Fetching the paper…
Reading the bibliography…
Computational modeling plays an increasingly important role in neuroscience, highlighting the philosophical question of how computational models explain.
The roles of mutation, inbreeding, crossbreeding and selection in evolution
Wright, S · 1932
Earlier work this paper cites.
Receptive fields of single neurones in the cat’s striate cortex
Hubel, D. H. & Wiesel, T. N · 1959
Earlier work this paper cites.
Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex
Hubel, D. H. & Wiesel, T. N · 1962
Earlier work this paper cites.
Mathematics, matter and method, Collected Papers Vol 2. , chap. What is mathematical truth? (Cambridge University Press, 1975)
Putnam, H · 1975
Earlier work this paper cites.
Spatial summation in the receptive fields of simple cells in the cat’s striate cortex
Movshon, J. A., Thompson, I. D. & Tolhurst, D. J · 1978
Earlier work this paper cites.
A confutation of convergent realism
Laudan, L · 1981
Earlier work this paper cites.
Darwin’s dangerous idea
Dennett, D. C · 1996
Earlier work this paper cites.
Emergence of simple-cell receptive field properties by learning a sparse code for natural images
Olshausen, B. A · 1996
Earlier work this paper cites.
Evolution and speciation on holey adaptive landscapes
Gavrilets, S · 1997
Earlier work this paper cites.
Science without laws (University of Chicago Press, 1999)
Giere, R. N · 1999
Earlier work this paper cites.
Orientation selectivity in macaque v1: diversity and laminar dependence
Ringach, D. L., Shapley, R. M. & Hawken, M. J · 2002
Earlier work this paper cites.
Near-neutrality in evolution of genes and gene regulation
Ohta, T · 2002
Cited alongside, same era.
Do We Know What the Early Visual System Does?
Carandini, M · 2005
Cited alongside, same era.
The berkeley wavelet transform: a biologically inspired orthogonal wavelet transform
Willmore, B., Prenger, R. J., Wu, M. C.-K. & Gallant, J. L · 2008
Cited alongside, same era.
The end of the adaptive landscape metaphor?
Kaplan, J · 2008
Cited alongside, same era.
The end of the adaptive landscape metaphor?
Calcott, B · 2008
Cited alongside, same era.
Construction and analysis of a large scale image ontology
Deng, J., Li, K., Do, M., Su, H. & Fei-Fei, L · 2009
Cited alongside, same era.
Using goal-driven deep learning models to understand sensory cortex
Yamins, D. L. & DiCarlo, J. J · 2016
Later among the works it cites.
Explicit information for category-orthogonal object properties increases along the ventral stream
*Hong, H., *Yamins, D. L., Majaj, N. J. & DiCarlo, J. J · 2016
Later among the works it cites.
Explanation in computational neuroscience: Causal and non-causal
Chirimuuta, M · 2017
Later among the works it cites.
A theory of multineuronal dimensionality, dynamics and measurement
Gao, P · 2017
Later among the works it cites.
Angular velocity integration in a fly heading circuit
Turner-Evans, D · 2017
Later among the works it cites.
Large-scale, high-resolution comparison of the core visual object recognition behavior of humans, monkeys, and state-of-the-art deep artificial neural networks
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Howson, C · 2013
Cited alongside, same era.
Performance-optimized hierarchical models predict neural responses in higher visual cortex
Yamins*, D · 2014
Cited alongside, same era.
Deep neural networks rival the representation of primate it cortex for core visual object recognition
Cadieu, C. F · 2014
Cited alongside, same era.
Deep supervised, but not unsupervised, models may explain it cortical representation
Khaligh-Razavi, S. M. & Kriegeskorte, N · 2014
Cited alongside, same era.
Deep neural networks reveal a gradient in the complexity of neural representations across the ventral stream
Güçlü, U. & van Gerven, M. A · 2015
Cited alongside, same era.
Rajalingham, R · 2018
Later among the works it cites.
Task-driven convolutional recurrent models of the visual system
Nayebi, A · 2018
Later among the works it cites.
Agents and goals in evolution (Oxford University Press, 2018)
Okasha, S · 2018
Later among the works it cites.
Deep convolutional models improve predictions of macaque v1 responses to natural images
Cadena, S. A · 2019
Later among the works it cites.
Single cortical neurons as deep artificial neural networks
Beniaguev, D., Segev, I. & London, M · 2019
Later among the works it cites.
Explanatory models in neuroscience: Part 1 – taking mechanistic abstraction seriously (2021)
Cao, R. & Yamins, D · 2021
Closest in time.