About

Brent Doiron is a Professor at the University of Chicago in the Department of Neurobiology. His research activities and funding focus on neuronal population dynamics within and across cortical areas, multiregional neuronal computations underlying rapid and flexible visual categorical decisions, and the formation of stimulus-selective neural assemblies through excitatory and inhibitory circuit plasticity. He serves as Principal Investigator on projects supported by the NIH, including training in theory and computation for next-generation neuroscientists. His work involves understanding the stability, gain modulation, and correlated variability in cortical circuits, as well as the mechanisms of neural coding and communication between brain regions. Doiron's contributions include developing models of low-dimensional shared variability in cortical networks, analyzing the mechanics of correlated variability in segregated cortical excitatory subnetworks, and exploring the dynamics of recurrent networks that shape sensory processing and behavior. His research employs computational and theoretical approaches to elucidate the circuit mechanisms underlying neural activity and information transmission in the brain.

Research topics

• Artificial Intelligence
• Computer Science
• Psychology
• Neuroscience
• Biology
• Optoelectronics
• Medicine
• Communication
• Physics
• Internal medicine
• Cognitive science

Selected publications

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

  2025-04-30

  peer-reviewOpen accessSenior author
  PublisherDOI

• Interleaving asynchronous and synchronous activity in balanced cortical networks with short-term synaptic depression

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-03-18

  preprintOpen accessSenior authorCorresponding

  Cortical populations are in a broadly asynchronous state that is sporadically interrupted by brief epochs of coordinated population activity. Cortical models are at a loss to explain this combination of states. At one extreme are network models where recurrent inhibition dynamically stabilizes an asynchronous low activity state. While these networks are widely used, they cannot produce the coherent population-wide activity that is reported in a variety of datasets. At the other extreme are models in which strong recurrent excitation that is quickly tamed by short term synaptic depression between excitatory neurons leads to short epochs of population-wide activity. However, in these networks, inhibition plays only a perfunctory role in network stability, which is at odds with many reports across cortex. In this study we analyze spontaneously active in vitro preparations of primary auditory cortex that show dynamics that are emblematic of this mixture of states. To capture this complex population activity we use firing rate-based networks as well as biologically realistic networks of spiking neuron models where large excitation is balanced by recurrent inhibition, yet we include short-term synaptic depression dynamics of the excitatory connections. These models give very rich nonlinear behavior that mimics the core features of the in vitro data, including the possibility of low frequency (2-12 Hz) rhythmic dynamics within population events. In these networks, synaptic depression enables activity fluctuations to induce a weakening of inhibitory recruitment, which in turn triggers population events. In sum, our study extends balanced network models to account for nonlinear, population-wide correlated activity, thereby providing a critical step in a mechanistic theory of realistic cortical activity.

  PublisherOA PDFDOI

• FACED 2.0 enables large-scale voltage and calcium imaging <i>in vivo</i>

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-03-10 · 7 citations

  preprintOpen access

  Abstract Monitoring neuronal activity at large scale and high spatiotemporal resolution is crucial for understanding information processing within the brain. We optimized a kilohertz-frame-rate two-photon fluorescence microscope with all-optical megahertz line-scan rate to achieve ultrafast imaging across large areas and volumes at subcellular resolution. Applying this technique to voltage and calcium imaging in vivo , we demonstrated simultaneous recording of voltage activity over 200 neurons and calcium activity over 14,000 neurons.

  PublisherOA PDFDOI

• Interleaving asynchronous and synchronous activity in balanced cortical networks with short term synaptic depression

  Nature Communications · 2025-09-30 · 1 citations

  articleOpen accessSenior authorCorresponding

  Cortical populations are in a broadly asynchronous state that is sporadically interrupted by brief epochs of coordinated population activity. Inhibitory stabilized networks reproduce a low activity asynchronous regime but cannot generate population events. In contrast, synaptic depression stabilized excitatory networks create transient surges of activity, yet give inhibition only a perfunctory role. We analyzed spontaneously active in vitro mouse auditory cortex slices that show both regimes, including slow (2-12 Hz) oscillations within some events. We built firing rate and biophysically realistic spiking models in which excitation is balanced by recurrent inhibition, yet all excitatory synapses undergo short term depression. In our model a depression of synaptic excitation onto inhibition neurons initiates events, while depression of excitation onto excitatory neurons shapes rhythmicity of the events, reproducing the full repertoire observed experimentally. Our work unifies balanced and depression stabilized theories and provides a mechanistic framework for nonlinear, population wide correlations in cortex.

  PublisherDOI

• Causal Spike Timing Dependent Plasticity Prevents Assembly Fusion in Recurrent Networks

  ArXiv.org · 2025-01-16

  preprintOpen accessSenior author

  The organization of neurons into functionally related assemblies is a fundamental feature of cortical networks, yet our understanding of how these assemblies maintain distinct identities while sharing members remains limited. Here we analyze how spike-timing-dependent plasticity (STDP) shapes the formation and stability of overlapping neuronal assemblies in recurrently coupled networks of spiking neuron models. Using numerical simulations and an associated mean-field theory, we demonstrate that the temporal structure of the STDP rule, specifically its degree of causality, critically determines whether assemblies that share neurons maintain segregation or merge together after training is completed. We find that causal STDP rules, where potentiation/depression occurs strictly when presynaptic spikes precede/proceed postsynaptic spikes, allow assemblies to remain distinct even with substantial overlap in membership. This stability arises because causal STDP effectively cancels the symmetric correlations introduced by common inputs from shared neurons. In contrast, acausal STDP rules lead to assembly fusion when overlap exceeds a critical threshold, due to unchecked growth of common input correlations. Our results provide theoretical insight into how spike-timing-dependent learning rules can support distributed representation where individual neurons participate in multiple assemblies while maintaining functional specificity.

  PublisherOA PDFDOI

• Causal Spike Timing Dependent Plasticity Prevents Assembly Fusion in Recurrent Networks

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-01-15 · 2 citations

  preprintOpen accessSenior authorCorresponding

  The organization of neurons into functionally related assemblies is a fundamental feature of cortical networks, yet our understanding of how these assemblies maintain distinct identities while sharing members remains limited. Here we analyze how spike-timing-dependent plasticity (STDP) shapes the formation and stability of overlapping neuronal assemblies in recurrently coupled networks of spiking neuron models. Using numerical simulations and an associated mean-field theory, we demonstrate that the temporal structure of the STDP rule, specifically its degree of causality, critically determines whether assemblies that share neurons maintain segregation or merge together after training is completed. We find that causal STDP rules, where potentiation/depression occurs strictly when presynaptic spikes precede/proceed postsynaptic spikes, allow assemblies to remain distinct even with substantial overlap in membership. This stability arises because causal STDP effectively cancels the symmetric correlations introduced by common inputs from shared neurons. In contrast, acausal STDP rules lead to assembly fusion when overlap exceeds a critical threshold, due to unchecked growth of common input correlations. Our results provide theoretical insight into how spike-timing-dependent learning rules can support distributed representation where individual neurons participate in multiple assemblies while maintaining functional specificity.

  PublisherDOI

• Noise Correlations in Balanced Networks with Unreliable Synapses

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-01-13

  preprintOpen accessSenior author

  Abstract Synaptic physiology is highly stochastic in the neocortex: immediately following an action potential, individual synapses release neurotransmitter unreliably, sometimes even failing to release any vesicles. However, theoretical models of neuronal networks typically neglect this well-established feature of biology, especially recurrent networks. In this work, to better understand the effects of synaptic unreliability in recurrent networks, we describe neuronal variability in a balanced network model of non-leaky integrate-and-fire neurons incorporating a Bernoulli model of synaptic release. For arbitrary network size, synaptic unreliability contributes non-negligibly to spike count variability. Most notably, this additional noise is overshadowed by effects on noise correlations. In particular, we find that feedforward and recurrent synaptic reliability have opposite influences on noise correlations: while increased reliability of synaptic input from neurons outside of the network increase correlations, reliability of recurrent synapses de-correlates population activity. We explain this dichotomy by examining the average input currents to cell pairs, and verify this effect with simulations of exponential integrate-and-fire neurons with adaptation and conductance-based synapses. Overall, our results emphasize the importance of synaptic unreliability in the study of noise correlations.

  PublisherOA PDFDOI

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

  2025-03-21

  peer-reviewOpen accessSenior author

  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

• Circuit-Based Understanding of Fine Spatial Scale Clustering of Orientation Tuning in Mouse Visual Cortex

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-02-13 · 6 citations

  preprintOpen accessSenior authorCorresponding

  In sensory cortex of brain it is often the case that neurons are spatially organized by their functional properties. A hallmark of primary visual cortex (V1) in higher mammals is a columnar functional map, where neurons tuned to different stimuli features are regularly organized in space. However, rodent visual cortex is at odds with this rule and lacks any spatially ordered functional architecture, and rather neuron feature preference is haphazardly organized in patterns termed "salt-and-pepper". This sharp contrast in feature organization between the visual cortices of rodents and higher mammals has been a persistent mystery, fueled in part by abundant evidence of conserved cortical physiology between species. In this work, we applied a novel GCaMP indicator that are localized in the nucleus of neurons during two photon imaging in mouse V1, which enabled us to overcome most spurious spatially correlated activity due to fluorescence contamination, and to ensure a faithful observation of functional organization over space. We found that the orientation tuning properties of distant neuron pairs (&gt; 20 um) are irregularly and randomly organized, while neuron pairs that are extremely close (&lt; 20 um) have strongly correlated orientation tuning, indicating a narrow yet strong spatially clustered organization of orientation preference, which we term "micro-clustered" organization. Exploring a circuit based model of recurrently coupled mouse V1 we derived two key predictions for the micro cluster: spatially localized recurrent connections over a comparable narrow spatial scale, and common relative spatial spreads of balanced excitation and inhibition in the network over broad spatial scales. These predictions are validated by both anatomical and optogenetic-based physiological circuit mapping experiments. Altogether, our work takes an important step in building a circuit-based theory of visual processing in mouse V1 over spatial scales that are often ignored, yet contain powerful synaptic interactions.

  PublisherDOI

• FACED 2.0 enables large-scale voltage and calcium imaging in vivo

  Nature Methods · 2025-12-09 · 6 citations

  articleOpen access

  Monitoring neuronal activity at large scale and high spatiotemporal resolution is crucial for understanding information processing within the brain. Here we optimized a kilohertz-frame-rate two-photon fluorescence microscope with an all-optical megahertz line-scan rate to achieve ultrafast imaging across large areas and volumes at subcellular resolution. Applying this technique to in vivo voltage and calcium imaging, we demonstrated simultaneous recording of voltage activity over 200 neurons and calcium activity over 14,000 neurons from the mouse visual cortex, as well as volumetric calcium imaging of the larval zebrafish brain.

  PublisherDOI

Recent grants

• INTERDISCIPLINARY TRAINING IN COMPUTATIONAL NEUROSCIENCE

  NIH · $4.5M · 2006–2022

• Modeling V1 circuit dynamics

  NIH · $6.8M · 2018–2023

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

  NIH · $801k · 2015–2019

• Neuronal population dynamics within and across cortical areas

  NIH · $1.0M · 2020–2022

• The Mechanics of Neural Variability

  NSF · $275k · 2013–2017

Frequent coauthors

• Gabriel Koch Ocker

  Center for Systems Biology

  87 shared

• Ashok Litwin-Kumar

  Columbia University

  52 shared

• Eric Shea‐Brown

  40 shared

• Chengcheng Huang

  Center for the Neural Basis of Cognition

  36 shared

• Anne-Marie M. Oswald

  University of Pittsburgh

  36 shared

• Robert Rosenbaum

  31 shared

• Krešimir Josić́

  University of Houston

  30 shared

• Matthew A. Smith

  Carnegie Mellon University

  27 shared

Labs

• Brent Doiron LabPI