Week 1 reading

Structure

• Scala et al. (2023) “Phenotypic variation of transcriptomic cell types in mouse motor cortex”
• Chandrasekhar and Navlakha (2019) “Neural arbors are Pareto optimal”
• Jones and Kording (2021) “Might a Single Neuron Solve Interesting Machine Learning Problems Through Successive Computations on Its Dendritic Tree?”

Function

• Mensi et al. (2012) “Parameter extraction and classification of three cortical neuron types reveals two distinct adaptation mechanisms”
• Perez-Nieves et al. (2021) “Neural heterogeneity promotes robust learning”

Abstract neuron models

• LaFosse et al. (2023) “Single cell optogenetics reveals attenuation-by-suppression in visual cortical neurons”
• Mainen and Sejnowski (1995) “Reliability of Spike Timing in Neocortical Neurons”
• Brette and Guigon (2003) “Reliability of Spike Timing Is a General Property of Spiking Model Neurons”
• Salaj et al. (2021) “Spike frequency adaptation supports network computations on temporally dispersed information”
• Allen Institute Cell Types Database
• Gutkin and Zeldenrust (2014) “Spike frequency adaptation”
• Gerstner and Brette (2009) “ Adaptive exponential integrate-and-fire model”
• Gerstner et al. (2014) “Neuronal Dynamics” - Part 1 Foundations of Neuronal Dynamics
• Biological neuron models - good Wikipedia article

Biophysical neuron models

• Hodgkin and Huxley (1952) “A quantitative description of membrane current and its application to conduction and excitation in nerve”
• Gerstner et al. (2014) “Neuronal Dynamics” - Chapter 2 Ion Channels and the Hodgkin-Huxley Model
• Gerstner et al. (2014) “Neuronal Dynamics” - Chapter 3 Dendrites and Synapses
• Pagkalos et al. (2023) “Leveraging dendritic properties to advance machine learning and neuro-inspired computing”
• Dendrify