Seminars

In nature, sensory inputs are often highly structured, and statistical regularities of these signals can be extracted to form expectation about future sensorimotor associations, thereby optimizing behavior. One of the fundamental questions in neuroscience concerns the neural computations that underlie these probabilistic sensorimotor processing. Through a recurrent neural network (RNN) model and human psychophysics and electroencephalography (EEG), the present study investigates circuit mechanisms for processing probabilistic structures of sensory signals to guide behavior. We first constructed and trained a biophysically constrained RNN model to perform a series of probabilistic decision-making tasks similar to paradigms designed for humans. Specifically, the training environment was probabilistic such that one stimulus was more probable than the others. We show that both humans and the RNN model successfully extract information about stimulus probability and integrate this knowledge into their decisions and task strategy in a new environment. Specifically, performance of both humans and the RNN model varied with the degree to which the stimulus probability of the new environment matched the formed expectation. In both cases, this expectation effect was more prominent when the strength of sensory evidence was low, suggesting that like humans, our RNNs placed more emphasis on prior expectation (top-down signals) when the available sensory information (bottom-up signals) was limited, thereby optimizing task performance. Finally, by dissecting the trained RNN model, we demonstrate how competitive inhibition and recurrent excitation form the basis for neural circuitry optimized to perform probabilistic information processing.

Neuronal alternative splicing is a key gene regulatory mechanism in the brain. However, the spliceosome machinery is insufficient to fully specify splicing complexity. In considering the role of the epigenome in activity-dependent alternative splicing, we and others find the histone modification H3K36me3 to be a putative splicing regulator. In this study, we found that mouse cocaine self-administration caused widespread differential alternative splicing, concomitant with the enrichment of H3K36me3 at differentially spliced junctions. Importantly, only targeted epigenetic editing can distinguish between a direct role of H3K36me3 in splicing and an indirect role via regulation of splice factor expression elsewhere on the genome. We targeted Srsf11, which was both alternatively spliced and H3K36me3 enriched in the brain following cocaine self-administration. Epigenetic editing of H3K36me3 at Srsf11 was sufficient to drive its alternative splicing and enhanced cocaine self-administration, establishing the direct causal relevance of H3K36me3 to alternative splicing of Srsf11 and to reward behavior.

Traumatic brain injury (TBI), a leading cause of acquired epilepsy, results in primary cellular injury as well as secondary neurophysiological and inflammatory responses which contribute to epileptogenesis. I will present our recent studies identifying a role for neuro-immune interactions, specifically, the innate immune receptor Toll-like receptor 4 (TLR4), in enhancing network excitability and cell loss in hippocampal dentate gyrus early after concussive brain injury. I will describe results indicating that the transient post-traumatic increases in dentate neurogenesis which occurs during the same early post-injury period augments dentate network excitability and epileptogenesis. I will provide evidence for the beneficial effects of targeting TLR4 and neurogenesis early after brain injury in limiting epileptogenesis. We will discuss potential mechanisms for convergence of the post-traumatic neuro-immune and neurogenic changes and the implications for therapies to reduce neurological deficits and epilepsy after brain injury.

Spike-driven adaptation involves intracellular mechanisms that are initiated by spiking and lead to the subsequent reduction of spiking rate. One of its consequences is the temporal patterning of spike trains, as it imparts serial correlations between interspike intervals in baseline activity. Surprisingly the hidden adaptation states that lead to these correlations themselves exhibit quasi-independence. This talk will first discuss recent findings about the role of such adaptation in suppressing noise and extending sensory detection to weak stimuli that leave the firing rate unchanged. Further, a matching of the post-synaptic responses to the pre-synaptic adaptation time scale enables a recovery of the quasi-independence property, and can explain observations of correlations between post-synaptic EPSPs and behavioural detection thresholds. We then consider the involvement of spike-driven adaptation in the representation of intervals between sensory events. We discuss the possible link of this time-stamping mechanism to the conversion of egocentric to allocentric coordinates. The heterogeneity of the population parameters enables the representation and Bayesian decoding of time sequences of events which may be put to good use in path integration and hilus neuron function in hippocampus.

Psychological essentialism is ubiquitous. It is one of primary bases of thoughts and behaviours throughout our entire lifetime. Human's such characteristics that find an unseen hidden entity behind observable phenomena or exemplars, however, lead us to somehow biased thinking and reasoning even in the realm of science, including psychology. For example, a latent variable extracted from various measurements is just a statistical property calculated in structural equation modeling, therefore, is not necessary to be a fundamental reality. Yet, we occasionally feel that there is the essential nature of such a psychological construct a priori. This talk will demonstrate examples of psychological essentialism in psychology and examine its resultant influences on working memory related issues, e. g., working memory training. Such demonstration, examination, and subsequent discussions on these topics will provide us an opportunity to reconsider the concept of working memory.

Artists have been doing experiments on vision longer than neurobiologists. Some major works of art have provided insights as to how we see; some of these insights are so undamental that they can be understood in terms of the underlying neurobiology. For example, artists have long realized that color and luminance can play independent roles in visual perception. Picasso said, "Colors are only symbols. Reality is to be found in luminance alone." This observation has a parallel in the functional subdivision of our visual systems, where color and luminance are processed by the evolutionarily newer, primate-specific What system, and the older, colorblind, Where (or How) system. Many techniques developed over the centuries by artists can be understood in terms of the parallel organization of our visual systems. I will explore how the segregation of color and luminance processing are the basis for why some Impressionist paintings seem to shimmer, why some op art paintings seem to move, some principles of Matisse's use of color, and how the Impressionists painted "air". Central and peripheral vision are distinct, and I will show how the differences in resolution across our visual field make the Mona Lisa's smile elusive, and produce a dynamic illusion in Pointillist paintings, Chuck Close paintings, and photomosaics. I will explore how artists have figured out important features about how our brains extract relevant information about faces and objects, and I will discuss why learning disabilities may be associated with artistic talent.

Every year around 100,000 people in the UK will have a stroke. Stroke is a leading cause of adult disability, and cerebrovascular disease more broadly is a major cause of dementia. Understanding these diseases – both acute and chronic manifestations of cerebrovascular disease – requires consideration not only of the brain itself, but also the blood vessels supplying it. Atherosclerosis – the hardening of arteries as we age – may predispose to stroke by triggering the formation of blood clots that block the blood supply to the brain, but also involves inflammation that may cause chronic damage to the brain and prime both the brain and body for injury. Understanding this interaction between systemic disease and brain health may have important implications for our understanding of healthy ageing and provide novel therapeutic approaches for reducing the burden of cerebrovascular disease. This talk will consider how advances in imaging may facilitate our understanding of the processes underlying atherosclerosis and how it affects the brain in stroke, as well as work currently underway to translate this understanding into improving treatments for stroke.

Spatial neglect is a syndrome that is most frequently associated with damage to the right hemisphere, although damage to the left hemisphere can also result in signs of spatial neglect. It is characterised by absent or deficient awareness of the contralesional side of space. The screening and diagnosis of spatial neglect lacks a universal gold standard, but is usually achieved by using various modes of assessment. Spatial neglect is also difficult to treat, although prism adaptation training (PAT) has in the past reportedly showed some promise. This seminar will include highlights from a series of studies designed to identify knowledge gaps, and will suggest ways in which these can be bridged. The first study was conducted to identify and quantify clinicians’ use of assessment tools for spatial neglect, finding that several different tools are in use, but that there is an emerging consensus and appetite for harmonisation. The second study included PAT, and sought to uncover whether PAT can improve engagement in recommended therapy in order to improve the outcomes of stroke survivors with spatial neglect. The final study, a systematic review and meta-analysis, sought to investigate the scientific efficacy (rather than clinical effectiveness) of PAT, identifying several knowledge gaps in the existing literature and a need for a new approach in the study of PAT in the clinical setting.

My research interests are centered on animal ecology, and more specifically include the following areas: visual ecology, behavioral ecology, and conservation biology, as well as the interactions between them. My research is question-driven. I answer my questions in a comprehensive manner, using a combination of empirical, theoretical, and comparative approaches. My model species are usually birds, but I have also worked with fish, mammals, amphibians, and insects. ​I was fortunate to enrich my education by attending Universities in different parts of the world. I did my undergraduate, specialized in ecology and biodiversity, at the "Universidad Nacional de Cordoba", Argentina. My Ph.D. was in animal ecology and conservation biology at the "Universidad Complutense de Madrid", Spain. My two post-docs were focused on behavioral ecology; the first one at University of Oxford (United Kingdom), and the second one at University of Minnesota (USA). I was an Assistant Professor at California State University Long Beach for almost six years. I am now a Full Professor of Biological Sciences at Purdue University.

The head direction (HD) system is classically modeled as a ring attractor network which ensures a stable representation of the animal’s head direction. This unidimensional description popularized the view of the HD system as the brain’s internal compass. However, unlike a globally consistent magnetic compass, the orientation of the HD system is dynamic, depends on local cues and exhibits remapping across familiar environments5. Such a system requires mechanisms to remember and align to familiar landmarks, which may not be well described within the classic 1-dimensional framework. To search for these mechanisms, we performed large population recordings of mouse thalamic HD cells using calcium imaging, during controlled manipulations of a visual landmark in a familiar environment. First, we find that realignment of the system was associated with a continuous rotation of the HD network representation. The speed and angular distance of this rotation was predicted by a 2nd dimension to the ring attractor which we refer to as network gain, i.e. the instantaneous population firing rate. Moreover, the 360-degree azimuthal profile of network gain, during darkness, maintained a ‘memory trace’ of a previously displayed visual landmark. In a 2nd experiment, brief presentations of a rotated landmark revealed an attraction of the network back to its initial orientation, suggesting a time-dependent mechanism underlying the formation of these network gain memory traces. Finally, in a 3rd experiment, continuous rotation of a visual landmark induced a similar rotation of the HD representation which persisted following removal of the landmark, demonstrating that HD network orientation is subject to experience-dependent recalibration. Together, these results provide new mechanistic insights into how the neural compass flexibly adapts to environmental cues to maintain a reliable representation of the head direction.

Where in the brain consciousness resides remains unclear. It has been suggested that the subnetworks supporting consciousness should be bidirectionally (recurrently) connected because both feed-forward and feedback processing are necessary for conscious experience. Accordingly, evaluating which subnetworks are bidirectionally connected and the strength of these connections would likely aid the identification of regions essential to consciousness. Here, we propose a method for hierarchically decomposing a network into cores with different strengths of bidirectional connection, as a means of revealing the structure of the complex brain network. We applied the method to a whole-brain mouse connectome. We found that cores with strong bidirectional connections consisted of regions presumably essential to consciousness (e.g., the isocortical and thalamic regions, and claustrum) and did not include regions presumably irrelevant to consciousness (e.g., cerebellum). Contrarily, we could not find such correspondence between cores and consciousness when we applied other simple methods which ignored bidirectionality. These findings suggest that our method provides a novel insight into the relation between bidirectional brain network structures and consciousness. Our recent preprint on this work is here: https://doi.org/10.1101/2021.07.12.452022.

In this workshop you'll learn the fundamentals of PyTorch using an incremental, from-first-principles approach. We'll start with tensors, autograd, and the dynamic computation graph, and then move on to developing and training a simple model using PyTorch's model classes, datasets, data loaders, optimizers, and more. You should be comfortable using Python, Jupyter notebooks, Google Colab, Numpy and, preferably, object oriented programming.

Natural soundscapes such as from a forest, a busy restaurant, or a busy intersection are generally composed of a cacophony of sounds that the brain needs to interpret either independently or collectively. In certain instances sounds - such as from moving cars, sirens, and people talking - are perceived in unison and are recognized collectively as single sound (e.g., city noise). In other instances, such as for the cocktail party problem, multiple sounds compete for attention so that the surrounding background noise (e.g., speech babble) interferes with the perception of a single sound source (e.g., a single talker). I will describe results from my lab on the perception and neural representation of auditory textures. Textures, such as a from a babbling brook, restaurant noise, or speech babble are stationary sounds consisting of multiple independent sound sources that can be quantitatively defined by summary statistics of an auditory model (McDermott & Simoncelli 2011). How and where in the auditory system are summary statistics represented and the neural codes that potentially contribute towards their perception, however, are largely unknown. Using high-density multi-channel recordings from the auditory midbrain of unanesthetized rabbits and complementary perceptual studies on human listeners, I will first describe neural and perceptual strategies for encoding and perceiving auditory textures. I will demonstrate how distinct statistics of sounds, including the sound spectrum and high-order statistics related to the temporal and spectral correlation structure of sounds, contribute to texture perception and are reflected in neural activity. Using decoding methods I will then demonstrate how various low and high-order neural response statistics can differentially contribute towards a variety of auditory tasks including texture recognition, discrimination, and categorization. Finally, I will show examples from our recent studies on how high-order sound statistics and accompanying neural activity underlie difficulties for recognizing speech in background noise.

What is perception? Our sensory modalities transmit information about the external world into electrochemical signals that somehow give rise to our conscious experience of our environment. Normally there is too much information to be processed in any given moment, and the mechanisms of attention focus the limited resources of the mind to some information at the expense of others. My research has advanced from first examining visual perception and attention to now examine how multisensory processing contributes to perception and cognition. There are fundamental constraints on how much information can be processed by the different senses on their own and in combination. Here I will explore information processing from the perspective of sensory substitution and augmentation, and how "seeing" with the ears and tongue can advance fundamental and translational research.

This talk will address important aspects of pediatric migraine research, including: 1) Who is affected by pediatric migraine? 2) What does pediatric migraine look like, and what does a clinician need to do to reach a migraine diagnosis in a child? 3) When does pediatric migraine begin, and how might it present clinically before it presents as headache (e.g., infant colic, benign paroxysmal torticollis, cyclic vomiting syndrome etc.) 4) Where does responsibility for decreasing pediatric migraine frequency rest? What is society's role in preventing migraine in young people?

Human cognitive abilities seem qualitatively different from the cognitive abilities of other primates, a difference Penn, Holyoak, and Povinelli (2008) attribute to role-based relational reasoning—inferences and generalizations based on the relational roles to which objects (and other relations) are bound, rather than just the features of the objects themselves. Role-based relational reasoning depends on the ability to dynamically bind arguments to relational roles. But dynamic binding cannot be sufficient for relational thinking: Some non-human animals solve the dynamic binding problem, at least in some domains; and many non-human species generalize affordances to completely novel objects and scenes, a kind of universal generalization that likely depends on dynamic binding. If they can solve the dynamic binding problem, then why can they not reason about relations? What are they missing? I will present simulations with the LISA model of analogical reasoning (Hummel & Holyoak, 1997, 2003) suggesting that the missing pieces are multi-role integration (the capacity to combine multiple role bindings into complete relations) and structure mapping (the capacity to map different systems of role bindings onto one another). When LISA is deprived of either of these capacities, it can still generalize affordances universally, but it cannot reason symbolically; granted both abilities, LISA enjoys the full power of relational (symbolic) thought. I speculate that one reason it may have taken relational reasoning so long to evolve is that it required evolution to solve both problems simultaneously, since neither multi-role integration nor structure mapping appears to confer any adaptive advantage over simple role binding on its own.

Cortical functions result from the conjoint activity of different, reciprocally connected areas working together as large-scale functionally specialized networks. In the macaque brain, neural tracers and functional data have provided evidence for functionally specialized large-scale cortical networks involving temporal, parietal, and frontal areas. One of these networks, the lateral grasping network, appears to play a primary role in controlling hand action organization and recognition. Available functional and tractograpy data suggest the existence of a human counterpart of this network.

The success of deep learning sparked interest in whether the brain learns by using similar techniques for assigning credit to each synaptic weight for its contribution to the network output. However, the majority of current attempts at biologically-plausible learning methods are either non-local in time, require highly specific connectivity motives, or have no clear link to any known mathematical optimization method. Here, we introduce Deep Feedback Control (DFC), a new learning method that uses a feedback controller to drive a deep neural network to match a desired output target and whose control signal can be used for credit assignment. The resulting learning rule is fully local in space and time and approximates Gauss-Newton optimization for a wide range of feedback connectivity patterns. To further underline its biological plausibility, we relate DFC to a multi-compartment model of cortical pyramidal neurons with a local voltage-dependent synaptic plasticity rule, consistent with recent theories of dendritic processing. By combining dynamical system theory with mathematical optimization theory, we provide a strong theoretical foundation for DFC that we corroborate with detailed results on toy experiments and standard computer-vision benchmarks.