Datasets

• SWC file containing traces of the Neocortical Layer 1 Axons dataset featured at the DIADEM Challenge. The traces can be viewed in NCTracer Web by selecting the Neocortical Registered Translation image set and Neocortical Layer 1 Axons trace set.

• Dataset of connection probabilities and strengths in local brain circuits in mammals. The dataset is a compilation of 152 articles published in peer-reviewed journals from 1990 to 2016, describing a total of 856 projections. It was created by Rammy Dang and was later proofread and updated by Chi Zhang. The dataset is described in Zhang, D., Zhang, C., and Stepanyants, A., Robust associative learning is sufficient to explain the structural and dynamical properties of local cortical circuits, J Neuroscience, 39(35): 6888-6904 (2019) [10.1523/JNEUROSCI.3218-18.2019]

• Correlative light and electron microscopy dataset used to validate bouton detection and measurement methodology described in Gala, R., Lebrecht, D., Sahlender, D.A., Jorstad, A., Knott, G., Holtmaat, A., and Stepanyants, A., Computer assisted detection of axonal bouton structural plasticity in in vivo time-lapse images, eLife, 6:e29315 (2017). [10.7554/eLife.29315]

• Dataset of connection probabilities and PSP amplitudes used in Chapeton, J., Fares, T., LaSota, D., and Stepanyants, A., Efficient associative memory storage in cortical circuits of inhibitory and excitatory neurons, PNAS, 109(51): E3614–E3622 (2012). [10.1073/pnas.1211467109]

• Dataset of dendritic spine neck lengths and head sizes used in Stepanyants, A. and Escobar, G., Statistical traces of long-term memories stored in strengths and patterns of synaptic connections, J Neuroscience, 31(21): 7579–7590 (2011). [10.1523/JNEUROSCI.0255-11.2011]

Associative Learning

• Analytical and numerical solutions of biologically constrained models of associative memory storage as described in Chapeton, J., Gala, R., and Stepanyants, A., Effects of homeostatic constraints on associative memory storage and synaptic connectivity of cortical circuits, Frontiers in Computational Neuroscience, 9:74 (2015). [10.3389/fncom.2015.00074]

• MatLab code for solving the unconstrained, l0, and l1-norm constrained models of associative memory storage.