No abstract

During the generation of rhythmic movements, most spinal neurons receive an oscillatory synaptic drive. The neuronal architecture underlying this drive is unknown, and the corresponding network size and sparseness have not yet been addressed. If the input originates from a small central pattern generator (CPG) with dense divergent connectivity, it will induce correlated input to all receiving neurons, while sparse convergent wiring will induce a weak correlation, if any. Here, we use pairwise recordings of spinal neurons to measure synaptic correlations and thus infer the wiring architecture qualitatively. A strong correlation on a slow timescale implies functional relatedness and a common source, which will also cause correlation on fast timescale due to shared synaptic connections. However, we consistently find marginal coupling between slow and fast correlations regardless of neuronal identity. This suggests either sparse convergent connectivity or a CPG network with recurrent inhibition that actively decorrelates common input.Movement is an essential part of our daily lives, and disorders of the motor system, such as spasticity, amyotrophic lateral sclerosis, and spinal cord injury are particularly debilitating for individuals. Simple rhythmic movements, such as walking and breathing, have constituted models for fundamental aspects of the motor system. In spite of extensive investigations, 1, 2, 3, 4, 5, 6 the connectivity of the network responsible for generating the motor activity remains unknown. A circuit component, known as a central pattern generator (CPG), is believed to transmit command signals to motoneurons and local premotor interneurons. 7,8,9,10 Although the size of the respiratory motor network, i.e. the preBötzinger complex, 1, 11 is well-known, the size and wiring of other CPG networks are not well understood. A feedforward organization is often proposed between groups of neurons or modules, which exhibit alternating rhythmic bursting 12 (Fig. 1a). Common drive modules are thought to be small, e.g., the preBötzinger complex has only 600 neurons, 1 which provides rhythmic drive for the rest of the network. The projection is also believed to diverge onto a much larger population of receiving neurons. 13,14,15 Thus, the receiving neurons would share the same connections via a dense divergent connectivity ( Fig. 1b). Since the transmission is communicated by action potentials, which are precise in time, a dense connectivity will manifest as a strong temporal correlation between synaptic potentials in the receiving neurons, and this correlation can be verified experimentally through pairwise recordings. If the drive network is not a small but rather a large population, however, the receiver neurons are likely to collect sparse convergent input without correlation (Fig. 1c). Hence, the assessment of correlation CPG connectivity from decoupled timescales Extensor network Flexor network extensor Common drive Common drive flexor IN IN MN MN MN e x o r e x te ns or knee MN Dense Common drive Sparse c b ...

During the generation of rhythmic movements, most spinal neurons receive an oscillatory synaptic drive. The neuronal architecture underlying this drive is unknown, and the corresponding network size and sparseness have not yet been addressed. If the input originates from a small central pattern generator (CPG) with dense divergent connectivity, it will induce correlated input to all receiving neurons, while sparse convergent wiring will induce a weak correlation, if any. Here, we use pairwise recordings of spinal neurons to measure synaptic correlations and thus infer the wiring architecture qualitatively. A strong correlation on a slow timescale implies functional relatedness and a common source, which will also cause correlation on fast timescale due to shared synaptic connections. However, we consistently find marginal coupling between slow and fast correlations regardless of neuronal identity. This suggests either sparse convergent connectivity or a CPG network with recurrent inhibition that actively decorrelates common input.

A major goal of neuroscience is to reveal mechanisms supporting collaborative actions of neurons in local and larger-scale networks. However, no clear overall principle of operation has emerged despite decades-long experimental efforts. Here we used an unbiased method to extract and identify the dynamics of local postsynaptic network states contained in the cortical field potential. Field potentials were recorded by depth electrodes targeting a wide selection of cortical regions during spontaneous activities, and sensory, motor, and cognitive experimental tasks. Despite different architectures and different activities, all local cortical networks generated the same type of dynamic confined to one region only of state space. Surprisingly, within this region state trajectories expanded and contracted continuously during all brain activities and generated a single expansion followed by a contraction in a single trial. This behavior deviates from known attractors and attractor networks. The state-space contractions of particular subsets of brain regions cross-correlated during perceptive, motor and cognitive tasks. Our results imply that the cortex does not need to change its dynamic to shift between different activities, making task-switching inherent in the dynamic of collective cortical operations. Our results provide a mathematically described general explanation of local and larger-scale cortical dynamic.

We lack viable explanations of how collective activities of neurons in networks produce brain functions. We recorded field potentials from many local networks in the human cerebral cortex during a wide variety of brain functions. The network dynamics showed that each local cortical network produced fluctuating attractor states. The state trajectories continuously stretched and contracted during all brain functions, leaving no stable patterns. Different local networks all produced this dynamic, despite different architectures. Single trial stimuli and tasks modified the stretching and contractions. These modified fluctuations cross-correlated among particular networks during specific brain functions. Spontaneous activity, rest, sensory, motor and cognitive functions all emerged from this dynamic. Its mathematical structure provides a general theoretical model of cortical dynamics that can be tested experimentally. This universal dynamic is a simple functional organizing principle for brain functions at the mm3 scale that is distinct from existing frameworks.Graphical abstractIn briefWillumsen et al. developed a method to show that local cortical networks contribute to sensory, motor and cognitive functions by stretching and contracting the trajectory of the multidimensional field potential. In single trials the networks communicate by cross-correlating the stretching and contracting. This ubiquitous attractor dynamic forms a departure from existing models of how postsynaptic dynamics contribute to sensory, motor and cognitive brain functions.HighlightsCortical fluctuating expanding and contacting attractor dynamics (FECAT) drive collective postsynaptic operations at the mm3 scaleFECAT dynamic accounted for all behavioral conditions and all tested cortical areasCortical states show no stationary patterns, but continuously expand and contract with a stable attractor dynamicOur method reveals multi-dimensional cortical dynamics in field potentials, also useful for EEG and MEG