Biology subjects

Buendia, V.

Publications and source records attributed to Buendia, V..

3 recordsLinked to original sources

bioRxiv2024

Connectome-based models of feature selectivity in a cortical circuit

Feature selectivity, the ability of neurons to respond preferentially to specific stimuli, is a defining property of cortical computation. Competing theories attribute selectivity to structured, tuning-dependent "like-to-like" feedforward or recurrent connections, whereas others propose that it can emerge without specific structure in randomly connected, inhibition-dominated networks. The relative contribution of these mechanisms remains unclear. Here, we investigate the circuit basis of feature selectivity in mouse visual cortex, focusing on how orientation selectivity arises in layer 2/3 neurons driven by input from layer 4. We developed a data-driven modeling framework integrating network modeling with functional imaging and synaptic-resolution connectomics from the MICrONS dataset. Our analyses show that randomness in connectivity is the dominant source of selectivity, while structured "like-to-like" feedforward and recurrent connections play comparable secondary roles in amplifying it. These findings refine classical theories of cortical selectivity and demonstrate how connectome-constrained modeling can reveal the circuit principles underlying cortical computation.

neuroscience↗

bioRxiv2024

Effective excitability captures network dynamics across development and phenotypes

Neuronal cultures in vitro are a versatile system for studying the fundamental properties of individual neurons and neuronal networks. Recently, this approach has gained attention as a precision medicine tool. Mature neuronal cultures in vitro exhibit synchronized collective dynamics called network bursting. If analyzed appropriately, this activity could offer insights into the networks properties, such as its composition, topology, and developmental and pathological processes. A promising method for investigating the collective dynamics of neuronal networks is to map them onto simplified dynamical systems. This approach allows the study of dynamical regimes and the characteristics of the parameters that lead to data-consistent activity. We designed a simple biophysically inspired dynamical system and used Bayesian inference to fit it to a large number of recordings of in vitro population activity. Even with a small number of parameters, the model showed strong inter-parameter dependencies leading to invariant bursting dynamics for many parameter combinations. We further validated this observation in our analytical solution. We found that in vitro bursting can be well characterized by each of three dynamical regimes: oscillatory, bistable, and excitable. The probability of finding a data-consistent match in a particular regime changes with network composition and development. The more informative way to describe the in vitro network bursting is the effective excitability, which we analytically show to be related to the parameter-invariance of the models dynamics. We establish that the effective excitability can be estimated directly from the experimentally recorded data. Finally, we demonstrate that effective excitability reliably detects the differences between cultures of cortical, hippocampal, and human pluripotent stem cell-derived neurons, allowing us to map their developmental trajectories. Our results open a new avenue for the model-based description of in vitro network phenotypes emerging across different experimental conditions.

neuroscience↗

bioRxiv2023

Recurrent connectivity structure controls the emergence of co-tuned excitation and inhibition

Cortical neurons are versatile and efficient coding units that develop strong preferences for specific stimulus characteristics. The sharpness of tuning and coding efficiency is hypothesized to be controlled by delicately balanced excitation and inhibition. These observations suggest a need for detailed co-tuning of excitatory and inhibitory populations. Theoretical studies have demonstrated that a combination of plasticity rules can lead to the emergence of excitation/inhibition (E/I) cotuning in neurons driven by independent, low-noise signals. However, cortical signals are typically noisy and originate from highly recurrent networks, generating correlations in the inputs. This raises questions about the ability of plasticity mechanisms to self-organize co-tuned connectivity in neurons receiving noisy, correlated inputs. Here, we study the emergence of input selectivity and weight co-tuning in a neuron receiving input from a recurrent network via plastic feedforward connections. We demonstrate that while strong noise levels destroy the emergence of co-tuning in the readout neuron, introducing specific structures in the non-plastic pre-synaptic connectivity can re-establish it by generating a favourable correlation structure in the population activity. We further show that structured recurrent connectivity can impact the statistics in fully plastic recurrent networks, driving the formation of co-tuning in neurons that do not receive direct input from other areas. Our findings indicate that the network dynamics created by simple, biologically plausible structural connectivity patterns can enhance the ability of synaptic plasticity to learn input-output relationships in higher brain areas.

neuroscience↗