Cortical dynamics

…inhibitory-cell diversity, large-scale cortical dynamics and an explanatory approach that links mechanisms across levels rather than treating them separately.

…On the other hand, cortical microcircuits seem to implement them via (near) orthogonal population-activity patterns. But how are these local geometries combined at the level of cortex-wide dynamics? To answer this, we developed a new task for mice navigating in virtual reality (VR) to temporally disentangle different cognitive computations. Mice first accumulate evidence to determine the predominant color of a random

Complex movements, such as riding a bike, require rich, coordinated, self-sustaining patterns of neural activity. Experimental studies show that such activity patterns are often controlled by brief, intermittent inputs. In contrast, computational models usually rely on constant external inputs or generate only transient --ballistic-- responses in networks tuned for that purpose. Here, we propose a novel approach that selects for locally amplifying network motifs to generate a diverse repertoire of dynamic behaviors without continuous external stimulation. Brief inputs to these models can induc

…and connectivity motifs in shaping cortical dynamics. In addition, cortical circuits comprise a large diversity

…crucial in explaining key aspects of cortical dynamics, including the nonlinear integration of visual inputs and the modulation of response variability. Yet, the absence of spike-based signals in these frameworks has hindered the study of millisecond-scale interactions between pairs of neurons, which have been shown to drive synaptic plasticity. For instance, spike-timing dependent plasticity (STDP), a phenomenological description of long-term synaptic plasticity, can shape neural circuits based on spike-bas

The power of deep neural networks for pattern recognition tasks lies in their universal approximation abilities, and their theoretical expressivity is well-studied. Practical expressivity, however, is known to lag behind theoretical expressivity. Network performance achieved in practice is determined by a set of inductive biases such as architectural constraints (e.g. residual connections). In this work, we introduce weakly coupled residual recurrent networks (WCRNNs) in which the residual connections result in well-defined Lyapunov exponents. This allows for studying properties of their dynam

In the cerebral cortex, activity dynamics measured at a mesoscopic (0.1 to 1 mm) scale are characterized by both spontaneous and task-related dynamical waves of synchronized neuronal activity ranging from spreading stationary depolarizations (blobs) to traveling waves that can follow complex trajectories throughout the cortical network. These waves are thought to participate in information propagation and processing, but remain poorly understood. To test whether cortical waves can be actively generated and their trajectory controlled, we have implemented a goal-directed task for head-fixed mic

Cortical dynamics are slow and low dimensional. Relaxation, as measured for instance by the auto-correlation of the spike counts, can be slow (~seconds) compared to single neurons and synapses’ time constants (~hundreds of milliseconds). Moreover, spiking activity is coordinated across neural circuits despite weak, zero-lag cross-correlations between pairs of neurons. Thus, neural circuit dynamics can be described by a small number of collective variables compared to the number of neurons. By which mechanism(s) do slow and low-dimensional dynamics arise in cortical networks, and how are they r

Cortical dynamics obey a 1/f power law, exhibiting an exponential decay of spectral power with increasing frequency. The slope and offset of this 1/f decay reflect the timescale and magnitude of aperiodic neural activity, respectively. These properties are tightly linked to cellular and circuit mechanisms (e.g. excitation:inhibition balance and firing rates) as well as cognitive processes (e.g. perception, memory, and behavioral state). However, the physiology underlying the 1/f power law in cortical dynamics is not well understood. Here, we compared laminar recordings from human, macaque and

One of the fundamental functions of the brain is to flexibly plan and control movement production at different timescales to efficiently shape structured behaviors. I will present a model that clarifies how these complex computations could be performed in the mammalian brain, with an emphasis on the learning of an extendable library of autonomous motor motifs and the flexible stringing of these motifs in motor sequences. To build this model, we took advantage of the fact that the anatomy of the circuits involved is well known. Our results show how these architectural constraints lead to a prin

…precision has revealed new levels of cortical sensitivity: stimulating tens of neurons can yield changes in the mean firing rate of thousands of similarly tuned neurons. This extreme sensitivity suggests that cortical dynamics are near criticality. Criticality is often studied in neural systems as a non-equilibrium thermodynamic process in which scale-free patterns of activity, called avalanches, emerge between distinct states of spontaneous activity. W

…the fine-grained spatiotemporal dynamics of spontaneous activity in the entire dorsal cortex. through simultaneous recordings of wide-field voltage sensitive dye transients (VS), cortical ECoG, and hippocampal LFP in anesthetized mice. Both VS and ECoG show cortical avalanches. When measuring avalanches from the VS

…neural circuit model of a visual cortical hypercolumn to perform sampling-based probabilistic inference. The optimized network displayed several key biological properties, including divisive normalization, as well as stimulus-modulated noise variability, inhibition-dominated transients at stimu

…connected to a recurrent cortical network. During movement sequences, electrophysiological recordings of basal ganglia output neurons show sustained activity patterns that switch at the boundaries between motifs. Thus, we model these inhibitory patterns as silencing some thalamic neurons while leaving others disinhib