Seminars

Karl Deisseroth explains how light-sensitive microbial proteins became tools for investigating brain circuits in living, freely moving animals. By controlling selected classes of neurons, and sometimes individual cells, optogenetics allows researchers to ask how cellular activity contributes to behaviour and mental experience. The lecture connects the discovery and development of these tools with questions about emotions, the neural pathways that generate our inner experience, and processes disrupted in psychiatric illness. It considers both basic research and clinical possibilities, including the importance of neuronal and social connections for emotional well-being.

Sensory experience often depends on one’s own actions, including self-motion. Theories of predictive coding postulate that actions are regulated by calculating prediction error, which is the difference between sensory experience and expectation based on self-generated actions. Signals consistent with prediction error have been reported in mouse visual cortex (V1) when visual flow coupled to running was unexpectedly stopped. Here, we show such signals can be elicited by visual stimuli uncoupled to animal’s running. We recorded V1 neurons while presenting drifting gratings that unexpectedly stopped. We found strong responses to visual perturbations, which were enhanced during running. Perturbation responses were strongest in the preferred orientation of individual neurons and perturbation responsive neurons were more likely to prefer slow visual speeds. Our results indicate that prediction error signals can be explained by the convergence of known motor and sensory signals, providing a purely sensory and motor explanation for purported mismatch signals.

A recent formulation of predictive coding theory proposes that a subset of neurons in each cortical area encodes sensory prediction errors, the difference between predictions relayed from higher cortex and the sensory input. Here, we test for evidence of prediction error responses in spiking responses and local field potentials (LFP) recorded in primary visual cortex and area V4 of macaque monkeys, and in complementary electroencephalographic (EEG) scalp recordings in human participants. We presented a fixed sequence of visual stimuli on most trials, and violated the expected ordering on a small subset of trials. Under predictive coding theory, pattern-violating stimuli should trigger robust prediction errors, but we found that spiking, LFP and EEG responses to expected and pattern-violating stimuli were nearly identical. Our results challenge the assertion that a fundamental computational motif in sensory cortex is to signal prediction errors, at least those based on predictions derived from temporal patterns of visual stimulation.

The long-term goal of our research is to understand the mechanisms of SUDEP, defined as Sudden, Unexpected, witnessed or unwitnessed, nontraumatic and non-drowning Death in patients with EPilepsy, excluding cases of documented status epilepticus. The majority of SUDEP patients die during sleep. SUDEP is the most devastating consequence of epilepsy, yet little is understood about its causes and no biomarkers exist to identify at risk patients. While SUDEP accounts for 7.5-20% of all epilepsy deaths, SUDEP risk in the genetic epilepsies varies with affected genes. Patients with ion channel gene variants have the highest SUDEP risk. Indirect evidence variably links SUDEP to seizure-induced apnea, pulmonary edema, dysregulation of cerebral circulation, autonomic dysfunction, and cardiac arrhythmias. Arrhythmias may be primary or secondary to hormonal or metabolic changes, or autonomic discharges. When SUDEP is compared to Sudden Cardiac Death secondary to Long QT Syndrome, especially to LQT3 linked to variants in the voltage-gated sodium channel (VGSC) gene SCN5A, there are parallels in the circumstances of death. To gain insight into SUDEP mechanisms, our approach has focused on channelopathies with high SUDEP incidence. One such disorder is Dravet syndrome (DS), a devastating form of developmental and epileptic encephalopathy (DEE) characterized by multiple pharmacoresistant seizure types, intellectual disability, ataxia, and increased mortality. While all patients with epilepsy are at risk for SUDEP, DS patients may have the highest risk, up to 20%, with a mean age at SUDEP of 4.6 years. Over 80% of DS is caused by de novo heterozygous loss-of-function (LOF) variants in SCN1A, encoding the VGSC Nav1.1  subunit, resulting in haploinsufficiency. A smaller cohort of patients with DS or a more severe DEE have inherited, homozygous LOF variants in SCN1B, encoding the VGSC 1/1B non-pore-forming subunits. A related DEE, Early Infantile EE (EIEE) type 13, is linked to de novo heterozygous gain-of-function variants in SCN8A, encoding the VGSC Nav1.6. VGSCs underlie the rising phase and propagation of action potentials in neurons and cardiac myocytes. SCN1A, SCN8A, and SCN1B are expressed in both the heart and brain of humans and mice. Because of this, we proposed that cardiac arrhythmias contribute to the mechanism of SUDEP in DEE. We have taken a novel approach to the development of therapeutics for DS in collaboration with Stoke Therapeutics. We employed Targeted Augmentation of Nuclear Gene Output (TANGO) technology, which modulates naturally occurring, non-productive splicing events to increase target gene and protein expression and ameliorate disease phenotype in a mouse model. We identified antisense oligonucleotides (ASOs) that specifically increase the expression of productive Scn1a transcript in human and mouse cell lines, as well as in mouse brain. We showed that a single intracerebroventricular dose of a lead ASO at postnatal day 2 or 14 reduced the incidence of electrographic seizures and SUDEP in the F1:129S-Scn1a+/- x C57BL/6J mouse model of DS. Increased expression of productive Scn1a transcript and NaV1.1 protein were confirmed in brains of treated mice. Our results suggest that TANGO may provide a unique, gene-specific approach for the treatment of DS.

Human vision is full of puzzles. Observers can grasp the essence of a scene in an instant, yet when probed for details they are at a loss. People have trouble finding their keys, yet they may be quite visible once found. How does one explain this combination of marvelous successes with quirky failures? I will describe our attempts to develop a unifying theory that brings a satisfying order to multiple phenomena. One key is to understand peripheral vision. A visual system cannot process everything with full fidelity, and therefore must lose some information. Peripheral vision must condense a mass of information into a succinct representation that nonetheless carries the information needed for vision at a glance. We have proposed that the visual system deals with limited capacity in part by representing its input in terms of a rich set of local image statistics, where the local regions grow — and the representation becomes less precise — with distance from fixation. This scheme trades off computation of sophisticated image features at the expense of spatial localization of those features. What are the implications of such an encoding scheme? Critical to our understanding has been the use of methodologies for visualizing the equivalence classes of the model. These visualizations allow one to quickly see that many of the puzzles of human vision may arise from a single encoding mechanism. They have suggested new experiments and predicted unexpected phenomena. Furthermore, visualization of the equivalence classes has facilitated the generation of testable model predictions, allowing us to study the effects of this relatively low-level encoding on a wide range of higher-level tasks. Peripheral vision helps explain many of the puzzles of vision, but some remain. By examining the phenomena that cannot be explained by peripheral vision, we gain insight into the nature of additional capacity limits in vision. In particular, I will suggest that decision processes face general-purpose limits on the complexity of the tasks they can perform at a given time.

One of Tali's cherished goals was to transform biology into physics. In his view, biologists were far too enamored by the details of the specific models they studied, losing sight of the big principles that may govern the behavior of these models. One such big principle that he suggested was the 'information bottleneck (IB) principle'. The iIB principle is an information-theoretical approach for extracting the relevant information that one random variable carries about another. Tali applied the IB principle to numerous problems in biology, gaining important insights in the process. Here I will describe two applications of the IB principle to neurobiological data. The first is the formalization of the notion of surprise that allowed us to rigorously estimate the memory duration and content of neuronal responses in auditory cortex, and the second is an application to behavior, allowing us to estimate 'optimal policies under information constraints' that shed interesting light on rat behavior.

Tali's work emphasized the tradeoff between compression and information preservation. In this talk I will explore this theme in the context of deep learning. Artificial neural networks have recently revolutionized the field of machine learning. However, we still do not have sufficient theoretical understanding of how such models can be successfully learned. Two specific questions in this context are: how can neural nets be learned despite the non-convexity of the learning problem, and how can they generalize well despite often having more parameters than training data. I will describe our recent work showing that gradient-descent optimization indeed leads to 'simpler' models, where simplicity is captured by lower weight norm and in some cases clustering of weight vectors. We demonstrate this for several teacher and student architectures, including learning linear teachers with ReLU networks, learning boolean functions and learning convolutional pattern detection architectures.

Smooth pursuit eye movements are among our most intriguing motor behaviors. They are able to keep the line of sight on smoothly moving targets with little or no overt effort or deliberate planning, and they can respond quickly and accurately to changes in the trajectory of motion of targets. Nevertheless, despite these seeming automatic characteristics, pursuit is highly sensitive to high-level factors, such as the choices made about attention, or beliefs about the direction of upcoming motion. Investigators have struggled for decades with the problem of incorporating both high- and low-level processes into a single coherent model. This talk will present an overview of the current state of efforts to incorporate high- and low-level influences, as well as new observations that add to our understanding of both types of influences. These observations (in contrast to much of the literature) focus on the directional properties of pursuit. Studies will be presented that show: (1) the direction of smooth pursuit made to pursue fields of noisy random dots depends on the relative reliability of the sensory signal and the expected motion direction; (2) smooth pursuit shows predictive responses that depend on the interpretation of cues that signal an impending collision; and (3) smooth pursuit during a change in target direction displays kinematic properties consistent with the well-known two-thirds power law. Implications for incorporating high- and low-level factors into the same framework will be discussed.

The human gut microbiome has emerged as a key player in the bidirectional communication of the gut-brain axis, affecting various aspects of homeostasis and pathophysiology. Until recently, the majority of studies that seek to explore the mechanisms underlying the microbiome-gut-brain axis cross-talk relied almost exclusively on animal models, and particularly gnotobiotic mice. Despite the great progress made with these models, various limitations, including ethical considerations and interspecies differences that limit the translatability of data to human systems, pushed researchers to seek for alternatives. Over the past decades, the field of in vitro modelling of tissues has experienced tremendous growth, thanks to advances in 3D cell biology, materials, science and bioengineering, pushing further the borders of our ability to more faithfully emulate the in vivo situation. Organ-on-chip technology and bioengineered tissues have emerged as highly promising alternatives to animal models for a wide range of applications. In this talk I’ll discuss our progress towards generating a complete platform of the human microbiota-gut-brain axis with integrated monitoring and sensing capabilities. Bringing together principles of materials science, tissue engineering, 3D cell biology and bioelectronics, we are building advanced models of the GI and the BBB /NVU, with real-time and label-free monitoring units adapted in the model architecture, towards a robust and more physiologically relevant human in vitro model, aiming to i) elucidate the role of microbiota in the gut-brain axis communication, ii) to study how diet and impaired microbiota profiles affect various (patho-)physiologies, and iii) to test personalised medicine approaches for disease modelling and drug testing.

All mammals transition from breastfeeding to independent feeding during the lactation period. In humans and other mammals, this critical transition is important for later in life metabolic control and, consequently, for the development of many chronic conditions. Here, Dr. Dietrich will discuss the work of his lab studying the function of hypothalamic neurons involved in homeostatic control during the transition from breastfeeding to independent feeding. His work illuminates novel properties of hypothalamic neurons in early life, suggesting mechanisms by which early life events shape homeostatic regulation throughout the individual’s lifespan.

The mammalian retina is considered an autonomous neuronal tissue, yet there is evidence that it receives inputs from the brain in the form of retinopetal axons. A sub-population of these axons was suggested to belong to histaminergic neurons located in the tuberomammillarynucleus (TMN) of the hypothalamus. Using viral injections to the TMN, we identified these retinopetal axons and found that although few in number, they extensively branch to cover a large portion of the retina. Using Ca2+ imaging and electrophysiology, we show that histamine application increases spontaneous firing rates and alters the light responses of a significant portion of retinal ganglion cells (RGCs). Direct activation of the histaminergic axons also induced significant changes in RGCs activity. Since activity in the TMN was shown to correlate with arousal state, our data suggest the retinal code may change with the animal's behavioral state through the release of histamine from TMN histaminergic neurons.

Extensive research has revealed that the hippocampus and entorhinal cortex maintain a rich representation of space through the coordinated activity of place cells, grid cells, and other spatial cell types. Frequently described as a ‘cognitive map’ or a ‘hippocampal map’, these maps are thought to support episodic memory through their instantiation and retrieval. Though often a useful and intuitive metaphor, a map typically evokes a static representation of the external world. However, the world itself, and our experience of it, are intrinsically dynamic. In order to make the most of their maps, a navigator must be able to adapt to, incorporate, and overcome these dynamics. Here I describe three projects where we address how hippocampal and entorhinal representations do just that. In the first project, I describe how boundaries dynamically anchor entorhinal grid cells and human spatial memory alike when the shape of a familiar environment is changed. In the second project, I describe how the hippocampus maintains a representation of the recent past even in the absence of disambiguating sensory and explicit task demands, a representation which causally depends on intrinsic hippocampal circuitry. In the third project, I describe how the hippocampus preserves a stable representation of context despite ongoing representational changes across a timescale of weeks. Together, these projects highlight the dynamic and adaptive nature of our hippocampal and entorhinal representations, and set the stage for future work building on these techniques and paradigms.

Anjan Chatterjee is a Professor of Neurology, Psychology, and Architecture and the founding Director of the Penn Center for Neuroaesthetics. His research explores the field of neuroaesthetics: how our brain experiences and responds to art. Lucas Kelly is a renowned visual artist, with work featured across several solo and group exhibitions, most notably in the survey of abstract painting “The Painted World” at PS1 Museum of Modern Art. As the inaugural Artist in Residence for the Penn Center for Neuroaesthetics, Lucas has collaborated with Anjan on a forthcoming exhibition, considering the emotions involved in aesthetic engagement informed by research. This event will feature a moderated conversation between Anjan and Lucas, discussing topics at the intersection of neuroscience and experience of visual art.

A major challenge of cognitive neuroscience is to understand how the brain as a network gives rise to our cognition. Simultaneous [18F]-fluorodeoxyglucose positron emission tomography functional magnetic resonance imaging (FDG-PET/fMRI) provides the opportunity to investigate brain connectivity not only via spatially distant, synchronous cerebrovascular hemodynamic responses (functional connectivity), but also glucose metabolism (metabolic connectivity). However, how these two modalities of brain connectivity differ in their relation to cognition is unknown. In this webinar, Dr Katharina Voigt will discuss recent findings demonstrating the advantage of simultaneous FDG-PET/fMRI in providing a more complete picture of the neural mechanisms underlying cognition, that calls for a combination of both modalities in future cognitive neuroscience. Dr Katharina Voigt is a Research Fellow within the Turner Institute for Brain and Mental Health, Monash University. Her research interests include systems neuroscience, simultaneous PET-MRI, and decision-making.