Sandia Neuroscience

Kristofor D. Carlson, Ph.D.

Computational Neuroscience

Carlson_picKristofor Carlson is a postdoctoral appointee in the Data-driven and Neural Computing group (1462) at Sandia National Laboratories, where he is a researcher in computational neuroscience and neuromorphic engineering. He is currently part of the Hardware Acceleration of Adaptive Neural Algorithms (HAANA) Grand Challenge, a research initiative aimed at developing novel neural based computing algorithms and computing architectures. Kristofor also performs verification and validation on large-scale neural simulations using uncertainty quantification and sensitivity analysis techniques.

Before joining Sandia, Kristofor was a postdoctoral scholar in the department of Cognitive Sciences at the University of California, Irvine. He participated in the DARPA-funded Systems of Neuromorphic Adaptive Plastic Scalable Electronics (SyNAPSE) project where he tested and developed large-scale spiking neural networks (SNNs) for implementation on neuromorphic hardware. Kristofor also extended and maintained an SNN simulator called CARLsim for use on graphics processing units (GPUs). He created a simulation framework that allowed for the parameter optimization of SNNs using evolutionary algorithms and GPUs. Kristofor has a long standing interest in synaptic plasticity and homeostasis at the molecular, cellular, and network levels. He received his B.S. in applied physics and Ph.D. in physics from Purdue University. His thesis work involved the development of biophysically realistic computational models of synaptic plasticity in the hippocampus.

Contact Information

kdcarls@sandia.gov

Research Areas

Computational Neuroscience

  • Analysis and development of spiking neural network models
  • Uncertainty quantification and sensitivity analysis of neural systems
  • Computational studies of the dentate gyrus and hippocampus

Neural Computing

  • Developing novel neural-inspired algorithms and computing architectures
  • Exploring the interface between machine learning and spiking neural networks    

Peer-Reviewed Publications

*Carlson, K.D., *Beyeler, M., *Chou, T.S., Krichmar, J.L., Dutt, N., (2015) CARLsim 3: A User-Friendly and Highly Optimized Library for the Creation of Neurobiologically Detailed Spiking Neural Networks. Proceedings of the International Joint Conference on Neural Networks (IJCNN'15)

Carlson, K.D., Nageswaran, J.M., Dutt, N., and Krichmar, J.L. (2014) An Efficient Automated Parameter Tuning Framework for Spiking Neural Networks. Frontiers in Neuroscience 8(10)

Carlson, K.D., Richert, M., Dutt, N., and Krichmar, J.L. (2013) Biologically Plausible Models of Homeostasis and STDP: Stability and Learning in Spiking Neural Networks. Proceedings of the International Joint Conference on Neural Networks (IJCNN’13)

Carlson, K. D., Giordano, N. (2011) Interplay of the Magnitude and Time-Course of Postsynaptic Ca2+ Concentration in Producing Spike Timing-Dependent Plasticity. J Comput Neurosci, 30, 747-758.