John D. Murray is an Assistant Professor in the Department of Psychiatry, with secondary appointments in Neuroscience and Physics. Dr. Murray trained in Physics and Mathematics at Yale University (B.S. '06, Ph.D. '13). For his PhD in Physics, he worked with Dr. Xiao-Jing Wang in the field of Computational Neuroscience. Following his graduate training, he was a Postdoctoral Associate at New York University. In 2015 he joined the faculty at Yale, where he directs a computational neuroscience lab at the interface of physics and biology, studying the dynamics and function of neural circuits across multiple scales of complexity.
* Murray JD, Bernacchia A, Roy NA, Constantinidis C, Romo R, Wang X-J (2017) Stable population coding for working memory coexists with heterogeneous neural dynamics in prefrontal cortex. Proceedings of the National Academy of Sciences 114:394
* Mejias JF, Murray JD, Kennedy H, Wang X-J (2016) Feedforward and feedback frequency-dependent interactions in a large-scale laminar network of the primate cortex. Science Advances 2:e1601335
* Yang GR, Murray JD, Wang X-J (2016) Dendritic disinhibition as a mechanism for pathway-specific gating. Nature Communications
* Yang GJ, Murray JD, Repovs G, Cole MW, Wang X-J, Glahn DC, Krystal JH, Pearlson GD, Anticevic A (2016) Functional hierarchy underlies preferential connectivity disturbances in schizophrenia. Proceedings of the National Academy of Sciences 113:E219
* Murray JD, Bernacchia A, Freedman DJ, Romo R, Wallis JD, Cai X, Padoa-Schioppa C, Pasternak T, Seo H, Lee D, Wang X-J (2014) A hierarchy of intrinsic timescales across primate cortex. Nature Neuroscience 17:1661
* Murray JD, Anticevic A, Gancsos M, Ichinose M, Corlett PR, Krystal JH, Wang X-J (2014) Linking microcircuit dysfunction to cognitive impairment: effects of disinhibition associated with schizophrenia in a cortical working memory model. Cerebral Cortex 24:859
The brain achieves flexible cognition through specialization of cortical areas for different functions. To subserve working memory, prefrontal cortex can maintain a persistent pattern of activity in the absence of a stimulus, whereas sensory areas do not. What is the neural circuit basis of specialized function? Strong recurrent structure in synaptic connections endows a circuit with attractor dynamics that can subserve cognitive function. This suggests that areas achieve specialization in part through differences in structured local circuitry. We tested for physiological signatures of structure by measuring intrinsic timescales in single-neuron spiking activity, and found a hierarchy of timescales across cortical areas. Finally, we built a model of interacting areas, and found that specialization of local circuit properties improves working memory function. We propose that gradients of local structure across cortical areas give rise to diverse neural dynamics and specialized functions for cognition.