How many neurons are sufficient for perception of cortical activity?

Network influence determines the impact of cortical ensembles on stimulus detection

Causally connecting neural activity patterns to behavioral decisions is essential to understand the neural code but requires direct perturbation of the pattern of interest with high specificity. We combined two-photon imaging and cellular-resolution holographic optogenetic photostimulation to causally test how neural activity in the mouse visual cortex is read out to detect visual stimuli. Contrary to expectations, targeted activation of visually sensitive neural ensembles did not preferentially modify behavior compared with targeting randomly selected ensembles. Instead, an activated ensemble's effect on local network activity was the main predictor of its impact on perception. This suggests that downstream regions summate visual cortex activity without preferentially weighting more informative neurons, a notion confirmed by analyzing the impact of photostimulation on decoding models of neural activity. This work challenges conventional notions for how sensory representations mediate perception and demonstrates that perturbing activity is essential to determine which features of neural activity drive behavior.

The Duality of Astrocyte Neuromodulation: Astrocytes Sense Neuromodulators and Are Neuromodulators

Neuromodulation encompasses different processes that regulate neuronal and network function. Classical neuromodulators originating from long-range nuclei, such as acetylcholine, norepinephrine, or dopamine, act with a slower time course and wider spatial range than fast synaptic transmission and action potential firing. Accumulating evidence in vivo indicates that astrocytes, which are known to actively participate in synaptic function at tripartite synapses, are also involved in neuromodulatory processes. The present article reviews recent findings obtained in vivo indicating that astrocytes express receptors for neuromodulators that elevate their internal calcium and stimulate the release of gliotransmitters, which regulate synaptic and network function, and hence mediate, at least partially, the effects of neuromodulators. In addition, we propose that astrocytes act in local support of neuromodulators by spatially and temporally integrating neuronal and neuromodulatory signals to regulate neural network function. The presence of astrocyte-neuron hysteresis loops suggests astrocyte-neuron interaction at tripartite synapses scales up to astrocyte-neuronal networks that modulate neural network function. We finally propose that astrocytes sense the environmental conditions, including neuromodulators and network function states, and provide homeostatic control that maximizes the dynamic range of neural network activity. In summary, we propose that astrocytes are critical in mediating the effects of neuromodulators, and they also act as neuromodulators to provide neural network homeostasis thus optimizing information processing in the brain. Hence, astrocytes sense ongoing neuronal activity along with neuromodulators and, acting as neuromodulators, inform the neurons about the state of the internal system and the external world.

Information transfer and recovery for the sense of touch

Transformation of postsynaptic potentials into action potentials is the rate-limiting step of communication in neural networks. The efficiency of this intracellular information transfer also powerfully shapes stimulus representations in sensory cortices. Using whole-cell recordings and information-theoretic measures, we show herein that somatic postsynaptic potentials accurately represent stimulus location on a trial-by-trial basis in single neurons, even 4 synapses away from the sensory periphery in the whisker system. This information is largely lost during action potential generation but can be rapidly (<20 ms) recovered using complementary information in local populations in a cell-type-specific manner. These results show that as sensory information is transferred from one neural locus to another, the circuits reconstruct the stimulus with high fidelity so that sensory representations of single neurons faithfully represent the stimulus in the periphery, but only in their postsynaptic potentials, resulting in lossless information processing for the sense of touch in the primary somatosensory cortex.

A spatial code for temporal information is necessary for efficient sensory learning

The temporal structure of sensory inputs contains essential information for their interpretation. Sensory cortex represents these temporal cues through two codes: the temporal sequences of neuronal activity and the spatial patterns of neuronal firing rate. However, it is unknown which of these coexisting codes causally drives sensory decisions. To separate their contributions, we generated in the mouse auditory cortex optogenetically driven activity patterns differing exclusively along their temporal or spatial dimensions. Mice could rapidly learn to behaviorally discriminate spatial but not temporal patterns. Moreover, large-scale neuronal recordings across the auditory system revealed that the auditory cortex is the first region in which spatial patterns efficiently represent temporal cues on the timescale of several hundred milliseconds. This feature is shared by the deep layers of neural networks categorizing time-varying sounds. Therefore, the emergence of a spatial code for temporal sensory cues is a necessary condition to efficiently associate temporally structured stimuli with decisions.

Cellular-resolution optogenetics reveals attenuation-by-suppression in visual cortical neurons

The relationship between neurons' input and spiking output is central to brain computation. Studies in vitro and in anesthetized animals suggest that nonlinearities emerge in cells' input-output (IO; activation) functions as network activity increases, yet how neurons transform inputs in vivo has been unclear. Here, we characterize cortical principal neurons' activation functions in awake mice using two-photon optogenetics. We deliver fixed inputs at the soma while neurons' activity varies with sensory stimuli. We find that responses to fixed optogenetic input are nearly unchanged as neurons are excited, reflecting a linear response regime above neurons' resting point. In contrast, responses are dramatically attenuated by suppression. This attenuation is a powerful means to filter inputs arriving to suppressed cells, privileging other inputs arriving to excited neurons. These results have two major implications. First, somatic neural activation functions in vivo accord with the activation functions used in recent machine learning systems. Second, neurons' IO functions can filter sensory inputs-not only do sensory stimuli change neurons' spiking outputs, but these changes also affect responses to input, attenuating responses to some inputs while leaving others unchanged.

A latent pool of neurons silenced by sensory-evoked inhibition can be recruited to enhance perception

To investigate which activity patterns in sensory cortex are relevant for perceptual decision-making, we combined two-photon calcium imaging and targeted two-photon optogenetics to interrogate barrel cortex activity during perceptual discrimination. We trained mice to discriminate bilateral whisker deflections and report decisions by licking left or right. Two-photon calcium imaging revealed sparse coding of contralateral and ipsilateral whisker input in layer 2/3, with most neurons remaining silent during the task. Activating pyramidal neurons using two-photon holographic photostimulation evoked a perceptual bias that scaled with the number of neurons photostimulated. This effect was dominated by optogenetic activation of non-coding neurons, which did not show sensory or motor-related activity during task performance. Photostimulation also revealed potent recruitment of cortical inhibition during sensory processing, which strongly and preferentially suppressed non-coding neurons. Our results suggest that a pool of non-coding neurons, selectively suppressed by network inhibition during sensory processing, can be recruited to enhance perception.

Stimulus information guides the emergence of behavior-related signals in primary somatosensory cortex during learning

Neurons in the primary cortex carry sensory- and behavior-related information, but it remains an open question how this information emerges and intersects together during learning. Current evidence points to two possible learning-related changes: sensory information increases in the primary cortex or sensory information remains stable, but its readout efficiency in association cortices increases. We investigated this question by imaging neuronal activity in mouse primary somatosensory cortex before, during, and after learning of an object localization task. We quantified sensory- and behavior-related information and estimated how much sensory information was used to instruct perceptual choices as learning progressed. We find that sensory information increases from the start of training, while choice information is mostly present in the later stages of learning. Additionally, the readout of sensory information becomes more efficient with learning as early as in the primary sensory cortex. Together, our results highlight the importance of primary cortical neurons in perceptual learning.

Brain-state mediated modulation of inter-laminar dependencies in visual cortex

Spatial attention is critical for recognizing behaviorally relevant objects in a cluttered environment. How the deployment of spatial attention aids the hierarchical computations of object recognition remains unclear. We investigated this in the laminar cortical network of visual area V4, an area strongly modulated by attention. We found that deployment of attention strengthened unique dependencies in neural activity across cortical layers. On the other hand, shared dependencies were reduced within the excitatory population of a layer. Surprisingly, attention strengthened unique dependencies within a laminar population. Crucially, these modulation patterns were also observed during successful behavioral outcomes that are thought to be mediated by internal brain state fluctuations. Successful behavioral outcomes were also associated with phases of reduced neural excitability, suggesting a mechanism for enhanced information transfer during optimal states. Our results suggest common computation goals of optimal sensory states that are attained by either task demands or internal fluctuations.

Highly local activation of inhibition at the seizure wavefront in vivo

The propagation of a seizure wavefront in the cortex divides an intensely firing seizure core from a low-firing seizure penumbra. Seizure propagation is currently thought to generate strong activation of inhibition in the seizure penumbra that leads to its decreased neuronal firing. However, the direct measurement of neuronal excitability during seizures has been difficult to perform in vivo. We used simultaneous optogenetics and calcium imaging (all-optical interrogation) to characterize real-time neuronal excitability in an acute mouse model of seizure propagation. We find that single-neuron excitability is decreased in close proximity to the seizure wavefront but becomes increased distal to the seizure wavefront. This suggests that inhibitory neurons of the seizure wavefront create a proximal circumference of hypoexcitability but do not influence neuronal excitability in the penumbra.

GHSR in a Subset of GABA Neurons Controls Food Deprivation-Induced Hyperphagia in Male Mice

The growth hormone secretagogue receptor (GHSR), primarily known as the receptor for the hunger hormone ghrelin, potently controls food intake, yet the specific Ghsr-expressing cells mediating the orexigenic effects of this receptor remain incompletely characterized. Since Ghsr is expressed in gamma-aminobutyric acid (GABA)-producing neurons, we sought to investigate whether the selective expression of Ghsr in a subset of GABA neurons is sufficient to mediate GHSR's effects on feeding. First, we crossed mice that express a tamoxifen-dependent Cre recombinase in the subset of GABA neurons that express glutamic acid decarboxylase 2 (Gad2) enzyme (Gad2-CreER mice) with reporter mice, and found that ghrelin mainly targets a subset of Gad2-expressing neurons located in the hypothalamic arcuate nucleus (ARH) and that is predominantly segregated from Agouti-related protein (AgRP)-expressing neurons. Analysis of various single-cell RNA-sequencing datasets further corroborated that the primary subset of cells coexpressing Gad2 and Ghsr in the mouse brain are non-AgRP ARH neurons. Next, we crossed Gad2-CreER mice with reactivable GHSR-deficient mice to generate mice expressing Ghsr only in Gad2-expressing neurons (Gad2-GHSR mice). We found that ghrelin treatment induced the expression of the marker of transcriptional activation c-Fos in the ARH of Gad2-GHSR mice, yet failed to induce food intake. In contrast, food deprivation-induced refeeding was higher in Gad2-GHSR mice than in GHSR-deficient mice and similar to wild-type mice, suggesting that ghrelin-independent roles of GHSR in a subset of GABA neurons is sufficient for eliciting full compensatory hyperphagia in mice.

Single-cell optogenetics reveals attenuation-by-suppression in visual cortical neurons

The relationship between neurons' input and spiking output is central to brain computation. Studies in vitro and in anesthetized animals suggest nonlinearities emerge in cells' input-output (activation) functions as network activity increases, yet how neurons transform inputs in vivo has been unclear. Here, we characterize cortical principal neurons' activation functions in awake mice using two-photon optogenetics. We deliver fixed inputs at the soma while neurons' activity varies with sensory stimuli. We find responses to fixed optogenetic input are nearly unchanged as neurons are excited, reflecting a linear response regime above neurons' resting point. In contrast, responses are dramatically attenuated by suppression. This attenuation is a powerful means to filter inputs arriving to suppressed cells, privileging other inputs arriving to excited neurons. These results have two major implications. First, somatic neural activation functions in vivo accord with the activation functions used in recent machine learning systems. Second, neurons' IO functions can filter sensory inputs - not only do sensory stimuli change neurons' spiking outputs, but these changes also affect responses to input, attenuating responses to some inputs while leaving others unchanged.

Unifying network model links recency and central tendency biases in working memory

The central tendency bias, or contraction bias, is a phenomenon where the judgment of the magnitude of items held in working memory appears to be biased toward the average of past observations. It is assumed to be an optimal strategy by the brain and commonly thought of as an expression of the brain's ability to learn the statistical structure of sensory input. On the other hand, recency biases such as serial dependence are also commonly observed and are thought to reflect the content of working memory. Recent results from an auditory delayed comparison task in rats suggest that both biases may be more related than previously thought: when the posterior parietal cortex (PPC) was silenced, both short-term and contraction biases were reduced. By proposing a model of the circuit that may be involved in generating the behavior, we show that a volatile working memory content susceptible to shifting to the past sensory experience - producing short-term sensory history biases - naturally leads to contraction bias. The errors, occurring at the level of individual trials, are sampled from the full distribution of the stimuli and are not due to a gradual shift of the memory toward the sensory distribution's mean. Our results are consistent with a broad set of behavioral findings and provide predictions of performance across different stimulus distributions and timings, delay intervals, as well as neuronal dynamics in putative working memory areas. Finally, we validate our model by performing a set of human psychophysics experiments of an auditory parametric working memory task.

Stimulus encoding by specific inactivation of cortical neurons

Neuronal ensembles are groups of neurons with correlated activity associated with sensory, motor, and behavioral functions. To explore how ensembles encode information, we investigated responses of visual cortical neurons in awake mice using volumetric two-photon calcium imaging during visual stimulation. We identified neuronal ensembles employing an unsupervised model-free algorithm and, besides neurons activated by the visual stimulus (termed "onsemble"), we also find neurons that are specifically inactivated (termed "offsemble"). Offsemble neurons showed faster calcium decay during stimuli, suggesting selective inhibition. In response to visual stimuli, each ensemble (onsemble+offsemble) exhibited small trial-to-trial variability, high orientation selectivity, and superior predictive accuracy for visual stimulus orientation, surpassing the sum of individual neuron activity. Thus, the combined selective activation and inactivation of cortical neurons enhances visual encoding as an emergent and distributed neural code.

The influence of cortical activity on perception depends on behavioral state and sensory context

The mechanistic link between neural circuit activity and behavior remains unclear. While manipulating cortical activity can bias certain behaviors and elicit artificial percepts, some tasks can still be solved when cortex is silenced or removed. Here, mice were trained to perform a visual detection task during which we selectively targeted groups of visually responsive and co-tuned neurons in L2/3 of primary visual cortex (V1) for two-photon photostimulation. The influence of photostimulation was conditional on two key factors: the behavioral state of the animal and the contrast of the visual stimulus. The detection of low-contrast stimuli was enhanced by photostimulation, while the detection of high-contrast stimuli was suppressed, but crucially, only when mice were highly engaged in the task. When mice were less engaged, our manipulations of cortical activity had no effect on behavior. The behavioral changes were linked to specific changes in neuronal activity. The responses of non-photostimulated neurons in the local network were also conditional on two factors: their functional similarity to the photostimulated neurons and the contrast of the visual stimulus. Functionally similar neurons were increasingly suppressed by photostimulation with increasing visual stimulus contrast, correlating with the change in behavior. Our results show that the influence of cortical activity on perception is not fixed, but dynamically and contextually modulated by behavioral state, ongoing activity and the routing of information through specific circuits.

Brain-state mediated modulation of inter-laminar dependencies in visual cortex

Spatial attention is a quintessential example of adaptive information processing in the brain and is critical for recognizing behaviorally relevant objects in a cluttered environment. Object recognition is mediated by neural encoding along the ventral visual hierarchy. How the deployment of spatial attention aids these hierarchical computations is unclear. Prior studies point to two distinct mechanisms: an improvement in the efficacy of information directed from one encoding stage to another, and/or a suppression of shared information within encoding stages. To test these proposals, it is crucial to estimate the attentional modulation of unique information flow across and shared information within the encoding stages of the visual hierarchy. We investigated this in the multi-stage laminar network of visual area V4, an area strongly modulated by attention. Using network-based dependency estimation from multivariate data, we quantified the modulation of inter-layer information flow during a change detection task and found that deployment of attention indeed strengthened unique dependencies between the input and superficial layers. Using the partial information decomposition framework, we estimated the modulation of shared dependencies and found that they are reduced specifically in the putative excitatory subpopulations within a layer. Surprisingly, we found a strengthening of unique dependencies within the laminar populations, a finding not previously predicted. Crucially, these modulation patterns were also observed during successful behavioral outcomes (hits) that are thought to be mediated by endogenous brain state fluctuations, and not by experimentally imposed attentive states. Finally, phases of endogenous fluctuations that were optimal for 'hits' were associated with reduced neural excitability. A reduction in neural excitability, potentially mediated by diminished shared inputs, suggests a novel mechanism for enhancing unique information transmission during optimal states. By decomposing the modulation of multivariate information, and combined with prior theoretical work, our results suggest common computations of optimal sensory states that are attained by either task demands or endogenous fluctuations.

Optogenetics 2.0: challenges and solutions towards a quantitative probing of neural circuits

To exploit the full potential of optogenetics, we need to titrate and tailor optogenetic methods to emulate naturalistic circuit function. For that, the following prerequisites need to be met: first, we need to target opsin expression not only to genetically defined neurons per se, but to specifically target a functional node. Second, we need to assess the scope of optogenetic modulation, i.e. the fraction of optogenetically modulated neurons. Third, we need to integrate optogenetic control in a closed loop setting. Fourth, we need to further safe and stable gene expression and light delivery to bring optogenetics to the clinics. Here, we review these concepts for the human and rodent brain.

The logic of recurrent circuits in the primary visual cortex

Recurrent cortical activity sculpts visual perception by refining, amplifying or suppressing visual input. However, the rules that govern the influence of recurrent activity remain enigmatic. We used ensemble-specific two-photon optogenetics in the mouse visual cortex to isolate the impact of recurrent activity from external visual input. We found that the spatial arrangement and the visual feature preference of the stimulated ensemble and the neighboring neurons jointly determine the net effect of recurrent activity. Photoactivation of these ensembles drives suppression in all cells beyond 30 µm but uniformly drives activation in closer similarly tuned cells. In nonsimilarly tuned cells, compact, cotuned ensembles drive net suppression, while diffuse, cotuned ensembles drive activation. Computational modeling suggests that highly local recurrent excitatory connectivity and selective convergence onto inhibitory neurons explain these effects. Our findings reveal a straightforward logic in which space and feature preference of cortical ensembles determine their impact on local recurrent activity.

Contribution of optical resolution to the spatial precision of two-photon optogenetic photostimulation in vivo

Significance

Two-photon optogenetics combines nonlinear excitation with noninvasive activation of neurons to enable the manipulation of neural circuits with a high degree of spatial precision. Combined with two-photon population calcium imaging, these approaches comprise a flexible platform for all-optical interrogation of neural circuits. However, a multitude of optical and biological factors dictate the exact precision of this approach in vivo, where it is most usefully applied.

Aim

We aimed to assess how the optical point spread function (OPSF) contributes to the spatial precision of two-photon photostimulation in neurobiology.

Approach

We altered the axial spread of the OPSF of the photostimulation beam using a spatial light modulator. Subsequently, calcium imaging was used to monitor the axial spatial precision of two-photon photostimulation of layer 2 neurons in the mouse neocortex.

Results

We found that optical resolution is not always the limiting factor of the spatial precision of two-photon optogenetic photostimulation and, by doing so, reveal the key factors that must be improved to achieve maximal precision.

Conclusions

Our results enable future work to focus on the optimal factors by providing key insight from controlled experiments in a manner not previously reported. This research can be applied to advance the state-of-the-art of all-optical interrogation, extending the toolkit for neuroscience research to achieve spatiotemporal precision at the crucial levels in which neural circuits operate.

Disrupted synaptic homeostasis and partial occlusion of associative long-term potentiation in the human cortex during social isolation

Social isolation often occurs in the military mission of soldiers but has increased in the general population since the COVID-19 epidemic. Overall synaptic homeostasis along with associative plasticity for the activity-dependent refinement of transmission across single synapses represent basic neural network function and adaptive behavior mechanisms. Here, we use electrophysiological and behavioral indices to non-invasively study the net synaptic strength and long-term potentiation (LTP)-like plasticity of humans in social isolation environments. The theta activity of electroencephalography (EEG) signals and transcranial magnetic stimulation (TMS) intensity to elicit a predefined amplitude of motor-evoked potential (MEP) demonstrate the disrupted synaptic homeostasis in the human cortex during social isolation. Furthermore, the induced MEP change by paired associative stimulation (PAS) demonstrates the partial occlusion of LTP-like plasticity, further behavior performances in a word-pair task are also identified as a potential index. Our study indicates that social isolation disrupts synaptic homeostasis and occludes associative LTP-like plasticity in the human cortex, decreasing behavior performance related to declarative memory.

Mechanisms underlying reshuffling of visual responses by optogenetic stimulation in mice and monkeys

The ability to optogenetically perturb neural circuits opens an unprecedented window into mechanisms governing circuit function. We analyzed and theoretically modeled neuronal responses to visual and optogenetic inputs in mouse and monkey V1. In both species, optogenetic stimulation of excitatory neurons strongly modulated the activity of single neurons yet had weak or no effects on the distribution of firing rates across the population. Thus, the optogenetic inputs reshuffled firing rates across the network. Key statistics of mouse and monkey responses lay on a continuum, with mice/monkeys occupying the low-/high-rate regions, respectively. We show that neuronal reshuffling emerges generically in randomly connected excitatory/inhibitory networks, provided the coupling strength (combination of recurrent coupling and external input) is sufficient that powerful inhibitory feedback cancels the mean optogenetic input. A more realistic model, distinguishing tuned visual vs. untuned optogenetic input in a structured network, reduces the coupling strength needed to explain reshuffling.

Neurological Considerations for "Nerve-Sparing" Cosmetic Genital Surgeries Performed on Children with XX Chromosomes Diagnosed with 21-Hydroxylase Congenital Adrenal Hyperplasia and Clitoromegaly

Congenital adrenal hyperplasia (CAH) is most often caused by adrenal deficiency of 21-hydroxylase (21-OH). The resulting increase in androgens can cause clitoromegaly in fetuses with XX chromosomes. 21-OH CAH is the most common reason for cosmetic clitoroplasty in childhood. "Nerve-sparing" (NS) clitoral reduction surgeries are described as offering optimal cosmesis, while sparing sensation and nerve function. The methods used to demonstrate NS surgery efficacy, however, such as electromyography and optical coherence tomography, do not evaluate the small-fiber axons that comprise the majority of axons in the clitoris and that transduce sexual pleasure. Although some data show sparing of a portion of the main dorsal nerve trunk of the clitoris, the overall neurobiological consequences of elective clitoral reductions have received little attention. NS surgeries remove dorsal nerve branches that transduce sexual sensation, as well as the corpora cavernosa and cavernous nerve, which provide clitoral autonomic function. While most outcome studies focus on surgeons' perceptions of cosmetic results, studies that assess small-fiber function indicate significant nervous system and sexual impairment. Studies assessing children's clitoral function after surgery with vibrational testing have been ethically condemned. Decades of advocacy against medically unnecessary childhood genital surgeries have highlighted the subsequent physical and psychological harm. Recent studies with CAH patients indicate gender diversity and a lower prevalence of female gender identification than is often cited to justify feminizing surgery. The most effective and ethical NS technique for CAH may be acceptance of gender, sexual, and genital diversity as the infant develops into childhood, adolescence, and adulthood.

Compressive sensing of functional connectivity maps from patterned optogenetic stimulation of neuronal ensembles

Mapping functional connectivity between neurons is an essential step toward probing the neural computations mediating behavior. Accurately determining synaptic connectivity maps in populations of neurons is challenging in terms of yield, accuracy, and experimental time. Here, we developed a compressive sensing approach to reconstruct synaptic connectivity maps based on random two-photon cell-targeted optogenetic stimulation and membrane voltage readout of many putative postsynaptic neurons. Using a biophysical network model of interconnected populations of excitatory and inhibitory neurons, we characterized mapping recall and precision as a function of network observability, sparsity, number of neurons stimulated, off-target stimulation, synaptic reliability, propagation latency, and network topology. We found that mapping can be achieved with far fewer measurements than the standard pairwise sequential approach, with network sparsity and synaptic reliability serving as primary determinants of the performance. Our results suggest a rapid and efficient method to reconstruct functional connectivity of sparsely connected neuronal networks.

FIOLA: an accelerated pipeline for fluorescence imaging online analysis

Optical microscopy methods such as calcium and voltage imaging enable fast activity readout of large neuronal populations using light. However, the lack of corresponding advances in online algorithms has slowed progress in retrieving information about neural activity during or shortly after an experiment. This gap not only prevents the execution of real-time closed-loop experiments, but also hampers fast experiment-analysis-theory turnover for high-throughput imaging modalities. Reliable extraction of neural activity from fluorescence imaging frames at speeds compatible with indicator dynamics and imaging modalities poses a challenge. We therefore developed FIOLA, a framework for fluorescence imaging online analysis that extracts neuronal activity from calcium and voltage imaging movies at speeds one order of magnitude faster than state-of-the-art methods. FIOLA exploits algorithms optimized for parallel processing on GPUs and CPUs. We demonstrate reliable and scalable performance of FIOLA on both simulated and real calcium and voltage imaging datasets. Finally, we present an online experimental scenario to provide guidance in setting FIOLA parameters and to highlight the trade-offs of our approach.

Propagation of activity through the cortical hierarchy and perception are determined by neural variability

Brains are composed of anatomically and functionally distinct regions performing specialized tasks, but regions do not operate in isolation. Orchestration of complex behaviors requires communication between brain regions, but how neural dynamics are organized to facilitate reliable transmission is not well understood. Here we studied this process directly by generating neural activity that propagates between brain regions and drives behavior, assessing how neural populations in sensory cortex cooperate to transmit information. We achieved this by imaging two densely interconnected regions-the primary and secondary somatosensory cortex (S1 and S2)-in mice while performing two-photon photostimulation of S1 neurons and assigning behavioral salience to the photostimulation. We found that the probability of perception is determined not only by the strength of the photostimulation but also by the variability of S1 neural activity. Therefore, maximizing the signal-to-noise ratio of the stimulus representation in cortex relative to the noise or variability is critical to facilitate activity propagation and perception.

All-optical recreation of naturalistic neural activity with a multifunctional transgenic reporter mouse

Determining which features of the neural code drive behavior requires the ability to simultaneously read out and write in neural activity patterns with high precision across many neurons. All-optical systems that combine two-photon calcium imaging and targeted photostimulation enable the activation of specific, functionally defined groups of neurons. However, these techniques are unable to test how patterns of activity across a population contribute to computation because of an inability to both read and write cell-specific firing rates. To overcome this challenge, we make two advances: first, we introduce a genetic line of mice for Cre-dependent co-expression of a calcium indicator and a potent soma-targeted microbial opsin. Second, using this line, we develop a method for read-out and write-in of precise population vectors of neural activity by calibrating the photostimulation to each cell. These advances offer a powerful and convenient platform for investigating the neural codes of computation and behavior.

Convolutional neural networks for vision neuroscience: significance, developments, and outstanding issues

Convolutional Neural Networks (CNN) are a class of machine learning models predominately used in computer vision tasks and can achieve human-like performance through learning from experience. Their striking similarities to the structural and functional principles of the primate visual system allow for comparisons between these artificial networks and their biological counterparts, enabling exploration of how visual functions and neural representations may emerge in the real brain from a limited set of computational principles. After considering the basic features of CNNs, we discuss the opportunities and challenges of endorsing CNNs as in silico models of the primate visual system. Specifically, we highlight several emerging notions about the anatomical and physiological properties of the visual system that still need to be systematically integrated into current CNN models. These tenets include the implementation of parallel processing pathways from the early stages of retinal input and the reconsideration of several assumptions concerning the serial progression of information flow. We suggest design choices and architectural constraints that could facilitate a closer alignment with biology provide causal evidence of the predictive link between the artificial and biological visual systems. Adopting this principled perspective could potentially lead to new research questions and applications of CNNs beyond modeling object recognition.

Learning in a sensory cortical microstimulation task is associated with elevated representational stability

Sensory cortical representations can be highly dynamic, raising the question of how representational stability impacts learning. We train mice to discriminate the number of photostimulation pulses delivered to opsin-expressing pyramidal neurons in layer 2/3 of primary vibrissal somatosensory cortex. We simultaneously track evoked neural activity across learning using volumetric two-photon calcium imaging. In well-trained animals, trial-to-trial fluctuations in the amount of photostimulus-evoked activity predicted animal choice. Population activity levels declined rapidly across training, with the most active neurons showing the largest declines in responsiveness. Mice learned at varied rates, with some failing to learn the task in the time provided. The photoresponsive population showed greater instability both within and across behavioral sessions among animals that failed to learn. Animals that failed to learn also exhibited a faster deterioration in stimulus decoding. Thus, greater stability in the stimulus response is associated with learning in a sensory cortical microstimulation task.

Neural Plasticity in Sensorimotor Brain-Machine Interfaces

Brain-machine interfaces (BMIs) aim to treat sensorimotor neurological disorders by creating artificial motor and/or sensory pathways. Introducing artificial pathways creates new relationships between sensory input and motor output, which the brain must learn to gain dexterous control. This review highlights the role of learning in BMIs to restore movement and sensation, and discusses how BMI design may influence neural plasticity and performance. The close integration of plasticity in sensory and motor function influences the design of both artificial pathways and will be an essential consideration for bidirectional devices that restore both sensory and motor function.

Visual cortex: How mice learn to detect entirely novel inputs

A new study has revealed that neural amplification in mouse primary visual cortex substantially increases between training sessions as mice learn to detect novel optogenetic input directly into visual cortex, suggesting consolidation and recurrent network plasticity contribute to learning the behavior.

Amplified cortical neural responses as animals learn to use novel activity patterns

Cerebral cortex supports representations of the world in patterns of neural activity, used by the brain to make decisions and guide behavior. Past work has found diverse, or limited, changes in the primary sensory cortex in response to learning, suggesting that the key computations might occur in downstream regions. Alternatively, sensory cortical changes may be central to learning. We studied cortical learning by using controlled inputs we insert: we trained mice to recognize entirely novel, non-sensory patterns of cortical activity in the primary visual cortex (V1) created by optogenetic stimulation. As animals learned to use these novel patterns, we found that their detection abilities improved by an order of magnitude or more. The behavioral change was accompanied by large increases in V1 neural responses to fixed optogenetic input. Neural response amplification to novel optogenetic inputs had little effect on existing visual sensory responses. A recurrent cortical model shows that this amplification can be achieved by a small mean shift in recurrent network synaptic strength. Amplification would seem to be desirable to improve decision-making in a detection task; therefore, these results suggest that adult recurrent cortical plasticity plays a significant role in improving behavioral performance during learning.

Distributing task-related neural activity across a cortical network through task-independent connections

Task-related neural activity is widespread across populations of neurons during goal-directed behaviors. However, little is known about the synaptic reorganization and circuit mechanisms that lead to broad activity changes. Here we trained a subset of neurons in a spiking network with strong synaptic interactions to reproduce the activity of neurons in the motor cortex during a decision-making task. Task-related activity, resembling the neural data, emerged across the network, even in the untrained neurons. Analysis of trained networks showed that strong untrained synapses, which were independent of the task and determined the dynamical state of the network, mediated the spread of task-related activity. Optogenetic perturbations suggest that the motor cortex is strongly-coupled, supporting the applicability of the mechanism to cortical networks. Our results reveal a cortical mechanism that facilitates distributed representations of task-variables by spreading the activity from a subset of plastic neurons to the entire network through task-independent strong synapses.

Rapid detection of neurons in widefield calcium imaging datasets after training with synthetic data

Widefield microscopy can provide optical access to multi-millimeter fields of view and thousands of neurons in mammalian brains at video rate. However, tissue scattering and background contamination results in signal deterioration, making the extraction of neuronal activity challenging, laborious and time consuming. Here we present our deep-learning-based widefield neuron finder (DeepWonder), which is trained by simulated functional recordings and effectively works on experimental data to achieve high-fidelity neuronal extraction. Equipped with systematic background contribution priors, DeepWonder conducts neuronal inference with an order-of-magnitude-faster speed and improved accuracy compared with alternative approaches. DeepWonder removes background contaminations and is computationally efficient. Specifically, DeepWonder accomplishes 50-fold signal-to-background ratio enhancement when processing terabytes-scale cortex-wide functional recordings, with over 14,000 neurons extracted in 17 h.

Probing inter-areal computations with a cellular resolution two-photon holographic mesoscope

Brain computation depends on intricately connected yet highly distributed neural networks. Due to the absence of the requisite technologies, causally testing fundamental hypotheses on the nature of inter-areal processing have remained largely out-of-each. Here we developed the first two photon holographic mesoscope, a system capable of simultaneously reading and writing neural activity patterns with single cell resolution across large regions of the brain. We demonstrate the precise photo-activation of spatial and temporal sequences of neurons in one brain area while reading out the downstream effect in several other regions. Investigators can use this new platform to understand feed-forward and feed-back processing in distributed neural circuits with single cell precision for the first time.

Ultrafast light targeting for high-throughput precise control of neuronal networks

Two-photon, single-cell resolution optogenetics based on holographic light-targeting approaches enables the generation of precise spatiotemporal neuronal activity patterns and thus a broad range of experimental applications, such as high throughput connectivity mapping and probing neural codes for perception. Yet, current holographic approaches limit the resolution for tuning the relative spiking time of distinct cells to a few milliseconds, and the achievable number of targets to 100-200, depending on the working depth. To overcome these limitations and expand the capabilities of single-cell optogenetics, we introduce an ultra-fast sequential light targeting (FLiT) optical configuration based on the rapid switching of a temporally focused beam between holograms at kHz rates. We used FLiT to demonstrate two illumination protocols, termed hybrid- and cyclic-illumination, and achieve sub-millisecond control of sequential neuronal activation and high throughput multicell illumination in vitro (mouse organotypic and acute brain slices) and in vivo (zebrafish larvae and mice), while minimizing light-induced thermal rise. These approaches will be important for experiments that require rapid and precise cell stimulation with defined spatio-temporal activity patterns and optical control of large neuronal ensembles.

Bicistronic Expression of a High-Performance Calcium Indicator and Opsin for All-Optical Stimulation and Imaging at Cellular Resolution

State-of-the-art all-optical systems promise unprecedented access to neural activity in vivo, using multiphoton optogenetics to allow simultaneous imaging and control of activity in selected neurons at cellular resolution. However, to achieve wide use of all-optical stimulation and imaging, simple strategies are needed to robustly and stably express opsins and indicators in the same cells. Here, we describe a bicistronic adeno-associated virus (AAV) that expresses both the fast and bright calcium indicator jGCaMP8s, and a soma-targeted (st) and two-photon-activatable opsin, ChrimsonR. With this method, stChrimsonR stimulation with two-photon holography in the visual cortex of mice drives robust spiking in targeted cells, and neural responses to visual sensory stimuli and spontaneous activity are strong and stable. Cells expressing this bicistronic construct show responses to both photostimulation and visual stimulation that are similar to responses measured from cells expressing the same opsin and indicator via separate viruses. This approach is a simple and robust way to prepare neurons in vivo for two-photon holography and imaging.

A flexible two-photon fiberscope for fast activity imaging and precise optogenetic photostimulation of neurons in freely moving mice

We developed a flexible two-photon microendoscope (2P-FENDO) capable of all-optical brain investigation at near cellular resolution in freely moving mice. The system performs fast two-photon (2P) functional imaging and 2P holographic photostimulation of single and multiple cells using axially confined extended spots. Proof-of-principle experiments were performed in freely moving mice co-expressing jGCaMP7s and the opsin ChRmine in the visual or barrel cortex. On a field of view of 250 μm in diameter, we demonstrated functional imaging at a frame rate of up to 50 Hz and precise photostimulation of selected groups of cells. With the capability to simultaneously image and control defined neuronal networks in freely moving animals, 2P-FENDO will enable a precise investigation of neuronal functions in the brain during naturalistic behaviors.

Angular gyrus: an anatomical case study for association cortex

The angular gyrus is associated with a spectrum of higher order cognitive functions. This mini-review undertakes a broad survey of putative neuroanatomical substrates, guided by the premise that area-specific specializations derive from a combination of extrinsic connections and intrinsic area properties. Three levels of spatial resolution are discussed: cellular, supracellular connectivity, and synaptic micro-scale, with examples necessarily drawn mainly from experimental work with nonhuman primates. A significant factor in the functional specialization of the human parietal cortex is the pronounced enlargement. In addition to "more" cells, synapses, and connections, however, the heterogeneity itself can be considered an important property. Multiple anatomical features support the idea of overlapping and temporally dynamic membership in several brain wide subnetworks, but how these features operate in the context of higher cognitive functions remains for continued investigations.

Viral Tracing Confirms Paranigral Ventral Tegmental Area Dopaminergic Inputs to the Interpeduncular Nucleus Where Dopamine Release Encodes Motivated Exploration

Midbrain dopaminergic (DAergic) neurons of the ventral tegmental area (VTA) are engaged by rewarding stimuli and encode reward prediction error to update goal-directed learning. However, recent data indicate that VTA DAergic neurons are functionally heterogeneous with emerging roles in aversive signaling, salience, and novelty, based in part on anatomic location and projection, highlighting a need to functionally characterize the repertoire of VTA DAergic efferents in motivated behavior. Previous work identifying a mesointerpeduncular circuit consisting of VTA DAergic neurons projecting to the interpeduncular nucleus (IPN), a midbrain area implicated in aversion, anxiety-like behavior, and familiarity, has recently come into question. To verify the existence of this circuit, we combined presynaptic targeted and retrograde viral tracing in the dopamine transporter-Cre mouse line. Consistent with previous reports, synaptic tracing revealed that axon terminals from the VTA innervate the caudal IPN; whereas, retrograde tracing revealed DAergic VTA neurons, predominantly in the paranigral region, project to the nucleus accumbens shell, as well as the IPN. To test whether functional DAergic neurotransmission exists in the IPN, we expressed the genetically encoded DA sensor, dLight 1.2, in the IPN of C57BL/6J mice and measured IPN DA signals in vivo during social and anxiety-like behavior using fiber photometry. We observed an increase in IPN DA signal during social investigation of a novel but not familiar conspecific and during exploration of the anxiogenic open arms of the elevated plus maze. Together, these data confirm VTA DAergic neuron projections to the IPN and implicate this circuit in encoding motivated exploration.

Columnar Lesions in Barrel Cortex Persistently Degrade Object Location Discrimination Performance

Primary sensory cortices display functional topography, suggesting that even small cortical volumes may underpin perception of specific stimuli. Traditional loss-of-function approaches have a relatively large radius of effect (>1 mm), and few studies track recovery following loss-of-function perturbations. Consequently, the behavioral necessity of smaller cortical volumes remains unclear. In the mouse primary vibrissal somatosensory cortex (vS1), "barrels" with a radius of ∼150 μm receive input predominantly from a single whisker, partitioning vS1 into a topographic map of well defined columns. Here, we train animals implanted with a cranial window over vS1 to perform single-whisker perceptual tasks. We then use high-power laser exposure centered on the barrel representing the spared whisker to produce lesions with a typical volume of one to two barrels. These columnar-scale lesions impair performance in an object location discrimination task for multiple days without disrupting vibrissal kinematics. Animals with degraded location discrimination performance can immediately perform a whisker touch detection task with high accuracy. Animals trained de novo on both simple and complex whisker touch detection tasks showed no permanent behavioral deficits following columnar-scale lesions. Thus, columnar-scale lesions permanently degrade performance in object location discrimination tasks.

Transformation of primary sensory cortical representations from layer 4 to layer 2

Sensory input arrives from thalamus in cortical layer (L) 4, which outputs predominantly to superficial layers. L4 to L2 thus constitutes one of the earliest cortical feedforward networks. Despite extensive study, the transformation performed by this network remains poorly understood. We use two-photon calcium imaging to record neural activity in L2-4 of primary vibrissal somatosensory cortex (vS1) as mice perform an object localization task with two whiskers. Touch responses sparsen and become more reliable from L4 to L2, with nearly half of the superficial touch response confined to ~1 % of excitatory neurons. These highly responsive neurons have broad receptive fields and can more accurately decode stimulus features. They participate disproportionately in ensembles, small subnetworks with elevated pairwise correlations. Thus, from L4 to L2, cortex transitions from distributed probabilistic coding to sparse and robust ensemble-based coding, resulting in more efficient and accurate representations.

Formation and computational implications of assemblies in neural circuits

In the brain, patterns of neural activity represent sensory information and store it in non-random synaptic connectivity. A prominent theoretical hypothesis states that assemblies, groups of neurons that are strongly connected to each other, are the key computational units underlying perception and memory formation. Compatible with these hypothesised assemblies, experiments have revealed groups of neurons that display synchronous activity, either spontaneously or upon stimulus presentation, and exhibit behavioural relevance. While it remains unclear how assemblies form in the brain, theoretical work has vastly contributed to the understanding of various interacting mechanisms in this process. Here, we review the recent theoretical literature on assembly formation by categorising the involved mechanisms into four components: synaptic plasticity, symmetry breaking, competition and stability. We highlight different approaches and assumptions behind assembly formation and discuss recent ideas of assemblies as the key computational unit in the brain. Abstract figure legend Assembly Formation. Assemblies are groups of strongly connected neurons formed by the interaction of multiple mechanisms and with vast computational implications. Four interacting components are thought to drive assembly formation: synaptic plasticity, symmetry breaking, competition and stability. This article is protected by copyright. All rights reserved.

Behaviorally relevant decision coding in primary somatosensory cortex neurons

Primary sensory cortex is thought to process incoming sensory information, while decision variables important for driving behavior are assumed to arise downstream in the processing hierarchy. Here, we used population two-photon calcium imaging and targeted two-photon optogenetic stimulation of neurons in layer 2/3 of mouse primary somatosensory cortex (S1) during a texture discrimination task to test for the presence of decision signals and probe their behavioral relevance. Small but distinct populations of neurons carried information about the stimulus irrespective of the behavioral outcome (stimulus neurons), or about the choice irrespective of the presented stimulus (decision neurons). Decision neurons show categorical coding that develops during learning, and lack a conclusive decision signal in Miss trials. All-optical photostimulation of decision neurons during behavior improves behavioral performance, establishing a causal role in driving behavior. The fact that stimulus and decision neurons are intermingled challenges the idea of S1 as a purely sensory area, and causal perturbation suggests a direct involvement of S1 decision neurons in the decision-making process.

All-optical inter-layers functional connectivity investigation in the mouse retina

We developed a multi-unit microscope for all-optical inter-layers circuits interrogation. The system performs two-photon (2P) functional imaging and 2P multiplexed holographic optogenetics at axially distinct planes. We demonstrated the capability of the system to map, in the mouse retina, the functional connectivity between rod bipolar cells (RBCs) and ganglion cells (GCs) by activating single or defined groups of RBCs while recording the evoked response in the GC layer with cell-type specificity and single-cell resolution. We then used a logistic model to probe the functional connectivity between cell types by deriving the "cellular receptive field" describing how RBCs impact each GC type. With the capability to simultaneously image and control neuronal activity at axially distinct planes, the system enables a precise interrogation of multi-layered circuits. Understanding this information transfer is a promising avenue to dissect complex neural circuits and understand the neural basis of computations.

Optogenetics for light control of biological systems

Optogenetic techniques have been developed to allow control over the activity of selected cells within a highly heterogeneous tissue, using a combination of genetic engineering and light. Optogenetics employs natural and engineered photoreceptors, mostly of microbial origin, to be genetically introduced into the cells of interest. As a result, cells that are naturally light-insensitive can be made photosensitive and addressable by illumination and precisely controllable in time and space. The selectivity of expression and subcellular targeting in the host is enabled by applying control elements such as promoters, enhancers and specific targeting sequences to the employed photoreceptor-encoding DNA. This powerful approach allows precise characterization and manipulation of cellular functions and has motivated the development of advanced optical methods for patterned photostimulation. Optogenetics has revolutionized neuroscience during the past 15 years and is primed to have a similar impact in other fields, including cardiology, cell biology and plant sciences. In this Primer, we describe the principles of optogenetics, review the most commonly used optogenetic tools, illumination approaches and scientific applications and discuss the possibilities and limitations associated with optogenetic manipulations across a wide variety of optical techniques, cells, circuits and organisms.

All-optical interrogation of neural circuits in behaving mice

Recent advances combining two-photon calcium imaging and two-photon optogenetics with computer-generated holography now allow us to read and write the activity of large populations of neurons in vivo at cellular resolution and with high temporal resolution. Such 'all-optical' techniques enable experimenters to probe the effects of functionally defined neurons on neural circuit function and behavioral output with new levels of precision. This greatly increases flexibility, resolution, targeting specificity and throughput compared with alternative approaches based on electrophysiology and/or one-photon optogenetics and can interrogate larger and more densely labeled populations of neurons than current voltage imaging-based implementations. This protocol describes the experimental workflow for all-optical interrogation experiments in awake, behaving head-fixed mice. We describe modular procedures for the setup and calibration of an all-optical system (~3 h), the preparation of an indicator and opsin-expressing and task-performing animal (~3-6 weeks), the characterization of functional and photostimulation responses (~2 h per field of view) and the design and implementation of an all-optical experiment (achievable within the timescale of a normal behavioral experiment; ~3-5 h per field of view). We discuss optimizations for efficiently selecting and targeting neuronal ensembles for photostimulation sequences, as well as generating photostimulation response maps from the imaging data that can be used to examine the impact of photostimulation on the local circuit. We demonstrate the utility of this strategy in three brain areas by using different experimental setups. This approach can in principle be adapted to any brain area to probe functional connectivity in neural circuits and investigate the relationship between neural circuit activity and behavior.

Advantages, Pitfalls, and Developments of All Optical Interrogation Strategies of Microcircuits in vivo

The holy grail for every neurophysiologist is to conclude a causal relationship between an elementary behaviour and the function of a specific brain area or circuit. Our effort to map elementary behaviours to specific brain loci and to further manipulate neural activity while observing the alterations in behaviour is in essence the goal for neuroscientists. Recent advancements in the area of experimental brain imaging in the form of longer wavelength near infrared (NIR) pulsed lasers with the development of highly efficient optogenetic actuators and reporters of neural activity, has endowed us with unprecedented resolution in spatiotemporal precision both in imaging neural activity as well as manipulating it with multiphoton microscopy. This readily available toolbox has introduced a so called all-optical physiology and interrogation of circuits and has opened new horizons when it comes to precisely, fast and non-invasively map and manipulate anatomically, molecularly or functionally identified mesoscopic brain circuits. The purpose of this review is to describe the advantages and possible pitfalls of all-optical approaches in system neuroscience, where by all-optical we mean use of multiphoton microscopy to image the functional response of neuron(s) in the network so to attain flexible choice of the cells to be also optogenetically photostimulated by holography, in absence of electrophysiology. Spatio-temporal constraints will be compared toward the classical reference of electrophysiology methods. When appropriate, in relation to current limitations of current optical approaches, we will make reference to latest works aimed to overcome these limitations, in order to highlight the most recent developments. We will also provide examples of types of experiments uniquely approachable all-optically. Finally, although mechanically non-invasive, all-optical electrophysiology exhibits potential off-target effects which can ambiguate and complicate the interpretation of the results. In summary, this review is an effort to exemplify how an all-optical experiment can be designed, conducted and interpreted from the point of view of the integrative neurophysiologist.

The structures and functions of correlations in neural population codes

The collective activity of a population of neurons, beyond the properties of individual cells, is crucial for many brain functions. A fundamental question is how activity correlations between neurons affect how neural populations process information. Over the past 30 years, major progress has been made on how the levels and structures of correlations shape the encoding of information in population codes. Correlations influence population coding through the organization of pairwise-activity correlations with respect to the similarity of tuning of individual neurons, by their stimulus modulation and by the presence of higher-order correlations. Recent work has shown that correlations also profoundly shape other important functions performed by neural populations, including generating codes across multiple timescales and facilitating information transmission to, and readout by, downstream brain areas to guide behaviour. Here, we review this recent work and discuss how the structures of correlations can have opposite effects on the different functions of neural populations, thus creating trade-offs and constraints for the structure-function relationships of population codes. Further, we present ideas on how to combine large-scale simultaneous recordings of neural populations, computational models, analyses of behaviour, optogenetics and anatomy to unravel how the structures of correlations might be optimized to serve multiple functions.

The geometry of robustness in spiking neural networks

Neural systems are remarkably robust against various perturbations, a phenomenon that still requires a clear explanation. Here, we graphically illustrate how neural networks can become robust. We study spiking networks that generate low-dimensional representations, and we show that the neurons’ subthreshold voltages are confined to a convex region in a lower-dimensional voltage subspace, which we call a 'bounding box'. Any changes in network parameters (such as number of neurons, dimensionality of inputs, firing thresholds, synaptic weights, or transmission delays) can all be understood as deformations of this bounding box. Using these insights, we show that functionality is preserved as long as perturbations do not destroy the integrity of the bounding box. We suggest that the principles underlying robustness in these networks — low-dimensional representations, heterogeneity of tuning, and precise negative feedback — may be key to understanding the robustness of neural systems at the circuit level.

Tracking connectivity maps in human stem cell-derived neuronal networks by holographic optogenetics

Holographic optogenetic stimulation of human iPSC–derived neuronal networks was exploited to map precise functional connectivity motifs and their long-term dynamics during network development.

Neuronal networks derived from human induced pluripotent stem cells have been exploited widely for modeling neuronal circuits, neurological diseases, and drug screening. As these networks require extended culturing periods to functionally mature in vitro, most studies are based on immature networks. To obtain insights on long-term functional features, we improved a glia–neuron co-culture protocol within multi-electrode arrays, facilitating continuous assessment of electrical features in weekly intervals. By full-field optogenetic stimulation, we detected an earlier onset of neuronal firing and burst activity compared with spontaneous activity. Full-field stimulation enhanced the number of active neurons and their firing rates. Compared with full-field stimulation, which evoked synchronized activity across all neurons, holographic stimulation of individual neurons resulted in local activity. Single-cell holographic stimulation facilitated to trace propagating evoked activities of 400 individually stimulated neurons per multi-electrode array. Thereby, we revealed precise functional neuronal connectivity motifs. Holographic stimulation data over time showed increasing connection numbers and strength with culture age. This holographic stimulation setup has the potential to establish a profound functional testbed for in-depth analysis of human-induced pluripotent stem cell-derived neuronal networks.