Harnessing Intelligence from Brain Cells In Vitro

Abstract

Harnessing intelligence from brain cells in vitro requires a multidisciplinary approach integrating wetware, hardware, and software. Wetware comprises the in vitro brain cells themselves, where differentiation from induced pluripotent stem cells offers ethical scalability; hardware typically involves a life support system and a setup to record the activity from and deliver stimulation to the brain cells; and software is required to control the hardware and process the signals coming from and going to the brain cells. This review provides a broad summary of the foundational technologies underpinning these components, along with outlining the importance of technology integration. Of particular importance is that this new technology offers the ability to extend beyond traditional methods that assess primarily the survival and spontaneous activity of neural cultures. Instead, the focus returns to the core function of neural tissue: the neurocomputational ability to process information and respond accordingly. Therefore, this review also covers work that, despite the relatively early state of current technology, has provided novel and meaningful understandings in the field of neuroscience along with opening exciting avenues for future research.

Keywords: criticality; functional connectivity; induced pluripotent stem cells; microelectrode arrays; neural cell cultures; organoid intelligence; perfusion circuit; synthetic biological intelligence; wetware/hardware/software.

Figure 1.

Key steps from donation of genetic material from a consenting volunteer to generation of induced pluripotent stem cells (iPSCs) and either neural monolayers or organoids for plating onto microelectrode arrays (MEA).

Figure 2.

Schematic of integrated system for controlled media change. A comparison of perfusion circuit layouts: (A) and open loop system where the aim is to automatically feed cells without filtration yet separate used media from fresh media; (B) a closed loop system where the media is reused by cells in a limited number of cycles before the media reservoir is refreshed; and (C) a closed loop configuration that also includes multiple filtration units. MEA, microelectrode array.

Figure 3.

Detection and visualization of neural tissue activity through optogenetic approaches. (A) Light stimulation of genetically encoded voltage indicator (GEVI) fluorescent tags in neuronal membrane during action potential. (B) Sodium ion influx and potassium ion efflux corresponding to action potential propagation along a neuron can be concurrently visualized with targeted fluorescent (Fl) molecules at discrete wavelengths. The accumulation of calcium ions is visualized at the axon termini upon successful firing.

Figure 4.

Open-loop systems do not update information inputs based on the output received. Closed-loop systems have a feedback loop that updates future input to a system based on the outputs of the system.

Figure 5.

Schematic describing the core components of a real-time closed-loop instance of a simplified version of the game Pong was tested by combining cortical cells taken from embryonic mouse primary cells or differentiated from human-induced pluripotent stem cells (hIPSCs). Implications from the free energy principle were applied as the feedback was based on whether the cultures successfully or unsuccessfully altered their activity to move a Pong paddle to intercept the virtual ball. CMOS = complementary metal-oxide-semiconductor; HD-MEA = high-density microelectrode array; I/O = input/output.

Figure 6.

Understanding functional connectivity within in vitro neuronal cultures. Schematic representation of a network of in vitro neurons, highlighting sparse spiking activity and the inference of a functional connectivity network from spiking time series before and after dimensionality reduction.

Figure 7.

Understanding population dynamics within in vitro neuronal cultures. (A) Comparison of key characteristics between a critical system and systems exhibiting subcritical or supercritical dynamics. Figure adapted from Habibollahi and others (2023). (B) Illustration of how the network adapts to external stimuli within a task environment, transitioning toward a critical state and away from states that are subcritical (resting-state spontaneous activity) or supercritical (runaway excitation).

Box.

The Concept of Criticality.

Figure 8.

Some possibilities for pharmacologic and translational research via a synthetic biological intelligence (SBI) platform. The gray area on the left represents the components and workflow composing a typical SBI platform capable of interrogating the information-processing capabilities of human neurons. Human neurons are generated in extremely high quantities from induced pluripotent stem cells (iPSCs) for embedding in a closed-loop feedback environment (black arrows), and in the case of a patient with a known disease or mutation, these cells represent an in vitro model of the condition (red arrows). The possibilities for basic drug-testing research and, in the case of patient cells, translational clinical outcomes arising from the SBI platform are listed on the right.