At every moment in time, the brain receives a vast amount of sensory information about the environment. This makes attention, the process by which we select currently relevant stimuli for processing and ignore irrelevant input, a fundamentally important brain function. Studies in primates have yielded a detailed description of how attention to a stimulus modifies the responses of neuronal ensembles in visual cortex, but how this modulation is produced mechanistically in the circuit is not well understood. Neuronal circuits comprise a large variety of neuron types, and to gain mechanistic insights, and to treat specific diseases of the nervous system, it is crucial to characterize the contribution of different identified cell types to information processing. Inhibition supplied by a small yet highly diverse set of interneurons controls all aspects of cortical function, and the central hypothesis of this proposal is that differential modulation of genetically-defined interneuron types is a key mechanism of attention in visual cortex. To identify the interneuron types underlying attentional modulation and to investigate how this, in turn, affects computations in the circuit we will use an innovative multidisciplinary approach combining genetic targeting in mice with cutting-edge in vivo 2-photon microscopy-based recordings and selective optogenetic manipulation of activity. Importantly, a key set of experiments will test whether the observed neuronal mechanisms are causally involved in attention at the level of behavior, the ultimate readout of the computations we are interested in. The expected results will provide a detailed, mechanistic dissection of the neuronal basis of attention. Beyond attention, selection of different functional states of the same hard-wired circuit by modulatory input is a fundamental, but poorly understood, phenomenon in the brain, and we predict that our insights will elucidate similar mechanisms in other brain areas and functional contexts.
Field of science
- /natural sciences/computer and information sciences/data science/data processing
Call for proposal
See other projects for this call