Events

Speaker: Prof. William Lytton, SUNY Downstate Health Sciences University

Title: Neurons and synapses working together happily in brain health; not so happily in brain disease

Abstract: At first approximation, we currently think of the brain as a set of neurons as nodes connected by directed edges, akin to the mathematical description of an Erdős–Rényi graph model. It is now time to redirect attention on the individual neurons, the massive complex entities that are often a locus of disease progression and may also be an additional locus of computation.  I will focus on the role of the cortical corticospinal cell in Parkinson's disease (PD) and in migraine/ischemia. In both cases a class of neuron becomes damaged as an effect of disease: the effect becomes a site for the burgeoning disorder.

More generally, I wish to refocus on cell physiology as a basis of brain function. This will help us to better explain how cell pathology produces dysfunction in neurodegenerative disorders such as Alzheimer's, Parkinson's, and mild cognitive impairment. The roles played by particular neuron types in performing the computations that underlie brain function will provide a new Neuron-based Computational Theory (NCT) to complement and augment the current dominant Synapse-based Computational Theory (SCT), which gave us Hebbian/Hopfieldian cell assemblies reified in modern
large-language models (LLMs).

Speaker: Patrick Schöfer,

Department of Neuromorphic Information Processing

University of Leipzig

Title: Boredom as Homeostasis of Cognitive Resource Utilization using Spiking Neural Networks

Abstract: In this talk, I will present our approach to modelling boredom as a homeostatic mechanism that maintains an optimal level of cognitive engagement. When engagement deviates from this “Goldilocks zone” due to under- or overstimulation, the system dynamically adjusts neural activity to restore balance. Implemented as a control loop in a spiking neural network, the model monitors and regulates simulated cognitive resource utilization through excitation and inhibition.

The purpose of the meeting is to share with all the CTN faculty what we are each up to and interested in, and to hear from some of the graduate students in each lab.

Location: DC 1304 

Title: Sensory expectations shape neural population dynamics in motor circuits

Abstract: The neural basis of movement preparation has been extensively studied during self-initiated actions where motor cortical activity during preparation shows a lawful relationship to the parameters of the subsequent action. However, movements are regularly triggered or corrected based on sensory inputs caused by disturbances to the body. Since such disturbances are often predictable and since preparing for disturbances would make movements better, we hypothesized that expectations about sensory inputs also influence preparatory activity in motor circuits. Here we show that when humans and monkeys are probabilistically cued about the direction of future mechanical perturbations, they incorporate sensory expectations into their movement preparation and improve their corrective responses. Using high-density neural recordings, we establish that sensory expectations are widespread across the brain, including the motor cortical areas involved in preparing self-initiated actions. The geometry of these preparatory signals in the neural population state is simple, directly scaling with the probability of each perturbation direction. After perturbation onset, a condition-independent signal shifts the neural state leading to rapid responses that initially reflect sensory expectations. Based on neural networks coupled to a biomechanical model of the arm, we show that this neural geometry emerges only when sensory inputs signal that a perturbation has occurred before resolving the direction of the perturbation. Thus, just as preparatory activity sets the stage for self-initiated movement, it also configures motor circuits to respond efficiently to sensory inputs.

Room: DC1304

Title: Why Simplicity Enables Intelligence: Efficient Coding in Human Learning and Generalization

Abstract:

Human intelligence depends critically on the ability to learn representations that generalize beyond past experience. While reinforcement learning theory formalizes how agents should act to maximize reward, it provides little guidance on how internal representations should be structured to support generalization. In this talk, I propose that efficient coding provides a unifying representational principle. When agents are constrained to use the simplest representations compatible with reward maximization, they are forced to discover abstract structure in the environment and to selectively encode features that matter for behaviour. I present a computational framework in which efficient coding augments the classical reinforcement learning objective, leading to compact internal state spaces that support robust generalization. Behavioural experiments show that this framework accounts for human generalization patterns that standard models struggle to explain. I further demonstrate that the same principle explains long-standing regularities in perceptual generalization, including the universal law of generalization. These results suggest that abstraction, generalization, and perceptual similarity arise from a common normative pressure to efficiently encode information under resource constraints.

Four internationally renowned speakers join us to present lectures from each of the perspectives of neuroscience, computational neuroscience, psychology, and philosophy on the ideas of mind, brain, theories, and models.

This is a free event, and we have a marvellous lineup of speakers. The CTN looks forward to greeting you all on April 7, 2026