Seminars

“Of pain you could wish only one thing: that it should stop. Nothing in the world was so bad as physical pain. In the face of pain there are no heroes.- George Orwell, ‘1984’ " "Neuroscience has revealed the secrets of the brain and nervous system to an extent that was beyond the realm of imagination just 10-20 years ago, let alone in 1949 when Orwell wrote his prophetic novel. Understanding pain, however, presents a unique challenge to academia, industry and medicine, being both a measurable physiological process as well as deeply personal and subjective. Given the millions of people who suffer from pain every day, wishing only, “that it should stop”, the need to find more effective treatments cannot be understated." "‘New treatments for pain’ will bring together approximately 120 people from the commercial, academic, and not-for-profit sectors to share current knowledge, identify future directions, and enable collaboration, providing delegates with meaningful and practical ways to accelerate their own work into developing treatments for pain.

Drosophila olfactory sensory hairs ("sensilla") typically house two olfactory receptor neurons (ORNs) which can laterally inhibit each other via electrical ("ephaptic") coupling. ORN pairing is highly stereotyped and genetically determined. Thus, olfactory signals arriving in the Antennal Lobe (AL) have been pre-processed by a fixed and shallow network at the periphery. To uncover the functional significance of this organization, we developed a nonlinear phenomenological model of asymmetrically coupled ORNs responding to odor mixture stimuli. We derived an analytical solution to the ORNs’ dynamics, which shows that the peripheral network can extract the valence of specific odor mixtures via transient amplification. Our model predicts that for efficient read-out of the amplified valence signal there must exist specific patterns of downstream connectivity that reflect the organization at the periphery. Analysis of AL→Lateral Horn (LH) fly connectomic data reveals evidence directly supporting this prediction. We further studied the effect of ephaptic coupling on olfactory processing in the AL→Mushroom Body (MB) pathway. We show that stereotyped ephaptic interactions between ORNs lead to a clustered odor representation of glomerular responses. Such clustering in the AL is an essential assumption of theoretical studies on odor recognition in the MB. Together our work shows that preprocessing of olfactory stimuli by a fixed and shallow network increases sensitivity to specific odor mixtures, and aids in the learning of novel olfactory stimuli. Work led by Palka Puri, in collaboration with Chih-Ying Su and Shiuan-Tze Wu.

Adolescent development is not only shaped by the mere passing of time and accumulating experience, but it also depends on pubertal timing and the cascade of maturational processes orchestrated by gonadal hormones. Although individual variability in puberty onset confounds adolescent studies, it has not been efficiently controlled for. Here we introduce ultrasonic bone age assessment to estimate biological maturity and disentangle the independent effects of chronological and biological age on adolescent cognitive abilities, emotional development, and brain maturation. Comparing cognitive performance of participants with different skeletal maturity we uncover the impact of biological age on both IQ and specific abilities. With respect to emotional development, we find narrow windows of highest vulnerability determined by biological age. In terms of neural development, we focus on the relevance of neural states unrelated to sensory stimulation, such as cortical activity during sleep and resting states, and we uncover a novel anterior-to-posterior pattern of human brain maturation. Based on our findings, bone age is a promising biomarker of adolescent maturity.

Language appears to be the most complex system of animal communication described to date. However, its precursors were present in the communication of our evolutionary ancestors and are likely shared by our modern ape cousins. All great apes, including humans, employ a rich repertoire of vocalizations, facial expressions, and gestures. Great ape gestural repertoires are particularly elaborate, with ape species employing over 80 different gesture types intentionally: that is towards a recipient with a specific goal in mind. Intentional usage allows us to ask not only what information is encoded in ape gestures, but what do apes mean when they use them. I will discuss recent research on ape gesture, on how we approach the question of decoding meaning, and how with new methods we are starting to integrate long overlooked aspects of ape gesture such as group and individual variation, and expression and emotion into our study of these signals.

The hypothalamus controls diverse homeostatic functions including fertility. Neural episode generators are required to drive the intermittent pulsatile and surge profiles of reproductive hormone secretion that control gonadal function. Studies in genetic mouse models have been fundamental in defining the neural circuits forming these central pattern generators and the full range of in vitro and in vivo optogenetic and chemogenetic methodologies have enabled investigation into their mechanism of action. The seminar will outline studies defining the hypothalamic “GnRH pulse generator network” and current understanding of its operation to drive pulsatile hormone secretion.

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. The exact nature of this process represents a classic example of neural computation and has been a longstanding question in the field. Our results obtained in the fruit fly Drosophila 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.

Francis Halzen explains how the IceCube telescope uses detectors buried deep in Antarctic ice to observe high-energy neutrinos from the cosmos. The lecture describes construction of the observatory, the measurement of particle energies and directions, and the distinction between atmospheric backgrounds and neutrinos arriving from distant astrophysical sources. It connects the discovery of cosmic neutrinos with the search for the powerful accelerators that produce cosmic rays. Examples include the association with a flaring blazar and evidence identifying the active galaxy NGC 1068 as a neutrino source. Halzen discusses the role of rotating supermassive black holes, coordination with other observatories, and prospects for extending the detector.

The ability to record large neural populations—hundreds to thousands of cells simultaneously—is a defining feature of modern systems neuroscience. Aside from improved experimental efficiency, what do these technologies fundamentally buy us? I'll argue that they provide an exciting opportunity to move beyond studying the "average" neural response. That is, by providing dense neural circuit measurements in individual subjects and moments in time, these recordings enable us to track changes across repeated behavioral trials and across experimental subjects. These two forms of variability are still poorly understood, despite their obvious importance to understanding the fidelity and flexibility of neural computations. Scientific progress on these points has been impeded by the fact that individual neurons are very noisy and unreliable. My group is investigating a number of customized statistical models to overcome this challenge. I will mention several of these models but focus particularly on a new framework for quantifying across-subject similarity in stochastic trial-by-trial neural responses. By applying this method to noisy representations in deep artificial networks and in mouse visual cortex, we reveal that the geometry of neural noise correlations is a meaningful feature of variation, which is neglected by current methods (e.g. representational similarity analysis).

Graphics processing units (GPUs) are widely available and have been used with great success to accelerate scientific computing in the last decade. These advances, however, are often not available to researchers interested in simulating spiking neural networks, but lacking the technical knowledge to write the necessary low-level code. Writing low-level code is not necessary when using the popular Brian simulator, which provides a framework to generate efficient CPU code from high-level model definitions in Python. Here, we present Brian2CUDA, an open-source software that extends the Brian simulator with a GPU backend. Our implementation generates efficient code for the numerical integration of neuronal states and for the propagation of synaptic events on GPUs, making use of their massively parallel arithmetic capabilities. We benchmark the performance improvements of our software for several model types and find that it can accelerate simulations by up to three orders of magnitude compared to Brian’s CPU backend. Currently, Brian2CUDA is the only package that supports Brian’s full feature set on GPUs, including arbitrary neuron and synapse models, plasticity rules, and heterogeneous delays. When comparing its performance with Brian2GeNN, another GPU-based backend for the Brian simulator with fewer features, we find that Brian2CUDA gives comparable speedups, while being typically slower for small and faster for large networks. By combining the flexibility of the Brian simulator with the simulation speed of GPUs, Brian2CUDA enables researchers to efficiently simulate spiking neural networks with minimal effort and thereby makes the advancements of GPU computing available to a larger audience of neuroscientists.

We hardly notice when there is a speck on our glasses, the obstructed visual information seems to be magically filled in. The mechanistic basis for this fundamental perceptual phenomenon has, however, remained obscure. What enables neurons in the visual system to respond to context when the stimulus is not available? While feedforward information drives the activity in cortex, feedback information is thought to provide contextual signals that are merely modulatory. We have made the discovery that mouse primary visual cortical neurons are strongly driven by feedback projections from higher visual areas when their feedforward sensory input from the retina is missing. This drive is so strong that it makes visual cortical neurons fire as much as if they were receiving a direct sensory input. These signals are likely used to predict input from the feedforward pathway. Preliminary results show that these feedback projections are strongly influenced by experience and learning.

Epilepsy is a common and disabling neurologic condition affecting adults and children that results from complex dysfunction of neural networks and is ineffectively treated with current therapies in up to one third of patients. This dysfunction can have especially severe consequences in pediatric age group, where neurodevelopment may be irreversibly affected. Furthermore, although seizures are the most obvious manifestation of epilepsy, the cognitive and psychiatric dysfunction that often coexists in patients with this disorder has the potential to be equally disabling. Given these challenges, her research program aims to better understand how epileptic activity disrupts the proper development and function of neural networks, with the overall goal of identifying novel biomarkers and systems level treatments for epileptic disorders and their comorbidities, especially those affecting children.

Research in Neuroscience, as in many scientific disciplines, is undergoing a renaissance based on deep learning. Unique to Neuroscience, deep learning models can be used not only as a tool but interpreted as models of the brain. The central claims of recent deep learning-based models of brain circuits are that they shed light on fundamental functions being optimized or make novel predictions about neural phenomena. We show, through the case-study of grid cells in the entorhinal-hippocampal circuit, that one may get neither. We rigorously examine the claims of deep learning models of grid cells using large-scale hyperparameter sweeps and theory-driven experimentation, and demonstrate that the results of such models are more strongly driven by particular, non-fundamental, and post-hoc implementation choices than fundamental truths about neural circuits or the loss function(s) they might optimize. We discuss why these models cannot be expected to produce accurate models of the brain without the addition of substantial amounts of inductive bias, an informal No Free Lunch result for Neuroscience.

Studies of the mouse visual system have revealed a variety of visual brain areas in a roughly hierarchical arrangement, together with a multitude of behavioral capacities, ranging from stimulus-reward associations, to goal-directed navigation, and object-centric discriminations. However, an overall understanding of the mouse’s visual cortex organization, and how this organization supports visual behaviors, remains unknown. Here, we take a computational approach to help address these questions, providing a high-fidelity quantitative model of mouse visual cortex. By analyzing factors contributing to model fidelity, we identified key principles underlying the organization of mouse visual cortex. Structurally, we find that comparatively low-resolution and shallow structure were both important for model correctness. Functionally, we find that models trained with task-agnostic, unsupervised objective functions, based on the concept of contrastive embeddings were substantially better than models trained with supervised objectives. Finally, the unsupervised objective builds a general-purpose visual representation that enables the system to achieve better transfer on out-of-distribution visual, scene understanding and reward-based navigation tasks. Our results suggest that mouse visual cortex is a low-resolution, shallow network that makes best use of the mouse’s limited resources to create a light-weight, general-purpose visual system – in contrast to the deep, high-resolution, and more task-specific visual system of primates.