Cortical networks

…explanations of irregular activity in cortical networks.

…hypothesize that the claustrum, a subcortical nucleus highly interconnected with the neocortex, encodes decision variables and aids in sustaining their representations in cerebral cor

Brain networks communicate through anatomical connections which are thought to mediate flexible, state-dependent interactions. We call the resulting pattern of communication “effective” or “functional” connectivity. In the motor system, within-day learning may rely primarily on modulation of networks, although the source of this modulation is unclear [1]. Over longer timescales, changes in anatomical connectivity can shape behavior through processes like Hebbian plasticity [2]. Understanding how these mechanisms combine to shape learning will require measurements of anatomical and functional c

…and computations in highly recurrent cortical networks? Seminal studies suggest that large-scale information processing results from interactions between the task environment, neuronal biophysics, and cortical architecture. While experimental advances have provided detailed cortical connectivity data, i.e. the connectome, it remains unclear which are fundamental for cognitive processing. Theoretically, Rec

Functional connectivity has been the focus of many research groups aiming to study the interaction between cells and brain regions. A standard method for analyzing connectivity is to statistically compare pairwise interactions between cells or brain regions across behavioral states or conditions. This methodology ignores the intrinsic properties of functional connectivity as a multivariate and dynamic signal, expressing the correlational configuration of the network. In this talk, I will present a geometric approach, combining Graph Theory and Riemannian Geometry to build "a graph of graphs" a

…neural input/output relationships in cortical networks and regulating the dynamic range of their responses to stimuli. To infer this balance using connectomics, we recently introduced a computational framework based on the Ising model, first developed to explain phase transitions in ferromagnets, and proposed a novel hybri

…In our study, we explore networks with balanced excitation and inhibition, demonstrating that short-distance connections increase the number of common presynaptic inputs through spatial clustering. This topological feature enhances the correlations of neural responses. Strengthened recurrent correlations can lead to bursting states, which amplify postsynaptic neuron activity and support the propagation of sig

…of synchronous spikes through the cortical networks [1,2,3]. Synfire-chain-like activity can also be found in spiking artificial neural networks trained for a classification task [4]. Understanding the mechanism for generating such activity would provide better insights into the functioning of real brains and artificial neural networks. Here we consider an analytically tractable network of binary units to study the conditions for the emergence of synchronous spikes and their stable propagation. Our network is organized in la

Amyotrophic Lateral Sclerosis (ALS) is a progressive neurological disorder characterized by the gradual degeneration of cells in the spinal cord, brainstem, and motor cortex, leading to manifestations such as weakness, muscle atrophy, and spasticity (Elisen, 2009). A comprehensive understanding of the disease process is currently lacking, making preclinical ALS models crucial to study the mechanisms of pathology insurgence, characterize the disease and test therapeutics approaches. Pathological in vitro models emerged as viable alternative to in vivo studies, consolidating a non-invasive appro

…are currently investigating how wider cortical networks support memory reprocessing. We are doing so by combining optical (wide-field) voltage imaging with laminarly-resolved hippocampal electrophysiology in mice, to get a large-scale view of the brain activity. Animals perform two virtual reality memory tas

…interneurons impact behavior and cortical networks. We used behavioral assays, patch-clamp recordings and immunostainings in two mouse models in

…for gaining better insights into cortical networks due to these characteristics. To comprehend local networks of the motor cortex and monitoring network changes during stimulation, we utilized subdurally positioned graphene electrodes for simultaneous recording and stimulation of the motor cortex in rhesus monkeys. Additionally, we implemented a wireless recordin

In vitro models of neuronal networks have proven to be a valuable tool for gaining deeper insights into the intricate mechanisms governing the human brain. Notably, the integration of human induced pluripotent stem cells (hiPSCs) with micro-electrode arrays (MEAs) provides a controlled in vitro environment to replicate and analyse both the structural and functional elements of the human brain. As neuronal communication relies on the emission of electrical (and chemical) stimuli, the employment of electrical stimulation becomes crucial for a comprehensive exploration of neuronal assemblies, to

…interactions between different cortical networks facilitate the integration of information and the execution of complex tasks. Yet even the simplest behavioral tasks are complex, requiring integration over m

Inhibition has long been thought to stabilize the activity of cortical networks at low rates, and to shape significantly their response to sensory inputs. In this talk, I will describe three recent collaborative projects that shed light on these issues. (1) I will show how optogenetic excitation of inhibition neurons is consistent with cortex being inhibition stabilized even in the absence of sensory inputs, and how this data can constrain the coupling strengths of E-I cortical network models. (2) Recent analysis of the effects of optogenetic excitation of pyramidal cells in V1 of mice and mon

Cortical networks have the remarkable ability to self-assemble into dynamic regimes in which excitatory positive feedback is balanced by recurrent inhibition. This inhibition-stabilized regime is increasingly viewed as the default dynamic regime of the cortex but how it emerges in an unsupervised manner remains an open question. Theoretical work has established that four sets of weights (WE←E, WE←I, WI←E, WI←I) must obey specific relationships to produce inhibition-stabilized dynamics, but it is not known how the brain autonomously sets the values of all four weight classes to be in the suitab

…the geometry of efficient spike-coding networks, we consider a population of neurons with low-rank connectivity, allowing

One of the key questions in neuroscience is how the connectivity structure of cortical networks determines the collective dynamics of neural activity. Two complementary approaches have been developed to address this question: (i) specifying connectivity in terms of local statistics of excitatory-inhibitory motifs [Trousdale et al. 2012, Aljadeff et al. 2015]; (ii) specifying connectivity through a global low-rank structure that determines the low-dimensional dynamics [Mastrogiuseppe and Ostojic 2018, Beiran et al. 2021, Dubreuil et al. 2020]. It is however currently unclear how local connectiv

…from the dynamics of recurrent neural networks (RNNs) performing error correction. These networks achieve best performance when processing pulsed inputs with noise levels proportional to those observed in cortical networks. In this optimal regime, the performance

…of neuronal spiking activity in cortical networks. Simplified theoretical models of cortical circuits show that these features can be mechanistically accounted for if cortex operates in a fluctuation-driven regime (FDR). In FDR, excitatory and inhibitory currents are approximately balanced, and activity fluctuations emerge intrinsically from the non-linear network dynamics. However, it is still unclear if cortex operates in this regime. We address this question by analyzing sub-threshold membrane potential of neurons in sensory and fr

…of single neurons and cortex-wide networks, we correlated electrophysiol

…biological and of artificial neuronal networks alike arise from the interaction of large numbers of neurons. A central quest is thus to characterize their collective states. The directed coupling between pairs of neurons and their continuous dissipation of energy, moreover, cause dynamics of neuronal networks outside thermodynamic equilibrium. Tools from non-equilibrium statistical mechanics and field theory are thus instrumental to obtain a quantitative understanding. We here present progress with this recent approach [1]. On the experime

…tools to characterize exactly how cortical networks change in the build-up to a seizure. I will show how intermittent optogenetic stimulation ("active probing") reveals a latent change in dendritic excitability that is tightly correlated to the onset of seizure activity. This data relates back to old work from the 1980s suggesting a critical role in epileptic pathophysiolo

…computational models of large-scale cortical networks of monkeys, and how this approach can help to answer important questions in large