Rapid generation of functional engineered 3D human neuronal assemblies: network dynamics evaluated by Micro-Electrodes Arrays

Objective: In this work we propose a method for producing engineered human derived three-dimensional neuronal assemblies coupled to Micro-Electrode Array (MEA) substrates for studying the electrophysiological activity of such networks. Approach: We used biocompatible chitosan microbeads as scaffold to build 3D networks and to ensure nutrients-medium exchange from the core of the structure to the external environment. We used excitatory neurons derived from human-induced Pluripotent Stem Cells (hiPSCs) co-cultured with astrocytes. By adapting the well-established NgN2 differentiation protocol, we obtained 3D engineered networks with good control over cell density, volume and cell composition. We coupled the 3D neuronal networks to 60-channel Micro Electrode Arrays (MEAs) to evaluate and monitor the functional activity of the neuronal population. In parallel, we generated two-dimensional neuronal networks to compare the results of the two models. Main results: 3D cultures were healthy and functional up to 42 Days In Vitro (DIVs). From the structural point of view, the hiPSC derived neurons were able to adhere to chitosan microbeads and to form a stable 3D assembly thanks to the connections among cells. From a functional point of view, neuronal networks showed spontaneous activity after a couple of weeks. We monitored the functional electrophysiological behavior up to 6 weeks and we compared the network dynamic with 2D models. Significance: We presented for the first time a method to generate 3D engineered cultures with human-derived neurons coupled to MEAs, overcoming some of the limitations related to 2D and 3D neuronal networks and thus increasing the therapeutic target potential of these models for biomedical applications.

Awake perception is associated with dedicated neuronal assemblies in cerebral cortex

Neural activity in sensory cortex combines stimulus responses and ongoing activity, but it remains unclear whether they reflect the same underlying dynamics or separate processes. Here we show that during wakefulness, the neuronal assemblies evoked by sounds in the auditory cortex and thalamus are specific to the stimulus and distinct from the assemblies observed in ongoing activity. In contrast, during anesthesia, evoked assemblies are indistinguishable from ongoing assemblies in cortex, while they remain distinct in the thalamus. A strong remapping of sensory responses accompanies this dynamical state change produced by anesthesia. Together, these results show that the awake cortex engages dedicated neuronal assemblies in response to sensory inputs, which we suggest is a network correlate of sensory perception.One-Sentence SummarySensory responses in the awake cortex engage specific neuronal assemblies that disappear under anesthesia.

Phase-specific pooling of sparse assembly activity by respiration-related brain oscillations

Nasal breathing affects cognitive functions, but it has remained largely unclear how respiration-driven inputs shape information processing in neuronal circuits. Current theories emphasize the role of neuronal assemblies, coalitions of transiently active pyramidal cells, as the core unit of cortical network computations. Here, we show that respiration-related oscillations (RROs) directly pace the activation of neuronal assemblies in the medial prefrontal cortex (mPFC) of mice. Neuronal assemblies are more efficiently entrained than single neurons and activate preferentially during the descending phase of RROs. At the same time, overlap between individual assemblies is minimized during descending RRO due to the efficient recruitment of GABAergic neurons by assemblies. Our results thus suggest the RROs support cortical operations by defining time windows of enhanced yet segregated assembly activity.

Flexibility of Functional Neuronal Assemblies Supports Human Memory

Episodic memories, or consciously accessible memories of unique events, represent a key aspect of human cognition. Evidence from rodent models suggests that the neural representation of these complex memories requires cooperative firing of groups of neurons on short time scales, organized by gamma oscillations. These co-firing groups, termed "neuronal assemblies," represent a fundamental neurophysiological unit supporting memory. Using microelectrode data from neurosurgical patients, we identify neuronal assemblies in the human MTL and show that they exhibit consistent organization in their firing pattern based on gamma phase information. We connect these properties to memory performance across recording sessions. Finally, we describe how human neuronal assemblies flexibly adjust over longer time scales. Our findings provide key evidence linking assemblies to human episodic memory for the first time.

Learning excitatory-inhibitory neuronal assemblies in recurrent networks

Understanding the connectivity observed in the brain and how it emerges from local plasticity rules is a grand challenge in modern neuroscience. In the primary visual cortex (V1) of mice, synapses between excitatory pyramidal neurons and inhibitory parvalbumin-expressing (PV) interneurons tend to be stronger for neurons that respond to similar stimulus features, although these neurons are not topographically arranged according to their stimulus preference. The presence of such excitatory-inhibitory (E/I) neuronal assemblies indicates a stimulus-specific form of feedback inhibition. Here, we show that activity-dependent synaptic plasticity on input and output synapses of PV interneurons generates a circuit structure that is consistent with mouse V1. Computational modeling reveals that both forms of plasticity must act in synergy to form the observed E/I assemblies. Once established, these assemblies produce a stimulus-specific competition between pyramidal neurons. Our model suggests that activity-dependent plasticity can refine inhibitory circuits to actively shape cortical computations.

Excitatory-inhibitory balance modulates the formation and dynamics of neuronal assemblies in cortical networks

Repetitive activation of subpopulation of neurons in cortical networks leads to the formation of neuronal assemblies, which can guide learning and behavior. Recent technological advances have made the artificial induction of such assemblies feasible, yet how various patterns of activation can shape their emergence in different operating regimes is not clear. Here we studied this question in large-scale cortical networks composed of excitatory (E) and inhibitory (I) neurons. We found that the dynamics of the network in which neuronal assemblies are embedded is important for their induction. In networks with strong E-E coupling at the border of E-I balance, increasing the number of perturbed neurons enhanced the potentiation of ensembles. This was, however, accompanied by off-target potentiation of connections from unperturbed neurons. When strong E-E connectivity was combined with dominant E-I interactions, formation of ensembles became specific. Counter-intuitively, increasing the number of perturbed neurons in this regime decreased the average potentiation of individual synapses, leading to an optimal assembly formation at intermediate sizes. This was due to potent lateral inhibition in this regime, which also slowed down the formation of neuronal assemblies, resulting in a speed-accuracy trade-off in the performance of the networks in pattern completion and behavioral discrimination. Our results therefore suggest that the two regimes might be suited for different cognitive tasks, with fast regimes enabling crude detections and slow but specific regimes favoring finer discriminations. Functional connectivity inferred from recent experiments in mouse cortical networks seems to be consistent with the latter regime, but we show that recurrent and top-down mechanisms can dynamically modulate the networks to switch between different states. Our work provides a framework to study how neuronal perturbations can lead to network-wide plasticity under biologically realistic conditions, and sheds light on the design of future experiments to optimally induce behaviorally relevant neuronal assemblies.