idw – Informationsdienst Wissenschaft
Nachrichten, Termine, Experten
Nachrichten, Termine, Experten
idw - Informationsdienst
Wissenschaft
Researchers investigate learning processes by simulating a cubic millimeter of the brain
The ability of the brain to learn lies in the special properties of the nerve cells and in particular of their connections, the synapses. All brain activity is mediated by information in the form of short electric impulses that are passed from one "firing" cell to the next. In so doing, the cells cultivate their capability to propagate signals. If cell A emits a pulse that evokes a response in cell B, this strengthens the contact between cells A and B. If there is no such causal relationship, or if cell B fires before cell A, the connection is weakened. As a result of this phenomenon, known as "spike-timing dependent plasticity" (STDP), frequent pairings cause strong neural pathways to develop. Conversely, connections which are infrequently used decline. This "plasticity" of the brain, its ability to adapt physiologically and structurally, is considered to be the foundation of learning. On the basis of a complex computer simulation of 100,000 neurons with 10,000 contacts each - corresponding to about one cubic millimeter of cortex - Abigail Morrison, Ad Aertsen and Markus Diesmann have discovered that STDP may be insufficient to explain the learning processes of nerve cells. The results of the scientists from the Bernstein Center for Computational Neuroscience, the University of Freiburg and the RIKEN Brain Science Institute in Tokyo will be published in the July issue of Neural Computation.
From earlier studies the researchers knew that their computer simulation reproduced many dynamical properties of living cortical tissue. The virtual neurons fire with about the same frequency as in the brain, and the activity neither escalates nor decays - the system exists in a "dynamic equilibrium". Now they have extended their model to take the plasticity of neuronal connections into account. To this end, Morrison developed a mathematical formulation of the STDP learning rule that fitted the available experimental data significantly better. This development allows the model to become considerably more realistic.
To investigate whether the computer model can simulate learning processes, the researchers repeatedly stimulated a specific group of neurons. Their initial observations were in agreement with the predictions of the learning model: as the stimulated neurons transmitted the stimuli to their downstream neurons, these contacts were strengthened. However, this occurred at the expense of the contacts from their upstream neurons in the network. As the stimulated group responded to the external stimulus, their other inputs became redundant and decayed. After a while, the researchers determined that the entire stimulated group had decoupled itself from the rest of the network.
STDP is therefore not sufficient to explain learning in large neuronal networks, additional requirements must be satisfied to enable the system to learn. There are already strong indications as to what these requirements might be. With the simulation of large networks, Morrison and colleagues have a powerful tool to appraise a variety of different models and uncover the secret of neuronal learning.
Source: Morrison, A., Aertsen, A., & Diesmann, M. (2007). Spike-timing dependent plasticity in balanced random networks. Neural Computation, 19 (6) 1437-1467
http://www.mitpressjournals.org/doi/abs/10.1162/neco.2007.19.6.1437
Contakt:
Dr. Abigail Morrison
Diesmann Research Unit
Computational Neuroscience Group
RIKEN Brain Science Institute
2-1 Hirosawa
Wako City, Saitama 351-0198
Japan
tel: +81 48 467 9644
abigail@brain.riken.jp
Prof. Dr. Ad Aertsen
Bernstein Center für Computational Neuroscience
Albert-Ludwigs-Universität
Hansastrasse 9a
79104 Freiburg i.Br.
Tel: +49 (761) 203-9549
ad.aertsen@biologie.uni-freiburg.de
Merkmale dieser Pressemitteilung:
Biologie, Ernährung / Gesundheit / Pflege, Informationstechnik, Mathematik, Medizin, Physik / Astronomie
überregional
Forschungsergebnisse, Wissenschaftliche Publikationen
Englisch
Sie können Suchbegriffe mit und, oder und / oder nicht verknüpfen, z. B. Philo nicht logie.
Verknüpfungen können Sie mit Klammern voneinander trennen, z. B. (Philo nicht logie) oder (Psycho und logie).
Zusammenhängende Worte werden als Wortgruppe gesucht, wenn Sie sie in Anführungsstriche setzen, z. B. „Bundesrepublik Deutschland“.
Die Erweiterte Suche können Sie auch nutzen, ohne Suchbegriffe einzugeben. Sie orientiert sich dann an den Kriterien, die Sie ausgewählt haben (z. B. nach dem Land oder dem Sachgebiet).
Haben Sie in einer Kategorie kein Kriterium ausgewählt, wird die gesamte Kategorie durchsucht (z.B. alle Sachgebiete oder alle Länder).
