The receptive field is dead. Long live the receptive field?

Advances in experimental techniques, including behavioral paradigms using rich stimuli under closed loop conditions and the interfacing of neural systems with external inputs and outputs, reveal complex dynamics in the neural code and require a revisiting of standard concepts of representation. High-throughput recording and imaging methods along with the ability to observe and control neuronal subpopulations allow increasingly detailed access to the neural circuitry that subserves neural representations and the computations they support. How do we harness theory to build biologically grounded models of complex neural function?

[1]  H. Nakahara Multiplexing signals in reinforcement learning with internal models and dopamine , 2014, Current Opinion in Neurobiology.

[2]  Wolfgang Maass,et al.  A Reward-Modulated Hebbian Learning Rule Can Explain Experimentally Observed Network Reorganization in a Brain Control Task , 2010, The Journal of Neuroscience.

[3]  L. F. Abbott,et al.  A Complex-Valued Firing-Rate Model That Approximates the Dynamics of Spiking Networks , 2013, PLoS Comput. Biol..

[4]  David D. Cox,et al.  Do we understand high-level vision? , 2014, Current Opinion in Neurobiology.

[5]  E. Marder,et al.  From the connectome to brain function , 2013, Nature Methods.

[6]  E. Fetz Operant Conditioning of Cortical Unit Activity , 1969, Science.

[7]  Rajesh P. N. Rao,et al.  Brain–computer interfaces: a powerful tool for scientific inquiry , 2014, Current Opinion in Neurobiology.

[8]  Shihab Shamma,et al.  Adaptive auditory computations , 2014, Current Opinion in Neurobiology.

[9]  J. Carmena Advances in Neuroprosthetic Learning and Control , 2013, PLoS biology.

[10]  Miguel Maravall,et al.  Algorithms of whisker-mediated touch perception , 2014, Current Opinion in Neurobiology.

[11]  Yoshua. Bengio,et al.  Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..

[12]  Eero P. Simoncelli,et al.  Spatio-temporal correlations and visual signalling in a complete neuronal population , 2008, Nature.

[13]  Emery N Brown,et al.  Modeling the dynamical effects of anesthesia on brain circuits , 2014, Current Opinion in Neurobiology.

[14]  Lorenzo Rosasco,et al.  The computational magic of the ventral stream: sketch of a theory (and why some deep architectures work). , 2012 .

[15]  E Roth,et al.  A comparative approach to closed-loop computation , 2014, Current Opinion in Neurobiology.

[16]  Louis K. Scheffer,et al.  A visual motion detection circuit suggested by Drosophila connectomics , 2013, Nature.

[17]  N. Sobel,et al.  The perceptual logic of smell , 2014, Current Opinion in Neurobiology.

[18]  K. Harris,et al.  Cortical connectivity and sensory coding , 2013, Nature.

[19]  Jefferson E. Roy,et al.  Prefrontal Cortex Activity during Flexible Categorization , 2010, The Journal of Neuroscience.

[20]  Alexander Borst,et al.  Different receptive fields in axons and dendrites underlie robust coding in motion-sensitive neurons , 2009, Nature Neuroscience.

[21]  Ari Rosenberg,et al.  Models and processes of multisensory cue combination , 2014, Current Opinion in Neurobiology.

[22]  N. Cohen,et al.  Nematode locomotion: dissecting the neuronal–environmental loop , 2014, Current Opinion in Neurobiology.

[23]  David Sussillo,et al.  Neural circuits as computational dynamical systems , 2014, Current Opinion in Neurobiology.

[24]  Bruno A Olshausen,et al.  Sparse coding of sensory inputs , 2004, Current Opinion in Neurobiology.

[25]  Rainer Engelken,et al.  Dynamical models of cortical circuits , 2014, Current Opinion in Neurobiology.

[26]  Andrew S. Whitford,et al.  Cortical control of a prosthetic arm for self-feeding , 2008, Nature.

[27]  Naoshige Uchida,et al.  Odor Representations in Olfactory Cortex: Distributed Rate Coding and Decorrelated Population Activity , 2012, Neuron.

[28]  Nicolas Brunel,et al.  Single neuron dynamics and computation , 2014, Current Opinion in Neurobiology.

[29]  Sreekanth H. Chalasani,et al.  Information theory of adaptation in neurons, behavior, and mood , 2014, Current Opinion in Neurobiology.

[30]  C. Eliasmith,et al.  The use and abuse of large-scale brain models , 2014, Current Opinion in Neurobiology.

[31]  M. Fee The role of efference copy in striatal learning , 2014, Current Opinion in Neurobiology.

[32]  W. Newsome,et al.  Context-dependent computation by recurrent dynamics in prefrontal cortex , 2013, Nature.

[33]  Adrienne L. Fairhall,et al.  Single Neuron Computation: From Dynamical System to Feature Detector , 2006, Neural Computation.

[34]  Adrienne L. Fairhall,et al.  Implications of single-neuron gain scaling for information transmission in networks , 2011 .

[35]  Georg B. Keller,et al.  Sensorimotor Mismatch Signals in Primary Visual Cortex of the Behaving Mouse , 2012, Neuron.

[36]  Fred Rieke,et al.  Review the Challenges Natural Images Pose for Visual Adaptation , 2022 .

[37]  Adrienne L Fairhall,et al.  Emergence of Adaptive Computation by Single Neurons in the Developing Cortex , 2013, The Journal of Neuroscience.

[38]  Srinivas C. Turaga,et al.  Connectomic reconstruction of the inner plexiform layer in the mouse retina , 2013, Nature.

[39]  Tatyana O Sharpee,et al.  Computational identification of receptive fields. , 2013, Annual review of neuroscience.

[40]  Colin W G Clifford,et al.  Adaptation Improves Neural Coding Efficiency Despite Increasing Correlations in Variability , 2013, The Journal of Neuroscience.

[41]  James J. DiCarlo,et al.  How Does the Brain Solve Visual Object Recognition? , 2012, Neuron.

[42]  Steven J. Cox,et al.  Structure-preserving model reduction of passive and quasi-active neurons , 2013, Journal of Computational Neuroscience.

[43]  Stephen A Baccus,et al.  Insights from the retina into the diverse and general computations of adaptation, detection, and prediction , 2014, Current Opinion in Neurobiology.

[44]  M. Sahani,et al.  Cortical control of arm movements: a dynamical systems perspective. , 2013, Annual review of neuroscience.