About

Anne-Marie Oswald is an Associate Professor of Neurobiology and Neuroscience at the University of Chicago's Biological Sciences Division. Her research focuses on understanding the neural mechanisms underlying cortical circuit stability and gain modulation, particularly involving multiple interneuron classes. She investigates how disinhibitory circuitry influences associative synaptic plasticity in the olfactory cortex and explores the interactions between behaviorally relevant rhythms and synaptic plasticity to understand coding in the piriform cortex. Her work contributes to elucidating the inhibitory dynamics within cortical circuits, including the differential inhibition of pyramidal cells and interneurons, as well as the development of inhibitory timescales in auditory cortex. Oswald is actively involved in the academic community through participation in the Institute Committee on Computational Neuroscience and the Committee on Neurobiology, and she is engaged in graduate programs in Computational Neuroscience and Neurobiology.

Research topics

• Neuroscience
• Biology
• Psychology

Selected publications

• Author response: Untangling stability and gain modulation in cortical circuits with multiple interneuron classes

  2025-03-21

  peer-reviewOpen access

  Synaptic inhibition is the mechanistic backbone of a suite of cortical functions, not the least of which are maintaining network stability and modulating neuronal gain. In cortical models with a single inhibitory neuron class, network stabilization and gain control work in opposition to one another – meaning high gain coincides with low stability and vice versa. It is now clear that cortical inhibition is diverse, with molecularly distinguished cell classes having distinct positions within the cortical circuit. We analyze circuit models with pyramidal neurons (E) as well as parvalbumin (PV) and somatostatin (SOM) expressing interneurons. We show how in E – PV – SOM recurrently connected networks an SOM-mediated modulation can lead to simultaneous increases in neuronal gain and network stability. Our work exposes how the impact of a modulation mediated by SOM neurons depends critically on circuit connectivity and the network state.

  PublisherOA PDFDOI

• Untangling stability and gain modulation in cortical circuits with multiple interneuron classes

  eLife · 2025-03-21 · 2 citations

  preprintOpen access

  Abstract Synaptic inhibition is the mechanistic backbone of a suite of cortical functions, not the least of which are maintaining network stability and modulating neuronal gain. In cortical models with a single inhibitory neuron class, network stabilization and gain control work in opposition to one another – meaning high gain coincides with low stability and vice versa. It is now clear that cortical inhibition is diverse, with molecularly distinguished cell classes having distinct positions within the cortical circuit. We analyze circuit models with pyramidal neurons (E) as well as parvalbumin (PV) and somatostatin (SOM) expressing interneurons. We show how in E – PV – SOM recurrently connected networks an SOM-mediated modulation can lead to simultaneous increases in neuronal gain and network stability. Our work exposes how the impact of a modulation mediated by SOM neurons depends critically on circuit connectivity and the network state.

  PublisherDOI

• Untangling stability and gain modulation in cortical circuits with multiple interneuron classes

  eLife · 2025-04-14 · 4 citations

  articleOpen access

  Synaptic inhibition is the mechanistic backbone of a suite of cortical functions, not the least of which are maintaining network stability and modulating neuronal gain. In cortical models with a single inhibitory neuron class, network stabilization and gain control work in opposition to one another – meaning high gain coincides with low stability and vice versa. It is now clear that cortical inhibition is diverse, with molecularly distinguished cell classes having distinct positions within the cortical circuit. We analyze circuit models with pyramidal neurons (E) as well as parvalbumin (PV) and somatostatin (SOM) expressing interneurons. We show how, in E – PV – SOM recurrently connected networks, SOM-mediated modulation can lead to simultaneous increases in neuronal gain and network stability. Our work exposes how the impact of a modulation mediated by SOM neurons depends critically on circuit connectivity and the network state.

  PublisherDOI

• Author response: Untangling stability and gain modulation in cortical circuits with multiple interneuron classes

  2025-04-30

  peer-reviewOpen access
  PublisherDOI

• Untangling stability and gain modulation in cortical circuits with multiple interneuron classes

  eLife · 2024-07-30 · 6 citations

  articleOpen access

  Synaptic inhibition is the mechanistic backbone of a suite of cortical functions, not the least of which are maintaining network stability and modulating neuronal gain. In cortical models with a single inhibitory neuron class, network stabilization and gain control work in opposition to one another - meaning high gain coincides with low stability and vice versa. It is now clear that cortical inhibition is diverse, with molecularly distinguished cell classes having distinct positions within the cortical circuit. We analyze circuit models with pyramidal neurons (E) as well as parvalbumin (PV) and somatostatin (SOM) expressing interneurons. We show how, in E - PV - SOM recurrently connected networks, SOM-mediated modulation can lead to simultaneous increases in neuronal gain and network stability. Our work exposes how the impact of a modulation mediated by SOM neurons depends critically on circuit connectivity and the network state.

  PublisherDOI

• Untangling stability and gain modulation in cortical circuits with multiple interneuron classes

  eLife · 2024-12-03

  preprintOpen access

  Abstract Synaptic inhibition is the mechanistic backbone of a suite of cortical functions, not the least of which are maintaining network stability and modulating neuronal gain. In cortical models with a single inhibitory neuron class, network stabilization and gain control work in opposition to one another – meaning high gain coincides with low stability and vice versa. It is now clear that cortical inhibition is diverse, with molecularly distinguished cell classes having distinct positions within the cortical circuit. We analyze circuit models with pyramidal neurons (E) as well as parvalbumin (PV) and somatostatin (SOM) expressing interneurons. We show how in E – PV – SOM recurrently connected networks an SOM-mediated modulation can lead to simultaneous increases in neuronal gain and network stability. Our work exposes how the impact of a modulation mediated by SOM neurons depends critically on circuit connectivity and the network state.

  PublisherDOI

• Author response: Untangling stability and gain modulation in cortical circuits with multiple interneuron classes

  2024-12-03

  peer-reviewOpen access

  Synaptic inhibition is the mechanistic backbone of a suite of cortical functions, not the least of which are maintaining network stability and modulating neuronal gain. In cortical models with a single inhibitory neuron class, network stabilization and gain control work in opposition to one another – meaning high gain coincides with low stability and vice versa. It is now clear that cortical inhibition is diverse, with molecularly distinguished cell classes having distinct positions within the cortical circuit. We analyze circuit models with pyramidal neurons (E) as well as parvalbumin (PV) and somatostatin (SOM) expressing interneurons. We show how in E – PV – SOM recurrently connected networks an SOM-mediated modulation can lead to simultaneous increases in neuronal gain and network stability. Our work exposes how the impact of a modulation mediated by SOM neurons depends critically on circuit connectivity and the network state.

  PublisherOA PDFDOI

• Untangling stability and gain modulation in cortical circuits with multiple interneuron classes

  eLife · 2024-07-30

  preprintOpen access

  Abstract Synaptic inhibition is the mechanistic backbone of a suite of cortical functions, not the least of which are maintaining network stability and modulating neuronal gain. In cortical models with a single inhibitory neuron class, network stabilization and gain control work in opposition to one another – meaning high gain coincides with low stability and vice versa. It is now clear that cortical inhibition is diverse, with molecularly distinguished cell classes having distinct positions within the cortical circuit. We analyze circuit models with pyramidal neurons (E) as well as parvalbumin (PV) and somatostatin (SOM) expressing interneurons. We show when E – PV networks recurrently connect with SOM neurons then an SOM mediated modulation that leads to increased neuronal gain can also yield increased network stability. Our work exposes how the impact of an inhibition mediated modulation depends critically on how inhibition is recruited from within the circuit.

  PublisherDOI

• Disinhibitory Circuitry Gates Associative Synaptic Plasticity in Olfactory Cortex

  Journal of Neuroscience · 2022 · 56 citations

  Senior authorCorresponding
  • Neuroscience
  • Biology

  Inhibitory interneurons stabilize neural activity during sensory processing. However, inhibition must also be modulated to allow sensory experience shape neural responses. In olfactory cortex, inhibition regulates activity-dependent increases in excitatory synaptic strength that accompany odor learning. We identify two inhibitory interneuron classes that act as gatekeepers preventing excitatory enhancement. We demonstrate that driving a third class of interneurons inhibits the gatekeepers and opens the gate for excitatory enhancement. All three inhibitory neuron classes comprise disinhibitory microcircuit motifs found throughout the cortex. Our findings suggest that a common disinhibitory microcircuit promotes changes in synaptic strength during sensory processing and learning.

  PublisherOA PDFDOI

• Assembly formation is stabilized by Parvalbumin neurons and accelerated by Somatostatin neurons

  bioRxiv (Cold Spring Harbor Laboratory) · 2021-09-07 · 40 citations

  preprintOpen access

  Abstract Learning entails preserving the features of the external world in the neuronal representations of the brain, and manifests itself in the form of strengthened interactions between neurons within assemblies. Hebbian synaptic plasticity is thought to be one mechanism by which correlations in spiking promote assembly formation during learning. While spike timing dependent plasticity (STDP) rules for excitatory synapses have been well characterized, inhibitory STDP rules remain incomplete, particularly with respect to sub-classes of inhibitory interneurons. Here, we report that in layer 2/3 of the orbitofrontal cortex of mice, inhibition from parvalbumin (PV) interneurons onto excitatory (E) neurons follows a symmetric STDP function and mediates homeostasis in E-neuron firing rates. However, inhibition from somatostatin (SOM) interneurons follows an asymmetric, Hebbian STDP rule. We incorporate these findings in both large scale simulations and mean-field models to investigate how these differences in plasticity impact network dynamics and assembly formation. We find that plasticity of SOM inhibition builds lateral inhibitory connections and increases competition between assemblies. This is reflected in amplified correlations between neurons within assembly and anti-correlations between assemblies. An additional finding is that the emergence of tuned PV inhibition depends on the interaction between SOM and PV STDP rules. Altogether, we show that incorporation of differential inhibitory STDP rules promotes assembly formation through competition, while enhanced inhibition both within and between assemblies protects new representations from degradation after the training input is removed.

  PublisherOA PDFDOI

Recent grants

• CIRCUIT MECHANISMS SHAPING OLFACTORY CORTICAL RESPONSES DURING PASSIVE AND ACTIVE

  NIH · $432k · 2010–2013

• CRCNS: Formation of stimulus selective neural assemblies in piriform cortex

  NIH · $801k · 2015–2019

Frequent coauthors

• Brent Doiron

  University of Chicago

  36 shared

• Martha Canto-Bustos

  University of Pittsburgh

  28 shared

• F. Kathryn Friason

  Center for the Neural Basis of Cognition

  16 shared

• Hannah Bos

  University of Pittsburgh

  15 shared

• Christoph Miehl

  University of Pittsburgh

  12 shared

• Adam M. Large

  University of Wisconsin–Madison

  12 shared

• Fereshteh Lagzi

  University of Washington

  9 shared

• Paul Schick

  University of Pittsburgh

  8 shared

Labs

• Anne-Marie OswaldPI

Education

• PhD-Neuroscience, Cellular and Molecular Medicine

  University of Ottawa

• BSc, Biology

  Simon Fraser University