Papers
Before looking at the list here we encourage you to look for a recent paper which is of interested to you.
You can choose paper from the list or find one on your own.
- Urs Köster et al., 2014, Modeling Higher-Order Correlations within Cortical Microcolumns
- Kaspar Meyer et al., 2010, Predicting visual stimuli on the basis of activity in auditory cortices
(Computational neuroscience, Machine Learning) - John P Cunningham & Byron M Yu, 2014, Dimensionality reduction for large-scale neural recordings
(Computational neuroscience, Machine Learning) - Wolfgang Maass, 2014, Noise as a Resource for Computation and Learning in Networks of Spiking Neurons
(Neuroscience, Machine Learning) - Dileep George & Jeff Hawkins, 2009, Towards a Mathematical Theory of Cortical Micro-circuits
(Computational neuroscience) - Fred Wolf et al., 2014, Dynamical models of cortical circuits
(Modeling) - Andre M. Bastos et al., 2012, Canonical Microcircuits for Predictive Coding
(Modeling, Computational Neuroscience) - Mia Xu Chen et al., 2014, Unsupervised Learning by Deep Scattering Contractions
(Neuroscience, Machine Learning) - Daniel L. K. Yamins, 2014, Performance-optimized hierarchical models predict neural responses in higher visual cortex
(Neuroscience, Machine Learning) - McClelland et al., 1995, Why There Are Complementary Learning Systems in the Hippocampus and Neocortex: Insights From the Successes and Failures of Connectionist Models of Learning and Memory,
a classic paper about the dual process approach to memory (hippocampus, cortex, their different computations and different time-courses for memory processing). Is a bit more complex and maybe not so easy to read but it is one of the foundations for current neuroscientific and computational thinking about memory
(Cognitive science, neuroscience, computational neuroscience). - Olshausen & Field, 2005, How Close Are We to Understanding V1?
Primary visual cortex (V1) is the mostly studied and understood brain area. This paper shows the gaps in our understanding of V1. One of the best critical papers on neuroscience.
(Neuroscience) - Nirenberg et al., 2009, Ruling out and ruling in neural codes,
Also an experimental paper, where the group of Nirenberg studies the neural code with a very clever method - they measure all the input the brain gets from the retina and then use different codes for decoding, which are all compared to the behavior of the animal. They can for example show that at this stage, on retina, the rate code cannot work - it performs much worse than the animal. Easy to understand, great paper.
(Computational neuroscience) - Eliasmith et al., 2012 A large-scale model of the functioning brain,
nice paper showing the emergence of human-like cognitive functions in a computational model of the brain.
(Computational neuroscience) - Körding & Wolpert, 2004, Bayesian Integration in Sensorimotor Learning,
empirical paper showing how people do implicitly Bayesian statistics by movement control.
(Computational neuroscience) - Berkes et al., 2011 - Spontaneous Cortical Activity Reveals Hallmarks of an Optimal Internal Model of the Environment,
empirical paper showing that spontaneous activity of the visual cortex might reflect the internal model of the environment.
(Neuroscience) - Wei Ji Ma, 2012, Organizing probabilistic models of perception,
overview paper which clarifies many misconceptions about Bayesian inference in systems neuroscience
(Cognitive neuroscience)