Mouse V1

Recurrent networks with balanced excitation-inhibition explain a wide range of neurophysiological observations, but can only implement a limited set of transformations on their input. On the other hand networks of firing-rate units with low-rank connectivity have universal computational capabilities, but do not work with spikes or generate noise self-consistently. Although empirical approaches to merge these two computational frameworks have been constructed, there is no established theory describing their unification. Here we develop such a theory. We study analytically and numerically networ

The ability to reconstruct imagery represented by the brain has the potential to give us an intuitive understanding of what the brain “sees”. Reconstruction of visual input from human fMRI data has garnered significant attention in recent years. Comparatively less focus has been directed towards vision reconstruction from single-cell recordings, despite its potential to provide a more direct measure of the information represented by the brain. Here, we achieve high-quality reconstructions of videos presented to mice, from the activity of neurons in their visual cortex. With our method of video

…of a cortical column representing the mouse V1 area, balancing morphological realism and practicality for large-network simulations. The model includes 17 sub-populations encompassing pyramidal neurons, PV

Recent studies showed that flexible, ultra-thin, polyimide electrodes implanted in mouse V1 elicit chronically stable intracortical microstimulation (ICM) with low activation threshold and high biocompatibility, demonstrating their promising use as prosthetics for visual restoration. However, these works did not explore the full repertoire of parameters that can shape the ICM. Besides, to achieve precise spatial encoding of the visual field, it is necessary to identify the stimulation conditions that lead to localized neuronal activation around the stimulation site(s). We have developed a pipe

It has been commonly reported across species and brain regions that neural population dynamics are low-dimensional, leading to a broad range of theories on the significance of this structure [1, 2]. However, several recent studies have reported that spontaneous activity in rodent sensory regions is high-dimensional [3], presenting a challenge to explanations which posit low-dimensional dynamics as integral to neural computations. Here we hypothesized that the observation of high-dimensional population dynamics in these datasets results from neural activity progressing through a series of low-d

…Here we study recurrent influences in mouse V1 by experimentally stimulating excitatory neurons. To do this, we selectively express an excitatory opsin (stChrimsonR) in excitatory cells, and record activity using electrophysiology, 2-photon, and widefield calcium imaging

Mapping the spatial patterns of synaptic connectivity is essential for understanding neural circuit function. Two-photon optogenetics combined with whole-cell patch clamp enable synaptic circuit mapping in mammalian cortex. We consider an experimental set-up in which the experimenter patches a single target cell and uses two-photon optogenetics to stimulate surrounding tissue in a 3-dimensional grid while measuring postsynaptic currents (PSCs). This yields a high resolution spatial map of putative synaptic connections to a single neuron. However, these connectivity maps are noisy due to biolog

Inhibitory interneurons come in different classes and form intricate circuits. While our knowledge of these circuits has advanced substantially over the last decades, it is not fully understood how the structure of these circuits relates to their function. I will present some of our recent attempts to “understand” the structure of interneuron circuits by means of computational modeling. Surprisingly (at least for us), we found that prominent features of inhibitory circuitry can be accounted for by an optimisation for excitation-inhibition (E/I) balance. In particular, we find that such an opti