Synthetic nervous system

Synthetic nervous system (SNS) is a computational neuroscience model that may be developed with the Functional Subnetwork Approach (FSA) to create biologically plausible models of circuits in a nervous system. The FSA enables the direct analytical tuning of dynamical networks that perform specific operations within the nervous system without the need for global optimization methods like genetic algorithms and reinforcement learning. The primary use case for a SNS is system control, where the system is most often a simulated biomechanical model or a physical robotic platform. An SNS is a form of a neural network much like artificial neural networks (ANNs), convolutional neural networks (CNN), and recurrent neural networks (RNN). The building blocks for each of these neural networks is a series of nodes and connections denoted as neurons and synapses. More conventional artificial neural networks rely on training phases where they use large data sets to form correlations and thus "learn" to identify a given object or pattern. When done properly this training results in systems that can produce a desired result, sometimes with impressive accuracy. However, the systems themselves are typically "black boxes" meaning there is no readily distinguishable mapping between structure and function of the network. This makes it difficult to alter the function, without simply starting over, or extract biological meaning except in specialized cases. The SNS method differentiates itself by using details of both structure and function of biological nervous systems. The neurons and synapse connections are intentionally designed rather than iteratively changed as part of a learning algorithm.

As in many other computational neuroscience models (Rybak, Eliasmith), the details of a neural model are informed by experimental data wherever possible. Not every study can measure every parameter of the network under investigation, requiring the modeler to make assumptions regarding plausible parameter values. Rybak uses a sampling method where each node is composed of many neurons and each particular neuron's parameters are pulled from a probability distribution. Eliasmith uses what they call the Neural Engineering Framework (NEF) in which the user specifies the functions of the network and the synaptic and neural properties are learned over time. SNS follows a similar approach via the Functional Subnetwork Approach (FSA). FSA allows parameters within the network (e.g., membrane conductances, synaptic conductances) to be designed analytically based on their intended function. As a result, it is possible to use this approach to directly assemble networks that perform basic functions, like addition or subtraction, as well as dynamical operations like differentiation and integration.

Background and history of synthetic nervous systems

Background The details of the underlying control networks for many biological systems are not very well understood. However, recent advancements in neuroscience tools and techniques have clarified the cellular and biophysical mechanisms of these networks, and their operation during behavior in complex environments. Although there is a long-standing interest in biologically-inspired robots and robotic platforms, there is a recent interest in incorporating features of biomechanics and neural control, e.g., biomimicry. The SNS method uses data from neuroscience in control systems for neuromechanical simulations and robots. Designing both a robot's mechanics and controller to capture key aspects of a particular animal may lead to more flexible functionality while suggesting new hypotheses for how the animal's nervous system works. Keeping neural models simple facilitates analysis, real time operation, and tuning. To this end, SNSs primarily model neurons as leaky integrators, which are reasonable approximations of sub-threshold passive membrane dynamics. The leaky integrator also models non-spiking interneurons which contribute to motor control in some invertebrates (locust, stick insect, C. elegans ). If spiking needs to be incorporated into the model, nodes may be represented using the leaky integrate-and-fire models. In addition, other conductances like those of the Hodgkin-Huxley model can be incorporated into the model. A model may be initialized with simple components (e.g., leaky integrators), and then details added to incorporate additional biological details. The modeler may then increase or decrease the level of biological detail depending upon the intended application. Keeping models simple in this way offers:

The ability to use dynamical systems analysis by way of balancing biological detail with analytical tractability. Fast and computationally inexpensive network dynamic simulations to work effectively in a robotic controller. Thus, complex traditional models, like the cable equation or the full Hodgkin-Huxley action potential model, are typically avoided or simplified for the sake of computational efficiency. Sparse function-dependent connectivity via the Functional Subnetwork (FSA) instead of fully connected (i.e., all-to-all connected) topologies, common in machine learning. While the neuroscientific models are typically simplified for SNS, the method is flexible enough that more features can be incorporated. Consequently, the SNS method can accommodate demand driven complexity, only adding features specifically where they are needed. For example, persistent sodium channels can be added to just two neurons in a neural circuit to create a half- center oscillator pattern generator without changing the other neurons in the circuit. While these additions may increase computational cost, they grant the system the ability to perform a wider array of interesting behaviors.

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