Development of coherent neuronal activity patterns in mammalian cortical networks: Common principles and local hetereogeneity

Abstract

Many mammals are born in a very immature state and develop their rich repertoire of behavioral and cognitive functions postnatally. This development goes in parallel with changes in the anatomical and functional organization of cortical structures which are involved in most complex activities. The emerging spatiotemporal activity patterns in multi-neuronal cortical networks may indeed form a direct neuronal correlate of systemic functions like perception, sensorimotor integration, decision making or memory formation. During recent years, several studies--mostly in rodents--have shed light on the ontogenesis of such highly organized patterns of network activity. While each local network has its own peculiar properties, some general rules can be derived. We therefore review and compare data from the developing hippocampus, neocortex and--as an intermediate region--entorhinal cortex. All cortices seem to follow a characteristic sequence starting with uncorrelated activity in uncoupled single neurons where transient activity seems to have mostly trophic effects. In rodents, before and shortly after birth, cortical networks develop weakly coordinated multineuronal discharges which have been termed synchronous plateau assemblies (SPAs). While these patterns rely mostly on electrical coupling by gap junctions, the subsequent increase in number and maturation of chemical synapses leads to the generation of large-scale coherent discharges. These patterns have been termed giant depolarizing potentials (GDPs) for predominantly GABA-induced events or early network oscillations (ENOs) for mostly glutamatergic bursts, respectively. During the third to fourth postnatal week, cortical areas reach their final activity patterns with distinct network oscillations and highly specific neuronal discharge sequences which support adult behavior. While some of the mechanisms underlying maturation of network activity have been elucidated much work remains to be done in order to fully understand the rules governing transition from immature to mature patterns of network activity.

Molecular, cellular and functional events in axonal sprouting after stroke

“…When synchronized neuronal activity after stroke is blocked axonal sprouting from contralateral cortex into ipsilateral striatum does not occur. In serving as a trigger, this synchronized neuronal activity in peri-infarct cortex after stroke resembles similar activity patterns in the formation of brain connections during development in the retina, hippocampus and cortex (Katz and Shatz, 1996; Stellwagon and Shatz, 2002; Egorov and Draguhn, 2013). Synchronized neuronal activity may activate a downstream molecular program for neuronal growth, or may stimulate the co-activation of many synapses in a particular region of neurons, stimulating a Hebbian plasticity and synaptic sprouting after stroke, as it does in the developing brain.…”

The 3 Rs of Stroke Biology: Radial, Relayed, and Regenerative

“…Blocking peri-infarct synchronized neuronal activity blocks poststroke axonal sprouting in peri-infarct cortex and in contralateral cortex to the infarct [23]. In serving as a trigger, this synchronized neuronal activity in peri-infarct cortex after stroke resembles similar activity patterns in the formation of brain connections during development in the retina, hippocampus, and cortex [49][50][51]. Synchronized neuronal activity may activate a downstream molecular program for neuronal growth, or may stimulate the co-activation of many synapses in a particular region of neurons, stimulating a Hebbian plasticity and synaptic sprouting after stroke, as it does in the developing brain.…”

Annual Research Review: Growth connectomics – the organization and reorganization of brain networks during normal and abnormal development

“…MEA recordings from cultures of dissociated neocortical neurons have been used to study changes in the neuronal dynamics at different stages of development in the mouse (Egorov & Draguhn, 2013; Kamioka, Maeda, Jimbo, Robinson, & Kawana, 1996; Muramoto, Ichikawa, Kawahara, Kobayashi, & Kuroda, 1993). It is only recently, however, that graph theoretical analyses have been used explicitly to map the development of MEA-based functional networks (Chiappalone, Bove, Vato, Tedesco, & Martinoia, 2006; Downes et al., 2012).…”