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2 Publications

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    10/19/16 | A designer AAV variant permits efficient retrograde access to projection neurons.
    Tervo DG, Hwang B, Viswanathan S, Gaj T, Lavzin M, Ritola KD, Lindo S, Michael S, Kuleshova E, Ojala D, Huang C, Gerfen CR, Schiller J, Dudman JT, Hantman AW, Looger LL, Schaffer DV, Karpova AY
    Neuron. 2016 Oct 19;92(2):372-82. doi: 10.1016/j.neuron.2016.09.021

    Efficient retrograde access to projection neurons for the delivery of sensors and effectors constitutes an important and enabling capability for neural circuit dissection. Such an approach would also be useful for gene therapy, including the treatment of neurodegenerative disorders characterized by pathological spread through functionally connected and highly distributed networks. Viral vectors, in particular, are powerful gene delivery vehicles for the nervous system, but all available tools suffer from inefficient retrograde transport or limited clinical potential. To address this need, we applied in vivo directed evolution to engineer potent retrograde functionality into the capsid of adeno-associated virus (AAV), a vector that has shown promise in neuroscience research and the clinic. A newly evolved variant, rAAV2-retro, permits robust retrograde access to projection neurons with efficiency comparable to classical synthetic retrograde tracers and enables sufficient sensor/effector expression for functional circuit interrogation and in vivo genome editing in targeted neuronal populations. VIDEO ABSTRACT.

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    02/11/16 | Toward the neural implementation of structure learning.
    Tervo DG, Tenenbaum JB, Gershman SJ
    Current Opinion in Neurobiology. 2016 Feb 11;37:99-105. doi: 10.1016/j.conb.2016.01.014

    Despite significant advances in neuroscience, the neural bases of intelligence remain poorly understood. Arguably the most elusive aspect of intelligence is the ability to make robust inferences that go far beyond one's experience. Animals categorize objects, learn to vocalize and may even estimate causal relationships - all in the face of data that is often ambiguous and sparse. Such inductive leaps are thought to result from the brain's ability to infer latent structure that governs the environment. However, we know little about the neural computations that underlie this ability. Recent advances in developing computational frameworks that can support efficient structure learning and inductive inference may provide insight into the underlying component processes and help pave the path for uncovering their neural implementation.

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