The wiring and rewiring of the brain never ends. Neural pathways are constantly reshaped as we interact with the world and learn new things. At MIT’s McGovern Institute for Brain Research and York University, scientists are combining detailed analysis of brain activity with computational modeling to better understand these changes. Postdoc Lynn Sörensen, investigator James DiCarlo, and assistant professor Kohitij Kar worked together to compare what happened when animals and an artificial neural network with brain-like architecture were trained to visually identify the same objects. As the model’s performance improved, it reorganized itself in ways that closely paralleled changes detected in the animal brains. Their open-access work, reported on July 8 in Nature Communications, shows how changes in visual processing support animals’ ability to learn to discriminate new kinds of objects.
Learning about a new object calls on many parts of the brain. Visual-processing areas work together to make sense of information taken in through the eyes, then communicate with other brain areas to give the visual information meaning and guide behavior. Multiple parts of this system likely change during learning, and the research team wanted a clearer understanding of how that change is distributed. Neuroscientists have debated the extent of changes in the brain’s visual-processing areas when an animal learns to recognize new objects. Some suspected that visual-processing pathways remain largely unchanged during learning to avoid broadly disrupting visual perception, while others reported changes in activity within dedicated visual-processing areas with this kind of learning in humans and other primates.
To take a closer look, the team focused on neural activity in a key component of the brain’s visual object-processing network, the inferior temporal (IT) cortex. By the time visual information reaches the IT cortex, key object features are clearly represented, allowing researchers to “decode” what object the subject is seeing and even predict errors in identification by analyzing patterns of neural activity. The team recorded neural activity in the IT cortex from animals as they looked at and identified images of objects. Some animals were untrained, so the images they saw had little meaning to them, while others had learned to identify similar objects and could usually discriminate between elephants, chairs, and other select objects, even when presented at different sizes, angles, or backgrounds.
The broad pattern of activity in the IT cortex was largely similar in trained and untrained animals, suggesting that learning had not dramatically rewritten this high-level visual representation. However, reliable differences were found in the way neurons in the IT cortex responded to images in animals that had learned to recognize the kinds of objects they were shown, compared to untrained animals.
The group turned to computational models to investigate how those modest changes might contribute to learning. Sörensen trained a suite of artificial neural networks whose internal components had been mapped to the IT cortex to identify the same categories of objects the animals had seen. The models were designed to learn using gradient descent, meaning they continually improved their accuracy by adjusting their parameters in response to errors. Only some of the animal models showed learning behavior that matched that of the subjects. In those that did, the IT-like stage changed in ways resembling the learning-related changes observed in trained animals.
While gradient descent is commonly used to train artificial intelligence, it is generally considered biologically implausible as a direct model of how the brain learns. The strong match in learning effects between the animals and their model demonstrates that these kinds of artificial neural networks can offer insights into biological learning at a useful level of abstraction, even if the brain does not learn in the same way.
The researchers stress that their study allowed for more granular measurements of brain activity than would be possible in humans, and because animal brains are organized similarly to our own, their experiments have direct relevance to human learning. They say understanding the impact of plasticity in the subjects’ IT cortex could help design new learning strategies for humans. DiCarlo states, “Our prior conceptual model of you learning new objects was that your brain makes changes to synaptic connections that are largely downstream of your visual system, so you don’t destroy your visual system.” He adds that when you learn “elephant,” your IT does change a little bit to make it more relevant to elephants. This likely has consequences for recognizing other visual features too.
Subtle changes in the IT cortex that support elephant recognition might also improve your ability to identify other objects, while also making it a bit harder to identify something else. These kinds of consequences may be difficult to predict intuitively, but become clear with computational modeling. For instance, the team’s models revealed that after learning to recognize new objects, the IT cortex contained more information about objects’ locations. By providing insights like these, models could aid the design of more effective training strategies for visual tasks, including for people with altered sensory processing, who may learn from visual information in atypical ways.
Blogger's Review: This study combines biological neuroscience with computational modeling to delve into the neural mechanisms of visual learning. The findings not only reveal subtle changes in the brain during the learning process but also provide important insights for future educational strategies, particularly in enhancing visual information processing. The application of artificial neural networks in this research showcases the immense potential of interdisciplinary studies, promising breakthroughs in cognitive science.