An Information Theoretic Approach to Stimulus Processing in the Olfactory System II

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A fundamental question in neurobiology is how external stimuli are coded by the activity of neurons in the brain. An emerging prototypical model system to address this question is in the neuronal network of the mouse OB. For neurons in general, stimuli have dimensions of millivolts per millisecond and action potentials (spikes) are the sole binary means of both encoding and propagating information. How exactly stimulus information is transformed and contained in spike trains of output neurons, however, remains controversial. The relative anatomical simplicity of the OB, the combined computation of sensory and state-dependent activity within the OB network, and its direct (and partly reciprocal) connection to the cortex, bypassing the sensory thalamus, render the OB an attractive model to study the basic principles of sensory information processing.As a corollary to the on-going research detailed in our previous proposal, we now aim to extend and refine the scope of information theoretic models to more closely match / describe neuronal communication and network processing in the mouse OB model. Specific predictions / hypotheses based on extended channel models will then be systematically investigated in neurophysiological experiments and, in turn, model parameters will be adapted according to experimental findings. While we intentionally confine ourselves to the mouse olfactory system, we expect that the obtained results can be largely generalized to a variety of (neuro)biological communication systems. Following the research logic of the current funding period, we propose a multi-disciplinary strategy that will focus on two major goals:A. Neurobiologists will be provided with analytical models to simulate neural information processing on a purely numerical basis - an innovative approach that we expect to be instrumental in gaining novel insights into the principle rules that govern sensory coding in the brain.B. Neural information coding and propagation is robust, energy efficient and highly error tolerant. Nonetheless, neural networks are fast and efficient - features sought after in technical communication systems. Using models and analyses that emerge from our collaborative efforts, we will adapt bio-inspired design principles to the field of communications, which in the past has turned out very fruitful for a variety of applications.We firmly believe that synergy from theoretical and experimental approaches will substantially advance our understanding of brain computations and, in parallel, fuel novel ideas in communication theory.