Analysis of ongoing dynamics in neural networks

Spontaneous neural activities in the cerebral cortex exhibit complex spatio-temporal patterns in the absence of sensory inputs [Arieli, A., Shoham, D., Hildesheim, R., Grinvald, A., 1995. Coherent spatio-temporal patterns of ongoing activity revealed by real-time optical imaging coupled with single-unit recording in the cat visual cortex. J. Neurophysiol. 73, 2072–2093; Arieli, A., Sterkin, A., Grinvald, A., Aertsen, A., 1996. Dynamics of ongoing activity: explanation of the large variability in evoked cortical responses. Science 273, 1868–1871], wandering among the intrinsic set of cortical states [Tsodyks, M., Kenet, T., Grinvald, A., Arieli, A., 1999. Linking spontaneous activity of single cortical neurons and the underlying functional architecture. Science 286, 1943–1946; Kenet, T., Bibitchkov, D., Tsodyks, M., Grinvald, A., Arieli, A., 2003. Spontaneously emerging cortical representations of visual attributes. Nature 425, 954–956]. Elucidating the nature of such spontaneous activities is one of the most intriguing challenges in the effort to understand the computational principles employed by the brain. The precise mechanism behind these salient phenomena, however, is not known. Here we model the ongoing dynamics of generic neural networks with attractor states using a conductance-based neuron model. Our realistic modeling shows the existence of up-states and down-states in the membrane potential, where the up-states exist as spatially clustered patches moving within the network. Our analysis shows that up-states are sustained by the balance between excitatory and inhibitory inputs. Synaptic depression and depolarization-dependent potassium channels can cause the transitions from the up-states to down-states by affecting the dynamics in differential manners. The velocity of patches depends on the firing frequency of excitatory neurons affected by contributing factors. These results suggest that the switching dynamics can be produced by the interactions within the local network, revealing the constraints on the nature of autonomous dynamics within the cortex.