Pacific Cascade Chapter of the Society for Neuroscience

“Circuits and cellular mechanisms for the generation and control of breathing”
Monday, Feb 5, 2024, 3:30 – 4:30 p.m.; UW, HSB Room T-639
Nathan Baertsch will present “Circuits and cellular mechanisms for the generation and control of breathing.” The Baertsch Lab investigates how breathing is generated and regulated by the brain. By uncovering fundamental cellular and network mechanisms of respiratory control, we hope to inspire new therapeutic interventions to treat breathing disorders associated with neurological pathology, prematurity, and opioid use.

“Investigating brainwide anesthesia-activated ensembles in mice”
Monday, Feb 12, 2024, 3:30 – 4:30 p.m.; UW, HSB Room T-639
Dr. Mitra Heshmati will present “Investigating brainwide anesthesia-activated ensembles in mice.”  Our goal is to develop a comprehensive cell-type and circuit-specific understanding of anesthesia-induced brain plasticity to better inform translational approaches to expediting emergence from general anesthesia and mitigating postanesthetic agitation and delirium. We currently investigate the effects of general anesthesia on brain circuitry during anesthesia state transitions using brain clearing and whole brain imaging with light sheet microscopy, an approach that enables a single cell resolution snapshot of activity in circuits across the whole rodent brain. We then take advantage of genetically targeted biosensor imaging to interrogate the population activity of neurons within identified anesthesia-activated circuits during emergence. Using knockdown and overexpression of specific genes of interest within specified neural circuits, we examine the effects of targeted genetic manipulations on population activity and whole brain network connectivity under anesthesia and during emergence. We also use established preclinical models of social behavior and stress to investigate postanesthetic agitation and delirium across the lifespan. We plan to initiate parallel translational investigations in humans as our studies move toward identifying putative mechanistic targets to improve the experience of general anesthesia for patients.

“Differential encoding of mammalian proprioception by voltage-gated sodium channels”
Thursday, Feb 29, 2024, 9:30 – 10:30 a.m.; UW, HSB Room G-328
Presenter:  Dr. Theanna Griffith, Assistant Professor, Department of Physiology and Membrane Biology, UC Davis

Ulises Pereira-Obilinovic
Scientist, Allen Institute for Neural Dynamics

Dynamics in Networks with Learning Rules Inferred from Data

HSB G328, January 17, 1:30 PM

https://washington.zoom.us/j/99088001918

Attractor networks are an influential theory for memory storage in brain systems. However, this theory has recently been challenged by the observation of strong temporal variability in neuronal recordings during memory tasks. First, I will present a study of a recurrent network model in which both learning rules and the distribution of stored patterns are inferred from distributions of visual responses for novel and familiar images in the inferior temporal cortex (ITC) (Pereira & Brunel, 2018, Neuron). We show that there exist two types of retrieval states: one in which firing rates are constant in time and another in which firing rates fluctuate chaotically. We develop a dynamical mean field theory to analyze the network dynamics and compare the theory with simulations of large networks. In the online learning scenario in which the network learns and forgets continuously we show that for a forgetting timescale that optimizes storage capacity, the qualitative features of the network’s memory retrieval dynamics are age-dependent: most recent memories are retrieved as fixed-point attractors while older memories are retrieved as chaotic attractors characterized by strong heterogeneity and temporal fluctuations (Pereira-Obilinovic, Aljadeff, Brunel, 2023, PRX). When these learning rules are temporally asymmetric, they transform a sequence of random input patterns into synaptic weight updates. After learning, recalled sequential activity is reflected in the transient correlation of network activity with each of the stored input patterns. Using a mean-field theory, we derive a low-dimensional description of the network dynamics and compute the storage capacity of sequences (Gillett, Pereira, Brunel, 2020, PNAS). We found that multiple characteristics of the recalled attractor, chaotic and sequential activity are consistent with experimental observations. If time permits, I would like to give you a glimpse of how we are using similar network models and analytical tools for building multiregional models constrained by anatomy and trained in Neuropixels recordings and behavior.