Making Sense of Complexity Summary of the Workshop on Dynamical Modeling of Complex Biomedical Systems (2002) / Chapter Skim
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3 Probabilistic Models that Represent Biological Observations
3 Probabilistic Models that Represent Biological Observations
Pages 10-14
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From page 10... ...
Rinzel's formulation sheds light on the intrinsic mechanisms of nerve cells, such as repetitive firing and bursting oscillations of individual cells, and the models were able to successfully mimic the patterns exhibited experimentally. More detail may be accessed through his Web page, at |
From page 11... ...
It is of great interest to study the input and output relations in a single neuron, which has more than 10,000 excitatory and inhibitory inputs. Let I denote the mean input current, which measures the difference between activation and inhibitory status, and let (5~2 be the input variance.
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From page 12... ...
Figure courtesy of Larry Abbott.
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From page 13... ...
The workshop's last foray into neuroscience was through the work of Emery Brown, of the Harvard Medical School, whose goal was to answer two questions: · Do ensembles of neurons in the rat hippocampus maintain a dynamic representation of the animal's location in space? · How can we characterize the dynamics of the spatial receptive fields of neurons in the rat hippocampus?
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From page 14... ...
The model was then used to predict the location of brain activity and validated against the actual location. The agreement was reasonable, with the Poisson prediction interval covering the actual rate of activation 37 percent of the time and the inhomogeneous gamma distribution covering it 62 percent of the time.
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