Scientists from the UK Medical Research Council (MRC) Brain Network Dynamics Unit and Oxford University’s Department of Computer Science have set out a new principle to explain how the brain adjusts connections between neurons during learning.
This new insight may guide further research on learning in brain networks and could inspire faster and more robust learning algorithms in artificial intelligence.
The essence of the research is to pinpoint which components in the information-processing pipeline are responsible for an error in output. In artificial intelligence, this is achieved by backpropagation: adjusting a model’s parameters to reduce the error in the output. Many researchers believe that the brain employs a similar learning principle.
The biological brain is superior to current machine learning systems. For example, people can learn new information by just seeing it once. In contrast, artificial systems need to be trained hundreds of times with the same pieces of information to learn them. Furthermore, people an learn new information while maintaining the knowledge they already have, while learning new information in artificial neural networks often interferes with existing knowledge and degrades it rapidly.
These observations motivated the researchers to identify the fundamental principle employed by the brain during learning. For this they looked at some existing sets of mathematical equations describing changes in the behaviour of neurons and in the synaptic connections between them.
The researchers next analysed and simulated these information-processing models and found that they employ a fundamentally different learning principle from that used by artificial neural networks.
In artificial neural networks, an external algorithm tries to modify synaptic connections in order to reduce error, whereas the researchers propose that the human brain first settles the activity of neurons into an optimal balanced configuration before adjusting synaptic connections.
Consequently, the scientists posit that this is in fact an efficient feature of the way that human brains learn. This is because it reduces interference by preserving existing knowledge, which in turn speeds up learning.
In describing this new learning principle, the scientists use the term ‘prospective configuration’. In the research they demonstrate in computer simulations that models employing this prospective configuration can learn faster and more effectively than artificial neural networks in tasks that are typically faced by animals and humans in nature.
To illustrate this in an everyday setting, the researchers have used the real-life example of a bear fishing for salmon. The bear can see the river and it has learnt that if it can also hear the river and smell the salmon it is likely to catch one. But one day, the bear arrives at the river with a damaged ear, so it can’t hear it. In an artificial neural network information processing model, this lack of hearing would also result in a lack of smell (because while learning there is no sound, backpropagation would change multiple connections including those between neurons encoding the river and the salmon) and the bear would conclude that there is no salmon, and go hungry. But in the animal brain, the lack of sound does not interfere with the knowledge that there is still the smell of the salmon, therefore the salmon is still likely to be there for catching.
The researchers developed a mathematical theory showing that letting neurons settle into a prospective configuration reduces interference between information during learning. They demonstrated that prospective configuration explains neural activity and behaviour in multiple learning experiments better than artificial neural networks.
Future research seeks to bridge the gap between abstract models and real brains and understand how the algorithm of prospective configuration is implemented in anatomically identified cortical networks.
The research appears in the journal Nature Neuroscience, titled “‘Inferring Neural Activity Before Plasticity: A Foundation for Learning Beyond Backpropagation.”
