Search Results - "Abbott, L. F"

Training dynamically balanced excitatory-inhibitory networks by Ingrosso, Alessandro, Abbott, L F

“…The construction of biologically plausible models of neural circuits is crucial for understanding the computational properties of the nervous system…”
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Generation of stable heading representations in diverse visual scenes by Kim, Sung Soo, Hermundstad, Ann M., Romani, Sandro, Abbott, L. F., Jayaraman, Vivek

“…Many animals rely on an internal heading representation when navigating in varied environments 1 – 10 . How this representation is linked to the sensory cues…”
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Random convergence of olfactory inputs in the Drosophila mushroom body by Caron, Sophie J. C., Ruta, Vanessa, Abbott, L. F., Axel, Richard

“…In Drosophila , olfactory sensory neurons project to spatially invariant loci (glomeruli) and stereotyped circuitry is maintained in projections to a brain…”
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Building functional networks of spiking model neurons by Abbott, L F, DePasquale, Brian, Memmesheimer, Raoul-Martin

“…The networks used by computer scientists and by modelers in neuroscience frequently consider unit activities as continuous. Neurons, however, com­municate…”
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full-FORCE: A target-based method for training recurrent networks by DePasquale, Brian, Cueva, Christopher J, Rajan, Kanaka, Escola, G Sean, Abbott, L F

“…Trained recurrent networks are powerful tools for modeling dynamic neural computations. We present a target-based method for modifying the full connectivity…”
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Balanced excitation and inhibition are required for high-capacity, noise-robust neuronal selectivity by Rubin, Ran, Abbott, L. F., Sompolinsky, Haim

“…Neurons and networks in the cerebral cortex must operate reliably despite multiple sources of noise. To evaluate the impact of both input and output noise, we…”
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The complete connectome of a learning and memory centre in an insect brain by Eichler, Katharina, Li, Feng, Litwin-Kumar, Ashok, Park, Youngser, Andrade, Ingrid, Schneider-Mizell, Casey M., Saumweber, Timo, Huser, Annina, Eschbach, Claire, Gerber, Bertram, Fetter, Richard D., Truman, James W., Priebe, Carey E., Abbott, L. F., Thum, Andreas S., Zlatic, Marta, Cardona, Albert

“…Associating stimuli with positive or negative reinforcement is essential for survival, but a complete wiring diagram of a higher-order circuit supporting…”
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Dimension of Activity in Random Neural Networks by Clark, David G., Abbott, L. F., Litwin-Kumar, Ashok

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The neuronal architecture of the mushroom body provides a logic for associative learning by Aso, Yoshinori, Hattori, Daisuke, Yu, Yang, Johnston, Rebecca M, Iyer, Nirmala A, Ngo, Teri-T B, Dionne, Heather, Abbott, L F, Axel, Richard, Tanimoto, Hiromu, Rubin, Gerald M

“…We identified the neurons comprising the Drosophila mushroom body (MB), an associative center in invertebrate brains, and provide a comprehensive map…”
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Synaptic computation by Abbott, L. F, Regehr, Wade G

“…Neurons are often considered to be the computational engines of the brain, with synapses acting solely as conveyers of information. But the diverse types of…”
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Sparse RNNs can support high-capacity classification by Turcu, Denis, Abbott, L F

“…Feedforward network models performing classification tasks rely on highly convergent output units that collect the information passed on by preceding layers…”
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Eigenvalue spectra of random matrices for neural networks by Rajan, Kanaka, Abbott, L F

“…The dynamics of neural networks is influenced strongly by the spectrum of eigenvalues of the matrix describing their synaptic connectivity. In large networks,…”
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Sparse balance: Excitatory-inhibitory networks with small bias currents and broadly distributed synaptic weights by Khajeh, Ramin, Fumarola, Francesco, Abbott, L F

“…Cortical circuits generate excitatory currents that must be cancelled by strong inhibition to assure stability. The resulting excitatory-inhibitory (E-I)…”
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Signal Propagation and Logic Gating in Networks of Integrate-and-Fire Neurons by Vogels, Tim P, Abbott, L. F

“…Transmission of signals within the brain is essential for cognitive function, but it is not clear how neural circuits support reliable and accurate signal…”
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Gating multiple signals through detailed balance of excitation and inhibition in spiking networks by Vogels, Tim P, Abbott, L F

“…The balance of excitation and inhibition across large populations of spiking neurons has been suggested to be important. Here the authors model the effects of…”
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Meta-learning synaptic plasticity and memory addressing for continual familiarity detection by Tyulmankov, Danil, Yang, Guangyu Robert, Abbott, L.F.

“…Over the course of a lifetime, we process a continual stream of information. Extracted from this stream, memories must be efficiently encoded and stored in an…”
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Neural learning rules for generating flexible predictions and computing the successor representation by Fang, Ching, Aronov, Dmitriy, Abbott, L F, Mackevicius, Emily L

“…The predictive nature of the hippocampus is thought to be useful for memory-guided cognitive behaviors. Inspired by the reinforcement learning literature, this…”
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Flexible neural control of motor units by Marshall, Najja J., Glaser, Joshua I., Trautmann, Eric M., Amematsro, Elom A., Perkins, Sean M., Shadlen, Michael N., Abbott, L. F., Cunningham, John P., Churchland, Mark M.

“…Voluntary movement requires communication from cortex to the spinal cord, where a dedicated pool of motor units (MUs) activates each muscle. The canonical…”
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Presynaptic inhibition of spinal sensory feedback ensures smooth movement by Fink, Andrew J. P., Croce, Katherine R., Huang, Z. Josh, Abbott, L. F., Jessell, Thomas M., Azim, Eiman

“…The precision of skilled movement depends on sensory feedback and its refinement by local inhibitory microcircuits. One specialized set of spinal GABAergic…”
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A temporal basis for predicting the sensory consequences of motor commands in an electric fish by Kennedy, Ann, Wayne, Greg, Kaifosh, Patrick, Alviña, Karina, Abbott, L F, Sawtell, Nathaniel B

“…To adaptively navigate their environments organisms need to predict and cancel out the sensory consequences of their actions. Here the authors show that…”
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