Induced cortical oscillations in turtle cortex are coherent at the mesoscale of population activity, but not at the microscale of the membrane potential of neurons

Scale-free behavioral dynamics directly linked with scale-free cortical dynamics

Naturally occurring body movements and collective neural activity both exhibit complex dynamics, often with scale-free, fractal spatiotemporal structure. Scale-free dynamics of both brain and behavior are important because each is associated with functional benefits to the organism. Despite their similarities, scale-free brain activity and scale-free behavior have been studied separately, without a unified explanation. Here, we show that scale-free dynamics of mouse behavior and neurons in the visual cortex are strongly related. Surprisingly, the scale-free neural activity is limited to specific subsets of neurons, and these scale-free subsets exhibit stochastic winner-take-all competition with other neural subsets. This observation is inconsistent with prevailing theories of scale-free dynamics in neural systems, which stem from the criticality hypothesis. We develop a computational model which incorporates known cell-type-specific circuit structure, explaining our findings with a new type of critical dynamics. Our results establish neural underpinnings of scale-free behavior and clear behavioral relevance of scale-free neural activity.

Stability of motor cortex network states during learning-associated neural reorganizations

A substantial reorganization of neural activity and neuron-to-movement relationship in motor cortical circuits accompanies the emergence of reproducible movement patterns during motor learning. Little is known about how this tempest of neural activity restructuring impacts the stability of network states in recurrent cortical circuits. To investigate this issue, we reanalyzed data in which we recorded for 14 days via population calcium imaging the activity of the same neural populations of pyramidal neurons in layer 2/3 and layer 5 of forelimb motor and premotor cortex in mice during the daily learning of a lever-press task. We found that motor cortex network states remained stable with respect to the critical network state during the extensive reorganization of both neural population activity and its relation to lever movement throughout learning. Specifically, layer 2/3 cortical circuits unceasingly displayed robust evidence for operating at the critical network state, a regime that maximizes information capacity and transmission and provides a balance between network robustness and flexibility. In contrast, layer 5 circuits operated away from the critical network state for all 14 days of recording and learning. In conclusion, this result indicates that the wide-ranging malleability of synapses, neurons, and neural connectivity during learning operates within the constraint of a stable and layer-specific network state regarding dynamic criticality, and suggests that different cortical layers operate under distinct constraints because of their specialized goals.NEW & NOTEWORTHY The neural activity reorganizes throughout motor learning, but how this reorganization impacts the stability of network states is unclear. We used two-photon calcium imaging to investigate how the network states in layer 2/3 and layer 5 of forelimb motor and premotor cortex are modulated by motor learning. We show that motor cortex network states are layer-specific and constant regarding criticality during neural activity reorganization, and suggests that layer-specific constraints could be motivated by different functions.

Dynamics and sources of response variability and its coordination in visual cortex

The trial-to-trial response variability in sensory cortices and the extent to which this variability can be coordinated among cortical units have strong implications for cortical signal processing. Yet, little is known about the relative contributions and dynamics of defined sources to the cortical response variability and their correlations across cortical units. To fill this knowledge gap, here we obtained and analyzed multisite local field potential (LFP) recordings from visual cortex of turtles in response to repeated naturalistic movie clips and decomposed cortical across-trial LFP response variability into three defined sources, namely, input, network, and local fluctuations. We found that input fluctuations dominate cortical response variability immediately following stimulus onset, whereas network fluctuations dominate the response variability in the steady state during continued visual stimulation. Concurrently, we found that the network fluctuations dominate the correlations of the variability during the ongoing and steady-state epochs, but not immediately following stimulus onset. Furthermore, simulations of various model networks indicated that (i) synaptic time constants, leading to oscillatory activity, and (ii) synaptic clustering and synaptic depression, leading to spatially constrained pockets of coherent activity, are both essential features of cortical circuits to mediate the observed relative contributions and dynamics of input, network, and local fluctuations to the cortical LFP response variability and their correlations across recording sites. In conclusion, these results show how a mélange of multiscale thalamocortical circuit features mediate a complex stimulus-modulated cortical activity that, when naively related to the visual stimulus alone, appears disguised as high and coordinated across-trial response variability.

Coupling of synaptic inputs to local cortical activity differs among neurons and adapts after stimulus onset

Cortical activity contributes significantly to the high variability of sensory responses of interconnected pyramidal neurons, which has crucial implications for sensory coding. Yet, largely because of technical limitations of in vivo intracellular recordings, the coupling of a pyramidal neuron's synaptic inputs to the local cortical activity has evaded full understanding. Here we obtained excitatory synaptic conductance ( g) measurements from putative pyramidal neurons and local field potential (LFP) recordings from adjacent cortical circuits during visual processing in the turtle whole brain ex vivo preparation. We found a range of g-LFP coupling across neurons. Importantly, for a given neuron, g-LFP coupling increased at stimulus onset and then relaxed toward intermediate values during continued visual stimulation. A model network with clustered connectivity and synaptic depression reproduced both the diversity and the dynamics of g-LFP coupling. In conclusion, these results establish a rich dependence of single-neuron responses on anatomical, synaptic, and emergent network properties. NEW & NOTEWORTHY Cortical neurons are strongly influenced by the networks in which they are embedded. To understand sensory processing, we must identify the nature of this influence and its underlying mechanisms. Here we investigate synaptic inputs to cortical neurons, and the nearby local field potential, during visual processing. We find a range of neuron-to-network coupling across cortical neurons. This coupling is dynamically modulated during visual processing via biophysical and emergent network properties.

The turtle visual system mediates a complex spatiotemporal transformation of visual stimuli into cortical activity

The three-layered visual cortex of turtle is characterized by extensive intracortical axonal projections and receives non-retinotopic axonal projections from lateral geniculate nucleus. What spatiotemporal transformation of visual stimuli into cortical activity arises from such tangle of malleable cortical inputs and intracortical connections? To address this question, we obtained band-pass filtered extracellular recordings of neural activity in turtle dorsal cortex during visual stimulation of the retina. We discovered important spatial and temporal features of stimulus-modulated cortical local field potential (LFP) recordings. Spatial receptive fields span large areas of the visual field, have an intricate internal structure, and lack directional tuning. The receptive field structure varies across recording sites in a distant-dependent manner. Such composite spatial organization of stimulus-modulated cortical activity is accompanied by an equally multifaceted temporal organization. Cortical visual responses are delayed, persistent, and oscillatory. Further, prior cortical activity contributes globally to adaptation in turtle visual cortex. In conclusion, these results demonstrate convoluted spatiotemporal transformations of visual stimuli into stimulus-modulated cortical activity that, at present, largely evade computational frameworks.