This work is done together with the PET/Cognitive Neurophysiology Research group at the Karolinska Hospital.
A popular science presentation of my work, based on a lecture held at Connect Stockholm February 5 200.
Mikael Djurfeldt, Anders Sandberg, Örjan Ekeberg and Anders Lansner, See---a framework for simulation of biologically detailed and artificial neural networks and systems, Neurocomputing vol 25-27, pp. 999-1003 1999
Anders Sandberg, Anders Lansner, Karl-Magnus Petersson and Örjan Ekeberg, An incremental Bayesian learning rule, Tech. report TRITA-NA-P9908 NADA, KTH, Stockholm 1999.
Anders Sandberg, Anders Lansner, Karl-Magnus Petersson and Örjan Ekeberg, A Palimpsest Memory based on an Incremental Bayesian Learning Rule. Neurocomputing 32-33 (2000) 987-994.
Anders Lansner, Anders Sandberg and K. M. Petersson. On Forgetful Attractor Memories. ANNIMAB-1, Gothemburg, Sweden. 54-62. (2000)
Anders Sandberg, Anders Lansner, Karl-Magnus Petersson, Selective Enhancement of Recall through Plasticity Modulation in an Autoassociative Memory. Neurocomputing 38-40 (2001) 867-873.
Anders Sandberg, Anders Lansner, Karl-Magnus Petersson, Incremental Learning in Bayesian Attractor Networks. Presented at XXVII International Congress of Psychology.
Anders Sandberg, Anders Lansner, Karl-Magnus Petersson, A Bayesian Connectionist Model for Memory Scanning. Presented at XXVII International Congress of Psychology.
Anders Lansner and Anders Sandberg (2001). Functionality and Performance of Brain-Inspired Neural Networks. NOLTA 2001, Zao, Sendai, Japan. 501-504.
Anders Sandberg & Anders Lansner, Synaptic Depression as an Intrinsic Driver of Reinstatement Dynamics in an Attractor Network. To appear in Neurocomputing.
Anders Sandberg, Anders Lansner, Karl-Magnus Petersson & Örjan Ekeberg, A Bayesian attractor network with incremental learning. Network: Computation in Neural Systems volume 13 issue 2 (May 2002) 79-194.
Anders Sandberg, Bayesian Attractor Neural Network Models of Memory, Doctoral thesis, Nada 2003.
Anders Sandberg, Jesper Tegnér and Anders Lansner, A Working Memory Model Based on Fast Learning. Network: Computation in Neural Systems, volume 14, issue 4, pages 789-802, 2003.
Anders Sandberg, Erik Fransen, Achieving Temporal Precision Despite Slow Signals Using Feedback: An Autocatalytic Model of STDP Timing from Slow Calcium Signals, Poster presented at CNS 2004, work to be published in Neurocomputing.
Computer history, artificial intelligence and artificial life
Lecture notes on models of development and morphogenes, in PDF or Postscript
Lecture notes and examples about LaTeX
The SANS Group
Anders Main Page
nv91-asa@nada.kth.se