Biology subjects

Doiron, B.

Publications and source records attributed to Doiron, B..

At least 19 recordsLinked to original sources

bioRxiv2026

Spontaneous brain-wide rotational dynamics constrain cue-evoked behavior

Uncovering low-dimensional representations of brain-wide neural dynamics and their relationship to behavior remains a central challenge. Here, we show that spontaneous brain-wide rotational dynamics constrain cue-evoked behavior and learning. To systematically establish these findings, we developed a dimensionality-reduction technique called Rotational Component Analysis (RCA) that optimizes the detection of low-variance rotational modes even within individual trials. We applied RCA to motor-cortical spike-train data from monkeys and humans, mouse cortex-wide calcium imaging, and human functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG). In mice, a rotational mode identified from activity recorded at rest predicts cue-evoked behavior: the phase of rotation when a randomly timed cue arrives predicts whether the animal subsequently pulls the lever. We explain this surprisingly strong correlation using both a recurrent neural network (RNN) and a minimal two-dimensional model of response learning, in which the intrinsic rotational mode serves as a dynamical constraint on response and learning. We further identify double-rotation modes containing two centers of rotation that separate preparatory from task dynamics in both mouse and human data. Remarkably, corresponding modes also organize neural dynamics during a non-motor human task, and rotational irreversibility captures early psychosis even when principal component analysis shows no group difference. Our work establishes rotational dynamics as low-dimensional coordinates that constrain how ongoing brain states are converted into behavior. More broadly, RCA provides a versatile tool for detecting hidden nonreciprocal dynamics in biological and physical datasets.

neuroscience↗

bioRxiv2026

Operator-valued free probability applied to recurrent neural networks with heterogeneity in local and global circuit structures

Random connectivity is a common assumption in models of recurrent neural networks, yet biological circuits combine heterogeneous synaptic connections with organization into distinct anatomical regions and functional groups. How does this global structure interact with cellular and local circuit heterogeneity to shape the dimensionality of population activity? In our work, we use operator-valued free probability to derive the covariance spectrum and effective dimension of recurrent networks with heterogeneous gains and block-structured random connectivity. This framework handles the noncommuting matrix operations through which cellular and circuit heterogeneity jointly determine population variability. We show that the linear relationship between effective dimension and firing rate uniformity persists despite global connectivity structure. At fixed total connectivity variance and with identical firing rate distributions across areas, concentrating variance within one area decreases dimension, whereas concentrating it in directed connections between areas increases dimension. Together, these results establish a mechanistic connection between circuit organization and population dimensionality, and demonstrate how operator-valued free probability unifies cellular, local, and global sources of heterogeneity in recurrent networks.

neuroscience↗

bioRxiv2026

Synaptic Development of Fine Spatial Scale Organization of Neuronal Orientation Tuning in Mouse Primary Visual Cortex

Primary sensory cortices often organize neurons with similar stimulus preference into spatially functional maps. Recent work in mouse primary visual cortex (V1) has established that neuronal tuning to the orientation of visual grating stimuli is organized into micro-clusters, where physically close neuron pairs (~ 20 {micro}m) share highly similar orientation preferences, but the organization is unstructured beyond this narrow range. This fine-scale organization is seemingly at odds with the underlying intracortical circuitry in mouse V1 whose spatial extent is an order of magnitude broader (100 ~ 200 {micro}m). In this study, we explore an activity-dependent synaptic plasticity model of spatially structured thalamo-cortical connectivity. We develop theory under asymptotic conditions specific for mouse V1, and derive concrete circuit conditions under which micro-clusters naturally develop. In particular, the recurrent interaction among V1 neurons requires an additional component over a micro-spatial scale, while the spatial profiles of balanced excitation and inhibition support an effective micro-scale interaction. Together, our results provide a developmental mechanism and analytical framework linking thalamo-cortical development, recurrent circuit structure, and the emergence of functional organization in primary visual cortex.

neuroscience↗

bioRxiv2026

Frontal Eye Field Leads a Distributed Oculomotor Circuit for Abstract Categorical Decisions

Flexible decisions require the brain to transform sensory evidence into abstract, task-relevant variables and then into actions. Understanding this process requires identifying how distributed neural populations represent sensory, cognitive, and motor variables, and how interareal interactions mediate transformations between them. We simultaneously recorded population activity in frontal eye field (FEF), lateral intraparietal area (LIP) and superior colliculus (SC) while monkeys performed a flexible yet urgent visual motion-categorization task. Within this network, FEF first encoded abstract categories, followed by SC and then LIP. LIP showed the earliest encoding of visual stimulus features, but a later encoding of upcoming saccades. Single-trial analyses revealed directed information flow from FEF to LIP populations for category-and choice-related signals. Reversible FEF inactivation impaired categorization and saccadic choice, causally implicating FEF in category-guided action. These findings reveal a differentiated FEF-LIP-SC circuit for transforming sensory evidence into abstract categorical decisions and the actions used to report them.

neuroscience↗

bioRxiv2026

Homeostatic Plasticity Enables Stable, Flexible, and Tunable Assemblies

Strongly interconnected neuronal populations, called assemblies, dynamically form through synaptic plasticity mechanisms and are thought to be a substrate for memories in the brain. Many assembly formation models use Hebbian excitatory-to-excitatory plasticity, where coordinated activity strengthens recurrent structure. However, these models typically yield binary assembly outcomes: networks with either weak (no assembly) or maximally strong (assembly) connectivity. We consider networks with a combination of Hebbian excitatory-to-excitatory plasticity and inhibitory-to-excitatory synapses with plasticity that homeostatically stabilizes excitatory neuron firing at a target value. When we set excitatory-to-excitatory plasticity to be homeostatically compliant, in that potentiation and depression are balanced at the homeostatic target firing rate, we find a stable continuum of synaptic strengths, and assembly structure is no longer binary. We use a recurrent network of spiking neuron models and an associated mean-field theory to identify this continuum as a line attractor in synaptic weight space. While along the attractor, homeostasis ensures that neuronal firing rates are invariant, the dynamical response properties of the network are quite malleable, with strongly coupled networks having high gain and longer timescale responses. Using our mean-field theory we show how correlated stochastic spiking activity among the excitatory neurons can destroy the line attractor, yet this can be mitigated when correlated inputs are shared across the excitatory and inhibitory neurons. Altogether, we provide a learning framework based on homeostasis, where a tunable and flexible assembly structure is possible.

neuroscience↗

bioRxiv2026

Convergence of Cortical and Thalamic Origins of Free Behavior Modulation of Mouse Primary Visual Cortex

The quality of sensory information flow in the brain may be affected by uninstructed free animal behavior. However, the specific circuit pathways that merge sensory and free behavior remain obscure. In mouse primary visual cortex (V1), we combine two-photon calcium imaging of cortical neurons and thalamo-cortical LGN (lateral geniculate nucleus) boutons with simultaneous measurements of facial movements. When controlling for spurious time-structured nonsense correlations, we observed representations of both eye movements and non-ocular facial movement features in both LGN bouton and V1 cortical neuron activity during grating input stimulation. Further, the correlation between V1 neurons and facial movement is larger for grating stimulation compared to blank stimulation, likely due to the integration of LGN inputs and modulation of V1 from higher brain centers during grating stimulation. Together, our results suggest a convergence of uninstructed non-visual signals from a persistent top-down pathway and a stimulus-gated bottom-up pathway in primary visual cortex.

neuroscience↗

bioRxiv2025

Guided by Noise: Correlated Variability Channels Task-Relevant Information in Sensory Neurons

Shared trial-to-trial variability across sensory neurons is reliably reduced when perceptual performance improves, yet this variability is low-dimensional, so it could be ignored by an optimal readout mechanism. Why then is it so consistently related to behavior? We propose that shared variability both reflects circuit structure and reveals the information communicated to downstream areas. In this framework, the same connectivity that shapes signal propagation also shapes shared variability. Using a circuit model, we show that when sensory signals align with shared variability, behaviorally relevant information is amplified without compromising coding fidelity. Analyses of neural population recordings from multiple brain areas and tasks reveal that the dominant axis of shared variability consistently aligns with task-relevant stimulus features and action plans. Finally, the behavioral impact of microstimulation can be explained by the extent to which it changes projections onto the shared variability axis. These findings suggest that shared variability may illuminate, rather than obscure, the neural dimensions that guide behavior. Significance StatementThe brains ability to use different features of sensory information flexibly across many tasks is essential for complex decision-making. Our study reveals that the correlated variability in neural responses in mid-level visual areas indicates the visual feature that is relevant for behavior. Our biologically plausible network model predicted that it is beneficial for the representation of behaviorally relevant visual information to be aligned with the correlated variability in neurons. We tested and confirmed this in five independent neural data sets. Our results suggest that trial-by-trial variability does not affect the information encoded in sensory neurons but instead is a valuable signal for us to understand which combination of the encoded features is being used by the brain to guide choices.

neuroscience↗

bioRxiv2025

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

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.

neuroscience↗

bioRxiv2025

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

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.

neuroscience↗

bioRxiv2025

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

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 (> 20 {micro}m) are irregularly and randomly organized, while neuron pairs that are extremely close (< 20 {micro}m) 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.

neuroscience↗

bioRxiv2025

Causal Spike Timing Dependent Plasticity Prevents Assembly Fusion in Recurrent Networks

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.

neuroscience↗

bioRxiv2025

Noise Correlations in Balanced Networks with Unreliable Synapses

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.

neuroscience↗

bioRxiv2024

Neuronal firing rate diversity lowers the dimension of population covariability

Neuronal responses are very diverse, with specific stimuli or behaviors eliciting a range of activity across a neuronal population. These responses also show large and correlated trial-to-trial fluctuations that occupy a low-dimensional region in activity space. We link these two aspects of neuronal response in both feedforward and recurrent circuit models and derive the following relation: the more diverse the firing rates of neurons in a population, the lower the effective dimension of their trial-to-trial covariability. We test our prediction using simultaneously recorded neuronal populations from numerous brain areas in mice, non-human primates, and in the motor cortex of human participants. Finally, we show how diverse neural codes lead to better stimulus discrimination, and that when the brain is in a heightened state of processing responses are more diverse and have lower-dimensional fluctuations. In sum, we present an organizing principle of population response that is widely observed across the nervous system and acts to synergistically improve population representation.

neuroscience↗

bioRxiv2024

Opposing Motor Memories in the Direct and Indirect Pathways of the Basal Ganglia

Loss of dopamine neurons causes motor deterioration in Parkinsons disease patients. We have previously reported that in addition to acute motor impairment, the impaired motor behavior is encoded into long-term memory in an experience-dependent and task-specific manner, a phenomenon we refer to as aberrant inhibitory motor learning. Although normal motor learning and aberrant inhibitory learning oppose each other and this is manifested in apparent motor performance, in the present study, we found that normal motor memory acquired prior to aberrant inhibitory learning remains preserved in the brain, suggesting the existence of independent storage. To investigate the neuronal circuits underlying these two opposing memories, we took advantage of the RNA-binding protein YTHDF1, an m6A RNA methylation reader involved in the regulation of protein synthesis and learning/memory. Conditional deletion of Ythdf1 in either D1 or D2 receptor-expressing neurons revealed that normal motor memory is stored in the D1 (direct) pathway of the basal ganglia, while inhibitory memory is stored in the D2 (indirect) pathway. Furthermore, fiber photometry recordings of GCaMP signals from striatal D1 (dSPN) and D2 (iSPN) receptor-expressing neurons support the preservation of normal memory in the direct pathway after aberrant inhibitory learning, with activities of dSPN predictive of motor performance. Finally, a computational model based on activities of motor cortical neurons, dSPN and iSPN neurons, and their interactions through the basal ganglia loops supports the above observations. These findings have important implications for novel approaches in treating Parkinsons disease by reactivating preserved normal memory, and in treating hyperkinetic movement disorders such as chorea or tics by erasing aberrant motor memories.

neuroscience↗

bioRxiv2023

Automated customization of large-scale spiking network models to neuronal population activity

Understanding brain function is facilitated by constructing computational models that accurately reproduce aspects of brain activity. Networks of spiking neurons capture the underlying biophysics of neuronal circuits, yet the dependence of their activity on model parameters is notoriously complex. As a result, heuristic methods have been used to configure spiking network models, which can lead to an inability to discover activity regimes complex enough to match large-scale neuronal recordings. Here we propose an automatic procedure, Spiking Network Optimization using Population Statistics (SNOPS), to customize spiking network models that reproduce the population-wide covariability of large-scale neuronal recordings. We first confirmed that SNOPS accurately recovers simulated neural activity statistics. Then, we applied SNOPS to recordings in macaque visual and prefrontal cortices and discovered previously unknown limitations of spiking network models. Taken together, SNOPS can guide the development of network models and thereby enable deeper insight into how networks of neurons give rise to brain function.

neuroscience↗

bioRxiv2023

The mechanics of correlated variability in segregated cortical excitatory subnetworks

Understanding the genesis of shared trial-to-trial variability in neural activity within sensory cortex is critical to uncovering the biological basis of information processing in the brain. Shared variability is often a reflection of the structure of cortical connectivity since this variability likely arises, in part, from local circuit inputs. A series of experiments from segregated networks of (excitatory) pyramidal neurons in mouse primary visual cortex challenge this view. Specifically, the across-network correlations were found to be larger than predicted given the known weak cross-network connectivity. We aim to uncover the circuit mechanisms responsible for these enhanced correlations through biologically motivated cortical circuit models. Our central finding is that coupling each excitatory subpopulation with a specific inhibitory subpopulation provides the most robust network-intrinsic solution in shaping these enhanced correlations. This result argues for the existence of excitatory-inhibitory functional assemblies in early sensory areas which mirror not just response properties but also connectivity between pyramidal cells.

neuroscience↗

bioRxiv2022

Subpopulation Codes Permit Information Modulation Across Cortical States

Cortical state is modulated by myriad cognitive and physiological mechanisms. Yet it is still unclear how changes in cortical state relate to changes in neuronal processing. Previous studies have reported state dependent changes in response gain or population-wide shared variability, motivated by the fact that both are important determinants of the performance of any population code. However, if the state-conditioned cortical regime is well-captured by a linear input-output response (as is often the case), then the linear Fisher information (FI) about a stimulus available to a decoder is invariant to state changes. In this study we show that by contrast, when one restricts a decoder to a subset of a cortical population, information within the subpopulation can increase through a modulation of cortical state. A clear example of such a subpopulation code is one in which decoders only receive projections from excitatory cells in a recurrent excitatory/inhibitory (E/I) network. We demonstrate the counterintuitive fact that when decoding only from E cells, it is exclusively the I cell response gain and connectivity which govern how information changes. Additionally, we propose a parametrically simplified approach to studying the effect of state change on subpopulation codes. Our results reveal the importance of inhibitory circuitry in modulating information flow in recurrent cortical networks, and establish a framework in which to develop deeper mechanistic insight into the impact of cortical state changes on information processing in these circuits.

neuroscience↗

bioRxiv2022

The logic of recurrent circuits in the primary visual cortex

Recurrent cortical activity sculpts visual perception by refining, amplifying, or suppressing incoming visual signals. Despite the importance of recurrent circuits for cortical processing, the basic rules that govern how nearby cortical neurons influence each other remains enigmatic. We used two-photon holographic optogenetics to activate ensembles of neurons in Layer 2/3 of the primary visual cortex (V1) in the absence of external stimuli to isolate the impact of local recurrence from external inputs. We find that the spatial arrangement and the stimulus feature preference of both the stimulated and the target ensemble jointly determine the net effect of recurrent activity, defining the cortical activity patterns that drive competition versus facilitation in L2/3 circuits. Computational modeling suggests that a combination of highly local recurrent excitatory connectivity and selective convergence onto inhibitory neurons give rise to these principles of recurrent activity. Our data and modeling reveal that recurrent activity can have varied impact, but a logic emerges through an understanding of the precise spatial distribution and feature preference of the multicellular pattern of activity.

neuroscience↗