Vision begins in the eye, and what the “retina tells the brain” is a major interest in visual neuroscience. To deduce what the retina encodes (“tells”), computational models are essential. The most important models in the retina currently aim to understand the responses of the retinal output neurons – the ganglion cells. Typically, these models make simplifying assumptions about the neurons in the retinal network upstream of ganglion cells. One important assumption is linear spatial integration. In this talk, I first define what it means for a neuron to be spatially linear or nonlinear and how we can experimentally measure these phenomena. Next, I introduce the neurons upstream to retinal ganglion cells, with focus on bipolar cells, which are the connecting elements between the photoreceptors (input to the retinal network) and the ganglion cells (output). This pivotal position makes bipolar cells an interesting target to study the assumption of linear spatial integration, yet due to their location buried in the middle of the retina it is challenging to measure their neural activity. Here, I present bipolar cell data where I ask whether the spatial linearity holds under artificial and natural visual stimuli. Through diverse analyses and computational models, I show that bipolar cells are more complex than previously thought and that they can already act as nonlinear processing elements at the level of their somatic membrane potential. Furthermore, through pharmacology and current measurements, I illustrate that the observed spatial nonlinearity arises at the excitatory inputs to bipolar cells. In the final part of my talk, I address the functional relevance of the nonlinearities in bipolar cells through combined recordings of bipolar and ganglion cells and I show that the nonlinearities in bipolar cells provide high spatial sensitivity to downstream ganglion cells. Overall, I demonstrate that simple linear assumptions do not always apply and more complex models are needed to describe what the retina “tells” the brain.

Many studies have investigated the major glutamatergic inputs to the cerebellum, mossy fibres and climbing fibres, however far less is known about its neuromodulatory inputs. In particular, anatomical studies have described cholinergic input to the cerebellum, yet little is known about its role(s). In this talk, I will present our recent findings which demonstrate that manipulating acetylcholine receptors in the cerebellum causes effects at both a cellular and behavioural level. Activating acetylcholine receptors alters the intrinsic properties and synaptic inputs of cerebellar output neurons, and blocking these receptors results in deficits in a range of behavioural tasks.

Each day we experience myriad somatosensory stimuli: hugs from loved ones, warm showers, a mosquito bite, and sore muscles after a workout. These tactile, thermal, itch, and nociceptive signals are detected by peripheral sensory neuron terminals distributed throughout our body, propagated into the spinal cord, and then transmitted to the brain through ascending spinal pathways. Primary sensory neurons that detect a wide range of somatosensory stimuli have been identified and characterized. In contrast, very little is known about how peripheral signals are integrated and processed within the spinal cord and conveyed to the brain to generate somatosensory perception and behavioral responses. We tackled this question by developing new mouse genetic tools to define projection neuron (PN) subsets of the anterolateral pathway, a major ascending spinal cord pathway, and combining these new tools with advanced anatomical, physiological, and behavioral approaches. We found that Gpr83+ PNs, a newly identified subset of spinal cord output neurons, and Tacr1+ PNs are largely non-overlapping populations that innervate distinct sets of subnuclei within the lateral parabrachial nucleus (PBNL) of the pons in a zonally segregated manner. In addition, Gpr83+ PNs are highly sensitive to cutaneous mechanical stimuli, receive strong synaptic inputs from primary mechanosensory neurons, and convey tactile information bilaterally to the PBNL in a non-topographically organized manner. Remarkably, Gpr83+ mechanosensory limb of the anterolateral pathway controls behaviors associated with different hedonic values (appetitive or aversive) in a scalable manner. This is the first study to identify a dedicated spinal cord output pathway that conveys affective touch signals to the brain and to define parallel ascending circuit modules that cooperate to convey tactile, thermal and noxious cutaneous signals from the spinal cord to the brain. This study has also revealed exciting new therapeutic opportunities for developing treatments for neurological disorders associated with pain and affective touch.

Popular notions of how the retina encodes visual stimuli typically focus on the center-surround receptive fields of retinal ganglion cells, the output neurons of the retina. In this view, the receptive field acts as a linear filter on the visual stimulus, highlighting spatial contrast and providing efficient representations of natural images. Yet, we also know that many ganglion cells respond vigorously to fine spatial gratings that should not activate the linear filter of the receptive field. Thus, ganglion cells may integrate visual signals nonlinearly across space. In this talk, I will discuss how these (and other) nonlinearities relate to the encoding of natural visual stimuli in the retina. Based on electrophysiological recordings of ganglion and bipolar cells from mouse and salamander retina, I will present methods for assessing nonlinear processing in different cell types and examine their importance and potential function under natural stimulation.

The mechanisms by which neural circuits generate an extensible library of motor motifs and flexibly string them into arbitrary sequences are unclear. We developed a model in which inhibitory basal ganglia output neurons project to thalamic units that are themselves bidirectionally connected to a recurrent cortical network. During movement sequences, electrophysiological recordings of basal ganglia output neurons show sustained activity patterns that switch at the boundaries between motifs. Thus, we model these inhibitory patterns as silencing some thalamic neurons while leaving others disinhibited and free to interact with cortex during specific motifs. We show that a small number of disinhibited thalamic neurons can control cortical dynamics to generate specific motor output in a noise robust way. If the thalamic units associated with each motif are segregated, many motor outputs can be learned without interference and then combined in arbitrary orders for the flexible production of long and complex motor sequences.