A novel theoretical framework for mathematically modeling nerve cells has illuminated for the first time how small synaptic impulses enable non-linear information processing in the brain. Reported in PLoS Computational Biology, the findings offer fundamental insights relevant to a wide range of biological, physical and technical systems.
In the field of neuroscience, neurons are known to communicate via so-called gaction potentialsh, brief impulses which cause a cellfs membrane potential to rise and fall. Only when many such impulses together exceed a threshold value does the neuron gfireh, releasing its action potential to target neurons. How neurons transfer action potentials from inputs into outputs determines which elementary operations they are able to perform, and at what rate.
With their latest work, researchers at the RIKEN Brain Science Institute and Bernstein Center for Computational Neuroscience set out to resolve contradictory findings uncovered earlier regarding this input-output relationship. At issue was the conventional theory of spiking neuronal networks, which approximates impulses in the limit where they become vanishingly tiny and infinitely numerous, limiting the capabilities of individual neurons to simple addition of inputs.
Using a newly-developed high-precision method for simulating nonlinear neuron models (see references), the team had previously uncovered contradictions in this theory. To unravel this mystery, the researchers developed a new analytic framework which explicitly incorporates the finite effect of each input at the critical boundary near the firing threshold. With this change, they show that not only can neurons process information far faster than previously believed, they can also perform nonlinear operations such as multiplication that are key to complex information processing.
While more accurately capturing the network aspect of neural dynamics, the new framework also reveals how cooperation between seemingly uncoordinated input signals enables neurons to perform many non-linear operations at the same time. Future work will build on these findings toward a better understanding of brain function, a fundamental requirement for treating neural diseases.
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Hanuschkin A, Kunkel S, Helias M, Morrison A and Diesmann M (2010). A general and efficient method for incorporating precise spike times in globally time-driven simulations. Front. Neuroinform. 4:113. doi:10.3389/fninf.2010.00113The simulation software is freely available from the NEST Initiative: http://www.nest-initiative.org
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