Seminars

Animals modify their behavioral outputs in response to changes in external and internal environments. We use the nematode, C. elegans to probe the pathways linking changes in internal states like hunger with behavior. We find that acute food deprivation alters the localization of two transcription factors, likely releasing an insulin-like peptide from the intestine, which in turn modifies chemosensory neurons and alters behavior. These results present a model for how inter-tissue signals to generate flexible behaviors via gut-brain signaling.

How could machines learn as efficiently as humans and animals? How could machines learn to reason and plan? How could machines learn representations of percepts and action plans at multiple levels of abstraction, enabling them to reason, predict, and plan at multiple time horizons? I will propose a possible path towards autonomous intelligent agents, based on a new modular cognitive architecture and a somewhat new self-supervised training paradigm. The centerpiece of the proposed architecture is a configurable predictive world model that allows the agent to plan. Behavior and learning are driven by a set of differentiable intrinsic cost functions. The world model uses a new type of energy-based model architecture called H-JEPA (Hierarchical Joint Embedding Predictive Architecture). H-JEPA learns hierarchical abstract representations of the world that are simultaneously maximally informative and maximally predictable. The corresponding working paper is available here:https://openreview.net/forum?id=BZ5a1r-kVsf

Donna Blackmond examines two linked problems in the origin of life: how an initially balanced mixture of molecular mirror images could acquire a handedness bias, and how that small bias could be amplified. She compares four experimental approaches using chemical kinetics and solid–solution equilibria. The first is asymmetric autocatalysis, including the Soai reaction and kinetic models connecting self-replication with suppression of the opposite enantiomer. Blackmond distinguishes this powerful model from a demonstrated prebiotic pathway. She then discusses amplification through eutectic partitioning and through attrition-enhanced deracemization, where dissolution, crystal growth and interconversion can produce a single-handed solid. The final approach incorporates stereochemical selection into plausible prebiotic chemistry. Examples explore reciprocal enrichment involving sugars and amino-acid precursors. The lecture argues that accounts of how biological building blocks arose must explain their chirality alongside their synthesis.

Within the vertebrate neocortex and other telencephalic structures, molecularly-defined neurons tend to segregate at first order into GABAergic types and glutamatergic types. Two fundamental questions arise: (1) do non-telencephalic neurons similarly segregate by neurotransmitter status, and (2) do GABAergic (or glutamatergic) types sampled in different structures share many molecular features in common, beyond the few genes directly responsible for neurotransmitter synthesis and release? To address these questions, we used single-nucleus RNA sequencing, analyzing over 2.4 million brain cells sampled from 16 locations in a primate (the common marmoset). Unexpectedly, we find the answer to both is “no”. I will discuss implications for generalizing associations between neurotransmitter utilization and other phenotypes, and share ongoing efforts to map the biodistributions of cell types in the primate brain.

The Aging Brain Initiative (ABI) is an interdisciplinary effort by MIT focusing on understanding neurodegeneration and discovery efforts to find hallmarks of aging, both in health and disease." "The Alana Down Syndrome Center (ADSC) aims to deepen knowledge about Down syndrome and to improve health, autonomy and inclusion of people with this genetic condition." "The ABI and the ADSC have joined forces for this year's symposium to highlight how aging-related changes to the brain overlap with neurological aspects of Down syndrome. Our hope is to encourage greater collaboration between the brain aging and Down syndrome research communities.

Vocabulary learning applications like Duolingo have millions of users around the world, but yet are based on very simple heuristics to choose teaching material to provide to their users. In this presentation, we will discuss the possibility to develop more advanced artificial teachers, which would be based on modeling of the learner’s inner characteristics. In the case of teaching vocabulary, understanding how the learner memorizes is enough. When it comes to picking grammar exercises, it becomes essential to assess how the learner reasons, in particular by analogy. This second application will illustrate how analogical and case-based reasoning can be employed in an alternative way in education: not as the teaching algorithm, but as a part of the learner’s model.

How seizures emerge from the abnormal dynamics of neural networks within the epileptogenic tissue remains an enigma. Are seizures random events, or do detectable changes in brain dynamics precede them? Are mechanisms of seizure emergence identical at the onset and later stages of epilepsy? Is the risk of seizure occurrence stable, or does it change over time? A myriad of questions about seizure genesis remains to be answered to understand the core principles governing seizure genesis. The last decade has brought unprecedented insights into the complex nature of seizure emergence. It is now believed that seizure onset represents the product of the interactions between the process of a transition to seizure, long-term fluctuations in seizure susceptibility, epileptogenesis, and disease progression. During the lecture, we will review the latest observations about mechanisms of ictogenesis operating at multiple temporal scales. We will show how the latest observations contribute to the formation of a comprehensive theory of seizure genesis, and challenge the traditional perspectives on ictogenesis. Finally, we will discuss how combining conventional approaches with computational modeling, modern techniques of in vivo imaging, and genetic manipulation open prospects for exploration of yet hidden mechanisms of seizure genesis.

To understand neural computation and the dynamics in the brain, we usually focus on the connectivity among neurons. In contrast, the properties of single neurons are often thought to be negligible, at least as far as the activity of networks is concerned. In this talk, I will contradict this notion and demonstrate how the biophysics of action-potential generation can have a decisive impact on network behaviour. Our recent theoretical work shows that, among regularly firing neurons, the somewhat unattended homoclinic type (characterized by a spike onset via a saddle homoclinic orbit bifurcation) particularly stands out: First, spikes of this type foster specific network states - synchronization in inhibitory and splayed-out/frustrated states in excitatory networks. Second, homoclinic spikes can easily be induced by changes in a variety of physiological parameters (like temperature, extracellular potassium, or dendritic morphology). As a consequence, such parameter changes can even induce switches in network states, solely based on a modification of cellular voltage dynamics. I will provide first experimental evidence and discuss functional consequences of homoclinic spikes for the design of efficient pattern-generating motor circuits in insects as well as for mammalian pathologies like febrile seizures. Our analysis predicts an interesting role for homoclinic action potentials as an integral part of brain dynamics in both health and disease.

Research in the brain and cognitive sciences attempts to uncover the neural mechanisms underlying intelligent behavior in domains such as vision. Due to the complexities of brain processing, studies necessarily had to start with a narrow scope of experimental investigation and computational modeling. I argue that it is time for our field to take the next step: build system models that capture a range of visual intelligence behaviors along with the underlying neural mechanisms. To make progress on system models, we propose integrative benchmarking – integrating experimental results from many laboratories into suites of benchmarks that guide and constrain those models at multiple stages and scales. We show-case this approach by developing Brain-Score benchmark suites for neural (spike rates) and behavioral experiments in the primate visual ventral stream. By systematically evaluating a wide variety of model candidates, we not only identify models beginning to match a range of brain data (~50% explained variance), but also discover that models’ brain scores are predicted by their object categorization performance (up to 70% ImageNet accuracy). Using the integrative benchmarks, we develop improved state-of-the-art system models that more closely match shallow recurrent neuroanatomy and early visual processing to predict primate temporal processing and become more robust, and require fewer supervised synaptic updates. Taken together, these integrative benchmarks and system models are first steps to modeling the complexities of brain processing in an entire domain of intelligence.

Biologically plausible spiking neural networks (SNNs) are an emerging architecture for deep learning tasks due to their energy efficiency when implemented on neuromorphic hardware. However, many of the biological features are at best irrelevant and at worst counterproductive when evaluated in the context of task performance and suitability for neuromorphic hardware. In this talk, I will present an alternative paradigm to design deep learning architectures with good task performance in real-world benchmarks while maintaining all the advantages of SNNs. We do this by focusing on two main features -- event-based computation and activity sparsity. Starting from the performant gated recurrent unit (GRU) deep learning architecture, we modify it to make it event-based and activity-sparse. The resulting event-based GRU (EGRU) is extremely efficient for both training and inference. At the same time, it achieves performance close to conventional deep learning architectures in challenging tasks such as language modelling, gesture recognition and sequential MNIST

The Aging Brain Initiative (ABI) is an interdisciplinary effort by MIT focusing on understanding neurodegeneration and discovery efforts to find hallmarks of aging, both in health and disease." "The Alana Down Syndrome Center (ADSC) aims to deepen knowledge about Down syndrome and to improve health, autonomy and inclusion of people with this genetic condition." "The ABI and the ADSC have joined forces for this year's symposium to highlight how aging-related changes to the brain overlap with neurological aspects of Down syndrome. Our hope is to encourage greater collaboration between the brain aging and Down syndrome research communities.

Theory of mind, which consists of reasoning about the knowledge, belief, desire, and similar mental states of others, is a key component of social reasoning and social interaction. While it has been studied by cognitive scientists for decades, none of the prevailing theories of the processes that underlie theory of mind reasoning and development explain the breadth of experimental findings. I propose that this is because theory of mind is, like much of human reasoning, inherently analogical. In this talk, I will discuss several theory of mind findings from the psychology literature, the challenges they pose for our understanding of theory of mind, and bring in evidence from the Analogical Theory of Mind (AToM) cognitive model that demonstrates how these findings fit into an analogical understanding of theory of mind reasoning.

The peripheral airways are technically challenging to assess and have been overlooked in the assessment of chronic respiratory diseases such as Asthma, in both the clinical and research space. Evidence of the importance of the small airways in Asthma is building, and small airways dysfunction is implicated in poor Asthma control, airway hyperresponsiveness, and exacerbation risk. The aim of this research was to complete comprehensive global, regional, and spatial assessments of airway function and ventilation in Asthma using physiological and MRI techniques. Specific ventilation imaging (SVI) and Phase resolved functional lung imaging (PREFUL) formed the spatial assessments. SVI uses oxygen as a contrast agent and looks at rate of change in signal to assess ventilation heterogeneity, PREFUL is a completely contrast free technique that uses Fourier decomposition to determine fractional ventilation.

Academic bullying is a serious issue that affects all disciplines and people of all levels of experience. To create a truly safe, productive, and vibrant environment in academia requires coordinated and collaborative input as well as the action of a variety of stakeholders, including scholarly communities, funding agencies, and institutions. This talk will focus on a framework of integrated responding, in which stakeholders as responsible and response-able parties could proactively collaborate and coordinate to reduce the incidence and consequences of academic bullying while at the same time building constructive academic cultures. The outcome of such a framework would be to create novel entities and actions that accelerate successful responses to academic bullying.