Caudate nucleus (CN) neurons in camels and humans were examined using modified Golgi impregnation methods. Neurons were classified based on soma morphology, dendritic characteristics, and spine distribution. Three primary neuron types were identified in both species: rich-spiny (Type I), sparsely-spiny (Type II), and aspiny (Type III), each comprising subtypes with specific features. Comparative analysis revealed significant differences in soma size, dendritic morphology, and spine distribution between camels and humans. The study contributes to our understanding of structural diversity in CN neurons and provides insights into evolutionary neural adaptations.

A recent study by Cheung, Pauler, Koppensteiner et al. combining lineage tracing with single-cell RNA sequencing (scRNA-seq) has revealed unexpected features of the developing superior colliculus (SC). Extremely multipotent individual progenitors generate all types of SC neurons and glial cells that were found to localize in a non-predetermined pattern, demonstrating a remarkable degree of unpredictability in SC development.

How our brain generates diverse neuron types that assemble into precise neural circuits remains unclear. Using Drosophila lamina neuron types (L1-L5), we show that the primary homeodomain transcription factor (HDTF) brain-specific homeobox (Bsh) is initiated in progenitors and maintained in L4/L5 neurons to adulthood. Bsh activates secondary HDTFs Ap (L4) and Pdm3 (L5) and specifies L4/L5 neuronal fates while repressing the HDTF Zfh1 to prevent ectopic L1/L3 fates (control: L1-L5; Bsh-knockdown: L1-L3), thereby generating lamina neuronal diversity for normal visual sensitivity. Subsequently, in L4 neurons, Bsh and Ap function in a feed-forward loop to activate the synapse recognition molecule DIP-β, thereby bridging neuronal fate decision to synaptic connectivity. Expression of a Bsh:Dam, specifically in L4, reveals Bsh binding to the DIP-β locus and additional candidate L4 functional identity genes. We propose that HDTFs function hierarchically to coordinate neuronal molecular identity, circuit formation, and function. Hierarchical HDTFs may represent a conserved mechanism for linking neuronal diversity to circuit assembly and function.

Reproducible definition and identification of cell types is essential to enable investigations into their biological function, and understanding their relevance in the context of development, disease and evolution. Current approaches model variability in data as continuous latent factors, followed by clustering as a separate step, or immediately apply clustering on the data. We show that such approaches can suffer from qualitative mistakes in identifying cell types robustly, particularly when the number of such cell types is in the hundreds or even thousands. Here, we propose an unsupervised method, MMIDAS, which combines a generalized mixture model with a multi-armed deep neural network, to jointly infer the discrete type and continuous type-specific variability. Using four recent datasets of brain cells spanning different technologies, species, and conditions, we demonstrate that MMIDAS can identify reproducible cell types and infer cell type-dependent continuous variability in both uni-modal and multi-modal datasets.

Heterogeneity is the norm in biology. The brain is no different: Neuronal cell types are myriad, reflected through their cellular morphology, type, excitability, connectivity motifs, and ion channel distributions. While this biophysical diversity enriches neural systems\' dynamical repertoire, it remains challenging to reconcile with the robustness and persistence of brain function over time (resilience). To better understand the relationship between excitability heterogeneity (variability in excitability within a population of neurons) and resilience, we analyzed both analytically and numerically a nonlinear sparse neural network with balanced excitatory and inhibitory connections evolving over long time scales. Homogeneous networks demonstrated increases in excitability, and strong firing rate correlations-signs of instability-in response to a slowly varying modulatory fluctuation. Excitability heterogeneity tuned network stability in a context-dependent way by restraining responses to modulatory challenges and limiting firing rate correlations, while enriching dynamics during states of low modulatory drive. Excitability heterogeneity was found to implement a homeostatic control mechanism enhancing network resilience to changes in population size, connection probability, strength and variability of synaptic weights, by quenching the volatility (i.e., its susceptibility to critical transitions) of its dynamics. Together, these results highlight the fundamental role played by cell-to-cell heterogeneity in the robustness of brain function in the face of change.

A liquid state machine (LSM) is a biologically plausible model of a cortical microcircuit. It exists of a random, sparse reservoir of recurrently connected spiking neurons with fixed synapses and a trainable readout layer. The LSM exhibits low training complexity and enables backpropagation-free learning in a powerful, yet simple computing paradigm. In this work, the liquid state machine is enhanced by a set of bio-inspired extensions to create the extended liquid state machine (ELSM), which is evaluated on a set of speech data sets. Firstly, we ensure excitatory/inhibitory (E/I) balance to enable the LSM to operate in edge-of-chaos regime. Secondly, spike-frequency adaptation (SFA) is introduced in the LSM to improve the memory capabilities. Lastly, neuronal heterogeneity, by means of a differentiation in time constants, is introduced to extract a richer dynamical LSM response. By including E/I balance, SFA, and neuronal heterogeneity, we show that the ELSM consistently improves upon the LSM while retaining the benefits of the straightforward LSM structure and training procedure. The proposed extensions led up to an 5.2% increase in accuracy while decreasing the number of spikes in the ELSM up to 20.2% on benchmark speech data sets. On some benchmarks, the ELSM can even attain similar performances as the current state-of-the-art in spiking neural networks. Furthermore, we illustrate that the ELSM input-liquid and recurrent synaptic weights can be reduced to 4-bit resolution without any significant loss in classification performance. We thus show that the ELSM is a powerful, biologically plausible and hardware-friendly spiking neural network model that can attain near state-of-the-art accuracy on speech recognition benchmarks for spiking neural networks.

To understand how the nervous system develops from a small pool of progenitors during early embryonic development, it is fundamentally important to identify the diversity of neuronal subtypes, decode the origin of neuronal diversity, and uncover the principles governing neuronal specification across different regions. Recent single-cell analyses have systematically identified neuronal diversity at unprecedented scale and speed, leaving the deconstruction of spatiotemporal mechanisms for generating neuronal diversity an imperative and paramount challenge. In this review, we highlight three distinct strategies deployed by neural progenitors to produce diverse neuronal subtypes, including predetermined, stochastic, and cascade diversifying models, and elaborate how these strategies are implemented in distinct regions such as the neocortex, spinal cord, retina, and hypothalamus. Importantly, the identity of neural progenitors is defined by their spatial position and temporal patterning factors, and each type of progenitor cell gives rise to distinguishable cohorts of neuronal subtypes. Microenvironmental cues, spontaneous activity, and connectional pattern further reshape and diversify the fate of unspecialized neurons in particular regions. The illumination of how neuronal diversity is generated will pave the way for producing specific brain organoids to model human disease and desired neuronal subtypes for cell therapy, as well as understanding the organization of functional neural circuits and the evolution of the nervous system.

Realizing the full utility of brain organoids to study human development requires understanding whether organoids precisely replicate endogenous cellular and molecular events, particularly since acquisition of cell identity in organoids can be impaired by abnormal metabolic states. We present a comprehensive single-cell transcriptomic, epigenetic, and spatial atlas of human cortical organoid development, comprising over 610,000 cells, from generation of neural progenitors through production of differentiated neuronal and glial subtypes. We show that processes of cellular diversification correlate closely to endogenous ones, irrespective of metabolic state, empowering the use of this atlas to study human fate specification. We define longitudinal molecular trajectories of cortical cell types during organoid development, identify genes with predicted human-specific roles in lineage establishment, and uncover early transcriptional diversity of human callosal neurons. The findings validate this comprehensive atlas of human corticogenesis in vitro as a resource to prime investigation into the mechanisms of human cortical development.

Defining the origin of neuronal diversity is a major challenge in developmental neurobiology. The Drosophila visual system is an excellent paradigm to study how cellular diversity is generated. Photoreceptors from the eye disc grow their axons into the optic lobe and secrete Hedgehog (Hh) to induce the lamina, such that for every unit eye there is a corresponding lamina unit made up of post-mitotic precursors stacked into columns. Each differentiated column contains five lamina neuron types (L1-L5), making it the simplest neuropil in the optic lobe, yet how this diversity is generated was unknown. Here, we found that Hh pathway activity is graded along the distal-proximal axis of lamina columns, and further determined that this gradient in pathway activity arises from a gradient of Hh ligand. We manipulated Hh pathway activity cell autonomously in lamina precursors and non-cell autonomously by inactivating the Hh ligand and by knocking it down in photoreceptors. These manipulations showed that different thresholds of activity specify unique cell identities, with more proximal cell types specified in response to progressively lower Hh levels. Thus, our data establish that Hh acts as a morphogen to pattern the lamina. Although this is the first such report during Drosophila nervous system development, our work uncovers a remarkable similarity with the vertebrate neural tube, which is patterned by Sonic Hh. Altogether, we show that differentiating neurons can regulate the neuronal diversity of their distant target fields through morphogen gradients.

The development of the central nervous system (CNS) in flies and mammals requires the production of distinct neurons in different locations and times. Here we review progress on how Drosophila stem cells (neuroblasts; NBs) generate distinct neurons over time. There are two types of NBs: type I and type II NBs (defined below); here we focus on type I NBs; type II NBs are reviewed elsewhere in this issue. Type I NBs generate neural diversity via the cascading expression of specific temporal transcription factors (TTFs). TTFs are sequentially expressed in neuroblasts and required for the identity of neurons born during each TTF expression window. In this way TTFs specify the \"temporal identity\" or birth-order dependent identity of neurons. Recent studies have shown that TTF expression in neuroblasts alter the identity of their progeny, including directing motor neurons to form proper connectivity to the proper muscle targets, independent of their birth-order. Similarly, optic lobe (OL) type I NBs express a series of TTFs that promote proper neuron morphology and targeting to the four OL neuropils. Together, these studies demonstrate how temporal identity is crucial in promoting proper circuit assembly within the Drosophila CNS. In addition, TTF orthologs in mouse are good candidates for specifying neuron types in the neocortex and retina. In this review we highlight the recent advances in understanding the role of TTFs in CNS circuit assembly in Drosophila and reflect on the conservation of these mechanisms in mammalian CNS development.