December 04, 2015 13:30 - 14:30
BSI Central Building 1F Seminar Room
Recurrent network dynamics can lead to active decorrelation of synaptic currents, resulting in a state of arbitrarily low mean correlation . More recent studies have shown that in networks of spiking neurons the spike train cross-covariance functions obey balance relations that extend and generalize this result in several directions [2,3]. Under rather general conditions these balance equations, that relate spike train auto- and cross-covariance functions, necessarily have to be satisfied. Although both spike train and total current cross-covariance functions are small, in the asynchronous state cross-covariance functions between the current components are finite. Another signature of the asynchronous state is that the distribution of the spike count correlation coefficients has a small mean but a wide width. In two different network models, a purely inhibitory network and a network with excitatory and inhibitory populations, these predictions are explicitly shown to be true. Moreover, the balance equations for correlations are well-satisfied for network sizes in the physiological range.
 Renart, A., de la Rocha, J., Bartho, P., Hollender, L., Parga, N., Reyes, A. and Harris, P. (2010). The asynchronous state in cortical circuits, Science 327:587.
 Manrique, J. (2014). Correlations in spontaneous activity states in the brain. PhD Thesis (Universidad Autónoma de Madrid.
 Manrique, J. and Parga, N (2014). Decorrelation in networks of spiking neurons: from microscopic to macroscopic magnitudes. COSYNE.
- Open to Public
- Tomoki Fukai [Tomoki Fukai, Neural Circuit Theory ]
Name: Tomoki Fukai