Marius Schneider

Freely moving mice are generally thought to redirect gaze mainly through head-coupled movements, with eye movements stabilizing the retinal image or resetting eye position. This reflexive view leaves unresolved whether gaze shifts also include active coordination modes. Here we show that a single gaze-shift class resolves into multiple distinct head-eye coordination motifs. Using high-resolution head and eye tracking in freely moving mice, we identified four reproducible motifs: Head-before-Eye (HbE), Head-with-Eye (HwE), Head-Dominant (HD), and Eye-Dominant (ED). The motifs differed in head-eye timing, locomotor context, and pre-onset visual-behavioral structure. Their short-timescale sequential organization was non-random: HD formed alternating side-to-side head movements, whereas HwE-HbE-ED formed a directed transition chain preserved within each movement direction. Simultaneous neural recordings further revealed motif-dependent responses in primary visual cortex (V1) and superficial superior colliculus (sSC), with stronger sSC than V1 modulation for the three motifs distinguished by pre-onset visual-behavioral structure (all but HbE). HwE showed the clearest signature of active orienting, combining near-synchronous head-eye onset, strong relevance to visual-behavioral features, and the largest pre-onset and earliest peri-onset responses in sSC. These results expand a unitary view of gaze shifts in freely moving mice into a diverse, structured repertoire of coordination motifs, and identify HwE as a candidate active gaze-shift mode during natural behavior.

The electrical and computational properties of neurons in our brains are determined by a rich repertoire of membrane-spanning ion channels and elaborate dendritic trees. However, the precise reason for this inherent complexity remains unknown, given that simpler models with fewer ion channels are also able to functionally reproduce the behaviour of some neurons. Here, we stochastically varied the ion channel densities of a biophysically detailed dentate gyrus granule cell model to produce a large population of putative granule cells, comparing those with all 15 original ion channels to their reduced but functional counterparts containing only 5 ion channels. Strikingly, valid parameter combinations in the full models were dramatically more frequent at ~6% vs. ~1% in the simpler model. The full models were also more stable in the face of perturbations to channel expression levels. Scaling up the numbers of ion channels artificially in the reduced models recovered these advantages confirming the key contribution of the actual number of ion channel types. We conclude that the diversity of ion channels gives a neuron greater flexibility and robustness to achieve a target excitability.

Rhythmic flicker stimulation has gained interest as a treatment for neurodegenerative diseases and a method for frequency tagging neural activity in human EEG/MEG recordings. Yet, little is known about the way in which flicker-induced synchronization propagates across cortical levels and impacts different cell types. Here, we used Neuropixels to simultaneously record from LGN, V1, and CA1 while presenting visual flicker stimuli at different frequencies. LGN neurons showed strong phase locking up to 40Hz, whereas phase locking was substantially weaker in V1 units and absent in CA1 units. Laminar analyses revealed an attenuation of phase locking at 40Hz for each processing stage, with substantially weaker phase locking in the superficial layers of V1. Gamma-rhythmic flicker predominantly entrained fast-spiking interneurons. Optotagging experiments showed that these neurons correspond to either PV+ or narrow-waveform Sst+ neurons. A computational model could explain the observed differences in phase locking based on the neurons’ capacitative low-pass filtering properties. In summary, the propagation of synchronized activity and its effect on distinct cell types strongly depend on its frequency.

The electrical and computational properties of neurons in our brains are determined by a rich repertoire of membrane-bound ion channels and elaborate dendritic trees. However, the precise reason for this inherent complexity remains unknown. Here, we generated large stochastic populations of biophysically realistic hippocampal granule cell models comparing those with all 15 ion channels to their reduced but functional counterparts containing only 5 ion channels. Strikingly, valid parameter combinations in the full models were more frequent and more stable in the face of perturbations. Scaling up the numbers of ion channels artificially in the reduced models recovered these advantages confirming the key contribution of the actual number of ion channels. We conclude that the diversity of ion channels allows a neuron to achieve a target excitability through random channel expression with increased robustness and higher flexibility. Significance statement Over the course of billions of years, evolution has led to a wide variety of biological systems. The emergence of the more complex among these seems surprising in the light of the high demands on searching viable solutions in a correspondingly high-dimensional parameter space. In realistic neuron models with their inherently complex ion channel composition, we find a surprisingly large number of viable solutions when selecting parameters randomly. This effect is strongly reduced in models with less ion channel types but is recovered when inserting additional artificial ion channels. Because concepts from probability theory provide a plausible explanation for such an improved arrangement of valid model parameters, we propose that this generalises to evolutionary selection in other complex biological systems. In brief Studying ion channel diversity in neuronal models we show how robust biological systems may evolve not despite but through their complexity. Highlights 15 channel model of hippocampal granule cells (GCs) reduces to 5 ion channels without loss of spiking behaviour. But knocking out ion channels can be compensated only in the full model. Random sampling leads to ∼ 6% solutions in full but only ∼ 1% in reduced model. Law of large numbers generalises our observations to other complex biological systems.

OECD/EUIPO (2020), Trade in Counterfeit Pharmaceutical Products, Illicit Trade, OECD Publishing, Paris, https://doi.org/10.1787/a7c7e054-en