These are the peer-reviewed papers and book chapters of mine that have a DOI, newest first. Most come from my years at Boston University and the early days of Neurala, and most ask the same question in different ways: how does a brain learn all the time, on so little power, and can we build a machine that does the same?
It is not the complete list. For everything, with citation counts, see my Google Scholar profile or my ORCID record.
Papers and chapters
- S. Grossberg, J. Palma, M. Versace (2016). Resonant Cholinergic Dynamics in Cognitive and Motor Decision-Making: Attention, Category Learning, and Choice in Neocortex, Superior Colliculus, and Optic Tectum. Frontiers in Neuroscience. doi:10.3389/fnins.2015.00501 · Wikidata
- B. V. Galbraith, F. H. Guenther, M. Versace (2015). A neural network-based exploratory learning and motor planning system for co-robots. Frontiers in Neurorobotics. doi:10.3389/fnbot.2015.00007 · Wikidata
- F. Raudies, S. Eldridge, A. Joshi, M. Versace (2014). Learning to navigate in a virtual world using optic flow and stereo disparity signals. Artificial Life and Robotics. doi:10.1007/s10015-014-0153-1 · Wikidata
- C. Wong, M. Versace (2012). CARTMAP: a neural network method for automated feature selection in financial time series forecasting. Neural Computing and Applications. doi:10.1007/s00521-012-0830-8 · Wikidata
- J. Palma, S. Grossberg, M. Versace (2012). 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. doi:10.3389/fncom.2012.00042 · Wikidata
- J. Léveillé, H. Ames, B. Chandler, A. Gorchetchnikov, E. Mingolla, S. Patrick, M. Versace (2012). Learning in a Distributed Software Architecture for Large-Scale Neural Modeling. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. doi:10.1007/978-3-642-32615-8_65 · Wikidata
- M. Versace, R. T. Kozma, D. C. Wunsch (2012). Adaptive Resonance Theory Design in Mixed Memristive-Fuzzy Hardware. Advances in Neuromorphic Memristor Science and Applications. doi:10.1007/978-94-007-4491-2_9 · Wikidata
- S. Grossberg, J. Léveillé, M. Versace (2011). How do object reference frames and motion vector decomposition emerge in laminar cortical circuits?. Attention, Perception, & Psychophysics. doi:10.3758/s13414-011-0095-9 · Wikidata
- J. Palma, M. Versace, S. Grossberg (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 · Wikidata
- Z. Vasilkoski, H. Ames, B. Chandler, A. Gorchetchnikov, J. Leveille, G. Livitz, E. Mingolla, M. Versace (2011). Review of stability properties of neural plasticity rules for implementation on memristive neuromorphic hardware. The 2011 International Joint Conference on Neural Networks. doi:10.1109/ijcnn.2011.6033553 · Wikidata
- M. Versace, B. Chandler (2010). The brain of a new machine. IEEE Spectrum. doi:10.1109/mspec.2010.5644776 · Wikidata
- M. VERSACE, M. ZORZI (2010). The role of dopamine in the maintenance of working memory in prefrontal cortex neurons: input-driven versus internally-driven networks. International Journal of Neural Systems. doi:10.1142/s0129065710002401 · Wikidata
- J. Léveillé, M. Versace, S. Grossberg (2010). Running as fast as it can: How spiking dynamics form object groupings in the laminar circuits of visual cortex. Journal of Computational Neuroscience. doi:10.1007/s10827-009-0211-1 · Wikidata
- M. Versace, S. Grossberg (2010). From spikes to objects: How multiple levels of thalamic and cortical interactions control visual learning. attention, and recognition. Journal of Vision. doi:10.1167/6.6.885 · Wikidata
- S. Grossberg, M. Versace (2008). Spikes, synchrony, and attentive learning by laminar thalamocortical circuits. Brain Research. doi:10.1016/j.brainres.2008.04.024 · Wikidata
- M. Versace, H. Ames, J. Léveillé, B. Fortenberry, A. Gorchetchnikov (2008). KInNeSS: A Modular Framework for Computational Neuroscience. Neuroinformatics. doi:10.1007/s12021-008-9021-2 · Wikidata
- A. Gorchetchnikov, M. Versace, M. E. Hasselmo (2005). A model of STDP based on spatially and temporally local information: Derivation and combination with gated decay. Neural Networks. doi:10.1016/j.neunet.2005.06.019 · Wikidata
- M. Versace, R. Bhatt, O. Hinds, M. Shiffer (2004). Predicting the exchange traded fund DIA with a combination of genetic algorithms and neural networks. Expert Systems with Applications. doi:10.1016/j.eswa.2004.05.018 · Wikidata
Where to start
If you read only one, read “The brain of a new machine” (IEEE Spectrum, 2010). It is written for engineers, not for neuroscientists, and it explains why we wanted hardware that works more like a brain.
For the context around these papers, I keep a list of other people’s work that shaped mine: a Physical AI reading list.