Sandia Neuroscience

Neural Theory

Theoretical neuroscience research at Sandia focuses primarily on developing theories for different biological neural circuits and understanding basic neural computation concepts that may eventually impact neural machine learning.  

Formal characterization of neural computation 

While much of the attention on neural machine learning today focuses on deep artificial neural networks, such as deep convolutional networks and deep belief networks, we are looking towards understanding fundamental features of neural systems that will underlie the next generation of brain-like algorithms.  In particular, we are focusing on formally developing novel algorithms leveraging neural properties including

  • Spike-based representions and communication
  • Dynamical network-based computation
  • Neuromodulation
  • Synaptic and neuronal learning 

Understanding computational primatives of key neural regions

In addition to characterizing the potential of neural circuits broadly, we also are focusing on theoretically characterizing specific brain regions which are well suited for formal analysis, again with the of how these insights may influence either neural computing or broader neuroscience understanding.  

The Hippocampus

Much of our theory effort focuses on the hippocampus, a region of the brain that has long attracted the attention of theoretical neuroscientists.  While generally thought to be critical for declarative and episodic memory formation (i.e., memories of facts, events, etc) and presumed to be important for spatial processing, the underlying computational mechanisms are still widely debated.  


Notably, our theoretical work has focused significantly on the dentate gyrus (red in figure), a region that is unique among brain regions in that it incorporates new neurons throughout life (see sidebar).  Ongoing theory efforts are also considering the downstream hippocampal regions CA3  (blue) and CA1 (brown), which are experimentally well studied regions against which extensive place cell and other in vivo data can be used to validate theories.

Selected References

Severa W, Parekh O, James CD, and Aimone JB– “A Combinatorial Model for Dentate Gyrus Sparse Coding” – Neural Computation 2017

Aimone JB - “Computational Modeling of Adult Neurogenesis.” Adult Neurogenesis Cold Spring Harbor Perspectives, Cold Spring Harbor, NY, 2016. 

Rangel LM, Quinn L, Chiba AA, Gage FH and Aimone JB -“A Hypothesis for Temporal Coding of Young and Mature Granule Cells” Frontiers in Neurogenesis. 7(75), May 2013