A central question in neuroscience is how the structure of brain circuits determines their activity and function. To explore this systematically, we developed a 230,000-neuron model of mouse primary visual cortex (area V1). The model integrates a broad array of experimental data:Distribution and morpho-electric properties of different neuron types in V1.

Detecting the direction of image motion is important for visual navigation, predator avoidance and prey capture, and thus essential for the survival of all animals that have eyes. However, the direction of motion is not explicitly represented at the level of the photoreceptors: it rather needs to be computed by subsequent neural circuits, involving a comparison of the signals from neighboring photoreceptors over time. The exact nature of this process represents a classic example of neural computation and has been a longstanding question in the field. Much progress has been made in recent years in the fruit fly Drosophila melanogaster by genetically targeting individual neuron types to block, activate or record from them. Our results obtained this way demonstrate that the local direction of motion is computed in two parallel ON and OFF pathways. Within each pathway, a retinotopic array of four direction-selective T4 (ON) and T5 (OFF) cells represents the four Cartesian components of local motion vectors (leftward, rightward, upward, downward). Since none of the presynaptic neurons is directionally selective, direction selectivity first emerges within T4 and T5 cells. Our present research focuses on the cellular and biophysical mechanisms by which the direction of image motion is computed in these neurons.

The retina is probably the most accessible part of the vertebrate central nervous system. Its computational logic can be interrogated in a dish, from patterns of lights as the natural input, to spike trains on the optic nerve as the natural output. Consequently, retinal circuits include some of the best understood computational networks in neuroscience. The retina is also ancient, and central to the emergence of neurally complex life on our planet. Alongside new locomotor strategies, the parallel evolution of image forming vision in vertebrate and invertebrate lineages is thought to have driven speciation during the Cambrian. This early investment in sophisticated vision is evident in the fossil record and from comparing the retina’s structural make up in extant species. Animals as diverse as eagles and lampreys share the same retinal make up of five classes of neurons, arranged into three nuclear layers flanking two synaptic layers. Some retina neuron types can be linked across the entire vertebrate tree of life. And yet, the functions that homologous neurons serve in different species, and the circuits that they innervate to do so, are often distinct to acknowledge the vast differences in species-specific visuo-behavioural demands. In the lab, we aim to leverage the vertebrate retina as a discovery platform for understanding the evolution of computation in the nervous system. Working on zebrafish alongside birds, frogs and sharks, we ask: How do synapses, neurons and networks enable ‘function’, and how can they rearrange to meet new sensory and behavioural demands on evolutionary timescales?

Spike Timing Dependent Plasticity (STDP) is argued to modulate synaptic strength depending on the timing of pre- and postsynaptic spikes. Physiological experiments identified a variety of temporal kernels: Hebbian, anti-Hebbian and symmetrical LTP/LTD. In this work we present a novel plasticity model, the Voltage-Dependent Pathway Model (VDP), which is able to replicate those distinct kernel types and intermediate versions with varying LTP/LTD ratios and symmetry features. In addition, unlike previous models it retains these characteristics for different neuron models, which allows for comparison of plasticity in different neuron types. The plastic updates depend on the relative strength and activation of separately modeled LTP and LTD pathways, which are modulated by glutamate release and postsynaptic voltage. We used the 15 neuron type parametrizations in the GLIF5 model presented by Teeter et al. (2018) in combination with the VDP to simulate a range of standard plasticity protocols including standard STDP experiments, frequency dependency experiments and low frequency stimulation protocols. Slight variation in kernel stability and frequency effects can be identified between the neuron types, suggesting that the neuron type may have an effect on the effective learning rule. This plasticity model builds a middle ground between biophysical and phenomenological models allowing not just for the combination with more complex and biophysical neuron models, but is also computationally efficient so can be used in network simulations. Therefore it offers the possibility to explore the functional role of the different kernel types and electrophysiological differences in heterogeneous networks in future work.

Neuromodulators control information processing in cortical microcircuits by regulating the cellular and synaptic physiology of neurons. Computational models and detailed simulations of neocortical microcircuitry offer a unifying framework to analyze the role of neuromodulators on network activity. In the present study, to get a deeper insight in the organization of the cortical neuropil for modeling purposes, we quantify the fiber length per cortical volume and the density of varicosities for catecholaminergic, serotonergic and cholinergic systems using immunocytochemical staining and stereological techniques. The data obtained are integrated into a biologically detailed digital reconstruction of the rodent neocortex (Markram et al, 2015) in order to model the influence of modulatory systems on the activity of the somatosensory cortex neocortical column. Simulations of ascending modulation of network activity in our model predict the effects of increasing levels of neuromodulators on diverse neuron types and synapses and reveal a spectrum of activity states. Low levels of neuromodulation drive microcircuit activity into slow oscillations and network synchrony, whereas high neuromodulator concentrations govern fast oscillations and network asynchrony. The models and simulations thus provide a unifying in silico framework to study the role of neuromodulators in reconfiguring network activity.

The concept of a dynamic excitation / inhibition ratio, that can shape information flow in cortical circuits during complex behavioural tasks due to circuit disinhibition, has recently arisen as an important and conserved processing motif. It has been also recognized that, in cortical circuits, a subpopulation of GABAergic cells that express vasoactive intestinal polypeptide (VIP) innervates selectively inhibitory interneurons, providing for circuit disinhibition as a possible outcome, depending on the network state and behavioural context. In this talk, I will highlight the latest discoveries on the dynamic organization of hippocampal disinhibitory circuits with a focus on VIP-expressing interneurons. I will discuss the neuron types that can be involved in disinhibition and their local circuit and long-range synaptic connections. I will also discuss some recent findings on how hippocampal VIP circuits may coordinate spatial learning.

The vagus nerve contains a diversity of sensory neurons that detect peripheral stimuli such as blood pressure changes at the aortic arch, lung expansion during breathing, meal-induced stomach distension, and chemotherapeutics that induce nausea. Underlying vagal sensory mechanisms are largely unresolved at a molecular level, presenting tremendously important problems in sensory biology. We charted vagal sensory neurons by single cell RNA sequencing, identifying novel cell surface receptors and classifying a staggering diversity of sensory neuron types. We then generated a collection of ires-Cre knock-in mice to target each neuron type, and adapted genetic tools for Cre-based anatomical mapping, in vivo imaging, targeted ablation, and optogenetic control of vagal neuron activity. We found different sensory neuron types that innervate the lung and exert powerful effects on breathing, others that monitor and control the digestive system, and yet others that innervate that innervate the larynx and protect the airways. Together with Ardem Patapoutian, we also identified a critical role for Piezo mechanoreceptors in the sensation of airway stretch, which underlies a classical respiratory reflex termed the Hering-Breuer inspiratory reflex, as well as in the neuronal sensation of blood pressure and the baroreceptor reflex.