Working with week-old zebrafish larvae, Weill Cornell Medicine researchers and their colleagues decoded how connections formed by a network of neurons in the brain stem guide the fish’s gaze. The study, published Nov. 22 in Nature Neuroscience, found that a simplified artificial circuit, based on the architecture of this neural system, can predict activity in the network. In addition to shedding light on how the brain handles short-term memory, the findings could lead to novel approaches to treating eye movement disorders.
Organisms constantly absorb a series of sensory information about the environment that changes from one moment to the next. To accurately assess a situation, the brain must retain these nuggets of information long enough to use them to form a complete picture; for example, joining words into a sentence or allowing an animal to keep its eyes directed at an area of interest.
“Trying to understand how these short-term memory behaviors are generated at the neural mechanism level is the main goal of the project,” said senior author Dr. Emre Aksay, associate professor of physiology and biophysics at Weill Cornell Medicine, who led the study. study, along with Dr. Mark Goldman of the University of California Davis and Dr. Sebastian Seung of Princeton University.
Modeling a dynamic system
To decode the behavior of such neural circuits, neuroscientists use the tools of dynamical systems, which involve building mathematical models that describe how the state of a system changes over time, where the current state determines its future states according to a set of rules. A short-term memory circuit, for example, will remain in a single preferred state until a new stimulus appears, causing it to settle into a new state of activity. In the visuomotor system, each of these states can store the memory of where an animal should look.
But what parameters help configure that type of dynamic system? One possibility is the anatomy of the circuit: the connections that form between each neuron and how many connections they make. Another likely possibility is the physiological strength of those connections, which is established by a multitude of factors such as the amount of neurotransmitter released, the type of synaptic receptors, and the concentration of those receptors.
To understand the contributions of circuit anatomy, Dr. Aksay and his collaborators looked at zebrafish larvae. At five days old, these fish swim and hunt for prey, a skill that involves sustained visual attention. It is important to highlight that for the research team the region of the brain that controls eye movement is structurally similar in fish and mammals. But the zebrafish system contains only 500 neurons. “So, we can analyze the entire circuit, microscopically and functionally,” Dr. Aksay said. “That’s very difficult to do in other vertebrates.”
Zebrafish shed light on neural circuits
Using a series of advanced imaging techniques, Dr. Aksay and his colleagues identified the neurons involved in the animals’ gaze control and then determined how these neurons are connected to each other. They found that the system consists of two prominent feedback loops, each containing three groups of closely connected cells. The researchers used this distinctive architecture to build a computational model. They found that their artificial network could accurately predict zebrafish circuit activity patterns, which they validated by comparing their results with physiological data.
“I consider myself, first and foremost, a physiologist,” Dr. Aksay said. “So I was surprised how much of the circuit’s behavior we could predict just from the anatomical architecture.”
Next, the researchers will explore how cells in each group contribute to the behavior of the circuit and whether neurons in different groups have different genetic signatures. This information could allow doctors to therapeutically target those cells that may malfunction in eye movement disorders. The findings also provide a model for unraveling the brain’s more complex computational systems that rely on short-term memory, such as those involved in deciphering visual scenes or understanding speech.
This study was supported in part by National Institutes of Health grants National Institute of Neurological Disorders and Stroke R01 NS104926 and Brain Initiative Award 5U19NS104648; the National Eye Institute R01 EY027036, R01 EY021581 and K99 EY027017; and the National Cancer Institute UH2 CA203710.