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Precise Signal Transmission in the Brain

Scientists from Freiburg investigate why the brain produces signals that suppress neurons shortly after they were activated (June 2010).

In sensory perception, the brain processes information step by step in successive layers. Neurons in a given layer send signals – in the form of electrical impulses – to the next layer. There are two types of signals: those that activate downstream cells (excitatory signals), and those that suppress their activity (inhibitory signals). The latter may sound paradoxical at first: why should the brain invest energy to inhibit its own activity? Jens Kremkow and Ad Aertsen of the Bernstein Center for Computational Neuroscience and the Faculty of Biology at the University of Freiburg, together with colleagues from Marseille (France), have used computer models to systematically investigate the role of inhibitory connections in the information processing by the brain. They show that inhibitory connections strongly promote precise signal transmission.

A nerve cell often receives excitatory as well as inhibitory signals from the same upstream structure, with the inhibitory signal lagging a few milliseconds behind the excitatory one. This is caused by a defined circuitry scheme, the so-called "Feed Forward Inhibition" (FFI). In their study, the researchers investigated the influence of FFI on signal transmission within the brain – both at the level of single cells and in complex networks.


Schematic representation of "Feed Forward Inhibition". A neuronal signal reaches the downstream cell (E) once through a direct, activating connection and once, indirectly, through an inhibitory interneuron (I). This short detour via (I) is responsible for the time lag. The factors α and σ indicate the signal's strength and synchrony, respectively.

FFI causes individual nerve cells to work like filters for simultaneous signals. This effect has a straightforward explanation and can be understood even without computer modeling. Each nerve cell receives input signals from thousands of upstream cells, and "adds them up". Only if a certain threshold value is reached, it sends out a signal itself – one says, the neuron "fires". If each excitatory signal is followed by an inhibitory one, the threshold is hardly ever reached. Every "plus" that the cell counts is followed by a "minus" shortly after. The cell only has a chance to fire if a large number of excitatory signals comes in simultaneously, such that the threshold is reached before the inhibitory signals arrive. For the transmission of information within the brain, such a filter for simultaneity can be quite valuable, since sensory inputs may often lead to synchronous – i.e. simultaneous – activity of nerve cells in the brain; these are then preferentially transmitted.

In the nervous system, signals are transmitted by groups of nerve cells in recurrent networks, from layer to layer. In computer simulations, the Freiburg scientists investigated how FFI influences signal transmission in such structures. They found that, also in this context, FFI leads to a selective transmission of synchronous signals – asynchronous signals are filtered out. The amount of synchrony required for effective transmission depends on the strength of the inhibitory signal and the delay between the excitatory and the inhibitory signal. Thus, signal transmission in the nervous system can be fine tuned through these factors. In addition, the investigators showed that inhibitory signals keep the background activity of the network into which the neural connectivity scheme is embedded in a state that is favorable for signal transmission. Thus, taken together, inhibition in the form of FFI allows synchronous signals to be more effectively and selectively transmitted in the brain.

Ansprechpartner Link

Jens Kremkow and Ad Aertsen
Institut for Biology III
Albert Ludwigs University Freiburg

Jens Kremkow, Laurent U. Perrinet, Guillaume S. Masson and Ad Aertsen

Functional consequences of correlated excitatory and inhibitory conductances in cortical networks.

Journal of Computational Neuroscience, Online 19. Mai 2010

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