Computational models

…connects systems neuroscience with computational models, emphasizing explanations of brain function and exchange between empirical and theoretical researchers. The main meeting is at Fairmont The Queen Elizabeth and workshops are at Le Westin Montréal. Abstract submission is closed. Abstracts use double-blind review, with deci

…multidimensional spectroscopy and computational models will connect molecular variation with physiological function. Natural diversity is examined as a source of mechanistic insight and potential strategies for improving crop and a

Jiaqi Zhang examines how computational models can learn the effects of genetic interventions from single-cell experiments. Such experiments reveal causal relationships, but their high-dimensional measurements are costly to collect and difficult to interpret. The seminar connects identifiable causal representations with a predictive method for previously unseen perturbations. The approach incorporates prior biological knowledge and changes in data distributions to estimate responses at individual-cell resolution. It also uses predictions to guide subsequent experiments. An application identifi

…This talk presents computational models of that early learning and compares them with language-centred AI training. It asks whether the conceptual structures formed before language could provide useful ingredients for artificial learning systems.

…(RL) theory---and fits a small set of computational models---often inspired by RL algorithms---to that dataset. For example, researchers often model human behavior on bandit tasks using variants of Q-learning. While this approach has been highly productive, leading to landmark discoveries such as the dopamine reward prediction error hypothesis, it also has limitations. This talk focuses on the lack of generalizability of such models: Even if they close

A central goal of sensory neuroscience is to build parsimonious computational models that can both predict neural responses to natural stimuli and reveal interpretable functional organization in the brain. Statistical “component” models can learn interpretable, low-dimensional structure across different brain regions and subjects, but lack an explicit “encoding model” that links these components to the stimuli that drive them, and thus cannot generate predictions for new stimuli or generalize across different experiments. The predictive power of sensory encoding models has improved substantial

Integrating information is particularly crucial when decisions contain complex information. In decision neuroscience, it has been widely studied how overall decision value is encoded in the brain, but how information is integrated prior to encoding overall value remains largely unclear. Recently, our neuroimaging study has shown that the lateral frontopolar cortex (FPl), a uniquely well-developed region in the human brain, engaged specifically in complex choice but not in simple choice (Law et al., 2023). Aided by an artificial neural network, we found that FPl involved a parallel processing t

Decision making involves assessing potential options and their expected outcomes. In most laboratory studies, decision making is examined through perceptual discrimination tasks with constant net sensory information, and explained as an accumulation of evidence until a threshold is reached. However, natural behavior involves dynamic, intermittently presented perceptual information, necessitating the use of memory. A recent study indicates that, in such cases, decisions align better with an urgency mechanism, which combines sensory evidence from memory or perception with a growing urgency signa

The ability to flexibly adjust our decision making is crucial for navigating a complex world where optimal decisions depend on the current context. Crucially, it is thought that the bidirectional connection of the prefrontal cortex (PFC) and hippocampus (HPC) through the nucleus reuniens of the thalamus (nRE) is vital for coordinating context-dependent decisions. However, little is known about how interaction of PFC and HPC via nRE guides behaviour. To address this, we used a cued T-maze in mice, where a cue predicts a distal reward location, combined with circuit-specific modulation of nRE. U

…To address this question, we combined computational models of mentalising with a new experimental setup of Guinea baboons living in a social colony, which required no direct human intervention or teaching. Baboons freely came to play a strategic coordination game via touchscreen devices with any other baboon (social condition), or alone (solo condition). In fact, in both conditions, they were interacting with an identical Artificial Agent. The ability to suc

Computational models play a key role in elucidating the complex behaviour of neurons. However, many of these models are tuned to the electrophysiological features of adult neurons, leaving a significant gap in the modelling of immature perinatal neurons. This study aims to bridge this gap by generating a population of computational models representing hippocampal neurons from primary hippocampal cultures of neonatal rat brains and comparing them with models of adult neurons in terms of variability in ion channels and electrophysiological behaviour. Using the TREES toolbox, we reconstructed the

Locomotion is crucial for an animal’s survival. Despite its importance, our understanding of how movement is generated is limited. Zebrafish (Danio rerio) is a commonly used animal to study the spinal control of movement. Many neuronal populations found in zebrafish are evolutionarily conserved across species, including mammals. Even as early as 4-5 days post fertilization, larval zebrafish display a wide range of swimming speeds measured in tail beat frequencies, making it a great model to study locomotor control. Experimental studies have identified several populations of spinal neurons comp

Sustained action potential firing (SAPF) is a feature shared by multiple neuronal types and is crucial for stimulus encoding, neural communication and circuit plasticity. We aimed at unveiling the mechanisms underlying SAPF in Peripheral Sensory Neurons (PSNs), a convenient experimental model for studying single-cell excitability. Current-clamp recordings in primary-cultured PSNs revealed that tonic depolarization elicits Subthreshold Membrane Potential Transients (SMPTs) in 82% of this cells, and statistical analyses indicates that larger and faster SMPTs are causally associated with SAPF. Ph

Avrama Blackwell discusses computational models of chemical signalling inside neurons and their relationship to cellular information processing.

…forms the basis of connectome-based computational models, which combine real human data and theoret

…I will present a series of computational models investigating dynamics of central neuronal interactions as well as a neuromechanical model that integrates neuronal circuits with a model of th

…with the aid of forefront means of computational biomechanics. Our activity in the last years has been devoted to the development of a comprehensive model of the co

…Here we investigate how computational models of neural circuits can express the many behavioral strategies empirically observed in reversal learning. We report that simply introducing more neurons in a neu

…At the same time, computational models are becoming ever more powerful for reproducing behavior and neural activity. The complexities of these advances, however, can make it difficult to reap the benefits of bridging model and experiment, such as accelerating experiment development with in-silico prototyping or validating the output of a simulation against ex

…brain and neuromodulation is to use computational models. Computational models allow researchers to run realistic simulations that show how the brain is influenced by neuromodulation or predict what brain stimulation will change in brain activity and/or behaviour. This talk will introduce how Wilson Cowan,

…decisions and discuss some simple computational models of decision making and the neural basis. Towards the end, we will have a short, interactive session to engage in some easy decisions that will help us discover our own biases.

A principle of modern decision theories is reference dependence, in which the subjective value of a reward is computed relative to an internal reference point that is thought to reflect expectations. Previous work often assumes that reference points are retrospective, deriving directly from past experiences. We used behavioral modeling to show that, instead, rats infer underlying task states in order to switch between multiple reference points. We designed a behavioral paradigm with partially observable states – uncued blocks of trials offering only small rewards (low blocks) or large rewards

Ever since Bliss and Lømo discovered the phenomenon of long-term potentiation (LTP) in rabbit dentate gyrus in the 1960s, Hebb’s rule—neurons that fire together wire together—gained popularity to explain learning and memory. Accumulating evidence, however, suggests that neural activity is homeostatically regulated. Homeostatic mechanisms are mostly interpreted to stabilize network dynamics. However, recent theoretical work has shown that linking the activity of a neuron to its connectivity within the network provides a robust alternative implementation of Hebb’s rule, although entirely based o

…I will discuss evidence from computational models supporting a mix of categorical and fine-detail information in working memory. Having established the need for two memory formats in working memory, I will discuss whether categorical and fine-detailed information for a stimulus are represented separately or as a single unified representation. Computational models of these two potential