Publications

2026

1. 

   A unified theory of feature learning in RNNs and DNNs

   Jan P. Bauer, Kirsten Fischer, Moritz Helias, and 1 more author

   In International Conference on Machine Learning (ICML), 2026

   arXiv HTML

2025

1. 

   Exact linear theory of perturbation response in a space- and feature-dependent cortical circuit model

   Ho Yin Chau, Kenneth D. Miller, and Agostina Palmigiano

   Proceedings of the National Academy of Sciences, 2025

   Abs DOI

   What are the principles that govern the responses of cortical networks to their inputs and the emergence of these responses from recurrent connectivity? Recent experiments have probed these questions by measuring cortical responses to two-photon optogenetic perturbations of single cells in the mouse primary visual cortex. A robust theoretical framework is needed to determine the implications of these responses for cortical recurrence. Here we propose a novel analytical approach: a formulation of the dependence of cell-type-specific connectivity on spatial distance that yields an exact solution for the linear perturbation response of a model with multiple cell types and space- and feature-dependent connectivity. Importantly and unlike previous approaches, the solution is valid in regimes of strong as well as weak intra-cortical coupling. Analysis reveals the structure of connectivity implied by various features of single-cell perturbation responses, such as the surprisingly narrow spatial radius of nearby excitation beyond which inhibition dominates, the number of transitions between mean excitation and inhibition thereafter, and the dependence of these responses on feature preferences. Comparison of these results to existing optogenetic perturbation data yields constraints on cell-type-specific connection strengths and their tuning dependence. Finally, we provide experimental predictions regarding the response of inhibitory neurons to single-cell perturbations and the modulation of perturbation response by neuronal gain; the latter can explain observed differences in the feature-tuning of perturbation responses in the presence vs. absence of visual stimuli.

2. Theory of interaction between untuned modulatory inputs and tuned sensory inputs

   Tuan Nguyen, Agostina Palmigiano, and Kenneth D. Miller

   bioRxiv, 2025

   DOI

2023

1. 

   Mechanisms underlying reshuffling of visual responses by optogenetic stimulation in mice and monkeys

   Alessandro Sanzeni, Agostina Palmigiano, Tuan H. Nguyen, and 6 more authors

   Neuron, Dec 2023

   Abs DOI

   The ability to optogenetically perturb neural circuits opens an unprecedented window into mechanisms governing circuit function. We analyzed and theoretically modeled neuronal responses to visual and optogenetic inputs in mouse and monkey V1. In both species, optogenetic stimulation of excitatory neurons strongly modulated the activity of single neurons yet had weak or no effects on the distribution of firing rates across the population. Thus, the optogenetic inputs reshuffled firing rates across the network. Key statistics of mouse and monkey responses lay on a continuum, with mice/monkeys occupying the low-/high-rate regions, respectively. We show that neuronal reshuffling emerges generically in randomly connected excitatory/inhibitory networks, provided the coupling strength (combination of recurrent coupling and external input) is sufficient that powerful inhibitory feedback cancels the mean optogenetic input. A more realistic model, distinguishing tuned visual vs. untuned optogenetic input in a structured network, reduces the coupling strength needed to explain reshuffling.

2. 

   Boosting of neural circuit chaos at the onset of collective oscillations

   Agostina Palmigiano, Rainer Engelken, and Fred Wolf

   Nov 2023

   Abs DOI

   Neuronal spiking activity in cortical circuits is often temporally structured by collective rhythms. Rhythmic activity has been hypothesized to regulate temporal coding and to mediate the flexible routing of information flow across the cortex. Spiking neuronal circuits, however, are non-linear systems that, through chaotic dynamics, can amplify insignificant microscopic fluctuations into network-scale response variability. In nonlinear systems in general, rhythmic oscillatory drive can induce chaotic behavior or boost the intensity of chaos. Thus, neuronal oscillations could rather disrupt than facilitate cortical coding functions by flooding the finite population bandwidth with chaotically-boosted noise. Here we tackle a fundamental mathematical challenge to characterize the dynamics on the attractor of effectively delayed network models. We find that delays introduce a transition to collective oscillations, below which ergodic theory measures have a stereotypical dependence on the delay so far only described in scalar systems and low-dimensional maps. We demonstrate that the emergence of internally generated oscillations induces a complete dynamical reconfiguration, by increasing the dimensionality of the chaotic attractor, the speed at which nearby trajectories separate from one another, and the rate at which the network produces entropy. We find that periodic input drive leads to a dramatic increase of chaotic measures at a the resonance frequency of the recurrent network. However, transient oscillatory input only has a moderate role on the collective dynamics. Our results suggest that simple temporal dynamics of the mean activity can have a profound effect on the structure of the spiking patterns and therefore on the information processing capability of neuronal networks.

3. 

   Common rules underlying optogenetic and behavioral modulation of responses in multi-cell-type V1 circuits

   Agostina Palmigiano, Francesco Fumarola, Daniel P. Mossing, and 3 more authors

   bioRxiv, Nov 2023

   Abs DOI

   The visual cortex receives non-sensory inputs containing behavioral and brain state information. Here we propose a parallel between optogenetic and behavioral modulations of activity and characterize their impact on cell-type-specific V1 processing under a common theoretical framework. We infer cell-type-specific circuitry from large-scale V1 recordings and demonstrate that, given strong recurrent excitation, the cell-type-specific responses imply key aspects of the known connectivity. In the inferred models, parvalbumin-expressing (PV), but not other, interneurons have responses to perturbations that we show theoretically imply that their activity stabilizes the circuit. We infer inputs that explain locomotion-induced changes in firing rates and find that, contrary to hypotheses of simple disinhibition, locomotory drive to VIP cells and to SOM cells largely cancel, with enhancement of excitatory-cell visual responses likely due to direct locomotory drive to them. We show that this SOM/VIP cancellation is a property emerging from V1 connectivity structure. 2 (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint this version posted January 22, 2023. ; https://doi.

2022

1. 

   Targeted cortical stimulation reveals principles of cortical contextual interactions

   Shen Wang, Agostina Palmigiano, Kenneth D. Miller, and 1 more author

   bioRxiv, Nov 2022

   Abs DOI

   Cross-orientation suppression is a classic form of contextual normalization in visual cortex, yet the degree to which cortical circuits participate in the normalization computation is unclear. We visualized orientation maps of individual ferrets, and provided patterned optogenetic stimulation to both excitatory and inhibitory cells in orientation columns that either matched or were orthogonal to the preferred visual orientation of neurons recorded with electrodes. When visual or optogenetic stimulation of columns preferring one orientation was combined with optogenetic stimulation of columns preferring the orthogonal orientation, we observed less suppression than when orthogonal stimulation was provided visually, suggesting that cortical circuits do not provide a large fraction of visual cross-orientation suppression. Integration of visual and optogenetic signals was linear when neurons exhibited low firing rates and became sublinear when neurons exhibited higher firing rates. We probed the nature of sublinearities in cortex by examining the influence of optogenetic stimulation of cortical interneurons. We observed a range of responses, including evidence for paradoxical responses in which interneuron stimulation caused a decrease in inhibitory firing rate, presumably due to the withdrawal of recurrent excitation. These results are compatible with cortical circuits that exhibit strong recurrent excitation with stabilizing inhibition that provides normalization, albeit normalization that is too weak across columns to account for cross-orientation suppression.

2021

1. 

   Antagonistic inhibitory subnetworks control cooperation and competition across cortical space

   Daniel P Mossing, Julia Veit, Agostina Palmigiano, and 2 more authors

   bioRxiv, Nov 2021

   Abs DOI

   The cortical microcircuit can dynamically adjust to dramatic changes in the strength, scale, and complexity of its input. In the primary visual cortex (V1), pyramidal cells (PCs) integrate widely across space when signals are weak, but integrate narrowly when signals are strong, a phenomenon known as contrast-dependent surround suppression. Theoretical work has proposed that local interneurons could mediate a shift from cooperation to competition of PCs across cortical space, underlying this computation. We combine calcium imaging and electrophysiology to constrain a stabilized superlinear network model that explains how the four principal cell types in layer 2/3 (L2/3) of mouse V1, somatostatin (SST), parvalbumin (PV), and vasoactive intestinal peptide (VIP) interneurons, and PCs, transform inputs from layer 4 (L4) PCs to encode drifting gratings of varying size and contrast. Using bidirectional optogenetic perturbations, we confirm key predictions of the model. Our data and modeling show that network nonlinearities set up by recurrent amplification mediate a shift from a positive PC-VIP feedback loop at small size and low contrast to a negative PC-SST feedback loop at large size and high contrast to support this flexible computation. This may represent a widespread mechanism for gating competition across cortical space to optimally meet task demands.Competing Interest StatementThe authors have declared no competing interest.

2. 

   Interrogating theoretical models of neural computation with emergent property inference

   Sean R. Bittner, Agostina Palmigiano, Alex T. Piet, and 4 more authors

   eLife, Nov 2021

   Abs DOI

   A cornerstone of theoretical neuroscience is the circuit model: a system of equations that captures a hypothesized neural mechanism. Such models are valuable when they give rise to an experimentally observed phenomenon – whether behavioral or a pattern of neural activity – and thus can offer insights into neural computation. The operation of these circuits, like all models, critically depends on the choice of model parameters. A key step is then to identify the model parameters consistent with observed phenomena: to solve the inverse problem. In this work, we present a novel technique, emergent property inference (EPI), that brings the modern probabilistic modeling toolkit to theoretical neuroscience. When theorizing circuit models, theoreticians predominantly focus on reproducing computational properties rather than a particular dataset. Our method uses deep neural networks to learn parameter distributions with these computational properties. This methodology is introduced through a motivational example of parameter inference in the stomatogastric ganglion. EPI is then shown to allow precise control over the behavior of inferred parameters and to scale in parameter dimension better than alternative techniques. In the remainder of this work, we present novel theoretical findings in models of primary visual cortex and superior colliculus, which were gained through the examination of complex parametric structure captured by EPI. Beyond its scientific contribution, this work illustrates the variety of analyses possible once deep learning is harnessed towards solving theoretical inverse problems.

2020

1. 

   Generalized paradoxical effects in excitatory/inhibitory networks

   Kenneth D. Miller, and Agostina Palmigiano

   Oct 2020

   Abs DOI

   An inhibition-stabilized network (ISN) is a network of excitatory and inhibitory cells at a stable fixed point of firing rates for a given input, for which the excitatory subnetwork would be unstable if inhibitory rates were frozen at their fixed point values. It has been shown that in a low-dimensional model (one unit per neuronal subtype) of an ISN with a single excitatory and single inhibitory cell type, the inhibitory unit shows a “paradoxical” response, lowering (raising) its steady-state firing rate in response to addition to it of excitatory (inhibitory) input. This has been generalized to an ISN with multiple inhibitory cell types: if input is given only to inhibitory cells, the steady-state inhibition received by excitatory cells changes paradoxically, that is, it decreases (increases) if the steady-state excitatory firing rates decrease (increase).

2017

1. 

   Flexible information routing by transient synchrony

   Agostina Palmigiano, Theo Geisel, Fred Wolf, and 1 more author

   Nature Neuroscience, Oct 2017

   Abs DOI

   Perception, cognition and behavior rely on flexible communication between microcircuits in distinct cortical regions. The mechanisms underlying rapid information rerouting between such microcircuits are still unknown. It has been proposed that changing patterns of coherence between local gamma rhythms support flexible information rerouting. The stochastic and transient nature of gamma oscillations in vivo, however, is hard to reconcile with such a function. Here we show that models of cortical circuits near the onset of oscillatory synchrony selectively route input signals despite the short duration of gamma bursts and the irregularity of neuronal firing. In canonical multiarea circuits, we find that gamma bursts spontaneously arise with matched timing and frequency and that they organize information flow by large-scale routing states. Specific self-organized routing states can be induced by minor modulations of background activity.

2013

1. 

   Controlling the oscillation phase through precisely timed closed-loop optogenetic stimulation: a computational study

   Annette Witt, Agostina Palmigiano, Andreas Neef, and 3 more authors

   Frontiers in Neural Circuits, Oct 2013

   DOI

2012

1. 

   Stability of synchronization clusters and seizurability in temporal lobe epilepsy

   Agostina Palmigiano, Jesús Pastor, Rafael Sola, and 1 more author

   PLoS ONE, Oct 2012

   DOI