Neuroscientists have known for a long time that we can measure the activity of our brains by connecting electrodes to the scalp. But making sense of these measurements has proven to be a long and difficult project.
Nonetheless, experiments have revealed small electrical "spikes" produced by our brains in response to words, pictures, sounds, or even smells. One of these spikes is the N400, and it appears when we hear or see something surprising or unexpected, like an unusual word in a sentence. For example:
"He spread his warm toast with socks"
The N400 appears when we hear or see the word "socks", because its meaning is not what we would expect in this context. For this reason, the N400 is thought to be an insight into how the brain stores, retrieves, and combines the information in our heads. This includes words, but also other types of symbols and implicit information about the world.
However, what does a small electrical spike at the scalp really tell us about the activity of the billions of neurons in the human brain?
This is the question the project seeks to answer. Our goal is to build a neural model which recreates the N400 spike, as the model stores, retrieves, and combines meanings. The basic building blocks of the model resemble the building blocks of the brain. This means that, as the behaviour of our model edges closer to the real behaviour of the N400, so should the architecture of the model edge closer to the real architecture of the brain.
In this way, the model will help us to translate our understanding of the N400 into understanding of how the brain works.
The goal of this project is to develop a theoretical model of one of the most robust empirical findings of cognitive neuroscience, the so-called 'N400' - an electophysiological response to semantically anomalous words and other stimuli, which can be measured at the scalp using realtively cheap and non-invasive methods. Over several decades, neuroscientists have amassed an impressive corpus of data regarding what types of stimuli affect the amplitude and other properties of our N400 measurements. Despite this, we currently have little meaningful insight into what actually causes the N400 in neural terms, or indeed why it should exist in the first place.
Our goal is to build on existing empirical and theoretical work, and construct a neural model of the N400. This way we can not only propose a theory of why the N400 exists, but also gain new insight into what our knowledge of the N400 actually tell us about the way brain categorises and processes semantic information.
Our focus is on the use of latching Potts models. These belong to a broader class of models known as "attractor networks", which represent some of the best studied models of neural function. The latching Potts model is exceptional in its ability to spontaneously and successively recall memories, dependent on the relationship between those memories. In the case that the memories represent linguistics elements (words, etc.) it can compose structures (e.g. phrases, sentences) and thereby function as a dynamical model of language. This allows us to study the N400 as a function of complex linguistic contexts.