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Merkt, B.

Publications and source records attributed to Merkt, B..

2 recordsLinked to original sources

A theory of orientation selectivity emerging from randomly sampling the visual field

Neurons in rodent primary visual cortex are simultaneously tuned to several stimulus features, including orientation and spatial frequency of moving gratings used in experiments. Light-induced signals emitted by retinal ganglion cells (RGC) are relayed to the primary visual cortex (V1) via cells in the dorsal lateral geniculate nucleus (dLGN). However, there is currently no agreement on which thalamocortical transformation leads to the neuronal tuning curves observed in experiments. Here, we outline a model that explains the emergence of feature-specific neural responses as the result of a two-step integration process: First, the compound input to cortical neurons comes from a set of retinal sensors randomly placed in the receptive field. Second, the cortical responses to the combined input are shaped by the rectification caused by the spike threshold of the neurons. We performed numerical simulations of a thalamocortical network stimulated by moving gratings and found that simultaneous tuning to orientation and spatial frequency results from this spatio-temporal integration process. We also show how this tuning is related to the complex structure of the receptive fields that reflect the input. We conclude that different types of feature selectivity arise naturally from random thalamocortical projections. Moreover, we describe in detail the underlying neural mechanism.

neuroscience↗

Propagation of orientation selectivity in a spiking network model of layered primary visual cortex

We studied the propagation of orientation selectivity over layers in a model of rodent primary visual cortex in terms of the underlying connectivity. While significant progress has been made in measuring the activity in the different sub-populations as well as the connectivity between those in experiments, a comprehensive theoretical explanation of how network structure and its dynamics are related is still missing. We suggest a model of layered mouse visual cortex by extending the model suggested by Potjans and Diesmann (2014) with thalamic input that has an orientation bias according to Sadeh et al. (2014). We then studied the response properties of the network to such non-homogeneous input. We found that, without further assumptions, the connectivity derived from experimental data leads to layer-specific distributions of orientation selectivity very similar to what has been observed in mouse experiments. Interestingly, the network settles in a dynamical operating point, in which the efficacy of the different projections and their contribution to orientation tuning deviate strongly from the expectations based on the underlying anatomical connectivity. To understand the processes that shape the observed dynamics, it is essential to perform the analysis of the microcircuit on the system level. With that perspective, we find in particular that the difference in tuning of L2/3 and L4 neurons is an immediate, albeit unexpected consequence of the specific network connectivity. Furthermore, we introduce a novel method for predicting the effects of optogenetic stimulation of specific neuronal sub-populations and demonstrate its power in network simulations.\n\nSignificance StatementUnderstanding the precise roles of neuronal sub-populations in shaping the activity of neuronal networks is a fundamental objective of neuroscience research. To this end, our work makes three important contributions. First, we show that the experimentally extracted connectivity suffices to explain the degree of selectivity of sub-populations in mouse visual cortex to visual stimulation. Second, we introduce a novel system-level approach for the analysis of input-output relations of recurrent networks, which lead to distinct activity patterns. Third, we present a method for the design of optogenetic experiments that can be used to devise specific stimuli which result in a desired and predictable change of neuronal activity.

neuroscience↗