Motivation

It's exciting to think that nature has come up with a simple mechanism that seems to scale to make sense of any kind of sensory information (about the animal or the world in which it lives), adapt to any kind of physical body, and then navigate the world in it. Brains, which are broadly found in most animal species, come in a wide range of sizes and configurations.

At the lowest end, there are nervous systems with fixed wiring (i.e., C. Elegans) in the range of hundreds of neurons. All individuals are wired the same (although in the case of C. Elegans, males and females differ slightly.) These are the fixed function brains that actually carry all individuals through a range of behavior that allows them to navigate their world in a wide range of circumstances. This is incredible by itself.

But thre are also animals with variable wiring brains, which are even more interesting. Species with variable wiring each have their own species brain architecture, but it is up to the individual's neurogenesis process to build a brain to fit this architecture. The same basic regions and large connections are common, but the implementation details of those regions at the lowest level is left up to the growth process. This is a very exciting development, because it means that there are many ways to achieve a working brain, not just a few.

In fact, nature has shown us billions of species over millions of years that have adopted different brain architectures that have all successfully demonstrated behavior that allowing those animals to navigate their environments. Further, the individual animals developed with their own brain wiring within their species' brain plan architectural constraints. It is astonishing to come to the understanding that, if billions of species, each with at least billions of individuals, produced an enormous range of individual brain wirings that exhibited behavior, there is not one fixed wiring plan of spiking neurons that generates behavior. Instead, there must be a set of salient architectural elements of these brain plans that enable the brain to generate behavior.

Rather than isolate and study the connectome of one individual and attempt to understand how it functions, we focus on identifying the salient properties making neuron networks actually functional, and from there, build functional brains using tools that incorporate those properties.

When we study functionality in brains, we must take care to set up experiments that offer enough complexity for functional behavior to be recognized, without offering only those complexities that we are used to as humans. We must also take care to interpret results not through the lens of a human, expecting to see social communication, or goal-directed behavior surrounding the kinds of desires a human might have.

The term behavior is used intentionally broadly. The behavior of an ant is quite different from a fruit fly, rodent, human, or octopus. We can say, however, that any behavior that arises from the brain simply interacting with the body and the environment which enables the organism to navigate its world and lead a functional life, is evidence of sentience.

What is sentience though? In different cultures and over time, the term sentience has come to mean different things, largely because humans use it in conjunction with the seat of human consciousness or emotions, and this is by its very nature anthropomorphically limiting. At NeuroSynthetica, we come back to behavior and what it must mean for the experience of the sentient animal, which armed with a body and brain alone in a new world, is able to familiarize itself with its world and successfully navigate it throughout its life. The emergence of this type of meaningful and relevant behavior is an indication that the animal is sentient-- that it senses its world around it (as well as its internal state), and makes decisions about taking actions, not because it has been hardwired to do so, but because it has adapted to do so, all without the preprogrammed fixed control regimens of fixed-wiring animal brains.

In a general embodiment, the feedback loop associated with sensory inputs stimulating the neural network in one area of the animal's brain can affect the neural networks in other areas of the animal's brain that generate actions, causing a change in sensory input, which may give rise to more changes in actions. Though this feedback loop is not itself sufficient, we must remember that connections between neurons have strengths that are adjusted in real time as those connections encounter signals. It is likely that the network of neural networks in a typical brain can encode uncountable mathematical attractors that allow sensory inputs to give rise to behavior automatically at the lowest level, and sequences of patterns of attractors to arise under certain conditions. We believe that a paradigm similar to this may give rise to behavior.

NeuroSynthetica's goal is to identify the salient components of the biological mechanism that gives rise to sentience. We do this in simulo by creating models of brains, starting from small ones that exhibit small behaviors, and simulate them using our own simulator called the Sentience Engine, which runs as a server on COTS (Commercial Off The Shelf) computers.

We use an interactive Workbench to write code in the SOMA descriptive programming language, allowing us to define a brain's architecture. Then from the Workbench, we compile the model and generate it on the server. Finally, we use the Workbench to start and stop the simulation, and observe it with instrumentation while it interacts with robotic sensors and movement actuators.

We'll know success when we see real animal-level behavior emerge from a synthetic model of a brain described purely in SOMA, simulated in real-time, interacting with the real world. An example might be seeing walking emerge from a quadruped robot-- the coordinated movement of 12 distinct actuators in response to the environment, not because a gait was programmed a certain way in a programming language, but because the robot discovered how to ambulate by itself.

The understanding of how sentience emerges in animal brains may be assisted partially by reverse engineering real brains, but it is unlikely to produce real results because of the complexities of natural brains, which may be constructed with ad hoc architectural optimizations that won't make sense to us. Understanding first design principles in real time spiking neural networks that interact with their environment to produce meaningful behavior is much more likely with a steady stream of experiments-- starting small, and building on that, just as we built on automotive principles, or the transistor.