A new computational model of the brain based closely on its biology and physiology not only learned a simple visual category learning task exactly as well as lab animals, but even enabled the discovery of counterintuitive activity by a group of neurons that researchers working with animals to perform the same task had not noticed in their data before, says a team of scientists at Dartmouth College, MIT, and the State University of New York at Stony Brook.<\/p>\n
Notably, the model produced these achievements without ever being trained on any data from animal experiments. Instead, it was built from scratch to faithfully represent how neurons connect into circuits and then communicate electrically and chemically across broader brain regions to produce cognition and behavior. Then, when the research team asked the model to perform the same task that they had previously performed with the animals (looking at patterns of dots and deciding which of two broader categories they fit), it produced highly similar neural activity and behavioral results, acquiring the skill with almost exactly the same erratic progress.<\/p>\n
\u201cIt\u2019s just producing new simulated plots of brain activity that then only afterward are being compared to the lab animals. The fact that they match up as strikingly as they do is kind of shocking,\u201d says Richard Granger<\/a>, a professor of psychological and brain sciences at Dartmouth and senior author of a new study in Nature Communications that describes the model.<\/p>\n A goal in making the model, and newer iterations developed since the paper was written, is not only to offer insight into how the brain works, but also how it might work differently in disease and what interventions could correct those aberrations, adds co-author Earl K. Miller<\/a>, Picower Professor in The Picower Institute for Learning and Memory at MIT. Miller, Granger, and other members of the research team have founded the company Neuroblox.ai<\/a> to develop the models\u2019 biotech applications. Co-author Lilianne R. Mujica-Parodi, a biomedical engineering professor at Stony Brook who is lead principal investigator for the Neuroblox Project, is CEO of the company.<\/p>\n \u201cThe idea is to make a platform for biomimetic modeling of the brain so you can have a more efficient way of discovering, developing, and improving neurotherapeutics. Drug development and efficacy testing, for example, can happen earlier in the process, on our platform, before the risk and expense of clinical trials,\u201d says Miller, who is also a faculty member of MIT\u2019s Department of Brain and Cognitive Sciences.<\/p>\n Making a biomimetic model<\/p>\n Dartmouth postdoc Anand Pathak created the model, which differs from many others in that it incorporates both small details, such as how individual pairs of neurons connect with each other, and large-scale architecture, including how information processing across regions is affected by neuromodulatory chemicals such as acetylcholine. Pathak and the team iterated their designs to ensure they obeyed various constraints observed in real brains, such as how neurons become synchronized by broader rhythms. Many other models focus only on the small or big scales, but not both, he says.<\/p>\n \u201cWe didn\u2019t want to lose the tree, and we didn\u2019t want to lose the forest,\u201d Pathak says.<\/p>\n The metaphorical \u201ctrees,\u201d called \u201cprimitives\u201d in the study, are small circuits of a few neurons each that connect based on electrical and chemical principles of real cells to perform fundamental computational functions. For example, within the model\u2019s version of the brain\u2019s cortex, one primitive design has excitatory neurons that receive input from the visual system via synapse connections affected by the neurotransmitter glutamate. Those excitatory neurons then densely connect with inhibitory neurons in a competition to signal them to shut down the other excitatory neurons \u2014 a \u201cwinner-take-all\u201d architecture found in real brains that regulates information processing.<\/p>\n At a larger scale, the model encompasses four brain regions needed for basic learning and memory tasks: a cortex, a brainstem, a striatum, and a \u201ctonically active neuron\u201d (TAN) structure that can inject a little \u201cnoise\u201d into the system via bursts of aceytlcholine. For instance, as the model engaged in the task of categorizing the presented patterns of dots, the TAN at first ensured some variability in how the model acted on the visual input so that the model could learn by exploring varied actions and their outcomes. As the model continued to learn, cortex and striatum circuits strengthened connections that suppressed the TAN, enabling the model to act on what it was learning with increasing consistency.<\/p>\n As the model engaged in the learning task, real-world properties emerged, including a dynamic that Miller has commonly observed in his research with animals. As learning progressed, the cortex and striatum became more synchronized in the \u201cbeta\u201d frequency band of brain rhythms, and this increased synchrony correlated with times when the model (and the animals) made the correct category judgement about what they were seeing.<\/p>\n Revealing \u201cincongruent\u201d neurons<\/p>\n But the model also presented the researchers with a group of neurons \u2014 about 20 percent \u2014 whose activity appeared highly predictive of error. When these so-called \u201cincongruent\u201d neurons influenced circuits, the model would make the wrong category judgement. At first, Granger says, the team figured it was a quirk of the model. But then they looked at the real-brain data Miller\u2019s lab accumulated when animals performed the same task.<\/p>\n \u201cOnly then did we go back to the data we already had, sure that this couldn\u2019t be in there because somebody would have said something about it, but it was in there, and it just had never been noticed or analyzed,\u201d he says.<\/p>\n