Functional consequences of correlated excitation and inhibition on single neuron integration and signal propagation through synfire chains


Neurons receive a large number of excitatory and inhibitory synaptic inputs whose temporal interplay determines their spiking behavior. On average, excitation (Gexc) and inhibition (Ginh) balance each other, such that spikes are elicited by fluctuations [1]. In addition, it has been shown in vivo that Gexc and Ginh are correlated, with Ginh lagging Gexc only by few milliseconds (6ms), creating a small temporal integration window [2,3]. This correlation structure could be induced by feed-forward inhibition (FFI), which has been shown to be present at many sites in the central nervous system. To characterize the functional consequences of the FFI, we first modeled a simple circuit using spiking neurons with conductance based synapses and studied the effect on the single neuron integration. We then coupled many of such circuits to construct a feed-forward network (synfire chain [4,5]) and investigated the effect of FFI on signal propagation along such feed-forward network. We found that the small temporal integration window, induced by the FFI, changes the integrative properties of the neuron. Only transient stimuli could produce a response when the FFI was active whereas without FFI the neuron responded to both steady and transient stimuli. Due to the increase in selectivity to transient inputs, the conditions of signal propagation through the feed-forward network changed as well. Whereas synchronous inputs could reliable propagate, high asynchronous input rates, which are known to induce synfire activity [6], failed to do so. In summary, the FFI increased the stability of the synfire chain. Supported by DFG SFB 780, EU-15879-FACETS, BMBF 01GQ0420 to BCCN Freiburg [1] Kumar A., Schrader S., Aertsen A. and Rotter S. (2008). The high-conductance state of cortical networks. Neural Computation, 20(1):1–43. [2] Okun M. and Lampl I. (2008). Instantaneous correlation of excitation and inhibition during ongoing and sensory-evoked activities. Nat Neurosci, 11(5):535–7. [3] Baudot P., Levy M., Marre O., Monier C. and Frégnac (2008). [4] Abeles M. (1991). Corticonics: Neural circuits of the cerebral cortex. Cambridge, UK [5] Diesmann M., Gewaltig M-O and Aertsen A. (1999). Stable propagation of synchronous spiking in cortical neural networks. Nature, 402(6761):529–33. [6] Kumar A., Rotter S. and Aertsen A. (2008), Conditions for propagating synchronous spiking and asynchronous firing rates in a cortical network model. J Neurosci 28 (20), 5268–80. Preliminary Program

Eighth Göttingen Meeting of the German Neuroscience Society
Jens Kremkow
Jens Kremkow
Phd in Computational Neuroscience

During my PhD, I focused on the interplay of Excitation and Inhibition in Visual Cortical Circuits.

Laurent U Perrinet
Laurent U Perrinet
Researcher in Computational Neuroscience

My research interests include Machine Learning and computational neuroscience applied to Vision.