Computational neuroscience

One of the main goals of modern neuroscience is to understand how organisms adapt to their environment and what brain mechanisms subserve this process. My research focuses on the following areas:

  •  learning and memory: how does biological organisms learn and retain useful information in a dynamic world? How to model the biophysical mechanisms of learning and memory through mathematical simulations of large-scale neural systems? How do we regulate the granularity (specificity vs. generality) of what we learn?
  • visual perception: how do we perceive static and moving features/objects?
  • short-term memory and decision making: how is information reliably store in frontal cortical areas?


  • Palma, J., and Grossberg, S., and Versace, M. (2011) Persistence and storage of activity patterns in spiking recurrent cortical networks: Modulation of sigmoid signals by after-hyperpolarization currents and acetylcholine. Frontiers in Computational Neuroscience, 6:42. doi: 10.3389/fncom.2012.00042.
  • Palma, J., Versace, M., and Grossberg, S. (2011) After-hyperpolarization currents and acetylcholine control sigmoid transfer functions in a spiking cortical model. Journal of Computational Neuroscience, DOI: 10.1007/s10827-011-0354-8.
  • Leveille, J., Versace, M., and Grossberg, S. (2010) How do object reference frames and motion vector decomposition emerge in laminar cortical circuits? Attention, Perception, & Psychophysics, Vol. 73, No. 4, 1147-1170.
  • Versace, M., and Zorzi, M. (2010) The role of dopamine in the maintenance of working memory in prefrontal cortex neurons: input-driven versus internally-driven networks. International Journals of Neural Systems, Aug, 20(4):249-65
  • Leveille, J., Versace, M., and Grossberg, S. (2010) Running as fast as it can: How spikes form object groupings in the laminar circuits of visual cortex? Journal of Computational Neuroscience, 28(2):323-46.
  • Grossberg, S., and Versace, M. (2008) Spikes, synchrony, and attentive learning by laminar thalamocortical circuits. Brain Research, 1218C, 278-312 [Authors listed alphabetically].
  • Gorchetchnikov, A., Versace, M., and Hasselmo, M.E. (2005) A model of STDP based on spatially and temporally local information: Derivation and combination with gated decay. Neural Networks 18, 458–466.


Abstracts & Conference papers

  • Gorchetchnikov, A., Versace, M., Ames, H., Chandler, B., Laveille, J., Livitz, G., Mingolla, E., Snider, G., Amerson, R., Carter, D., Abdalla, H., and Qureshi, S. (2011) A Unified Learning Framework for Memristive Neuromorphic Hardware. Proceedings of the International Joint Conference on Neural Networks (IJCNN) 2011, San Jose, CA, USA.
  • Vasilkoski , Z., Versace, M., Ames, H., Chandler, B., Leveille, J., Gorchetchnikov, A., Livitz, G., and Mingolla, E. (2011) Stability analysis of neural plasticity rules for implementation on memristive neuromorphic hardware.Proceedings of the International Joint Conference on Neural Networks (IJCNN) 2011, San Jose, CA, USA.
  • Leveille, J., Ames, H., Chandler, B., Gorchetchnikov, A., Mingolla, E., Patrick, S., and Versace, M. (2010) Learning in a distributed software architecture for large-scale neural modeling. BIONETICS10, Boston, MA, USA.
  • Lorenz, S., Ames, H., and Versace, M. (2010) Consciousness and neuromorphic chips: A case for embodiment. Boston University Interdisciplinary Graduate Conference on Consciousness, Boston, MA.
  • Gorchetchnikov, A., Versace, M., and Hasselmo, M.E. (2005) Spatially and temporally local spike-timing-dependent plasticity rule. Proceedings of the International Joint Conference on Neural Networks (IJCNN) 2005, Montreal, QC, Canada. 1568, 390–396.

Tutorials and Resources


I am the leader of the Neuromorphics Lab, a highly collaborative lab with connections across both academia and industry.