Neural networks: artificial vs biological
The human brain is often said to be the most complex network known to man. Studies of the structure and connectivity of neurons in the brain served as inspiration for artificial intelligence (AI) systems known as Artificial Neural Networks, built to detect and learn patterns in data. Such neural networks are reaching incredible heights in solving real-world problems, however they fall short in some tasks which require higher cognitive abilities, such as intuition and judgment.
Brainvivo is proving that the brain and its incredible abilities can be used as more than inspiration. With cutting-edge technology based on over 20 years of neuroscience research, Brainvivo is unlocking this sophisticated biological neural network and digitizing the brain to automate tasks that require human understanding, perception, and decision-making.
Artificial Neural Networks
Neural networks are a type of machine learning model used for complex tasks such as advanced speech recognition and image classification. An 'artificial neural network' refers to a neural network made up of a collection of interconnected units joined together by edges of varying weights. First materialized in the 1940s and 1950s, this computational system was originally inspired by some of the top neuroscience research of that time on synaptic plasticity, and the structure and connectivity of neurons in the human brain.
A massive amount of computing power is required to run artificial neural networks and handle the large amount of data needed to properly train them. The computing power in the 1950s was far too weak to support these networks, however since then - and especially over the last decade - the era of neural networks has dawned as a result of significant improvements in GPU power, the quantity and quality of available data, and advances in research on AI and machine learning.
Machine learning models rely on features: measurable caharacteristics that a model uses to detect patterns in data. For example, in the domain of image classification, features are the visual elements of images such as edges or color patterns that enable the model to recognize the contents of that image. Classical machine learning models require engineers to manually design and select these features, however artificial neural networks are able to discover and extract features for themselves.
Artificial neural networks are reaching incredible heights in solving real-world problems like beating chess grandmasters, accurately identifying disease in medical scans, and voice recognition & speech generation for voice assistants like Siri and Alexa. There are, however, some critical limitations to these systems. AI are heavily reliant on the availability of computing power, large amounts of data, and accurate labels for that data. Most critically, AI is frequently limited when it comes to tasks that require higher-order executive functions such as perception, reasoning, and decision-making.
When it comes to us humans, we are not held back by these limitations as we use our inherent perception and intuition to solve problems. Brainvivo has pioneered a unique approach to digitizing the human brain's biological neural network, tapping into the brain's ability to perform these complex functions.
Biological Neural Networks: Brainvivo's Technology
While artificial neural networks are trained to learn patterns from a particular set of data, the human brain holds a significant advantage from years of continuous experience and millennia of evolution. Even by the first few months of life, the human brain is able to quickly and effectively learn, classify, and recognize; infants typically start to recognize their parents' faces in the first three months of their lives.
Brainvivo’s technology is built on the idea that the abilities of the brain can be replicated and digitized in order to create a powerful model.
Here is how the technology works:
A group of people were scanned in an MRI while viewing a set of images. The MRI recorded their brain activity across the entire brain at very high granularity. Concurrently, a set of features were extracted from those same images and matched to responses of relevant brain areas to create a Feature-to-Brain Map, which forms the foundation of Brainvivo’s Generalized Brain Model (GBM). The GBM can then take any new and unfamiliar image and generate a predictive brain response for that image!
The Generalized Brain Model can also be fine-tuned to create specialized models. For example, by training the GBM on images of silk and wool, Brainvivo successfully generated a model capable of recognizing whether an object is soft or rough! This achievement not only highlights Brainvivo’s ability to create models specifically suited for particular tasks, but also the capacity of these models to not just classify, but also understand the context of images and transfer that learning. Transfer Learning is the ability of a model trained on one task to be repurposed for a second related task, an ability with the potential to significantly improve the efficiency of machine learning models.
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What's the Difference?
Although artificial neural networks are directly inspired by the structure and connectivity of neurons, they may never reach the sophistication of the human mind, shaped by experience and brain evolution*. This experience and evolution has exposed the brain to an unfathomable amount of data, calibrating it to tackle both familiar and new situations with little data.
Humans have a unique advantage over machines when it comes to perception, as the brain does not perceive through a single lens. For example, when we look at an image, we do not just break it down into its visual features, but also apply context using experience, emotion, and language. allowing us to not just look at an image but also to understand it.
As previously mentioned, artificial neural networks were initially inspired by the connectivity of neurons; however, the design of neural networks has not caught up with the neuroscience of today, and neuroscience has come a very long way since the 1950s. These networks, as well as other efforts to computationally capture the brain assume that in order to build a brain, one must build all its individual neurons; there is also the assumption that the function of the brain is derived solely from neurons.
Brainvivo views the human brain not as a network of individual units but as an organ - a network of functionality. Behavior is not encoded by individual neurons but by functional areas - rather than build a digital brain neuron by neuron, Brainvivo focuses on the regions found in the brain and the behaviors and higher cognitive functions they generate, and uses them to create their Digitized Brain.
Another key difference between artificial neural networks and Brainvivo’s Digitized Brain is the impact each will have on the future of humanity. AI tools are already supporting humans in their day to day chores, and will inevitably take over the completion of redundant tasks. Brainvivo, on the other hand, foresees its Digitized Brains helping humans solve higher cognitive challenges like making judgment calls, untangling perception, bridging cross-cultural differences.