Intelligence can be defined as a predominantly human ability to accomplish tasks that are generally hard for computers and animals. Artificial Intelligence [AI] is a field attempting to accomplish such tasks with computers. AI is becoming increasingly widespread, as are claims of its relationship with Biological Intelligence. Often these claims are made to imply higher chances of a given technology succeeding, working on the assumption that AI systems which mimic the mechanisms of Biological Intelligence should be more successful. In this article I will discuss the similarities and differences between AI and the extent of our knowledge about the mechanisms of intelligence in biology, especially within humans. I will also explore the validity of the assumption that biomimicry in AI systems aids their advancement, and I will argue that existing similarity to biological systems in the way Artificial Neural Networks [ANNs] tackle tasks is due to design decisions, rather than inherent similarity of underlying mechanisms. This article is aimed at people who understand the basics of AI (especially ANNs), and would like to be better able to evaluate the often wild claims about the value of biomimicry in AI. Acknowledgements I thank Kate Wilkinson for extensive editing and proofreading of this article. Dr Kristjan Kalm's critical review of this article was essential for its scientific integrity; however, not all views expressed in this article reflect Kristjan's own views. Finally, I would like to thank illumr Ltd, especially its founder and CEO Jason Lee, for sponsoring this work. Symbolic AI was the prevailing approach to AI until the early 90's.
A synapse is the connection between nodes, or neurons, in an artificial neural network (ANN). Similar to biological brains, the connection is controlled by the strength or amplitude of a connection between both nodes, also called the synaptic weight. Multiple synapses can connect the same neurons, with each synapse having a different level of influence (trigger) on whether that neuron is "fired" and activates the next neuron. In ANNs, each neuron is defined through its input and its activation function, and its outputs. A synapse is often referred to as a node in the machine learning terminology.
This section introduces the integral components of deep learning and how they operate to emulate learning commonly found amongst humans. The brain is responsible for all human cognitive functions; in short, the brain is responsible for your ability to learn, acquire knowledge, retain information and recall knowledge. One of the fundamental building blocks of the learning systems within the brain is the biological neuron. A neuron is a cell responsible for transmitting signals to other neurons and, as a consequence, other parts of the body. Researchers, in some way, have replicated the functionality of the biological neuron into a mathematically representative model called the perceptron.
Bioelectronic medicine is driving the need for neuromorphic microcircuits that integrate raw nervous stimuli and respond identically to biological neurons. However, designing such circuits remains a challenge. Here we estimate the parameters of highly nonlinear conductance models and derive the ab initio equations of intracellular currents and membrane voltages embodied in analog solid-state electronics. By configuring individual ion channels of solid-state neurons with parameters estimated from large-scale assimilation of electrophysiological recordings, we successfully transfer the complete dynamics of hippocampal and respiratory neurons in silico. The solid-state neurons are found to respond nearly identically to biological neurons under stimulation by a wide range of current injection protocols.
The oddest thing about Artificial Neural Networks is that they actually work despite being based on a completely false model of a biological neuron. Why Artificial Neural Networks (ANN) work remains a mystery. Understanding that "Why" can inform us why real biological neurons might work. We can make progress by identifying universal characteristics between the biological and the synthetic. One universality that we can be certain of is that both biological and artificial neurons are pattern-matching machines.