Re-evaluating Circuit Mechanisms Underlying Pattern Separation

Cerebellar-driven cortical dynamics can enable task acquisition, switching and consolidation

The brain must maintain a stable world model while rapidly adapting to the environment, but the underlying mechanisms are not known. Here, we posit that cortico-cerebellar loops play a key role in this process. We introduce a computational model of cerebellar networks that learn to drive cortical networks with task-outcome predictions. First, using sensorimotor tasks, we show that cerebellar feedback in the presence of stable cortical networks is sufficient for rapid task acquisition and switching. Next, we demonstrate that, when trained in working memory tasks, the cerebellum can also underlie the maintenance of cognitive-specific dynamics in the cortex, explaining a range of optogenetic and behavioural observations. Finally, using our model, we introduce a systems consolidation theory in which task information is gradually transferred from the cerebellum to the cortex. In summary, our findings suggest that cortico-cerebellar loops are an important component of task acquisition, switching, and consolidation in the brain.

Neural stem and progenitor cells support and protect adult hippocampal function via vascular endothelial growth factor secretion

Adult neural stem and progenitor cells (NSPCs) reside in the dentate gyrus (DG) of the hippocampus throughout the lifespan of most mammalian species. In addition to generating new neurons, NSPCs may alter their niche via secretion of growth factors and cytokines. We recently showed that adult DG NSPCs secrete vascular endothelial growth factor (VEGF), which is critical for maintaining adult neurogenesis. Here, we asked whether NSPC-derived VEGF alters hippocampal function independent of adult neurogenesis. We found that loss of NSPC-derived VEGF acutely impaired hippocampal memory, caused neuronal hyperexcitability and exacerbated excitotoxic injury. Conversely, we observed that overexpression of VEGF reduced microglial response to excitotoxic injury. We also found that NSPCs generate substantial proportions of total DG VEGF and VEGF disperses widely throughout the DG, both of which help explain how this anatomically-restricted cell population could modulate function broadly. These findings suggest that NSPCs actively support and protect DG function via secreted VEGF, thereby providing a non-neurogenic functional dimension to endogenous NSPCs.

Neuroplasticity Meets Artificial Intelligence: A Hippocampus-Inspired Approach to the Stability-Plasticity Dilemma

The stability-plasticity dilemma remains a critical challenge in developing artificial intelligence (AI) systems capable of continuous learning. This perspective paper presents a novel approach by drawing inspiration from the mammalian hippocampus-cortex system. We elucidate how this biological system's ability to balance rapid learning with long-term memory retention can inspire novel AI architectures. Our analysis focuses on key mechanisms, including complementary learning systems and memory consolidation, with emphasis on recent discoveries about sharp-wave ripples and barrages of action potentials. We propose innovative AI designs incorporating dual learning rates, offline consolidation, and dynamic plasticity modulation. This interdisciplinary approach offers a framework for more adaptive AI systems while providing insights into biological learning. We present testable predictions and discuss potential implementations and implications of these biologically inspired principles. By bridging neuroscience and AI, our perspective aims to catalyze advancements in both fields, potentially revolutionizing AI capabilities while deepening our understanding of neural processes.

Increased understanding of complex neuronal circuits in the cerebellar cortex

The prevailing belief has been that the fundamental structures of cerebellar neuronal circuits, consisting of a few major neuron types, are simple and well understood. Given that the cerebellum has long been known to be crucial for motor behaviors, these simple yet organized circuit structures seemed beneficial for theoretical studies proposing neural mechanisms underlying cerebellar motor functions and learning. On the other hand, experimental studies using advanced techniques have revealed numerous structural properties that were not traditionally defined. These include subdivided neuronal types and their circuit structures, feedback pathways from output Purkinje cells, and the multidimensional organization of neuronal interactions. With the recent recognition of the cerebellar involvement in non-motor functions, it is possible that these newly identified structural properties, which are potentially capable of generating greater complexity than previously recognized, are associated with increased information capacity. This, in turn, could contribute to the wide range of cerebellar functions. However, it remains largely unknown how such structural properties contribute to cerebellar neural computations through the regulation of neuronal activity or synaptic transmissions. To promote further research into cerebellar circuit structures and their functional significance, we aim to summarize the newly identified structural properties of the cerebellar cortex and discuss future research directions concerning cerebellar circuit structures and their potential functions.

A daytime nap with REM sleep is linked to enhanced generalization of emotional stimuli

How memory representations are shaped during and after their encoding is a central question in the study of human memory. Recognition responses to stimuli that are similar to those observed previously can hint at the fidelity of the memories or point to processes of generalization at the expense of precise memory representations. Experimental studies utilizing this approach showed that emotions and sleep both influence these responses. Sleep, and more specifically rapid eye movement sleep, is assumed to facilitate the generalization of emotional memories. We studied mnemonic discrimination by the emotional variant of the Mnemonic Separation Task in participants (N = 113) who spent a daytime nap between learning and testing compared with another group that spent an equivalent time awake between the two sessions. Our findings indicate that the discrimination of similar but previously not seen items from previously seen ones is enhanced in case of negative compared with neutral and positive stimuli. Moreover, whereas the sleep and the wake groups did not differ in memory performance, participants entering rapid eye movement sleep exhibited increased generalization of emotional memories. Our findings indicate that entering into rapid eye movement sleep during a daytime nap shapes emotional memories in a way that enhances recognition at the expense of detailed memory representations.

An Information-Geometric Formulation of Pattern Separation and Evaluation of Existing Indices

Pattern separation is a computational process by which dissimilar neural patterns are generated from similar input patterns. We present an information-geometric formulation of pattern separation, where a pattern separator is modeled as a family of statistical distributions on a manifold. Such a manifold maps an input (i.e., coordinates) to a probability distribution that generates firing patterns. Pattern separation occurs when small coordinate changes result in large distances between samples from the corresponding distributions. Under this formulation, we implement a two-neuron system whose probability law forms a three-dimensional manifold with mutually orthogonal coordinates representing the neurons' marginal and correlational firing rates. We use this highly controlled system to examine the behavior of spike train similarity indices commonly used in pattern separation research. We find that all indices (except scaling factor) are sensitive to relative differences in marginal firing rates, but no index adequately captures differences in spike trains that result from altering the correlation in activity between the two neurons. That is, existing pattern separation metrics appear (A) sensitive to patterns that are encoded by different neurons but (B) insensitive to patterns that differ only in relative spike timing (e.g., synchrony between neurons in the ensemble).

Predictions enable top-down pattern separation in the macaque face-processing hierarchy

Distinguishing faces requires well distinguishable neural activity patterns. Contextual information may separate neural representations, leading to enhanced identity recognition. Here, we use functional magnetic resonance imaging to investigate how predictions derived from contextual information affect the separability of neural activity patterns in the macaque face-processing system, a 3-level processing hierarchy in ventral visual cortex. We find that in the presence of predictions, early stages of this hierarchy exhibit well separable and high-dimensional neural geometries resembling those at the top of the hierarchy. This is accompanied by a systematic shift of tuning properties from higher to lower areas, endowing lower areas with higher-order, invariant representations instead of their feedforward tuning properties. Thus, top-down signals dynamically transform neural representations of faces into separable and high-dimensional neural geometries. Our results provide evidence how predictive context transforms flexible representational spaces to optimally use the computational resources provided by cortical processing hierarchies for better and faster distinction of facial identities.

Cellular and circuit features distinguish dentate gyrus semilunar granule cells and granule cells activated during contextual memory formation

The dentate gyrus is critical for spatial memory formation and shows task related activation of cellular ensembles considered as memory engrams. Semilunar granule cells (SGCs), a sparse dentate projection neuron subtype distinct from granule cells (GCs), were recently reported to be enriched among behaviorally activated neurons. However, the mechanisms governing SGC recruitment during memory formation and their role in engram refinement remains unresolved. By examining neurons labeled during contextual memory formation in TRAP2 mice, we empirically tested competing hypotheses for GC and SGC recruitment into memory ensembles. In support of the proposal that more excitable neurons are preferentially recruited into memory ensembles, SGCs showed greater sustained firing than GCs. Additionally, SGCs labeled during memory formation showed less adapting firing than unlabeled SGCs. Our recordings did not reveal glutamatergic connections between behaviorally labeled SGCs and GCs, providing evidence against SGCs driving local circuit feedforward excitation in ensemble recruitment. Contrary to a leading hypothesis, there was little evidence for individual SGCs or labeled neuronal ensembles supporting lateral inhibition of unlabeled neurons. Instead, pairs of GCs and SGCs within labeled neuronal cohorts received more temporally correlated spontaneous excitatory synaptic inputs than labeled-unlabeled neuronal pairs, validating a role for correlated afferent inputs in neuronal ensemble selection. These findings challenge the proposal that SGCs drive dentate GC ensemble refinement, while supporting a role for intrinsic active properties and correlated inputs in preferential SGC recruitment to contextual memory engrams.

Impact Statement

Evaluation of semilunar granule cell involvement in dentate gyrus contextual memory processing supports recruitment based on intrinsic and input characteristics while revealing limited contribution to ensemble refinement.

Eight challenges in developing theory of intelligence

A good theory of mathematical beauty is more practical than any current observation, as new predictions about physical reality can be self-consistently verified. This belief applies to the current status of understanding deep neural networks including large language models and even the biological intelligence. Toy models provide a metaphor of physical reality, allowing mathematically formulating the reality (i.e., the so-called theory), which can be updated as more conjectures are justified or refuted. One does not need to present all details in a model, but rather, more abstract models are constructed, as complex systems such as the brains or deep networks have many sloppy dimensions but much less stiff dimensions that strongly impact macroscopic observables. This type of bottom-up mechanistic modeling is still promising in the modern era of understanding the natural or artificial intelligence. Here, we shed light on eight challenges in developing theory of intelligence following this theoretical paradigm. Theses challenges are representation learning, generalization, adversarial robustness, continual learning, causal learning, internal model of the brain, next-token prediction, and the mechanics of subjective experience.

Spatial constraints and cell surface molecule depletion structure a randomly connected learning circuit

The brain can represent almost limitless objects to "categorize an unlabeled world" (Edelman, 1989). This feat is supported by expansion layer circuit architectures, in which neurons carrying information about discrete sensory channels make combinatorial connections onto much larger postsynaptic populations. Combinatorial connections in expansion layers are modeled as randomized sets. The extent to which randomized wiring exists in vivo is debated, and how combinatorial connectivity patterns are generated during development is not understood. Non-deterministic wiring algorithms could program such connectivity using minimal genomic information. Here, we investigate anatomic and transcriptional patterns and perturb partner availability to ask how Kenyon cells, the expansion layer neurons of the insect mushroom body, obtain combinatorial input from olfactory projection neurons. Olfactory projection neurons form their presynaptic outputs in an orderly, predictable, and biased fashion. We find that Kenyon cells accept spatially co-located but molecularly heterogeneous inputs from this orderly map, and ask how Kenyon cell surface molecule expression impacts partner choice. Cell surface immunoglobulins are broadly depleted in Kenyon cells, and we propose that this allows them to form connections with molecularly heterogeneous partners. This model can explain how developmentally identical neurons acquire diverse wiring identities.

Computational role of structure in neural activity and connectivity

One major challenge of neuroscience is identifying structure in seemingly disorganized neural activity. Different types of structure have different computational implications that can help neuroscientists understand the functional role of a particular brain area. Here, we outline a unified approach to characterize structure by inspecting the representational geometry and the modularity properties of the recorded activity and show that a similar approach can also reveal structure in connectivity. We start by setting up a general framework for determining geometry and modularity in activity and connectivity and relating these properties with computations performed by the network. We then use this framework to review the types of structure found in recent studies of model networks performing three classes of computations.

Simple spike patterns and synaptic mechanisms encoding sensory and motor signals in Purkinje cells and the cerebellar nuclei

Whisker stimulation in awake mice evokes transient suppression of simple spike probability in crus I/II Purkinje cells. Here, we investigated how simple spike suppression arises synaptically, what it encodes, and how it affects cerebellar output. In vitro, monosynaptic parallel fiber (PF)-excitatory postsynaptic currents (EPSCs) facilitated strongly, whereas disynaptic inhibitory postsynaptic currents (IPSCs) remained stable, maximizing relative inhibitory strength at the onset of PF activity. Short-term plasticity thus favors the inhibition of Purkinje spikes before PFs facilitate. In vivo, whisker stimulation evoked a 2-6 ms synchronous spike suppression, just 6-8 ms (∼4 synaptic delays) after sensory onset, whereas active whisker movements elicited broadly timed spike rate increases that did not modulate sensory-evoked suppression. Firing in the cerebellar nuclei (CbN) inversely correlated with disinhibition from sensory-evoked simple spike suppressions but was decoupled from slow, non-synchronous movement-associated elevations of Purkinje firing rates. Synchrony thus allows the CbN to high-pass filter Purkinje inputs, facilitating sensory-evoked cerebellar outputs that can drive movements.

Hippocampus-to-amygdala pathway drives the separation of remote memories of related events

The mammalian brain can store and retrieve memories of related events as distinct memories and remember common features of those experiences. How it computes this function remains elusive. Here, we show in rats that recent memories of two closely timed auditory fear events share overlapping neuronal ensembles in the basolateral amygdala (BLA) and are functionally linked. However, remote memories have reduced neuronal overlap and are functionally independent. The activity of parvalbumin (PV)-expressing neurons in the BLA plays a crucial role in forming separate remote memories. Chemogenetic blockade of PV preserves individual remote memories but prevents their segregation, resulting in reciprocal associations. The hippocampus drives this process through specific excitatory connections with BLA GABAergic interneurons. These findings provide insights into the neuronal mechanisms that minimize the overlap between distinct remote memories and enable the retrieval of related memories separately.

Sensory encoding and memory in the mushroom body: signals, noise, and variability

To survive in changing environments, animals need to learn to associate specific sensory stimuli with positive or negative valence. How do they form stimulus-specific memories to distinguish between positively/negatively associated stimuli and other irrelevant stimuli? Solving this task is one of the functions of the mushroom body, the associative memory center in insect brains. Here we summarize recent work on sensory encoding and memory in the Drosophila mushroom body, highlighting general principles such as pattern separation, sparse coding, noise and variability, coincidence detection, and spatially localized neuromodulation, and placing the mushroom body in comparative perspective with mammalian memory systems.

Cognitive limits of larval Drosophila: testing for conditioned inhibition, sensory preconditioning, and second-order conditioning

Drosophila larvae are an established model system for studying the mechanisms of innate and simple forms of learned behavior. They have about 10 times fewer neurons than adult flies, and it was the low total number of their neurons that allowed for an electron microscopic reconstruction of their brain at synaptic resolution. Regarding the mushroom body, a central brain structure for many forms of associative learning in insects, it turned out that more than half of the classes of synaptic connection had previously escaped attention. Understanding the function of these circuit motifs, subsequently confirmed in adult flies, is an important current research topic. In this context, we test larval Drosophila for their cognitive abilities in three tasks that are characteristically more complex than those previously studied. Our data provide evidence for (i) conditioned inhibition, as has previously been reported for adult flies and honeybees. Unlike what is described for adult flies and honeybees, however, our data do not provide evidence for (ii) sensory preconditioning or (iii) second-order conditioning in Drosophila larvae. We discuss the methodological features of our experiments as well as four specific aspects of the organization of the larval brain that may explain why these two forms of learning are observed in adult flies and honeybees, but not in larval Drosophila.

Maladaptation of dentate gyrus mossy cells mediates contextual discrimination deficit after traumatic stress

Fear overgeneralization is a maladaptive response to traumatic stress that is associated with the inability to discriminate between threat and safety contexts, a hallmark feature of post-traumatic stress disorder (PTSD). However, the neural mechanisms underlying this deficit remain unclear. Here, we show that traumatic stress exposure impairs contextual discrimination between threat and safety contexts in the learned helplessness (LH) model. Mossy cells (MCs) in the dorsal hippocampus are suppressed in response to traumatic stress. Bidirectional manipulation of MC activity in the LH model reveals that MC inhibition is causally linked to impaired contextual discrimination. Mechanistically, MC inhibition increases the number of active granule cells in a given context, significantly overlapping context-specific ensembles. Our study demonstrates that maladaptive inhibition of MCs after traumatic stress is a substantial mechanism underlying fear overgeneralization with contextual discrimination deficit, suggesting a potential therapeutic target for cognitive symptoms of PTSD.

Retinoic acid modulation of granule cell activity and spatial discrimination in the adult hippocampus

Retinoic acid (RA), derived from vitamin A (retinol), plays a crucial role in modulating neuroplasticity within the adult brain. Perturbations in RA signaling have been associated with memory impairments, underscoring the necessity to elucidate RA's influence on neuronal activity, particularly within the hippocampus. In this study, we investigated the cell type and sub-regional distribution of RA-responsive granule cells (GCs) in the mouse hippocampus and delineated their properties. We discovered that RA-responsive GCs tend to exhibit a muted response to environmental novelty, typically remaining inactive. Interestingly, chronic dietary depletion of RA leads to an abnormal increase in GC activation evoked by a novel environment, an effect that is replicated by the localized application of an RA receptor beta (RARβ) antagonist. Furthermore, our study shows that prolonged RA deficiency impairs spatial discrimination-a cognitive function reliant on the hippocampus-with such impairments being reversible with RA replenishment. In summary, our findings significantly contribute to a better understanding of RA's role in regulating adult hippocampal neuroplasticity and cognitive functions.

Robust compression and detection of epileptiform patterns in ECoG using a real-time spiking neural network hardware framework

Interictal Epileptiform Discharges (IED) and High Frequency Oscillations (HFO) in intraoperative electrocorticography (ECoG) may guide the surgeon by delineating the epileptogenic zone. We designed a modular spiking neural network (SNN) in a mixed-signal neuromorphic device to process the ECoG in real-time. We exploit the variability of the inhomogeneous silicon neurons to achieve efficient sparse and decorrelated temporal signal encoding. We interface the full-custom SNN device to the BCI2000 real-time framework and configure the setup to detect HFO and IED co-occurring with HFO (IED-HFO). We validate the setup on pre-recorded data and obtain HFO rates that are concordant with a previously validated offline algorithm (Spearman's ρ = 0.75, p = 1e-4), achieving the same postsurgical seizure freedom predictions for all patients. In a remote on-line analysis, intraoperative ECoG recorded in Utrecht was compressed and transferred to Zurich for SNN processing and successful IED-HFO detection in real-time. These results further demonstrate how automated remote real-time detection may enable the use of HFO in clinical practice.

Distinct information conveyed to the olfactory bulb by feedforward input from the nose and feedback from the cortex

Sensory systems are organized hierarchically, but feedback projections frequently disrupt this order. In the olfactory bulb (OB), cortical feedback projections numerically match sensory inputs. To unravel information carried by these two streams, we imaged the activity of olfactory sensory neurons (OSNs) and cortical axons in the mouse OB using calcium indicators, multiphoton microscopy, and diverse olfactory stimuli. Here, we show that odorant mixtures of increasing complexity evoke progressively denser OSN activity, yet cortical feedback activity is of similar sparsity for all stimuli. Also, representations of complex mixtures are similar in OSNs but are decorrelated in cortical axons. While OSN responses to increasing odorant concentrations exhibit a sigmoidal relationship, cortical axonal responses are complex and nonmonotonic, which can be explained by a model with activity-dependent feedback inhibition in the cortex. Our study indicates that early-stage olfactory circuits have access to local feedforward signals and global, efficiently formatted information about odor scenes through cortical feedback.

A pilot study examining the impact of lithium treatment and responsiveness on mnemonic discrimination in bipolar disorder

INTRODUCTION

Mnemonic discrimination (MD), the ability to discriminate new stimuli from similar memories, putatively involves dentate gyrus pattern separation. Since lithium may normalize dentate gyrus functioning in lithium-responsive bipolar disorder (BD), we hypothesized that lithium treatment would be associated with better MD in lithium-responsive BD patients.

METHODS

BD patients (N = 69; NResponders = 16 [23 %]) performed the Continuous Visual Memory Test (CVMT), which requires discriminating between novel and previously seen images. Before testing, all patients had prophylactic lithium responsiveness assessed over ≥1 year of therapy (with the Alda Score), although only thirty-eight patients were actively prescribed lithium at time of testing (55 %; 12/16 responders, 26/53 nonresponders). We then used computational modelling to extract patient-specific MD indices. Linear models were used to test how (A) lithium treatment, (B) lithium responsiveness via the continuous Alda score, and (C) their interaction, affected MD.

RESULTS

Superior MD performance was associated with lithium treatment exclusively in lithium-responsive patients (Lithium x AldaScore β = 0.257 [SE 0.078], p = 0.002). Consistent with prior literature, increased age was associated with worse MD (β = -0.03 [SE 0.01], p = 0.005).

LIMITATIONS

Secondary pilot analysis of retrospectively collected data in a cross-sectional design limits generalizability.

CONCLUSION

Our study is the first to examine MD performance in BD. Lithium is associated with better MD performance only in lithium responders, potentially due to lithium's effects on dentate gyrus granule cell excitability. Our results may influence the development of behavioural probes for dentate gyrus neuronal hyperexcitability in BD.

Local synaptic inhibition mediates cerebellar granule cell pattern separation and enables learned sensorimotor associations

The cerebellar cortex has a key role in generating predictive sensorimotor associations. To do so, the granule cell layer is thought to establish unique sensorimotor representations for learning. However, how this is achieved and how granule cell population responses contribute to behavior have remained unclear. To address these questions, we have used in vivo calcium imaging and granule cell-specific pharmacological manipulation of synaptic inhibition in awake, behaving mice. These experiments indicate that inhibition sparsens and thresholds sensory responses, limiting overlap between sensory ensembles and preventing spiking in many granule cells that receive excitatory input. Moreover, inhibition can be recruited in a stimulus-specific manner to powerfully decorrelate multisensory ensembles. Consistent with these results, granule cell inhibition is required for accurate cerebellum-dependent sensorimotor behavior. These data thus reveal key mechanisms for granule cell layer pattern separation beyond those envisioned by classical models.

Robust and consistent measures of pattern separation based on information theory and demonstrated in the dentate gyrus

Pattern separation is a valuable computational function performed by neuronal circuits, such as the dentate gyrus, where dissimilarity between inputs is increased, reducing noise and increasing the storage capacity of downstream networks. Pattern separation is studied from both in vivo experimental and computational perspectives and, a number of different measures (such as orthogonalisation, decorrelation, or spike train distance) have been applied to quantify the process of pattern separation. However, these are known to give conclusions that can differ qualitatively depending on the choice of measure and the parameters used to calculate it. We here demonstrate that arbitrarily increasing sparsity, a noticeable feature of dentate granule cell firing and one that is believed to be key to pattern separation, typically leads to improved classical measures for pattern separation even, inappropriately, up to the point where almost all information about the inputs is lost. Standard measures therefore both cannot differentiate between pattern separation and pattern destruction, and give results that may depend on arbitrary parameter choices. We propose that techniques from information theory, in particular mutual information, transfer entropy, and redundancy, should be applied to penalise the potential for lost information (often due to increased sparsity) that is neglected by existing measures. We compare five commonly-used measures of pattern separation with three novel techniques based on information theory, showing that the latter can be applied in a principled way and provide a robust and reliable measure for comparing the pattern separation performance of different neurons and networks. We demonstrate our new measures on detailed compartmental models of individual dentate granule cells and a dentate microcircuit, and show how structural changes associated with epilepsy affect pattern separation performance. We also demonstrate how our measures of pattern separation can predict pattern completion accuracy. Overall, our measures solve a widely acknowledged problem in assessing the pattern separation of neural circuits such as the dentate gyrus, as well as the cerebellum and mushroom body. Finally we provide a publicly available toolbox allowing for easy analysis of pattern separation in spike train ensembles.

An enhancer-AAV approach selectively targeting dentate granule cells of the mouse hippocampus

The mammalian brain contains a diverse array of cell types, including dozens of neuronal subtypes with distinct anatomical and functional characteristics. The brain leverages these neuron-type specializations to perform diverse circuit operations and thus execute different behaviors properly. Through the use of Cre lines, access to specific neuron types has improved over past decades. Despite their extraordinary utility, development and cross-breeding of Cre lines is time consuming and expensive, presenting a significant barrier to entry for investigators. Furthermore, cell-based therapeutics developed in Cre mice are not clinically translatable. Recently, several adeno-associated virus (AAV) vectors utilizing neuron-type-specific regulatory transcriptional sequences (enhancer-AAVs) were developed that overcome these limitations. Using a publicly available RNA sequencing (RNA-seq) dataset, we evaluated the potential of several candidate enhancers for neuron-type-specific targeting in the hippocampus. Here, we demonstrate that a previously identified enhancer-AAV selectively targets dentate granule cells over other excitatory neuron types in the hippocampus of wild-type adult mice.

Granule cells perform frequency-dependent pattern separation in a computational model of the dentate gyrus

Mnemonic discrimination (MD) may be dependent on oscillatory perforant path input frequencies to the hippocampus in a "U"-shaped fashion, where some studies show that slow and fast input frequencies support MD, while other studies show that intermediate frequencies disrupt MD. We hypothesize that pattern separation (PS) underlies frequency-dependent MD performance. We aim to study, in a computational model of the hippocampal dentate gyrus (DG), the network and cellular mechanisms governing this putative "U"-shaped PS relationship. We implemented a biophysical model of the DG that produces the hypothesized "U"-shaped input frequency-PS relationship, and its associated oscillatory electrophysiological signatures. We subsequently evaluated the network's PS ability using an adapted spatiotemporal task. We undertook systematic lesion studies to identify the network-level mechanisms driving the "U"-shaped input frequency-PS relationship. A minimal circuit of a single granule cell (GC) stimulated with oscillatory inputs was also used to study potential cellular-level mechanisms. Lesioning synapses onto GCs did not impact the "U"-shaped input frequency-PS relationship. Furthermore, GC inhibition limits PS performance for fast frequency inputs, while enhancing PS for slow frequency inputs. GC interspike interval was found to be input frequency dependent in a "U"-shaped fashion, paralleling frequency-dependent PS observed at the network level. Additionally, GCs showed an attenuated firing response for fast frequency inputs. We conclude that independent of network-level inhibition, GCs may intrinsically be capable of producing a "U"-shaped input frequency-PS relationship. GCs may preferentially decorrelate slow and fast inputs via spike timing reorganization and high frequency filtering.

Reclusive chandeliers: Functional isolation of dentate axo-axonic cells after experimental status epilepticus

Axo-axonic cells (AACs) provide specialized inhibition to the axon initial segment (AIS) of excitatory neurons and can regulate network output and synchrony. Although hippocampal dentate AACs are structurally altered in epilepsy, physiological analyses of dentate AACs are lacking. We demonstrate that parvalbumin neurons in the dentate molecular layer express PTHLH, an AAC marker, and exhibit morphology characteristic of AACs. Dentate AACs show high-frequency, non-adapting firing but lack persistent firing in the absence of input and have higher rheobase than basket cells suggesting that AACs can respond reliably to network activity. Early after pilocarpine-induced status epilepticus (SE), dentate AACs receive fewer spontaneous excitatory and inhibitory synaptic inputs and have significantly lower maximum firing frequency. Paired recordings and spatially localized optogenetic stimulation revealed that SE reduced the amplitude of unitary synaptic inputs from AACs to granule cells without altering reliability, short-term plasticity, or AIS GABA reversal potential. These changes compromised AAC-dependent shunting of granule cell firing in a multicompartmental model. These early post-SE changes in AAC physiology would limit their ability to receive and respond to input, undermining a critical brake on the dentate throughput during epileptogenesis.

Increased neural differentiation after a single session of aerobic exercise in older adults

Aging is associated with decreased cognitive function. One theory posits that this decline is in part due to multiple neural systems becoming dedifferentiated in older adults. Exercise is known to improve cognition in older adults, even after only a single session. We hypothesized that one mechanism of improvement is a redifferentiation of neural systems. We used a within-participant, cross-over design involving 2 sessions: either 30 minutes of aerobic exercise or 30 minutes of seated rest (n = 32; ages 55-81 years). Both functional Magnetic Resonance Imaging (fMRI) and Stroop performance were acquired soon after exercise and rest. We quantified neural differentiation via general heterogeneity regression. There were 3 prominent findings following the exercise. First, participants were better at reducing Stroop interference. Second, while there was greater neural differentiation within the hippocampal formation and cerebellum, there was lower neural differentiation within frontal cortices. Third, this greater neural differentiation in the cerebellum and temporal lobe was more pronounced in the older ages. These data suggest that exercise can induce greater neural differentiation in healthy aging.

Early deficits in dentate circuit and behavioral pattern separation after concussive brain injury

Traumatic brain injury leads to cellular and circuit changes in the dentate gyrus, a gateway to hippocampal information processing. Intrinsic granule cell firing properties and strong feedback inhibition in the dentate are proposed as critical to its ability to generate unique representation of similar inputs by a process known as pattern separation. Here we evaluate the impact of brain injury on cellular decorrelation of temporally patterned inputs in slices and behavioral discrimination of spatial locations in vivo one week after concussive lateral fluid percussion injury (FPI) in mice. Despite posttraumatic increases in perforant path evoked excitatory drive to granule cells and enhanced ΔFosB labeling, indicating sustained increase in excitability, the reliability of granule cell spiking was not compromised after FPI. Although granule cells continued to effectively decorrelate output spike trains recorded in response to similar temporally patterned input sets after FPI, their ability to decorrelate highly similar input patterns was reduced. In parallel, encoding of similar spatial locations in a novel object location task that involves the dentate inhibitory circuits was impaired one week after FPI. Injury induced changes in pattern separation were accompanied by loss of somatostatin expressing inhibitory neurons in the hilus. Together, these data suggest that the early posttraumatic changes in the dentate circuit undermine dentate circuit decorrelation of temporal input patterns as well as behavioral discrimination of similar spatial locations, both of which could contribute to deficits in episodic memory.

Acute sleep loss impairs object but not spatial pattern separation in humans

Pattern separation allows us to form discrete representations of information in memory. Pattern separation can be measured in several domains including spatial and object-based discrimination. The brain area largely involved in this process is the dentate gyrus of the hippocampus, which has been shown to be particularly sensitive to the effects of sleep loss. However, methodology in rodent and human studies varies greatly making translational conclusions difficult. Therefore, the aim of the current study was to measure the effects of sleep deprivation on human hippocampal function, using well-validated spatial and object-based pattern separation tests. The effects of acute sleep loss were examined, as this method is frequently used in rodent research but not human studies. Results show that sleep loss impaired performance on the object-based version of the test, but not spatial pattern separation. The findings support the notion that these discrimination projections represent separate but complimentary hippocampal processes, and further elucidates how they may be discretely affected by acute sleep loss.

Reducing Catastrophic Forgetting With Associative Learning: A Lesson From Fruit Flies

Catastrophic forgetting remains an outstanding challenge in continual learning. Recently, methods inspired by the brain, such as continual representation learning and memory replay, have been used to combat catastrophic forgetting. Associative learning (retaining associations between inputs and outputs, even after good representations are learned) plays an important function in the brain; however, its role in continual learning has not been carefully studied. Here, we identified a two-layer neural circuit in the fruit fly olfactory system that performs continual associative learning between odors and their associated valences. In the first layer, inputs (odors) are encoded using sparse, high-dimensional representations, which reduces memory interference by activating nonoverlapping populations of neurons for different odors. In the second layer, only the synapses between odor-activated neurons and the odor's associated output neuron are modified during learning; the rest of the weights are frozen to prevent unrelated memories from being overwritten. We prove theoretically that these two perceptron-like layers help reduce catastrophic forgetting compared to the original perceptron algorithm, under continual learning. We then show empirically on benchmark data sets that this simple and lightweight architecture outperforms other popular neural-inspired algorithms when also using a two-layer feedforward architecture. Overall, fruit flies evolved an efficient continual associative learning algorithm, and circuit mechanisms from neuroscience can be translated to improve machine computation.

RECLUSIVE CHANDELIERS: FUNCTIONAL ISOLATION OF DENTATE AXO-AXONIC CELLS AFTER EXPERIMENTAL STATUS EPILEPTICUS

Axo-axonic cells (AACs) provide specialized inhibition to the axon initial segment (AIS) of excitatory neurons and can regulate network output and synchrony. Although hippocampal dentate AACs are structurally altered in epilepsy, physiological analyses of dentate AACs are lacking. We demonstrate that parvalbumin neurons in the dentate molecular layer express PTHLH, an AAC marker, and exhibit morphology characteristic of AACs. Dentate AACs show high-frequency, non-adapting firing but lack persistent firing in the absence of input and have higher rheobase than basket cells suggesting that AACs can respond reliably to network activity. Early after pilocarpine-induced status epilepticus (SE), dentate AACs receive fewer spontaneous excitatory and inhibitory synaptic inputs and have significantly lower maximum firing frequency. Paired recordings and spatially localized optogenetic stimulation revealed that SE reduced the amplitude of unitary synaptic inputs from AACs to granule cells without altering reliability, short-term plasticity, or AIS GABA reversal potential. These changes compromised AAC-dependent shunting of granule cell firing in a multicompartmental model. These early post-SE changes in AAC physiology would limit their ability to receive and respond to input, undermining a critical brake on the dentate throughput during epileptogenesis.

Effects of stochastic coding on olfactory discrimination in flies and mice

Sparse coding can improve discrimination of sensory stimuli by reducing overlap between their representations. Two factors, however, can offset sparse coding's benefits: similar sensory stimuli have significant overlap and responses vary across trials. To elucidate the effects of these 2 factors, we analyzed odor responses in the fly and mouse olfactory regions implicated in learning and discrimination-the mushroom body (MB) and the piriform cortex (PCx). We found that neuronal responses fall along a continuum from extremely reliable across trials to extremely variable or stochastic. Computationally, we show that the observed variability arises from noise within central circuits rather than sensory noise. We propose this coding scheme to be advantageous for coarse- and fine-odor discrimination. More reliable cells enable quick discrimination between dissimilar odors. For similar odors, however, these cells overlap and do not provide distinguishing information. By contrast, more unreliable cells are decorrelated for similar odors, providing distinguishing information, though these benefits only accrue with extended training with more trials. Overall, we have uncovered a conserved, stochastic coding scheme in vertebrates and invertebrates, and we identify a candidate mechanism, based on variability in a winner-take-all (WTA) inhibitory circuit, that improves discrimination with training.

High-resolution CMOS-based biosensor for assessing hippocampal circuit dynamics in experience-dependent plasticity

Experiential richness creates tissue-level changes and synaptic plasticity as patterns emerge from rhythmic spatiotemporal activity of large interconnected neuronal assemblies. Despite numerous experimental and computational approaches at different scales, the precise impact of experience on network-wide computational dynamics remains inaccessible due to the lack of applicable large-scale recording methodology. We here demonstrate a large-scale multi-site biohybrid brain circuity on-CMOS-based biosensor with an unprecedented spatiotemporal resolution of 4096 microelectrodes, which allows simultaneous electrophysiological assessment across the entire hippocampal-cortical subnetworks from mice living in an enriched environment (ENR) and standard-housed (SD) conditions. Our platform, empowered with various computational analyses, reveals environmental enrichment's impacts on local and global spatiotemporal neural dynamics, firing synchrony, topological network complexity, and large-scale connectome. Our results delineate the distinct role of prior experience in enhancing multiplexed dimensional coding formed by neuronal ensembles and error tolerance and resilience to random failures compared to standard conditions. The scope and depth of these effects highlight the critical role of high-density, large-scale biosensors to provide a new understanding of the computational dynamics and information processing in multimodal physiological and experience-dependent plasticity conditions and their role in higher brain functions. Knowledge of these large-scale dynamics can inspire the development of biologically plausible computational models and computational artificial intelligence networks and expand the reach of neuromorphic brain-inspired computing into new applications.

Phase information is conserved in sparse, synchronous population-rate-codes via phase-to-rate recoding

Neural computation is often traced in terms of either rate- or phase-codes. However, most circuit operations will simultaneously affect information across both coding schemes. It remains unclear how phase and rate coded information is transmitted, in the face of continuous modification at consecutive processing stages. Here, we study this question in the entorhinal cortex (EC)- dentate gyrus (DG)- CA3 system using three distinct computational models. We demonstrate that DG feedback inhibition leverages EC phase information to improve rate-coding, a computation we term phase-to-rate recoding. Our results suggest that it i) supports the conservation of phase information within sparse rate-codes and ii) enhances the efficiency of plasticity in downstream CA3 via increased synchrony. Given the ubiquity of both phase-coding and feedback circuits, our results raise the question whether phase-to-rate recoding is a recurring computational motif, which supports the generation of sparse, synchronous population-rate-codes in areas beyond the DG.

Early Deficits in Dentate Circuit and Behavioral Pattern Separation after Concussive Brain Injury

Traumatic brain injury leads to cellular and circuit changes in the dentate gyrus, a gateway to hippocampal information processing. Intrinsic granule cell firing properties and strong feedback inhibition in the dentate are proposed as critical to its ability to generate unique representation of similar inputs by a process known as pattern separation. Here we evaluate the impact of brain injury on cellular decorrelation of temporally patterned inputs in slices and behavioral discrimination of spatial locations in vivo one week after concussive lateral fluid percussion injury (FPI) in mice. Despite posttraumatic increases in perforant path evoked excitatory drive to granule cells and enhanced ΔFosB labeling, indicating sustained increase in excitability, the reliability of granule cell spiking was not compromised after FPI. Although granule cells continued to effectively decorrelate output spike trains recorded in response to similar temporally patterned input sets after FPI, their ability to decorrelate highly similar input patterns was reduced. In parallel, encoding of similar spatial locations in a novel object location task that involves the dentate inhibitory circuits was impaired one week after FPI. Injury induced changes in pattern separation were accompanied by loss of somatostatin expressing inhibitory neurons in the hilus. Together, these data suggest that the early posttraumatic changes in the dentate circuit undermine dentate circuit decorrelation of temporal input patterns as well as behavioral discrimination of similar spatial locations, both of which could contribute to deficits in episodic memory.

Mismatch negativity as a marker of auditory pattern separation

To what extent does incidental encoding of auditory stimuli influence subsequent episodic memory for the same stimuli? We examined whether the mismatch negativity (MMN), an event-related potential generated by auditory change detection, is correlated with participants' ability to discriminate those stimuli (i.e. targets) from highly similar lures and from dissimilar foils. We measured the MMN in 30 young adults (18-32 years, 18 females) using a passive auditory oddball task with standard and deviant 5-tone sequences differing in pitch contour. After exposure, all participants completed an incidental memory test for old targets, lures, and foils. As expected, participants at test exhibited high sensitivity in recognizing target items relative to foils and lower sensitivity in recognizing target items relative to lures. Notably, we found a significant correlation between MMN amplitude and lure discrimination, but not foil discrimination. Our investigation shows that our capacity to discriminate sensory inputs at encoding, as measured by the MMN, translates into precision in memory for those inputs.

Task-dependent optimal representations for cerebellar learning

The cerebellar granule cell layer has inspired numerous theoretical models of neural representations that support learned behaviors, beginning with the work of Marr and Albus. In these models, granule cells form a sparse, combinatorial encoding of diverse sensorimotor inputs. Such sparse representations are optimal for learning to discriminate random stimuli. However, recent observations of dense, low-dimensional activity across granule cells have called into question the role of sparse coding in these neurons. Here, we generalize theories of cerebellar learning to determine the optimal granule cell representation for tasks beyond random stimulus discrimination, including continuous input-output transformations as required for smooth motor control. We show that for such tasks, the optimal granule cell representation is substantially denser than predicted by classical theories. Our results provide a general theory of learning in cerebellum-like systems and suggest that optimal cerebellar representations are task-dependent.

Optimal routing to cerebellum-like structures

The vast expansion from mossy fibers to cerebellar granule cells (GrC) produces a neural representation that supports functions including associative and internal model learning. This motif is shared by other cerebellum-like structures and has inspired numerous theoretical models. Less attention has been paid to structures immediately presynaptic to GrC layers, whose architecture can be described as a 'bottleneck' and whose function is not understood. We therefore develop a theory of cerebellum-like structures in conjunction with their afferent pathways that predicts the role of the pontine relay to cerebellum and the glomerular organization of the insect antennal lobe. We highlight a new computational distinction between clustered and distributed neuronal representations that is reflected in the anatomy of these two brain structures. Our theory also reconciles recent observations of correlated GrC activity with theories of nonlinear mixing. More generally, it shows that structured compression followed by random expansion is an efficient architecture for flexible computation.

Adult-born granule cells improve stimulus encoding and discrimination in the dentate gyrus

Heterogeneity plays an important role in diversifying neural responses to support brain function. Adult neurogenesis provides the dentate gyrus with a heterogeneous population of granule cells (GCs) that were born and developed their properties at different times. Immature GCs have distinct intrinsic and synaptic properties than mature GCs and are needed for correct encoding and discrimination in spatial tasks. How immature GCs enhance the encoding of information to support these functions is not well understood. Here, we record the responses to fluctuating current injections of GCs of different ages in mouse hippocampal slices to study how they encode stimuli. Immature GCs produce unreliable responses compared to mature GCs, exhibiting imprecise spike timings across repeated stimulation. We use a statistical model to describe the stimulus-response transformation performed by GCs of different ages. We fit this model to the data and obtain parameters that capture GCs' encoding properties. Parameter values from this fit reflect the maturational differences of the population and indicate that immature GCs perform a differential encoding of stimuli. To study how this age heterogeneity influences encoding by a population, we perform stimulus decoding using populations that contain GCs of different ages. We find that, despite their individual unreliability, immature GCs enhance the fidelity of the signal encoded by the population and improve the discrimination of similar time-dependent stimuli. Thus, the observed heterogeneity confers the population with enhanced encoding capabilities.

Early molecular layer interneuron hyperactivity triggers Purkinje neuron degeneration in SCA1

Toxic proteinaceous deposits and alterations in excitability and activity levels characterize vulnerable neuronal populations in neurodegenerative diseases. Using in vivo two-photon imaging in behaving spinocerebellar ataxia type 1 (Sca1) mice, wherein Purkinje neurons (PNs) degenerate, we identify an inhibitory circuit element (molecular layer interneurons [MLINs]) that becomes prematurely hyperexcitable, compromising sensorimotor signals in the cerebellum at early stages. Mutant MLINs express abnormally elevated parvalbumin, harbor high excitatory-to-inhibitory synaptic density, and display more numerous synaptic connections on PNs, indicating an excitation/inhibition imbalance. Chemogenetic inhibition of hyperexcitable MLINs normalizes parvalbumin expression and restores calcium signaling in Sca1 PNs. Chronic inhibition of mutant MLINs delayed PN degeneration, reduced pathology, and ameliorated motor deficits in Sca1 mice. Conserved proteomic signature of Sca1 MLINs, shared with human SCA1 interneurons, involved the higher expression of FRRS1L, implicated in AMPA receptor trafficking. We thus propose that circuit-level deficits upstream of PNs are one of the main disease triggers in SCA1.

Neuroscience: Hacking development to understand sensory discrimination

Fine sensory discrimination abilities are enabled by specific neural circuit architectures. A new study reveals how manipulating particular network parameters in the fly's memory centre, the mushroom body, alters sensory coding and discrimination.

Assessments of dentate gyrus function: discoveries and debates

There has been considerable speculation regarding the function of the dentate gyrus (DG) - a subregion of the mammalian hippocampus - in learning and memory. In this Perspective article, we compare leading theories of DG function. We note that these theories all critically rely on the generation of distinct patterns of activity in the region to signal differences between experiences and to reduce interference between memories. However, these theories are divided by the roles they attribute to the DG during learning and recall and by the contributions they ascribe to specific inputs or cell types within the DG. These differences influence the information that the DG is thought to impart to downstream structures. We work towards a holistic view of the role of DG in learning and memory by first developing three critical questions to foster a dialogue between the leading theories. We then evaluate the extent to which previous studies address our questions, highlight remaining areas of conflict, and suggest future experiments to bridge these theories.

Input density tunes Kenyon cell sensory responses in the Drosophila mushroom body

The ability to discriminate sensory stimuli with overlapping features is thought to arise in brain structures called expansion layers, where neurons carrying information about sensory features make combinatorial connections onto a much larger set of cells. For 50 years, expansion coding has been a prime topic of theoretical neuroscience, which seeks to explain how quantitative parameters of the expansion circuit influence sensory sensitivity, discrimination, and generalization. Here, we investigate the developmental events that produce the quantitative parameters of the arthropod expansion layer, called the mushroom body. Using Drosophila melanogaster as a model, we employ genetic and chemical tools to engineer changes to circuit development. These allow us to produce living animals with hypothesis-driven variations on natural expansion layer wiring parameters. We then test the functional and behavioral consequences. By altering the number of expansion layer neurons (Kenyon cells) and their dendritic complexity, we find that input density, but not cell number, tunes neuronal odor selectivity. Simple odor discrimination behavior is maintained when the Kenyon cell number is reduced and augmented by Kenyon cell number expansion. Animals with increased input density to each Kenyon cell show increased overlap in Kenyon cell odor responses and become worse at odor discrimination tasks.

Behavioral pattern separation and cognitive flexibility are enhanced in a mouse model of increased lateral entorhinal cortex-dentate gyrus circuit activity

Behavioral pattern separation and cognitive flexibility are essential cognitive abilities that are disrupted in many brain disorders. A better understanding of the neural circuitry involved in these abilities will open paths to treatment. In humans and mice, discrimination and adaptation rely on the integrity of the hippocampal dentate gyrus (DG) which receives glutamatergic input from the entorhinal cortex (EC), including the lateral EC (LEC). An inducible increase of EC-DG circuit activity improves simple hippocampal-dependent associative learning and increases DG neurogenesis. Here, we asked if the activity of LEC fan cells that directly project to the DG (LEC → DG neurons) regulates the relatively more complex hippocampal-dependent abilities of behavioral pattern separation or cognitive flexibility. C57BL/6J male mice received bilateral LEC infusions of a virus expressing shRNA TRIP8b, an auxiliary protein of an HCN channel or a control virus (SCR shRNA). Prior work shows that 4 weeks post-surgery, TRIP8b mice have more DG neurogenesis and greater activity of LEC → DG neurons compared to SCR shRNA mice. Here, 4 weeks post-surgery, the mice underwent testing for behavioral pattern separation and reversal learning (touchscreen-based location discrimination reversal [LDR]) and innate fear of open spaces (elevated plus maze [EPM]) followed by quantification of new DG neurons (doublecortin-immunoreactive cells [DCX+] cells). There was no effect of treatment (SCR shRNA vs. TRIP8b) on performance during general touchscreen training, LDR training, or the 1st days of LDR testing. However, in the last days of LDR testing, the TRIP8b shRNA mice had improved pattern separation (reached the first reversal more quickly and had more accurate discrimination) compared to the SCR shRNA mice, specifically when the load on pattern separation was high (lit squares close together or "small separation"). The TRIP8b shRNA mice were also more cognitively flexible (achieved more reversals) compared to the SCR shRNA mice in the last days of LDR testing. Supporting a specific influence on cognitive behavior, the SCR shRNA and TRIP8b shRNA mice did not differ in total distance traveled or in time spent in the closed arms of the EPM. Supporting an inducible increase in LEC-DG activity, DG neurogenesis was increased. These data indicate that the TRIP8b shRNA mice had better pattern separation and reversal learning and more neurogenesis compared to the SCR shRNA mice. This study advances fundamental and translational neuroscience knowledge relevant to two cognitive functions critical for adaptation and survival-behavioral pattern separation and cognitive flexibility-and suggests that the activity of LEC → DG neurons merits exploration as a therapeutic target to normalize dysfunctional DG behavioral output.

A novel enhancer-AAV approach selectively targeting dentate granule cells

The mammalian brain contains the most diverse array of cell types of any organ, including dozens of neuronal subtypes with distinct anatomical and functional characteristics. The brain leverages these neuron-type-specializations to perform diverse circuit operations and thus execute different behaviors properly. Through the use of Cre lines, access to specific neuron types has steadily improved over past decades. Despite their extraordinary utility, development and cross-breeding of Cre lines is time-consuming and expensive, presenting a significant barrier to entry for many investigators. Furthermore, cell-based therapeutics developed in Cre mice are not clinically translatable. Recently, several AAV vectors utilizing neuron-type-specific regulatory transcriptional sequences (enhancer-AAVs) were developed which overcome these limitations. Using a publicly available RNAseq dataset, we evaluated the potential of several candidate enhancers for neuron-type-specific targeting in the hippocampus. Here we identified a promising enhancer-AAV for targeting dentate granule cells and validated its selectivity in wild-type adult mice.

Developmental timing-dependent organization of synaptic connections between mossy fibers and granule cells in the cerebellum

The long-standing hypothesis that synapses between mossy fibers (MFs) and cerebellar granule cells (GCs) are organized according to the origins of MFs and locations of GC axons, parallel fibers (PFs), is supported by recent findings. However, the mechanisms of such organized synaptic connections remain unknown. Here, using our technique that enabled PF location-dependent labeling of GCs in mice, we confirmed that synaptic connections of GCs with specific MFs originating from the pontine nucleus (PN-MFs) and dorsal column nuclei (DCoN-MFs) were gently but differentially organized according to their PF locations. We then found that overall MF-GC synaptic connectivity was biased in a way that dendrites of GCs having nearby PFs tended to connect with the same MF terminals, implying that the MF origin- and PF location-dependent organization is associated with the overall biased MF-GC synaptic connectivity. Furthermore, the development of PN-MFs preceded that of DCoN-MFs, which matches the developmental sequence of GCs that preferentially connect with each type of these MFs. Thus, our results revealed that overall MF-GC synaptic connectivity is biased in terms of PF locations, and suggested that such connectivity is likely the result of synaptic formation between developmental timing-matched partners.

Neural stem and progenitor cells support and protect adult hippocampal function via vascular endothelial growth factor secretion

Adult neural stem and progenitor cells (NSPCs) reside in the dentate gyrus (DG) of the hippocampus throughout the lifespan of most mammalian species. In addition to generating new neurons, NSPCs may alter their niche via secretion of growth factors and cytokines. We recently showed that adult DG NSPCs secrete vascular endothelial growth factor (VEGF), which is critical for maintaining adult neurogenesis. Here, we asked whether NSPC-derived VEGF alters hippocampal function independent of adult neurogenesis. We found that loss of NSPC-derived VEGF acutely impaired hippocampal memory, caused neuronal hyperexcitability and exacerbated excitotoxic injury. We also found that NSPCs generate substantial proportions of total DG VEGF and VEGF disperses broadly throughout the DG, both of which help explain how this anatomically-restricted cell population could modulate function broadly. These findings suggest that NSPCs actively support and protect DG function via secreted VEGF, thereby providing a non-neurogenic functional dimension to endogenous NSPCs.

Parametric control of flexible timing through low-dimensional neural manifolds

Biological brains possess an unparalleled ability to adapt behavioral responses to changing stimuli and environments. How neural processes enable this capacity is a fundamental open question. Previous works have identified two candidate mechanisms: a low-dimensional organization of neural activity and a modulation by contextual inputs. We hypothesized that combining the two might facilitate generalization and adaptation in complex tasks. We tested this hypothesis in flexible timing tasks where dynamics play a key role. Examining trained recurrent neural networks, we found that confining the dynamics to a low-dimensional subspace allowed tonic inputs to parametrically control the overall input-output transform, enabling generalization to novel inputs and adaptation to changing conditions. Reverse-engineering and theoretical analyses demonstrated that this parametric control relies on a mechanism where tonic inputs modulate the dynamics along non-linear manifolds while preserving their geometry. Comparisons with data from behaving monkeys confirmed the behavioral and neural signatures of this mechanism.

Restructuring of olfactory representations in the fly brain around odor relationships in natural sources

A core challenge of olfactory neuroscience is to understand how neural representations of odor are generated and progressively transformed across different layers of the olfactory circuit into formats that support perception and behavior. The encoding of odor by odorant receptors in the input layer of the olfactory system reflects, at least in part, the chemical relationships between odor compounds. Neural representations of odor in higher order associative olfactory areas, generated by random feedforward networks, are expected to largely preserve these input odor relationships1-3. We evaluated these ideas by examining how odors are represented at different stages of processing in the olfactory circuit of the vinegar fly D. melanogaster. We found that representations of odor in the mushroom body (MB), a third-order associative olfactory area in the fly brain, are indeed structured and invariant across flies. However, the structure of MB representational space diverged significantly from what is expected in a randomly connected network. In addition, odor relationships encoded in the MB were better correlated with a metric of the similarity of their distribution across natural sources compared to their similarity with respect to chemical features, and the converse was true for odor relationships encoded in primary olfactory receptor neurons (ORNs). Comparison of odor coding at primary, secondary, and tertiary layers of the circuit revealed that odors were significantly regrouped with respect to their representational similarity across successive stages of olfactory processing, with the largest changes occurring in the MB. The non-linear reorganization of odor relationships in the MB indicates that unappreciated structure exists in the fly olfactory circuit, and this structure may facilitate the generalization of odors with respect to their co-occurence in natural sources.

Behavioral pattern separation and cognitive flexibility are enhanced in a mouse model of increased lateral entorhinal cortex-dentate gyrus circuit activity

Behavioral pattern separation and cognitive flexibility are essential cognitive abilities which are disrupted in many brain disorders. Better understanding of the neural circuitry involved in these abilities will open paths to treatment. In humans and mice, discrimination and adaptation rely on integrity of the hippocampal dentate gyrus (DG) which both receive glutamatergic input from the entorhinal cortex (EC), including the lateral EC (LEC). Inducible increase of EC-DG circuit activity improves simple hippocampal-dependent associative learning and increases DG neurogenesis. Here we asked if the activity of LEC fan cells that directly project to the DG (LEC➔DG neurons) regulates behavioral pattern separation or cognitive flexibility. C57BL6/J male mice received bilateral LEC infusions of a virus expressing shRNA TRIP8b, an auxiliary protein of an HCN channel or a control virus (SCR shRNA); this approach increases the activity of LEC➔DG neurons. Four weeks later, mice underwent testing for behavioral pattern separation and reversal learning (touchscreen-based Location Discrimination Reversal [LDR] task) and innate fear of open spaces (elevated plus maze [EPM]) followed by counting of new DG neurons (doublecortin-immunoreactive cells [DCX+] cells). TRIP8b and SCR shRNA mice performed similarly in general touchscreen training and LDR training. However, in late LDR testing, TRIP8b shRNA mice reached the first reversal more quickly and had more accurate discrimination vs. SCR shRNA mice, specifically when pattern separation was challenging (lit squares close together or "small separation"). Also, TRIP8b shRNA mice achieved more reversals in late LDR testing vs. SCR shRNA mice. Supporting a specific influence on cognitive behavior, SCR shRNA and TRIP8b shRNA mice did not differ in total distance traveled or in time spent in the closed arms of the EPM. Supporting an inducible increase in LEC-DG activity, DG neurogenesis was increased. These data indicate TRIP8b shRNA mice had better pattern separation and reversal learning and more neurogenesis vs. SCR shRNA mice. This work advances fundamental and translational neuroscience knowledge relevant to two cognitive functions critical for adaptation and survival - behavioral pattern separation and cognitive flexibility - and suggests the activity of LEC➔DG neurons merits exploration as a therapeutic target to normalize dysfunctional DG behavioral output.

Hacking brain development to test models of sensory coding

Animals can discriminate myriad sensory stimuli but can also generalize from learned experience. You can probably distinguish the favorite teas of your colleagues while still recognizing that all tea pales in comparison to coffee. Tradeoffs between detection, discrimination, and generalization are inherent at every layer of sensory processing. During development, specific quantitative parameters are wired into perceptual circuits and set the playing field on which plasticity mechanisms play out. A primary goal of systems neuroscience is to understand how material properties of a circuit define the logical operations-computations--that it makes, and what good these computations are for survival. A cardinal method in biology-and the mechanism of evolution--is to change a unit or variable within a system and ask how this affects organismal function. Here, we make use of our knowledge of developmental wiring mechanisms to modify hard-wired circuit parameters in the Drosophila melanogaster mushroom body and assess the functional and behavioral consequences. By altering the number of expansion layer neurons (Kenyon cells) and their dendritic complexity, we find that input number, but not cell number, tunes odor selectivity. Simple odor discrimination performance is maintained when Kenyon cell number is reduced and augmented by Kenyon cell expansion.