Colloquium Series 2023-2024
Colloquia are generally on Tuesdays at 3:30 p.m., once per month or so. If you'd like to be on the mailing list announcing these events, please sign up here.
Here is a list of our speakers for the Winter Term 2024 (full titles and abstracts will follow as available)
Winter 2024
Feb 6, 3:30, E5 2004 , Milad Lankarany, Krembil Research Institute
Title: Using Computational Neuroscience and Neuromodulation Techniques to Uncover Mechanisms of Neural Systems
Abstract: Deep brain stimulation, as an invasive neuromodulation technique, has been successfully used to reduce symptoms of movement disorders by delivering electrical pulses to the substructures of the basal ganglia and thalamus. Existing efforts to understand the mechanisms of action of DBS rely on detecting biomarkers during (or after) delivering clinically approved high-frequency pulses and assessing their clinical outcomes. However, less consideration has been given to how DBS modulates information processing in the human brain. Specifically, it is not yet discovered how modulated neural activities observed in a sub-cortical region represent dynamics of underlying circuitry. Building on our published data of in-vivo single-unit recordings in patients with Essential Tremor (n = 20), we discovered that neuronal dynamics (in the sense of instantaneous firing rate) of thalamic ventral intermediate nucleus (Vim) in response to various frequencies of deep brain stimulation (DBS) can be explained by Balanced Amplification mechanism, an established theoretical framework that suggests the large amplification of excitation — caused by either strong external input or positive recurrent circuitry — can be balanced by strong feedback inhibition. We developed a network rate model, together with a sequential optimization algorithm, that accurately reproduces the instantaneous firing rate of Vim neurons, the primary surgical target of DBS for reducing symptoms of essential tremor, in response to low- and high-frequency DBS. Our study revealed that the computer simulation of a single population of Vim neurons (appeared in our recent publication PMID: 37140523) cannot capture the dynamics of the firing rate, thus the presence of other neuronal populations is essential to track the firing rate reliably and mechanistically. Interestingly, our work suggests, in an unsupervised manner, that the presence of an inhibitory neuronal population is necessary to replicate Vim firing rates across different DBS frequencies. We anticipate that our study provides a conceptual modeling framework to uncover mechanisms of information processing in different DBS-modulated sub-cortical regions in the human brain.
https://sites.google.com/view/lnsbsp/home
March 12, 3:30, NOTE Room Change E7-7363, Memming Park, Champalimaud Centre
Title: Persistent learning signals and working memory without continuous attractors
Abstract: Neural dynamical systems with stable attractor structures, such as point attractors and continuous attractors, are hypothesized to underlie meaningful temporal behavior that requires working memory. However, working memory may not support useful learning signals necessary to adapt to changes in the temporal structure of the environment. We show that in addition to the continuous attractors that are widely implicated, periodic and quasi-periodic attractors can also support learning arbitrarily long temporal relationships. Unlike the continuous attractors that suffer from the fine-tuning problem, the less explored quasi-periodic attractors are uniquely qualified for learning to produce temporally structured behavior. Our theory has broad implications for the design of artificial learning systems and makes predictions about observable signatures of biological neural dynamics that can support temporal dependence learning and working memory. Based on our theory, we developed a new initialization scheme for artificial recurrent neural networks that outperforms standard methods for tasks that require learning temporal dynamics. Moreover, we propose a robust recurrent memory mechanism for integrating and maintaining head direction without a ring attractor.
https://www.fchampalimaud.org/research/groups/memming-park
March 19, 3:30, Online, Megan Peters,
Title: Theory-informed methods for studying metacognition and consciousness
Abstract: Under typical, everyday scenarios, we ought to feel more confident when we are more likely to correctly perceive the world. But we also know that clever lab-based tasks can pull these capacities apart, creating conditions where observers’ confidence fails to adequately track their task accuracy. When, why, and how would you feel more confident than you “should”, or less confident than you “should”? How can we quantify this phenomenon, and use it to study how confidence judgments are constructed in the first place? How can theory-informed measures and paradigms help clarify the link between these dissociations and the neurocomputational architectures that drive not only metacognition, but also perceptual awareness? In this talk I will discuss recent findings probing these questions from perspectives of experimental paradigm design, analysis approaches, and theory.
UC Irvine https://faculty.sites.uci.edu/cnclab/
Here is a list of our speakers for the Fall Term 2023
Fall 2023 Term
September 26 15:30 (in person) - Jonathan Cannon, McMaster, head of the Trimba Lab, will give a CTN Seminar on Dynamic inference in rhythm perception, production, and synchronization in E5 2004
Abstract:
Moving
in
time
with
rhythmic
music
is
nearly
universal
across
human
cultures,
and
group
rhythmic
coordination
produces
remarkable
group
cohesion
effects,
in
part
by
dissolving
subjective
boundaries
between
self
and
other.
How
can
we
make
sense
of
this
unique
sensorimotor
behavior
in
the
context
of
the
wide
human
repertoire
of
perceptual
and
motor
processes?
In
this
talk,
I
propose
that
the
theory
of
Bayesian
predictive
processing
provides
not
only
a
conceptual
framework
but
also
a
clear,
intuitive
mathematical
modeling
language
for
rhythm
perception,
rhythm
production,
and
sensorimotor
synchronization
through
self/other
integration.
Following the predictive processing account of perceptual inference, I propose a computational model in which we perform approximate Bayesian inference to estimate the momentary phase and tempo of ongoing underlying metrical cycles using learned metrical models. Then, drawing on the theory of “active inference” which extends predictive processing to the realm of action, I propose a closely related computational model of rhythm production as a closed loop: timely feedback from our actions informs a dynamic model of our moving body, and that model guides the timing of subsequent action. Bringing these two computational models together, I propose a new formal account of sensorimotor synchronization: by modeling a heard rhythm and our own motor feedback as though they arise from the same underlying metrical cycle (i.e., modeling “self” and “other” as a single unified process), active inference naturally brings our actions into synch with what we hear. I explore evidence for this model, its new predictions, and experiments that might test those predictions.
Note (Oct 13 2023): Jonathan's talk was recorded and is available on his youtube channel.
October 24 15:30 (in person) Stephanie Palmer, Chicago in room E5 2004
Title:
How behavioral and evolutionary constraints sculpt early visual processing
Abstract:
Biological systems must selectively encode partial information about the environment, as dictated by the capacity constraints at work in all living organisms. For example, we cannot see every feature of the light field that reaches our eyes; temporal resolution is limited by transmission noise and delays, and spatial resolution is limited by the finite number of photoreceptors and output cells in the retina. Classical efficient coding theory describes how sensory systems can maximize information transmission given such capacity constraints, but it treats all input features equally. Not all inputs are, however, of equal value to the organism. Our work quantifies whether and how the brain selectively encodes stimulus features, specifically predictive features, that are most useful for fast and effective movements. We have shown that efficient predictive computation starts at the earliest stages of the visual system, in the retina. We borrow techniques from statistical physics and information theory to assess how we get terrific, predictive vision from these imperfect (lagged and noisy) component parts. In broader terms, we aim to build a more complete theory of efficient encoding in the brain, and along the way have found some intriguing connections between formal notions of coarse graining in biology and physics.
November 7 15:30 (in-person) Michael Anderson, Western in room E5 2004
Title: Neural reuse, dynamics, and constraints: Getting beyond componential mechanistic explanation of neural function
Abstract: In this talk, I will review some of the evidence for neural reuse--a form of neural plasticity whereby existing neural resources are put to many different uses--and use it to motivate an argument that we need to move beyond (although not necessarily abandon) componential mechanistic explanation in the neurosciences. I claim that what is needed to capture the full range of neural plasticity and dynamics is a style of explanation based on the notion of constraints--enabling constraints in particular. I will give examples of neural phenomena that are hard to capture in the mechanistic framework, and show that they are naturally handled by enabling constraints. As this moves us away from faculty psychology, it has some important implications for the ontology of cognition.
December 5 15:30 (in -person) Matt van der Meer, Dartmouth in room E5 2004
Title: Three lies, and a cognitive process model, about information processing in the rodent hippocampus
Abstract: I will present results from my lab that cast doubt on three commonly held ideas about the rodent hippocampus, and then propose a cognitive process model that assigns synergistic functions to each of the experimentally observed alternatives. First, contrary to the idea that hippocampal replay reflects recent experience and/or upcoming goals, we show “paradoxical replay” of non-chosen options. Second, contrary to the idea that remapping across different environments is random, we observe various kinds of structure across the encoding of different environments. Third, contrary to the idea that the hippocampus faithfully encodes ongoing experience, we synthesize evidence that it actually blends the past, present and future together into subjective belief states. We propose that paradoxical replay protects these subjective belief states from interference, and that representational similarity structure of these belief states underlies behavioral generalization.