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NIP Class Papers, Assignment 2

Deadline 5. April 2019, 4pm

You will be allocated to a group presenting one of the class papers. Your task is to write a short essay answerig the following questions.

• Describe the central problem the paper is attempting to address.
• How does it build on previous work and evidence?
• Describe the methods used, focussing on aspects central to the results.
• Summarise the key finding(s) and advances presented in the paper.
• Discuss the biological plausibility if you have chosen a paper about artificial systems, or the insights that could benefit artificial systems if you have chosen a biological paper.

Each question has equal weight in the final mark. There is no word limit or minimum length, but aim for about 2 pages/900 words. As a rough guide, if your answer to a question has less than 150 words, check if your answer is sufficiently informative. Ensure you provide sufficient information to address each question and demonstrate understanding.

Use the DICE submit command, in case this fails, you can hand in a hardcopy at the ITO. Command: submit nip cw2 file.pdf

Suggested papers:

Yamins et al, Performance-optimized hierarchical models predict neural responses in higher visual cortex
Neural inspired parallel hardware. Furber et al. (2014)
Spike based visual computation. Perez-Carrasco et al (2013)
Linking ML networks to neural data from monkey area IT. Yamins et al.(2014)
Sampling with neurons. Buesing et al.(2011)
Deep networks for imaging classification. Krizhevsky et al (2013)
Large scale unsupervised learning Le et al (2012)
Spontaneous cortical activity reveals hallmarks of an optimal internal model of the environment. Berkes et al. (2011)
Visual stimuli recruit intrinsically generated cortical ensembles. Miller et al. (2014)
Learning with hierarchical-deep models. Salakhutdinov et al. (2013)
Spiking Boltzmann machines. Hinton and Brown (2000)
Deep Learning Models of the Retinal Response to Natural Scenes. McIntosh et al. (2016).
Overcoming catastrophic forgetting in neural networks. Kirkpatrick et al. (2017)
Vector-based navigation using grid-like representations in artificial agents, Banino et al. (2018)
Signatures and mechanisms of low-dimensional neural predictive manifolds, Recanatesi et al. (2018)

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