Abstract
Gain control is essential for the proper function of any sensory system. However, the precise mechanisms for achieving effective gain control in the brain are unknown. Based on our understanding of the existence and strength of connections in the insect olfactory system, we analyze the conditions that lead to controlled gain in a randomly connected network of excitatory and inhibitory neurons. We consider two scenarios for the variation of input into the system. In the first case, the intensity of the sensory input controls the input currents to a fixed proportion of neurons of the excitatory and inhibitory populations. In the second case, increasing intensity of the sensory stimulus will both, recruit an increasing number of neurons that receive input and change the input current that they receive. Using a mean field approximation for the network activity we derive relationships between the parameters of the network that ensure that the overall level of activity of the excitatory population remains unchanged for increasing intensity of the external stimulation. We find that, first, the main parameters that regulate network gain are the probabilities of connections from the inhibitory population to the excitatory population and of the connections within the inhibitory population. Second, we show that strict gain control is not achievable in a random network in the second case, when the input recruits an increasing number of neurons. Finally, we confirm that the gain control conditions derived from the mean field approximation are valid in simulations of firing rate models and Hodgkin-Huxley conductance based models.
Highlights
In sensory perception the salient properties of signals need to be separated from their overall amplitude and, at some level in the neural processing cascade the response of neurons should become insensitive to the overall amplitude of stimulation
We evaluate whether it is possible to design stable neural circuits made of excitatory and inhibitory neurons that are capable of controlling the internal representation of a stimulus using network properties alone
It is known that one can control the internal representation by the saturating responses of neurons
Summary
In sensory perception the salient properties of signals need to be separated from their overall amplitude and, at some level in the neural processing cascade the response of neurons should become insensitive to the overall amplitude of stimulation. This is the role of gain control, which is ubiquitous for sensory processing in the brain [1]. During their foraging trips the intensity of stimulation fluctuates over a wide range of concentrations while they approach or leave their target flowers They are able to discern a preferred odor and reach their goal, consistent with perceiving the odor at different concentrations as a single perceptual object [3,4]. By recording the activity of more than 100 PNs it has been found in locusts [17] that the mean firing
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