📖 Reading material

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