INTRODUCTION
The Barccsyn Seminar Cycle, funded by the SGR “Network Dynamics”, aims to invite researchers from abroad in computational and systems neuroscience to Barcelona. We are excited to hear about their own work, as well as to have them get to know the growing Barccsyn community. In this vein we encourage our invited speakers to spend enough time in Barcelona to visit several labs, ideally one day and a half.
next session
Date: Friday, January 17th, 2025
Time: TBP
Place: Centre de Recerca Matemàtica
TBP
Abstract:
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N Alex Cayco Gajic
École Normale Supérieure
I am an applied mathematician working at the intersection of systems neuroscience and machine learning. I develop and apply data-driven dynamical systems and dimensionality reduction tools to questions in neural and behavioral learning.
Currently a Junior Professor in the Group for Neural Theory at the École Normale Supérieure in Paris, I received my PhD in applied mathematics at the University of Washington (under the supervision of Eric Shea Brown) where my dissertation focused on how the statistics of neural activity impact population coding. I then joined Angus Silver‘s lab in University College London to study learning in the cerebellum and get my hands dirty with data analysis of large-scale neural data.
My faculty research integrates these two sources of training with an aim to identify the fundamental principles of how task-relevant neural dynamics emerge over learning.
Personal website
A shared neural circuit for maintenance and integration of information over time
Abstract:
Working memory and decision-making are two higher cognitive functions for which detailed neurobiological models have been developed in recent years. Prominent classes of these models share key features – generation of persistent neural activity through recurrent excitation within stimulus-selective populations of neurons, and inhibition between these populations – which has led to the suggestion that both maintenance (for working memory) and integration (for deliberative decision-making) of information over time are implemented within the same neural circuits. I will present new lines of convergent evidence that support this idea. I will show how recurrent neural networks trained to perform both working memory and decision-making tasks recapitulate the same basic circuit configuration and activation patterns as the hand-crafted neurobiological models, and that memory and decision states within these trained networks share the same population code. I will present non-invasive electrophysiological data suggesting that humans exploit this same solution, highlighting a generalization of the task-relevant neural representations across tasks. I will also suggest a mechanism – dynamic modulation by brainstem arousal systems – by which the underlying shared circuit might be tuned to produce stable memory states that are robust to distraction in some contexts, versus flexible decision states reflecting integration of multiple information samples in others. This work promotes an integrative perspective of working memory and decision-making that holds promise for understanding disorders of the brain that are characterized by deficits in both functions.
Peter Murphy
Maynooth University, Ireland
I investigate how brains encode information about the world, maintain that information over time, and combine it in ways that allow us to make good decisions. I also study how these cognitive functions are altered in psychiatric and aging populations. To tackle these questions, I ask human participants to perform simple tasks that allow me to measure these functions in a controlled way; I usually measure brain activity from these participants through non-invasive methods (EEG, MEG, fMRI); I sometimes manipulate specific brain systems through pharmacological intervention; and I almost always monitor changes in each individual’s state of arousal by measuring their pupil size. I also use computational models to try to understand how the behaviour that participants produce on my tasks might emerge from the brain activity that I observe
Personal website
Future sessions
Date: Monday, February 17th, 2025
Time: TBP
Place: TBP
Probing economic decision preferences in mice
Abstract:
Real life decisions often occur in volatile environments and require strategic interaction between multiple decision makers. Imagine decisions at a poker table or foraging decisions in a competitive, changing environment. In both situations, animals make decisions based on incomplete information, under risk and uncertainty, and in a multi-agent social context. Due to the multiplexed and dynamic nature of these types of decisions, there are usually no uniquely correct answers. Instead, choices reflect individuals’ varied decision preferences that lead to differential short-term and long-term gains. Our goal is to understand how animals make flexible decisions under risk and social influence, and the neural circuit mechanisms underlying these choices. Towards this goal, we combine theory-motivated behavioural designs in mice, quantitative extraction of animals’ internal states, large-scale, cellular-resolution monitoring and manipulation of brain activity during decision tasks, and computational modelling. In this talk, I will present our progress in probing the behavioural and neural mechanisms for value-based decision-making under risk and in a multi-agent context.
Chunyu Ann Duan
Sainsbury Wellcome Centre - University College London
Ann Duan joined the faculty at the Sainsbury Wellcome Centre in summer 2021. She obtained her PhD in Neuroscience at Princeton University, where she studied prefrontal and collicular contributions to executive functions with Carlos Brody. In 2016, Ann became a Simons Collaboration on the Global Brain postdoctoral fellow in Ning-long Xu’s lab at the Institute of Neuroscience in Shanghai, where she used circuit-level tools to investigate cortico-subcortical cooperation during decision formation and maintenance.
organizers
Albert Compte | IDIBAPS
Ignasi Cos | UB
Alex Hyafil | CRM
Ernest Montbrió | UPF
Jaime de la Rocha | IDIBAPS
Alex Roxin | CRM
Klaus Wimmer | CRM
PAST SESSIONS
Date: Thursday, October 24th, 2024
Speaker: Nicolas Brunel (Duke University, Università Bocconi)
Title: Roles of inhibition in stabilizing and shaping the response of cortical networks
Abstract: Inhibition has long been thought to stabilize the activity of cortical networks at low rates, and to shape significantly their response to sensory inputs. In this talk, I will describe three recent collaborative projects that shed light on these issues. (1) I will show how optogenetic excitation of inhibition neurons is consistent with cortex being inhibition stabilized even in the absence of sensory inputs, and how this data can constrain the coupling strengths of E-I cortical network models. (2) Recent analysis of the effects of optogenetic excitation of pyramidal cells in V1 of mice and monkeys shows that in some cases this optogenetic input reshuffles the firing rates of neurons of the network, leaving the distribution of rates unaffected. I will show how this surprising effect can be reproduced in sufficiently strongly coupled E-I networks. (3) Another puzzle has been to understand the respective roles of different inhibitory subtypes in network stabilization. Recent data reveal a novel, state dependent, paradoxical effect of weakening AMPAR mediated synaptic currents onto SST cells. Mathematical analysis of a network model with multiple inhibitory cell types shows that this effect tells us in which conditions SST cells are required for network stabilization.
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Date:Thursday, June 13th, 2024
Speaker: Alaa Ahmed (University of Colorado Boulde)
Title: Movement vigor as a reflection of internal decision variables during deliberation and learning
Abstract: To understand subjective evaluation of an option, various disciplines have quantified the interaction between reward and effort during decision making, producing an estimate of economic utility, namely the subject ‘goodness’ of an option. However, those same variables that affect the utility of an option also influence the vigor (speed) of movements towards that option. To better understand this, we have developed a mathematical framework demonstrating how utility can influence not only the choice of what to do, but also the speed of the movement follows. I will present results demonstrating that expectation of reward increases speed of saccadic eye and reaching movements, whereas expectation of effort expenditure decreases this speed. Intriguingly, when deliberating between two visual options, saccade vigor to each option increases differentially, encoding their relative value. Even when option value is hidden and must be learned, vigor can reveal the trial-to-trial prediction error and the consequent update of learned value. These results and others imply that vigor may serve as a new, real-time metric with which to quantify and track the evolution of subjective value, and that the neural circuits responsible for the control of movement are inextricably linked to the circuits involved in decision making.
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Date: Thursday, May 23rd, 2024
Speaker: Ralf Haefner (Rochester University)
Title: Behavioral and neural signatures of approximate inference during passive and active vision
Abstract: Perception requires the combination of uncertain sensory inputs with prior expectations. How such probabilistic computations might be implemented in the brain is a key question in systems neuroscience. Most of my talk will present recent work on temporal biases in evidence accumulation tasks. We found that approximate hierarchical inference results in a confirmation bias whose strength depends on the task in a predictable way. We verified our predictions using psychophysical experiments and showed that the proposed underlying mechanism – a positive feedback loop between decision-making and sensory areas – can reconcile a wide range of prior studies who differed in the biases they found. Next, we extended our work to the case of active sensing and showed that human eye-movements suffer from a similar confirmation bias as they collect information across the visual scene, again explainable by an approximate Bayesian observer. Interestingly, a neural signature of such computations include an increase in differential correlations with task learning in contradiction to classic feedforward models of noise correlations, a prediction we confirmed in monkey neurophysiology experiments.
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This seminar series is carried out with the support of the Department of Research and Universities of the Government of Catalonia.
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For inquiries about this event please contact the Scientific Events Coordinator Ms. Núria Hernández at nhernandez@crm.cat
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