OpenWorm Foundation, Boston, MA USA Abstract While Artificial Neural Networks (ANNs) have yielded impressive results in the realm of simulated intelligent behavior, it is important to remember that they are but sparse approximations of Biological Neural Networks (BNNs). We go beyond comparison of ANNs and BNNs to introduce principles from BNNs that might guide the further development of ANNs as embodied neural models. These principles include representational complexity, complex network structure/energetics, and robust function. We then consider these principles in ways that might be implemented in the future development of ANNs. In conclusion, we consider the utility of this comparison, particularly in terms of building more robust and dynamic ANNs. This even includes constructing a morphology and sensory apparatus to create an embodied ANN, which when complemented with the organizational and functional advantages of BNNs unlocks the adaptive potential of lifelike networks. Introduction How can Artificial Neural Networks (ANNs) emulate the "lifelike" nature of Biological Neural Networks (BNNs)?

Although artificial neurons and perceptrons were inspired by the biological processes scientists were able to observe in the brain back in the 50s, they do differ from their biological counterparts in several ways. Birds have inspired flight and horses have inspired locomotives and cars, yet none of today's transportation vehicles resemble metal skeletons of living-breathing-self replicating animals. Still, our limited machines are even more powerful in their own domains (thus, more useful to us humans), than their animal "ancestors" could ever be. It is easy to draw the wrong conclusions from the possibilities in AI research by anthropomorphizing Deep Neural Networks, but artificial and biological neurons do differ in more ways than just the materials of their containers. The idea behind perceptrons (the predecessors to artificial neurons) is that it is possible to mimic certain parts of neurons, such as dendrites, cell bodies and axons using simplified mathematical models of what limited knowledge we have on their inner workings: signals can be received from dendrites, and sent down the axon once enough signals were received.

This workshop explores the interface between cognitive neuroscience and recent advances in AI fields that aim to reproduce human performance such as natural language processing and computer vision, and specifically deep learning approaches to such problems. When studying the cognitive capabilities of the brain, scientists follow a system identification approach in which they present different stimuli to the subjects and try to model the response that different brain areas have of that stimulus. The goal is to understand the brain by trying to find the function that expresses the activity of brain areas in terms of different properties of the stimulus. Experimental stimuli are becoming increasingly complex with more and more people being interested in studying real life phenomena such as the perception of natural images or natural sentences. There is therefore a need for a rich and adequate vector representation of the properties of the stimulus, that we can obtain using advances in machine learning. In parallel, new ML approaches, many of which in deep learning, are inspired to a certain extent by human behavior or biological principles. Neural networks for example were originally inspired by biological neurons. More recently, processes such as attention are being used which have are inspired by human behavior. However, the large bulk of these methods are independent of findings about brain function, and it is unclear whether it is at all beneficial for machine learning to try to emulate brain function in order to achieve the same tasks that the brain achieves.

To perform research on learning in cultures of mouse neurons, a hardware and software system for interfacing a biological neuronal culture to a robot arm has been constructed. The software architecture is modular, which permits simulated neurons to be used in place of biological neurons. In both cases, the activity of the culture over time is represented as an activation vector that captures recent spatiotemporal patterns of neuron firing. The activation vector is converted into control signals for the arm in a manner that can be generalized to multiple degrees of freedom. Preliminary results from the system with both simulated and biological cultures are presented.