Search Results - "McKinstry, Jeffrey L"

Convolutional networks for fast, energy-efficient neuromorphic computing by Esser, Steven K., Merolla, Paul A., Arthur, John V., Cassidy, Andrew S., Appuswamy, Rathinakumar, Andreopoulos, Alexander, Berg, David J., McKinstry, Jeffrey L., Melano, Timothy, Barch, Davis R., di Nolfoa, Carmelo, Datta, Pallab, Amir, Arnon, Taba, Brian, Flickner, Myron D., Modha, Dharmendra S.

“…Deep networks are now able to achieve human-level performance on a broad spectrum of recognition tasks. Independently, neuromorphic computing has now…”
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Spontaneous emergence of fast attractor dynamics in a model of developing primary visual cortex by Miconi, Thomas, McKinstry, Jeffrey L., Edelman, Gerald M.

“…Recent evidence suggests that neurons in primary sensory cortex arrange into competitive groups, representing stimuli by their joint activity rather than as…”
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Imagery May Arise from Associations Formed through Sensory Experience: A Network of Spiking Neurons Controlling a Robot Learns Visual Sequences in Order to Perform a Mental Rotation Task by McKinstry, Jeffrey L, Fleischer, Jason G, Chen, Yanqing, Gall, W Einar, Edelman, Gerald M

“…Mental imagery occurs "when a representation of the type created during the initial phases of perception is present but the stimulus is not actually being…”
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A Cerebellar Model for Predictive Motor Control Tested in a Brain-Based Device by McKinstry, Jeffrey L., Edelman, Gerald M., Krichmar, Jeffrey L.

“…The cerebellum is known to be critical for accurate adaptive control and motor learning. We propose here a mechanism by which the cerebellum may replace reflex…”
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Versatile networks of simulated spiking neurons displaying winner-take-all behavior by Chen, Yanqing, McKinstry, Jeffrey L, Edelman, Gerald M

“…We describe simulations of large-scale networks of excitatory and inhibitory spiking neurons that can generate dynamically stable winner-take-all (WTA)…”
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Temporal sequence learning in winner-take-all networks of spiking neurons demonstrated in a brain-based device by McKinstry, Jeffrey L, Edelman, Gerald M

“…Animal behavior often involves a temporally ordered sequence of actions learned from experience. Here we describe simulations of interconnected networks of…”
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Neural inference at the frontier of energy, space, and time by Modha, Dharmendra S., Akopyan, Filipp, Andreopoulos, Alexander, Appuswamy, Rathinakumar, Arthur, John V., Cassidy, Andrew S., Datta, Pallab, DeBole, Michael V., Esser, Steven K., Otero, Carlos Ortega, Sawada, Jun, Taba, Brian, Amir, Arnon, Bablani, Deepika, Carlson, Peter J., Flickner, Myron D., Gandhasri, Rajamohan, Garreau, Guillaume J., Ito, Megumi, Klamo, Jennifer L., Kusnitz, Jeffrey A., McClatchey, Nathaniel J., McKinstry, Jeffrey L., Nakamura, Yutaka, Nayak, Tapan K., Risk, William P., Schleupen, Kai, Shaw, Ben, Sivagnaname, Jay, Smith, Daniel F., Terrizzano, Ignacio, Ueda, Takanori

“…Computing, since its inception, has been processor-centric, with memory separated from compute. Inspired by the organic brain and optimized for inorganic…”
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Discovering Low-Precision Networks Close to Full-Precision Networks for Efficient Inference by McKinstry, Jeffrey L., Esser, Steven K., Appuswamy, Rathinakumar, Bablani, Deepika, Arthur, John V., Yildiz, Izzet B., Modha, Dharmendra S.

“…To realize the promise of ubiquitous embedded deep network inference, it is essential to seek limits of energy and area efficiency. Low-precision networks…”
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11.4 IBM NorthPole: An Architecture for Neural Network Inference with a 12nm Chip by Cassidy, Andrew S., Arthur, John V., Akopyan, Filipp, Andreopoulos, Alexander, Appuswamy, Rathinakumar, Datta, Pallab, Debole, Michael V., Esser, Steven K., Otero, Carlos Ortega, Sawada, Jun, Taba, Brian, Amir, Arnon, Bablani, Deepika, Carlson, Peter J., Flickner, Myron D., Gandhasri, Rajamohan, Garreau, Guillaume J., Ito, Megumi, Klamo, Jennifer L., Kusnitz, Jeffrey A., McClatchey, Nathaniel J., McKinstry, Jeffrey L., Nakamura, Yutaka, Nayak, Tapan K., Risk, William P., Schleupen, Kai, Shaw, Ben, Sivagnaname, Jay, Smith, Daniel F., Terrizzano, Ignacio, Ueda, Takanori, Modha, Dharmendra

“…The Deep Neural Network (DNN) era was ushered in by the triad of algorithms, big data, and more powerful hardware processors for training large-scale neural…”
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Visual Binding Through Reentrant Connectivity and Dynamic Synchronization in a Brain-based Device by Seth, Anil K., McKinstry, Jeffrey L., Edelman, Gerald M., Krichmar, Jeffrey L.

“…Effective visual object recognition requires mechanisms to bind object features (e.g. color, shape and motion) while distinguishing distinct objects…”
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Embodied models of delayed neural responses: Spatiotemporal categorization and predictive motor control in brain based devices by McKinstry, Jeffrey L., Seth, Anil K., Edelman, Gerald M., Krichmar, Jeffrey L.

“…In order to respond appropriately to environmental stimuli, organisms must integrate over time spatiotemporal signals that reflect object motion and…”
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Charting out the octopus connectome at submicron resolution using the knife-edge scanning microscope by Choe, Yoonsuck, Abbott, Louise C, Ponte, Giovanna, Keyser, John, Kwon, Jaerock, Mayerich, David, Miller, Daniel, Han, Donghyeop, Grimaldi, Anna Maria, Fiorito, Graziano, Edelman, David B, McKinstry, Jeffrey L

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IBM NorthPole Neural Inference Machine by Modha, Dharmendra S., Akopyan, Filipp, Andreopoulos, Alexander, Appuswamy, Rathinakumar, Arthur, John V., Cassidy, Andrew S., Datta, Pallab, DeBole, Michael V., Esser, Steven K., Otero, Carlos Ortega, Sawada, Jun, Taba, Brian, Amir, Arnon, Bablani, Deepika, Carlson, Peter J., Flickner, Myron D., Gandhasri, Rajamohan, Garreau, Guillaume J., Ito, Megumi, Klamo, Jennifer L., Kusnitz, Jeffrey A., McClatchey, Nathaniel J., McKinstry, Jeffrey L., Nakamura, Yutaka, Nayak, Tapan K., Risk, William P., Schleupen, Kai, Shaw, Ben, Sivagnaname, Jay, Smith, Daniel F., Terrizzano, Ignacio, Ueda, Takanori

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Temporal sequence learning in reentrantly coupled winner-take-all networks of spiking neurons by McKinstry, Jeffrey L

“…Doc number: P271…”
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Embodied models of delayed neural responses : Spatiotemporal categorization and predictive motor control in brain based devices: Robotics and Neuroscience by MCKINSTRY, Jeffrey L, SETH, Anil K, EDELMAN, Gerald M, KRICHMAR, Jeffrey L

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Efficient and Effective Methods for Mixed Precision Neural Network Quantization for Faster, Energy-efficient Inference by Bablani, Deepika, Mckinstry, Jeffrey L, Esser, Steven K, Appuswamy, Rathinakumar, Modha, Dharmendra S

“…For efficient neural network inference, it is desirable to achieve state-of-the-art accuracy with the simplest networks requiring the least computation,…”
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Texture discrimination by an autonomous mobile brain-based device with whiskers by Seth, A.K., McKinstry, J.L., Edelman, G.M., Krichmar, J.L.

“…Whiskers are widely used by many animal species for navigation and texture discrimination. This paper describes Darwin IX, a mobile physical device equipped…”
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Searching for an IT model with columnar organization by McKinstry, Jeffrey L.

“…The inferotemporal visual cortex (IT) is at the top of the ‘what’ stream. Cells in IT are known to respond to complex features and have a columnar…”
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Learned Step Size Quantization by Esser, Steven K, McKinstry, Jeffrey L, Bablani, Deepika, Appuswamy, Rathinakumar, Modha, Dharmendra S

“…Deep networks run with low precision operations at inference time offer power and space advantages over high precision alternatives, but need to overcome the…”
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Discovering Low-Precision Networks Close to Full-Precision Networks for Efficient Embedded Inference by McKinstry, Jeffrey L, Esser, Steven K, Appuswamy, Rathinakumar, Bablani, Deepika, Arthur, John V, Yildiz, Izzet B, Modha, Dharmendra S

“…To realize the promise of ubiquitous embedded deep network inference, it is essential to seek limits of energy and area efficiency. To this end, low-precision…”
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