a diode and used to charge a capacitor*. While the charge on the capacitor is above a certain voltage, an NPN transistor (the 2N3904) allows current to flow, amplifying this transient input current. The capacitor* discharges predictably (as an RC circuit) through a leak resistor. A large value leak resistor slows the discharge and allows that signal’s transistor to flow current for a longer duration. By having two signals (fast and slow) using RC circuits with different resistances (smaller and larger), the transistors are on for different durations (shorter and longer). By making the short pulse positive (using the NPN in common collector configuration) and the longer pulse negative (using the NPN in common emitter configuration), a resistor voltage divider can be designed to scale and combine these signals into an output waveform a few hundred mV in size with a 5V power supply. Pictured below is the output of this circuit realized on a breadboard. The blue trace is the output of the 555 timer.

*Between the capacitance of the rectification diode, input capacitance of the transistor, and stray parasitic capacitance from the physical construction of my wires and the rails on my breadboard, there is sufficient capacitance to accumulate charge which can be modified by changing the value of the leak resistor.

This circuit produces similar output when simulated. I’m using LTspice (free) to simulate this circuit. The circuit shown is identical to the one hand-drawn and built on the breadboard, with the exception that an additional 0.1 µF capacitor to ground is used on the output to smooth the signal. On the breadboard this capacitance-based low-pass filtering already exists due to the capacitive nature of the components, wires, and rails.

A few improvements naturally come to mind when considering this completed, functional circuit:

  • Action potential frequency: The resistor/capacitor network on the 555 timer determines the rate of square pulses which trigger action potentials. Changing these values will cause a different rate of action potential firing, but I haven’t attempted to push it too fast and suspect the result would not be stable is the capacitors are not given time to fully discharge before re-initiating subsequent action potentials.
  • Microcontroller-triggered action potentials: Since action potentials are triggered by any 5V rising edge signal, it is trivially easy to create action potentials from microcontrollers! You could create some very complex firing patterns, or even “reactive” firing patterns which respond to inputs. For example, add a TSL2561 I2C digital light sensor and you can have a light-to-frequency action potential generator!
  • Adjusting size and shape of action potentials: Since the waveform is the combination of two waveforms, you can really only adjust the duration (width) or amplitude (height) of each individual waveform, as well as the relative proportion of each used in creating the summation. Widths are adjusted by changing the leak resistor on the base of each transistor, or by adding additional capacitance. Amplitude and the ratio of each signal may be adjusted by changing the ratio of resistors on the output resistor divider.
  • Producing -70 mV (physiological) output: The current output is electirically decoupled (through a series capacitor) so it can float at whatever voltage you bias it to. Therefore, it is easy to “pull” in either direction. Adding a 10k potentiometer to bias the output is an easy way to let you set the voltage. A second potentiometer gating the magnitude of the output signal will let you adjust the height of the output waveform as desired.
  • The 555 could be replaced by an inverted ramp (sawtooth): An inverted ramp / sawtooth pattern which produces rapid 5V rising edges would drive this circuit equally well. A fully analog ramp generator circuit can be realized with 3 transistors: essentially a constant current capacitor charger with a threshold-detecting PNP/NPN discharge component.
  • This action potential is not all-or-nothing: In real life, small excitatory inputs which fail to reach the action potential threshold do not produce an action potential voltage waveform. This circuit uses 5V rising edges to produce action potential waveforms. However, feeding a 1V rising edge would produce an action potential 1/5 the size. This is not a physiological effect. However, it is unlikely (if not impossible) for many digital signal sources (i.e., common microcontrollers) to output anything other than sharp rising edge square waves of fixed voltages, so this is not a concern for my application.
  • Random action potentials: When pondering how to create randomly timed action potentials, the issue of how to generate random numbers arises. This is surprisingly difficult, especially in embedded devices. If a microcontroller is already being used, consider Make’s write-up on the subject, and I think personally I would go with a transistor-based avalanche nosie generator to create the randomness.
  • A major limitation is that irregularly spaced action potentials have slightly different amplitudes. I found this out the next day when I created a hardware random number generator (yes, that happened) to cause it to fire regularly, missing approximately half of the action potentials. When this happens, breaks in time result in a larger subsequent action potential. There are several ways to get around this, but it’s worth noting that the circuit shown here is best operated around 6 Hz with only continuous regularly-spaced action potentials.

In the video I also demonstrate how to record the output of this circuit using a high-speed (44.1 kHz) 16-bit analog-to-digital converter you already have (the microphone input of your sound card). I won’t go into all the details here, but below is the code to read data from a WAV file and plot it as if it were a real neuron. The graph below is an actual recording of the circuit described here using the microphone jack of my sound card.

import numpy as np
import matplotlib.pyplot as plt
Ys = np.memmap("recording.wav", dtype='h', mode='r')[1000:40000]
Ys = np.array(Ys)/max(Ys)*150-70
Xs = np.arange(len(Ys))/44100*1000
plt.title("Action Potential Circuit Output")
plt.ylabel("potential (mV)")
plt.xlabel("time (ms)")

Let’s make some noise! Just to see what it would look like, I created a circuit to generate slowly drifting random noise. I found this was a non-trivial task to achieve in hardware. Most noise generation circuits create random signals on the RF scale (white noise) which when low-pass filtered rapidly approach zero. I wanted something which would slowly drift up and down on a time scale of seconds. I achieved this by creating 4-bit pseudo-random numbers with a shift register (74HC595) clocked at a relatively slow speed (about 200 Hz) having essentially random values on its input. I used a 74HC14 inverting buffer (with Schmidt trigger inputs) to create the low frequency clock signal (about 200 Hz) and an extremely fast and intentionally unstable square wave (about 30 MHz) which was sampled by the shift register to generate the “random” data. The schematic illustrates these points, but note that I accidentally labeled the 74HC14 as a 74HC240. While also an inverting buffer the 74HC240 will not serve as a good RC oscillator buffer because it does not have Schmidt trigger inputs.

The addition of noise was a success, from an electrical and technical sense. It isn’t particularly physiological. Neurons would fire differently based on their resting membrane potential, and the peaks of action potential should all be about the same height regardless of the resting potential. However if one were performing an electrical recording through a patch-clamp pipette in perforated patch configuration (with high resistance between the electrode and the internal of the cell), a sharp microelectrode (with high resistance due to the small size of the tip opening), or were using electrical equipment or physical equipment with amplifier limitations, one could imagine that capacitance in the recording system would overcome the rapid swings in cellular potential and result in “noisy” recordings similar to those pictured above. They’re not physiological, but perhaps they’re a good electrical model of what it’s like trying to measure a physiological voltage in a messy and difficult to control experimental environment.

This project was an interesting exercise in analog land, and is completed sufficiently to allow me to move toward my initial goal: creating advanced action potential detection and measurement circuitry. There are many tweaks which may improve this circuit, but as it is good enough for my needs I am happy to leave it right where it is. If you decide to build a similar circuit (or a vastly different circuit to serve a similar purpose), send me an email! I’d love to see what you came up with.


Celebrity Dwarf Gouramis

So I was reviewing my website statistics generated by a Python script I wrote when I noticed a peculiarity so bizarre that it made me questin the very purpose of my life. Okay maybe it wasn’t that bizarre, but it was interesting. The python script (which is automatically run every hour) downloads my latest access.log and saves it to its own folder. It then analyzes the data, creates some charts and graphs, and dumps out a bare-bones results file displaying some of the information I found useful. Of note is the number of times each page is hit.

This is where things get funny. Outperforming my home page by nearly double was indexOld.php (now indexOld22.php) – a simple webpage I tossed of for about a year before I put my big blog back online! Why were people still going to this page? Further investigation (from the referring sites section of my stats page) revealed a lot of hits from Google image-searches. I started looking at the actual requests and realized that many of these hits were people searching for the term Dwarf Gouramis “a type of freshwater aquarium fish) which was mentioned on that old webpage. The ironic part about it is what happens when you google image search for dwarf gouramis there is a picture of an extremely rare zebra pleco which is actually a link to my website! However the link APPEARS to be to because on my page I just linked to their image.

My conclusion: People are Google image-searching for ‘dwarf gouramis’, and an amazing picture of a zebra pleco is coming up which links to my site (due to the fact that months ago I talked about dwarf gouramis but posted a photo of a zebra pleco) and people (in their awe at this amazing fish) are clicking on it. So what did I do? I pulled a bait-and-switch! You bet I did. Now when you go to indexOld2.php it just forwards you to my current website – mua ha ha ha ha

PS: I’m appending to this entry at 2:17pm to note that I made a wonderful breakthrough in the lab today. Due to intellectual property protection blah blah and the fact that I don’t want anyone else to beat me to my research goal I will not describe what this is, I’ll just say that it took months of preparation and today – presto! It worked beautifully =oD