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

Abstract

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 pre-motor 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.

Self-Organized Criticality in the Brain

“…These results demonstrate that the nLFP reflects local avalanche activity and should not be equated to single spikes. These findings also identify pyramidal neuron activity in superficial cortex layers to carry avalanche signature, which has recently been confirmed in vivo (Bellay et al, 2015;Karimipanah et al, 2017;Bowen et al, 2019;Ma et al, 2020).…”

“…In this context, the threshold, λ, applied to extract nLFPs is similar to the population threshold applied to summed spiking activity in identifying neuronal avalanches in network models (e.g., [72]). These findings identify pyramidal neuron activity in superficial cortex layers to carry signatures associated with the organization of avalanches, which, since then, has been confirmed in vivo [124,133,134,138]. The relationship between firing statistics of single neurons and critical exponents in avalanche dynamics has been a major research topic in neural network dynamics (e.g., [139][140][141][142]).…”

“…Even advanced recording techniques in vivo in the awake rodent demonstrate the absence of avalanches in deep layers. Using two-photon imaging in vivo, power laws in spike-based avalanches were identified in cortical layer 2/3 and layer 4 [133], but seem to be absent in deep layers [134].…”

Parabolic avalanche scaling in the synchronization of cortical cell assemblies

“…Avalanches are readily observed in the local field potential 12 , selectively engage single neurons 13,14 , and carry high information capacity 15,16 . Yet, it is currently not clear whether avalanches, when measured at the single cell level in vivo 8,17–19 , do exhibit robust neuronal synchronization that unfolds in a predictive manner.…”

Toward Reframing Brain-Social Dynamics: Current Assumptions and Future Challenges

“…It is commonly believed that these forms of spike-timing-dependent plasticity (Keysers & Gazzola, 2014) are central to cellular models of learning (Caya-Bissonnette, 2020) which was aptly named later as Hebbian learning. Studies focused on the learning-induced plasticity in adults have shown extensive reorganisations in cortical circuits (Jenkins et al, 1990;Karni et al, 1995;Ma, Liu, Komiyama, & Wessel, 2020;Meaux et al, 2019;Sotiras et al, 2017;G. Yang et al, 2009) such as modifications of synaptic transmission in response to new learning and experiences.…”