Biology subjects

Mastrogiuseppe, F.

Publications and source records attributed to Mastrogiuseppe, F..

5 recordsLinked to original sources

bioRxiv2026

Direct cortical feedforward and feedback pathways differentially shape visual responses and selectively engage looped circuits

Nearly every computational account of hierarchical cortex rests on assumptions about the influence of direct cortico-cortical inputs and which neurons they act upon. Yet, disentangling their direct functional contributions from indirect, multisynaptic influences remains difficult. We examined how feedforward (FF) and feedback (FB) pathways between mouse primary visual cortex (V1) and posteromedial cortex (PM) modulate visual responses, combining retrograde labelling, multi-area two-photon imaging and optogenetic perturbations. High or low activity in either pathway was associated with both excitation and inhibition of neurons in the targeted areas. FF activity was associated with stronger additive changes, while FB activity was related to stronger multiplicative modulations and improvements in visual decoding. Optogenetic manipulations causally confirmed these asymmetries. Both pathways preferentially modulated reciprocating "looped" neurons, with FF exciting them and FB either exciting or inhibiting them. These findings demonstrate that feedforward and feedback pathways serve distinct functional roles within recurrent cortical loops.

neuroscience↗

bioRxiv2026

Input-dependent directionality of interactions between cortical areas

Tracking signal flow across areas is essential for understanding brain function. Recent studies using cross-covariances show that activity directionality can shift rapidly with behavioral or task demands; yet, the circuit mechanisms underlying these changes remain unclear. Here, we use recurrent network models to investigate how directional interactions emerge and are flexibly reconfigured in multi-area cortical circuits. We show that, for fixed connectivity, directionality is shaped by how common inputs align with recurrent connectivity and the associated internal timescales of activity. In multi-area circuits with locally balanced excitation and inhibition, this reveals a predominant role for inputs to excitatory over inhibitory populations in controlling directionality. These inputs govern the directionality of the latent signals that account for most of the shared activity across areas, predominantly reflecting widespread and coherent activity fluctuations. Our models capture key features of cross-covariances from primate areas V1 and V2 and suggest parsimonious mechanisms for the shift in directionality reported in these areas. This work establishes a mechanistic framework for understanding dynamic changes in signal flow between brain areas.

neuroscience↗

bioRxiv2025

Stochastic activity in low-rank recurrent neural networks

The geometrical and statistical properties of brain activity depend on the way neurons connect to form recurrent circuits. However, the link between connectivity structure and emergent activity remains incompletely understood. We investigate this relationship in recurrent neural networks with additive stochastic inputs. We assume that the synaptic connectivity can be expressed in a low-rank form, parameterized by a handful of connectivity vectors, and examine how the geometry of emergent activity relates to these vectors. Our findings reveal that this relationship critically depends on the dimensionality of the external stochastic inputs. When inputs are low-dimensional, activity remains low-dimensional, and recurrent dynamics influence it within a subspace spanned by a subset of the connectivity vectors, with dimensionality equal to the rank of the connectivity matrix. In contrast, when inputs are high-dimensional, activity also becomes potentially high-dimensional. The contribution of recurrent dynamics is apparent within a subspace spanned by the totality of the connectivity vectors, with dimensionality equal to twice the rank of the connectivity matrix. Applying our formalism to excitatory-inhibitory networks, we discuss how the input configuration also plays a crucial role in determining the amount of amplification generated by non-normal dynamics. Our work provides a foundation for studying activity in structured brain circuits under realistic noise conditions, and offers a framework for interpreting stochastic models inferred from experimental data.

neuroscience↗

bioRxiv2022

Evolution of neural activity in circuits bridging sensory and abstract knowledge

The ability to associate sensory stimuli with abstract classes is critical for survival. How are these associations implemented in brain circuits? And what governs how neural activity evolves during abstract knowledge acquisition? To investigate these questions, we consider a circuit model that learns to map sensory input to abstract classes via gradient descent synaptic plasticity. We focus on typical neuroscience tasks (simple, and context-dependent, categorization), and study how both synaptic connectivity and neural activity evolve during learning. To make contact with the current generation of experiments, we analyze activity via standard measures such as selectivity, correlations, and tuning symmetry. We find that the model is able to recapitulate experimental observations, including seemingly disparate ones. We determine how, in the model, the behaviour of these measures depends on details of the circuit and the task. These dependencies make experimentally-testable predictions about the circuitry supporting abstract knowledge acquisition in the brain.

neuroscience↗

bioRxiv2020

Complementary roles of dimensionality and population structure in neural computations

Neural computations are currently investigated using two separate approaches: sorting neurons into functional populations, or examining the low-dimensional dynamics of collective activity. Whether and how these two aspects interact to shape computations is currently unclear. Using a novel approach to extract computational mechanisms from networks trained on neuroscience tasks, here we show that the dimensionality of the dynamics and cell-class structure play fundamentally complementary roles. While various tasks can be implemented by increasing the dimensionality in networks with fully random population structure, flexible input-output mappings instead required a non-random population structure that can be described in terms of multiple sub-populations. Our analyses revealed that such a population structure enabled flexible computations through a mechanism based on gain-controlled modulations that flexibly shape the dynamical landscape of collective dynamics. Our results lead to task-specific predictions for the structure of neural selectivity, inactivation experiments, and for the implication of different neurons in multi-tasking.

neuroscience↗