Biology subjects

Sprekeler, H.

Publications and source records attributed to Sprekeler, H..

4 recordsLinked to original sources

bioRxiv2018

A local measure of symmetry and orientation for individual spikes of grid cells

Grid cells have attracted broad attention because of their highly symmetric hexagonal firing patterns. Recently, research has shifted its focus from the global symmetry of grid cell activity to local distortions both in space and time, such as drifts in orientation, local defects of the hexagonal symmetry, and the decay and reappearance of grid patterns after changes in lighting condition. Here, we introduce a method that allows to visualize and quantify such local distortions, by assigning both a local grid score and a local orientation to each individual spike of a neuronal recording. The score is inspired by a standard measure from crystallography, which has been introduced to quantify local order in crystals. By averaging over spikes recorded within arbitrary regions or time periods, we can quantify local variations in symmetry and orientation of firing patterns in both space and time.

neuroscience↗

bioRxiv2018

Amplifying the redistribution of somato-dendritic inhibition by the interplay of three interneuron types

GABAergic interneurons play an important role in shaping the activity of excitatory pyramidal cells (PCs). How the various inhibitory cell types contribute to neuronal information processing, however, is not resolved. Here, we propose a functional role for a widespread network motif consisting of parvalbumin-(PV), somatostatin-(SOM) and vasoactive intestinal peptide (VIP)-expressing interneurons. Following the idea that PV and SOM interneurons control the distribution of somatic and dendritic inhibition onto PCs, we suggest that mutual inhibition between VIP and SOM cells translates weak inputs to VIP interneurons into large changes of somato-dendritic inhibition of PCs. Using a computational model, we show that the neuronal and synaptic properties of the circuit support this hypothesis. Moreover, we show that the SOM-VIP motif allows transient inputs to persistently switch the circuit between two processing modes, in which top-down inputs onto apical dendrites of PCs are either integrated or canceled.

neuroscience↗

bioRxiv2017

Burst Ensemble Multiplexing: A Neural Code Connecting Dendritic Spikes with Microcircuits

Many cortical neurons combine the information ascending and descending the cortical hierarchy. In the classical view, this information is combined nonlinearly to give rise to a single firing rate output, which collapses all input streams into one. We propose that neurons can simultaneously represent multiple input streams by using a novel code that distinguishes single spikes and bursts at the level of a neural ensemble. Using computational simulations constrained by experimental data, we show that cortical neurons are well suited to generate such multiplexing. Interestingly, this neural code maximizes information for short and sparse bursts, a regime consistent with in vivo recordings. It also suggests specific connectivity patterns that allows to demultiplex this information. These connectivity patterns can be used by the nervous system to maintain optimal multiplexing. Contrary to firing rate coding, our findings indicate that a single neural ensemble can communicate multiple independent signals to different targets.

neuroscience↗

bioRxiv2017

Learning place cells, grid cells and invariances: A unifying model

Neurons in the hippocampus and adjacent brain areas show a large diversity in their tuning to location and head direction. The underlying circuit mechanisms are not fully resolved. In particular, it is unclear why certain cell types are selective to one spatial variable, but invariant to another. For example, a place cell is highly selective to location, but typically invariant to head direction. Here, we propose that all observed spatial tuning patterns - in both their selectivity and their invariance - are a consequence of the same mechanism: Excitatory and inhibitory synaptic plasticity that is driven by the spatial tuning statistics of synaptic inputs. Using simulations and a mathematical analysis, we show that combined excitatory and inhibitory plasticity can lead to localized, grid-like or invariant activity. Combinations of different input statistics along different spatial dimensions reproduce all major spatial tuning patterns observed in rodents. The model is robust to changes in parameters, develops patterns on behavioral time scales and makes distinctive experimental predictions. Our results suggest that the interaction of excitatory and inhibitory plasticity is a general principle for the formation of neural representations.

neuroscience↗