TY - JOUR TI - Interrogating theoretical models of neural computation with emergent property inference AU - Bittner, Sean R AU - Palmigiano, Agostina AU - Piet, Alex T AU - Duan, Chunyu A AU - Brody, Carlos D AU - Miller, Kenneth D AU - Cunningham, John A2 - Huguenard, John R A2 - O'Leary, Timothy A2 - Goldman, Mark S VL - 10 PY - 2021 DA - 2021/07/29 SP - e56265 C1 - eLife 2021;10:e56265 DO - 10.7554/eLife.56265 UR - https://doi.org/10.7554/eLife.56265 AB - A cornerstone of theoretical neuroscience is the circuit model: a system of equations that captures a hypothesized neural mechanism. Such models are valuable when they give rise to an experimentally observed phenomenon -- whether behavioral or a pattern of neural activity -- and thus can offer insights into neural computation. The operation of these circuits, like all models, critically depends on the choice of model parameters. A key step is then to identify the model parameters consistent with observed phenomena: to solve the inverse problem. In this work, we present a novel technique, emergent property inference (EPI), that brings the modern probabilistic modeling toolkit to theoretical neuroscience. When theorizing circuit models, theoreticians predominantly focus on reproducing computational properties rather than a particular dataset. Our method uses deep neural networks to learn parameter distributions with these computational properties. This methodology is introduced through a motivational example of parameter inference in the stomatogastric ganglion. EPI is then shown to allow precise control over the behavior of inferred parameters and to scale in parameter dimension better than alternative techniques. In the remainder of this work, we present novel theoretical findings in models of primary visual cortex and superior colliculus, which were gained through the examination of complex parametric structure captured by EPI. Beyond its scientific contribution, this work illustrates the variety of analyses possible once deep learning is harnessed towards solving theoretical inverse problems. KW - theoretical neuroscience KW - deep learning KW - circuit models JF - eLife SN - 2050-084X PB - eLife Sciences Publications, Ltd ER -