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Volume 34, Issue 6

June 2022

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ISSN 0899-7667

EISSN 1530-888X

In this Issue

Review
Letters

Review

Advancements in Algorithms and Neuromorphic Hardware for Spiking Neural Networks

Amirhossein Javanshir, Thanh Thi Nguyen, M. A. Parvez Mahmud, Abbas Z. Kouzani

Neural Computation (2022) 34 (6): 1289–1328.

Abstract

View articletitled, Advancements in Algorithms and Neuromorphic Hardware for Spiking Neural Networks

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Letters

Predictive Coding Approximates Backprop Along Arbitrary Computation Graphs

Beren Millidge, Alexander Tschantz, Christopher L. Buckley

Neural Computation (2022) 34 (6): 1329–1368.

Abstract

View articletitled, Predictive Coding Approximates Backprop Along Arbitrary Computation Graphs

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Decoding Pixel-Level Image Features From Two-Photon Calcium Signals of Macaque Visual Cortex

Yijun Zhang, Tong Bu, Jiyuan Zhang, Shiming Tang, Zhaofei Yu ...

Neural Computation (2022) 34 (6): 1369–1397.

Abstract

View articletitled, Decoding Pixel-Level Image Features From Two-Photon Calcium Signals of Macaque Visual Cortex

Supplementary data

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Full-Span Log-Linear Model and Fast Learning Algorithm

Kazuya Takabatake, Shotaro Akaho

Neural Computation (2022) 34 (6): 1398–1424.

Abstract

View articletitled, Full-Span Log-Linear Model and Fast Learning Algorithm

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Role of Interaction Delays in the Synchronization of Inhibitory Networks

Nariman Roohi, Alireza Valizadeh

Neural Computation (2022) 34 (6): 1425–1447.

Abstract

View articletitled, Role of Interaction Delays in the Synchronization of Inhibitory Networks

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Hypothesis Test and Confidence Analysis With Wasserstein Distance on General Dimension

Masaaki Imaizumi, Hirofumi Ota, Takuo Hamaguchi

Neural Computation (2022) 34 (6): 1448–1487.

Abstract

View articletitled, Hypothesis Test and Confidence Analysis With Wasserstein Distance on General Dimension

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The Perils of Being Unhinged: On the Accuracy of Classifiers Minimizing a Noise-Robust Convex Loss

Philip M. Long, Rocco A. Servedio

Neural Computation (2022) 34 (6): 1488–1499.

Abstract

View articletitled, The Perils of Being Unhinged: On the Accuracy of Classifiers Minimizing a Noise-Robust Convex Loss

Review

Advancements in Algorithms and Neuromorphic Hardware for Spiking Neural Networks

Letters

Predictive Coding Approximates Backprop Along Arbitrary Computation Graphs

Decoding Pixel-Level Image Features From Two-Photon Calcium Signals of Macaque Visual Cortex

Full-Span Log-Linear Model and Fast Learning Algorithm

Role of Interaction Delays in the Synchronization of Inhibitory Networks

Hypothesis Test and Confidence Analysis With Wasserstein Distance on General Dimension

The Perils of Being Unhinged: On the Accuracy of Classifiers Minimizing a Noise-Robust Convex Loss

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Issues

Cover Image

Review

Advancements in Algorithms and Neuromorphic Hardware for Spiking Neural Networks

Letters

Predictive Coding Approximates Backprop Along Arbitrary Computation Graphs

Decoding Pixel-Level Image Features From Two-Photon Calcium Signals of Macaque Visual Cortex

Full-Span Log-Linear Model and Fast Learning Algorithm

Role of Interaction Delays in the Synchronization of Inhibitory Networks

Hypothesis Test and Confidence Analysis With Wasserstein Distance on General Dimension

The Perils of Being Unhinged: On the Accuracy of Classifiers Minimizing a Noise-Robust Convex Loss

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