Optimizing information processing in neuronal networks beyond critical states

The impact of antidepressants on human neurodevelopment: Brain organoids as experimental tools

The use of antidepressants during pregnancy benefits the mother's well-being, but the effects of such substances on neurodevelopment remain poorly understood. Moreover, the consequences of early exposure to antidepressants may not be immediately apparent at birth. In utero exposure to selective serotonin reuptake inhibitors (SSRIs) has been related to developmental abnormalities, including a reduced white matter volume. Several reports have observed an increased incidence of autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) after prenatal exposure to SSRIs such as sertraline, the most widely prescribed SSRI. The advent of human-induced pluripotent stem cell (hiPSC) methods and assays now offers appropriate tools to test the consequences of such compounds for neurodevelopment in vitro. In particular, hiPSCs can be used to generate cerebral organoids - self-organized structures that recapitulate the morphology and complex physiology of the developing human brain, overcoming the limitations found in 2D cell culture and experimental animal models for testing drug efficacy and side effects. For example, single-cell RNA sequencing (scRNA-seq) and electrophysiological measurements on organoids can be used to evaluate the impact of antidepressants on the transcriptome and neuronal activity signatures in developing neurons. While the analysis of large-scale transcriptomic data depends on dimensionality reduction methods, electrophysiological recordings rely on temporal data series to discriminate statistical characteristics of neuronal activity, allowing for the rigorous analysis of the effects of antidepressants and other molecules that affect the developing nervous system, especially when applied in combination with relevant human cellular models such as brain organoids.

Critical excitation-inhibition balance in dense neural networks

The "edge of chaos" phase transition in artificial neural networks is of renewed interest in light of recent evidence for criticality in brain dynamics. Statistical mechanics traditionally studied this transition with connectivity k as the control parameter and an exactly balanced excitation-inhibition ratio. While critical connectivity has been found to be low in these model systems, typically around k=2, which is unrealistic for natural neural systems, a recent study utilizing the excitation-inhibition ratio as the control parameter found a new, nearly degree independent, critical point when connectivity is large. However, the new phase transition is accompanied by an unnaturally high level of activity in the network. Here we study random neural networks with the additional properties of (i) a high clustering coefficient and (ii) neurons that are solely either excitatory or inhibitory, a prominent property of natural neurons. As a result, we observe an additional critical point for networks with large connectivity, regardless of degree distribution, which exhibits low activity levels that compare well with neuronal brain networks.

Robust entropy requires strong and balanced excitatory and inhibitory synapses

It is widely appreciated that balanced excitation and inhibition are necessary for proper function in neural networks. However, in principle, balance could be achieved by many possible configurations of excitatory and inhibitory synaptic strengths and relative numbers of excitatory and inhibitory neurons. For instance, a given level of excitation could be balanced by either numerous inhibitory neurons with weak synapses or a few inhibitory neurons with strong synapses. Among the continuum of different but balanced configurations, why should any particular configuration be favored? Here, we address this question in the context of the entropy of network dynamics by studying an analytically tractable network of binary neurons. We find that entropy is highest at the boundary between excitation-dominant and inhibition-dominant regimes. Entropy also varies along this boundary with a trade-off between high and robust entropy: weak synapse strengths yield high network entropy which is fragile to parameter variations, while strong synapse strengths yield a lower, but more robust, network entropy. In the case where inhibitory and excitatory synapses are constrained to have similar strength, we find that a small, but non-zero fraction of inhibitory neurons, like that seen in mammalian cortex, results in robust and relatively high entropy.