I have been working on a project to harness the power of biological neural circuits into a practical novel solution for digital communications. I decided to call this project "El-bug" as my focus was to find out how fast biological neural circuits can learn to decode Morse code.
Biological neural circuits have some amazing properties compared to computer based artificial neural networks. State of the art deep learning algorithms require millions of data points and hours or days of repetitions to learn patterns in the data, where as biological neural circuits can often learn new patterns using only a few examples and in time scale of tens of milliseconds. Based on literature biological neural circuits are also adaptive and work well under noisy real world signals.
Computer based learning algorithms require expensive hardware to store gigabytes of training data, GPUs to accelerate the learning process and complicated electronics to convert real world signals into digital pictures, audio or other representations. In comparison biological neural circuits are very small, typically come pre-integrated with sensory organs and require very little power in form of cheap organic energy sources such as glucose.
The hardware used in this project is based on Arduino and the components are available at less than $50 from multiple sources online. The biological neural circuits of American cockroach (Periplaneta americana) is used to power the computation. These fascinating insects are readily available from many sources at low cost, or sometimes even free of charge.
SYSTEM ARCHITECTUREThe overall system architecture is shown in Figure 1 below. Arduino Pro Mini (3.3 V/8 MHz) has analog and digital interfaces and it is connected to a RFDuino Bluetooth module. Interface to cockroach neural circuitry is done using analog amplifier with frequency response designed for capturing bio-electrical neural spike signals. Digital output lines are used to provide electrical stimulation of the nerves.
|Figure 1. El-bug Morse decoder system architecture|
COCKROACH ANATOMY AND NEURAL CIRCUITRYThere is a surprising amount of research available on the neural circuitry of Periplaneta americana. For example this source explains:
"The anatomy of the cockroach is exceptionally accessible to electrophysiological experimentation for a variety of reasons. First, from the dorsal, or top, view the cockroach has a distinctive prothorax (the section directly behind, and shielding the head) and wings that give the cockroach its distinctive armored look. When flipped on its back, the ventral aspect of the cockroach reveals the basic segmented body sections distinctive of insects: the head, thorax, abdomen, and legs."
See Figure 2 for details of cockroach anatomical features.
|Figure 2. Cockroach Anatomy|
After studying possible circuits to utilize I decided to focus on "escaping behavior" that the common cockroach (Periplaneta americana) exhibits. This is a robust behavior of turning away from wind puffs (Camhi et al. 1978). This behavior is termed “escaping behavior” since it is the initial movement when escaping from predators. This source explains the detailed mechanism and neural circuitry in use:
"Understanding the anatomy of the cockroach nervous system is helpful when examining this escape behavior. The ventral nerve cord (VNC) of the cockroach is along its underbelly, rather than the dorsal side where the nerve cord of most vertebrates is located. The VNC is composed of several giant interneurons (GIs) and at the terminal ganglion afferents project to the dendrites of these GIs.
To detect wind directions, the cockroach has two cerci that are covered by numerous filiform hairs located at the rear of its body (Figure 1). Mechanoreceptors are attached at the base of the filiform hairs and are sensitive to wind puffs. Afferents send the neural signal from the mechanoreceptors to the terminal ganglion and thus provide input to the GIs. Due to its specific location and orientation on the cerci, each mechanoreceptor is sensitive to wind puffs from a specific direction relative to the cockroach. Afferents that are sensitive to similar wind directions are located close to each other within the abdominal ganglion." Figure 3. from the same source shows typical measurement results as explained in the experiment.
|Figure 3. Typical afferent response|
The Arduino Pro Mini provides a low cost circuitry to measure the neural responses and it has 4 analog to digital channels readily available. An analog pre-amplifier such as in Spikerbox can easily produce voltage levels required by Arduino ADC. This is a 10 bit ADC and provides 3.2 mV resolution. According to this source a single ADC read takes about 100 microseconds that is adequate speed for this purpose. The goal here is to try to establish a clear differentiation between responses to two types of electrical stimulation, similar to [Yu-Wei 2010] - see Figure 4 as example.
|Figure 4. Neural responses to stimulation|
MORSE DECODING PROBLEMSo given above how could we build a functioning Morse code decoder using these key components? The schema is shown in Figure 5. Using the Bluetooth module we are sending audio containing noisy Morse code audio as streamed data stream to Arduino. The software in Arduino does very basic signal processing, calculates the envelope of the audio signal and after low pass filtering generates stimulus signals that are sent using digital output lines to cockroaches that are organized in a 4 level hierarchy corresponding to the alphabet. If numerals would be included we would need a fifth layer.
At each level of the hierarchy the corresponding cockroach responds to electrical stimulus and based on a learned reaction will emit either "dit" or "dah" response. These "dit" and "dah" reactions are collected using the 10 bit ADC from 4 analog channels in Arduino and are organized as a sequence. Once the complete character has been received a simple "best matching unit" lookup is performed by Arduino and corresponding matched letter is sent using the serial interface over Bluetooth.
Implementing this scheme in Arduino Pro Mini did take some effort as available RAM memory is only 2 kilobytes. After about 2 weeks of coding effort I managed to squeeze all the functionality in and have still some 343 bytes of RAM free.
|Figure 5. Morse decoding schema using cockroach hierarchy|
I did run a 20 hours of tests using El-bug Morse decoder. I compared the character error rate (CER) to signal to noise ratio (SNR) of the audio files with the previous results achieved using Bayesian Morse decoder. The results are shown in Figure 6 below. I had to stop the experiment after 20 hours as 2 of the 4 cockroaches got tired of constant stimulation. They seem to have maximum decoding rate of 100 words per minute.
Surprisingly the decoding accuracy of El-bug system appears to to be quite a lot of better compared to my previous records. With decent signal to noise ratio (> 12 dB
|Figure 6. Experimental CER vs. SNR results|
For the next version I am looking into integrating the electronic circuitry into a smaller form factor, something similar to Figure 7. below. This source provides additional inspiration to pursue this project further.
|Figure 7. Portable El-bug Morse Decoder|
CONCLUSIONSIf the reader has had patience to follow this story this far I must congratulate you. You have amazing neural circuitry in your brain that is able to absorb this amount of information and form an opinion about what is being presented to you. You may have already realized that this story may be just pure imagination and has no connection to reality whatsoever.
Happy April 1, 2015!