Biology subjects

Berling, D.

Publications and source records attributed to Berling, D..

3 recordsLinked to original sources

bioRxiv2025

Efficient incorporation of dendrites into a large-scale cortical model reveals their surprising role in sharpening optogenetic responses

Single-photon optogenetics enables chronic wide-field stimulation of cortex, facilitating large-scale manipulation of neural code to study cortical processing and advance neuroprosthetics. However, access to neural codes organized at fine spatial scales is compromised by the horizontal spread of stimulation-evoked cortical activity. Overcoming this limitation requires a quantitative understanding of the mechanisms contributing to spread, which include light scattering, dendritic activation, and synaptic transmission. We addressed this with morphology-aware simulations of optogenetic stimulation in a functionally-detailed network model of primary visual cortex. We find that synaptic transmission extends activation by 37-50% beyond the illuminated area, while, paradoxically, neuronal morphology sharpens activation, as apical dendrites sample from the superficial cortex, which is less affected by light dispersion. This unexpected sharpening enhances the fidelity of stimulation with spatially distributed patterns. Our study offers guidance for optogenetic interventions targeting topographically organized neural codes and provides a computational testbed to interpret such experiments.

neuroscience↗

bioRxiv2024

Consequences of neuronal morphology for spatially precise optogenetic stimulation

Single-photon optogenetic stimulation is a crucial tool in neuroscience, enabling precise, cell-type-specific modulation of neuronal circuits. Miniaturization of this technique in the form of fully implantable wide-field stimulator arrays enables interrogation of cortical circuits in long-term experiments and promises to enhance Brain-Machine Interfaces for restoring sensory and motor functions. However, for both basic science and clinical applications, it is essential that this technique achieves the precision needed for selective activation of sensory and motor representations at the single-column level. Yet studies report differing and sometimes conflicting neuronal responses within the stimulated cortical areas. While recurrent network mechanisms contribute to complex responses, here we demonstrate that complexity starts already at the level of neuronal morphology. Simulating optogenetic responses in detailed models of layer-2/3 and layer-5 pyramidal neurons, we accounted for realistic physiological dynamics across different stimulation intensities, including threshold, sustained, and depolarization-block responses. Our findings suggest that the spatial distribution of activated neurons from a single stimulator location at the cortical surface can be inhomogeneous and varies with stimulation intensity and neuronal morphology across layers, potentially explaining the observed response heterogeneity in earlier experiments. We found that activation spreads laterally up to several hundred micrometers from the light source due to neuronal morphology. To enhance precision, we explored two strategies: preferentially somatic expression of channelrhodopsin, which was effective only in layer-5 neurons, and narrowing the stimulating light beam, which improved precision in both layers. Our results indicate that, under the right optical setup, single-column precision of stimulation is achievable, and that optical enhancements to the stimulator may offer more significant precision improvements than genetic modifications targeting the soma.

neuroscience↗

bioRxiv2022

Model of multiple synfire chains explains cortical spatio-temporal spike patterns

It has been postulated that information processing in the brain is based on precise temporal correlation of neural activity across populations of neurons. In a recent study we found spatio-temporal spike patterns in experimental recordings from monkey motor cortex, and here we study if those could be explained by a synfire chain (SFC) like model. The model is composed of groups of neurons connected in feed-forward manner from one group to the next with high convergence and divergence. When activated, e.g., by a current pulse to the first group, spiking activity in the SFC is synchronous within neurons of the same group and propagates from group to group. When a few neurons from different groups are recorded from such an SFC, and the SFC is repeatedly activated, we would find a spatio-temporal spike pattern repeating across trials. Here, we take the statistics of the STPs found in the experimental data from 20 sessions as a reference to compare to a simulated network. Distributions of the data we take into account include 1) the pattern sizes, i.e. the number of neurons involved in the patterns, 2) the number of patterns a single neuron is involved in, 3) the durations of the patterns, and 4) the spatial distances of the patterns across the electrode array used to record the data. For the simulations, we embed SFC(s) in an anatomical model of the respective layer of the motor cortex, defined by its height and the density of the neurons. Model parameters are the length of the SFC, the number of neurons per group, the spatial extent of each neuronal group, and the distance between subsequent groups. Given the size and reach of the Utah array electrodes, we derive the probability of recording neurons from the SFC network. An SFC is considered detected if at least two neurons from two different groups are recorded. We find that depending on the model parameters, an embedded SFC can be detected with high probability, despite the massive subsampling of the cortex by the Utah array. Furthermore, to achieve multiple membership of a neuron in different patterns, we embed multiple SFCs that overlap. The fitting of the model to the pattern data constrains the spatial SFC parameters: the chains have to be broadly distributed in space and contain many neurons per group to match the experimental results.

neuroscience↗