Recurrent Excitatory Connections Research Articles

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.

Interaction Between HCN and Slack Channels Regulates mPFC Pyramidal Cell Excitability in Working Memory Circuits.

The ability of monkeys and rats to carry out spatial working memory tasks has been shown to depend on the persistent firing of pyramidal cells in the prefrontal cortex (PFC), arising from recurrent excitatory connections on dendritic spines. These spines express hyperpolarization-activated cyclic nucleotide-gated (HCN) channels whose open state is increased by cAMP signaling, and which markedly alter PFC network connectivity and neuronal firing. In traditional neural circuits, activation of these non-selective cation channels leads to neuronal depolarization and increased firing rate. Paradoxically, cAMP activation of HCN channels in PFC pyramidal cells reduces working memory-related neuronal firing. This suggests that activation of HCN channels may hyperpolarize rather than depolarize these neurons. The current study tested the hypothesis that Na+ influx through HCN channels activates Slack Na+-activated K+ (KNa) channels to hyperpolarize the membrane. We have found that HCN and Slack KNa channels co-immunoprecipitate in cortical extracts and that, by immunoelectron microscopy, they colocalize at postsynaptic spines of PFC pyramidal neurons. A specific blocker of HCN channels, ZD7288, reduces KNa current in pyramidal cells that express both HCN and Slack channels, but has no effect on KNa currents in an HEK cell line expressing Slack without HCN channels, indicating that blockade of HCN channels in neurons reduces K+ current indirectly by lowering Na+ influx. Activation of HCN channels by cAMP in a cell line expressing a Ca2+ reporter results in elevation of cytoplasmic Ca2+, but the effect of cAMP is reversed if the HCN channels are co-expressed with Slack channels. Finally, we used a novel pharmacological blocker of Slack channels to show that inhibition of Slack in rat PFC improves working memory performance, an effect previously demonstrated for blockers of HCN channels. Our results suggest that the regulation of working memory by HCN channels in PFC pyramidal neurons is mediated by an HCN-Slack channel complex that links activation HCN channels to suppression of neuronal excitability.

Excitation creates a distributed pattern of cortical suppression due to varied recurrent input

Dense local, recurrent connections are a major feature of cortical circuits, yet how they affect neurons' responses has been unclear, with some studies reporting weak recurrent effects, some reporting amplification, and others indicating local suppression. Here, we show that optogenetic input to mouse V1 excitatory neurons generates salt-and-pepper patterns of both excitation and suppression. Responses in individual neurons are not strongly predicted by that neuron's direct input. A balanced-state network model reconciles a set of diverse observations: the observed dynamics, suppressed responses, decoupling of input and output, and long tail of excited responses. The model shows recurrent excitatory-excitatory connections are strong and also variable across neurons. Together, these results demonstrate that excitatory recurrent connections can have major effects on cortical computations by shaping and changing neurons' responses to input.

Inhibitory stabilized network behaviour in a balanced neural mass model of a cortical column

Strong inhibitory recurrent connections can reduce the tendency for a neural network to become unstable. This is known as inhibitory stabilization; networks that are unstable in the absence of strong inhibitory feedback because of their unstable excitatory recurrent connections are known as Inhibition Stabilized Networks (ISNs). One of the characteristics of ISNs is their “paradoxical response”, where perturbing the inhibitory neurons with additional excitatory input results in a decrease in their activity after a temporal delay instead of increasing their activity. Here, we develop a model of populations of neurons across different layers of cortex. Within each layer, there is one population of inhibitory neurons and one population of excitatory neurons. The connectivity weights across different populations in the model are derived from a synaptic physiology database provided by the Allen Institute. The model shows a gradient of excitation–inhibition balance across different layers in the cortex, where superficial layers are more inhibitory dominated compared to deeper layers. To investigate the presence of ISNs across different layers, we measured the membrane potentials of neural populations in the model after perturbing inhibitory populations. The results show that layer 2/3 in the model does not operate in the ISN regime but layers 4 and 5 do operate in the ISN regime. These results accord with neurophysiological findings that explored the presence of ISNs across different layers in the cortex. The results show that there may be a systematic macroscopic gradient of inhibitory stabilization across different layers in the cortex that depends on the level of excitation–inhibition balance, and that the strength of the paradoxical response increases as the model moves closer to bifurcation points.

Inhibitory control of sharp-wave ripple duration during learning in hippocampal recurrent networks.

Recurrent excitatory connections in hippocampal regions CA3 and CA2 are thought to play a key role in the generation of sharp-wave ripples (SWRs), electrophysiological oscillations tightly linked with learning and memory consolidation. However, it remains unknown how defined populations of inhibitory interneurons regulate these events during behavior. Here, we use large-scale, three-dimensional calcium imaging and retrospective molecular identification in the mouse hippocampus to characterize molecularly identified CA3 and CA2 interneuron activity during SWR-associated memory consolidation and spatial navigation. We describe subtype- and region-specific responses during behaviorally distinct brain states and find that SWRs are preceded by decreased cholecystokinin-expressing interneuron activity and followed by increased parvalbumin-expressing basket cell activity. The magnitude of these dynamics correlates with both SWR duration and behavior during hippocampal-dependent learning. Together these results assign subtype- and region-specific roles for inhibitory circuits in coordinating operations and learning-related plasticity in hippocampal recurrent circuits.

Processing of cell assemblies in the lateral entorhinal cortex.

There is evidence that olfactory cortex responds to its afferent input with the generation of cell assemblies: collections of principal neurons that fire together over a time scale of tens of ms. If such assemblies form an odor representation, then a fundamental question is how each assembly then induces neuronal activity in downstream structures. We have addressed this question in a detailed model of superficial layers of lateral entorhinal cortex, a recipient of input from olfactory cortex and olfactory bulb. Our results predict that the response of the fan cell subpopulation can be approximated by a relatively simple Boolean process, somewhat along the lines of the McCulloch/Pitts scheme; this is the case because of the sparsity of recurrent excitation amongst fan cells. However, because of recurrent excitatory connections between layer 2 and layer 3 pyramidal cells, synaptic and probably also gap junctional, the response of pyramidal cell subnetworks cannot be so approximated. Because of the highly structured anatomy of entorhinal output projections, our model suggests that downstream targets of entorhinal cortex (dentate gyrus, hippocampal CA3, CA1, piriform cortex, olfactory bulb) receive differentially processed information.

Nonlinear transient amplification in recurrent neural networks with short-term plasticity.

To rapidly process information, neural circuits have to amplify specific activity patterns transiently. How the brain performs this nonlinear operation remains elusive. Hebbian assemblies are one possibility whereby strong recurrent excitatory connections boost neuronal activity. However, such Hebbian amplification is often associated with dynamical slowing of network dynamics, non-transient attractor states, and pathological run-away activity. Feedback inhibition can alleviate these effects but typically linearizes responses and reduces amplification gain. Here, we study nonlinear transient amplification (NTA), a plausible alternative mechanism that reconciles strong recurrent excitation with rapid amplification while avoiding the above issues. NTA has two distinct temporal phases. Initially, positive feedback excitation selectively amplifies inputs that exceed a critical threshold. Subsequently, short-term plasticity quenches the run-away dynamics into an inhibition-stabilized network state. By characterizing NTA in supralinear network models, we establish that the resulting onset transients are stimulus selective and well-suited for speedy information processing. Further, we find that excitatory-inhibitory co-tuning widens the parameter regime in which NTA is possible in the absence of persistent activity. In summary, NTA provides a parsimonious explanation for how excitatory-inhibitory co-tuning and short-term plasticity collaborate in recurrent networks to achieve transient amplification.

Dissecting the neural underpinnings of the short-latency response to transcranial magnetic stimulation of motor cortex: A computational modeling study

Abstract Objective: Transcranial magnetic stimulation (TMS) over the primary motor cortex (M1) elicits a series of high frequency volleys termed D- and I-waves measured epidurally in the corticospinal tract of awake humans. The D-wave is thought to be caused by direct activation of corticospinal axons, while I-waves are believed to be generated by trans-synaptic activation of corticospinal axons. The cortical circuits and mechanisms involved in the generation of I-waves by TMS of M1 remain unclear. Methods: We implemented a computational model of a cortical column with biophysically-based cortical neurons, following an existing model (Traub 2005). The TMS induced E-field was modeled as intracellular current injections of monophasic TMS pulses into the axon terminals of pyramidal neurons (PN). We constructed dose–response curves of spiking activity averaged across layer 5 (L5) PN as a function of the proportion of activated PN. Results: Model-based responses were compared to I-waves recorded from L5 PN in rodent M1 during TMS (Li 2017), and the network model reproduced I-waves only during the activation of axon terminals of L5 PN in contrast to activation of axon terminals of L2/3 or L6 PN. Inhibitory interneurons in deep layers fired in synchrony during the late phases of I-waves offering a potential explanation for the reduction in magnitude of late I-waves seen in experimental studies after administration of GABA-A agonists. Virtual lesioning of model neurons revealed that recurrent synaptic connections between L5 PN mediated by AMPA receptors play a prominent role in the generation of I-waves. Conclusions: Activation of presynaptic terminal of L5 PN is necessary to generate an I-wave response to TMS. Further, recurrent excitatory synaptic connections between L5 PN play a prominent role in the generation of I-wave response. Significance: Gaining a better understanding of circuit mechanisms of TMS of the M1 could enable optimization of stimulation paradigms. Keywords: Transcranial magnetic stimulation, I-waves, Cortical column model, Motor cortex

State transitions through inhibitory interneurons in a cortical network model.

Inhibitory interneurons shape the spiking characteristics and computational properties of cortical networks. Interneuron subtypes can precisely regulate cortical function but the roles of interneuron subtypes for promoting different regimes of cortical activity remains unclear. Therefore, we investigated the impact of fast spiking and non-fast spiking interneuron subtypes on cortical activity using a network model with connectivity and synaptic properties constrained by experimental data. We found that network properties were more sensitive to modulation of the fast spiking population, with reductions of fast spiking excitability generating strong spike correlations and network oscillations. Paradoxically, reduced fast spiking excitability produced a reduction of global excitation-inhibition balance and features of an inhibition stabilised network, in which firing rates were driven by the activity of excitatory neurons within the network. Further analysis revealed that the synaptic interactions and biophysical features associated with fast spiking interneurons, in particular their rapid intrinsic response properties and short synaptic latency, enabled this state transition by enhancing gain within the excitatory population. Therefore, fast spiking interneurons may be uniquely positioned to control the strength of recurrent excitatory connectivity and the transition to an inhibition stabilised regime. Overall, our results suggest that interneuron subtypes can exert selective control over excitatory gain allowing for differential modulation of global network state.

Cell assembly formation and structure in a piriform cortex model.

The piriform cortex is rich in recurrent excitatory synaptic connections between pyramidal neurons. We asked how such connections could shape cortical responses to olfactory lateral olfactory tract (LOT) inputs. For this, we constructed a computational network model of anterior piriform cortex with 2000 multicompartment, multiconductance neurons (500 semilunar, 1000 layer 2 and 500 layer 3 pyramids; 200 superficial interneurons of two types; 500 deep interneurons of three types; 500 LOT afferents), incorporating published and unpublished data. With a given distribution of LOT firing patterns, and increasing the strength of recurrent excitation, a small number of firing patterns were observed in pyramidal cell networks: first, sparse firings; then temporally and spatially concentrated epochs of action potentials, wherein each neuron fires one or two spikes; then more synchronized events, associated with bursts of action potentials in some pyramidal neurons. We suggest that one function of anterior piriform cortex is to transform ongoing streams of input spikes into temporally focused spike patterns, called here "cell assemblies", that are salient for downstream projection areas.

Attractor-state itinerancy in neural circuits with synaptic depression

Neural populations with strong excitatory recurrent connections can support bistable states in their mean firing rates. Multiple fixed points in a network of such bistable units can be used to model memory retrieval and pattern separation. The stability of fixed points may change on a slower timescale than that of the dynamics due to short-term synaptic depression, leading to transitions between quasi-stable point attractor states in a sequence that depends on the history of stimuli. To better understand these behaviors, we study a minimal model, which characterizes multiple fixed points and transitions between them in response to stimuli with diverse time- and amplitude-dependencies. The interplay between the fast dynamics of firing rate and synaptic responses and the slower timescale of synaptic depression makes the neural activity sensitive to the amplitude and duration of square-pulse stimuli in a nontrivial, history-dependent manner. Weak cross-couplings further deform the basins of attraction for different fixed points into intricate shapes. We find that while short-term synaptic depression can reduce the total number of stable fixed points in a network, it tends to strongly increase the number of fixed points visited upon repetitions of fixed stimuli. Our analysis provides a natural explanation for the system’s rich responses to stimuli of different durations and amplitudes while demonstrating the encoding capability of bistable neural populations for dynamical features of incoming stimuli.

Prefrontal Computation as Active Inference.

The prefrontal cortex is vital for a range of cognitive processes, including working memory, attention, and decision-making. Notably, its absence impairs the performance of tasks requiring the maintenance of information through a delay period. In this paper, we formulate a rodent task—which requires maintenance of delay-period activity—as a Markov decision process and treat optimal task performance as an (active) inference problem. We simulate the behavior of a Bayes optimal mouse presented with 1 of 2 cues that instructs the selection of concurrent visual and auditory targets on a trial-by-trial basis. Formulating inference as message passing, we reproduce features of neuronal coupling within and between prefrontal regions engaged by this task. We focus on the micro-circuitry that underwrites delay-period activity and relate it to functional specialization within the prefrontal cortex in primates. Finally, we simulate the electrophysiological correlates of inference and demonstrate the consequences of lesions to each part of our in silico prefrontal cortex. In brief, this formulation suggests that recurrent excitatory connections—which support persistent neuronal activity—encode beliefs about transition probabilities over time. We argue that attentional modulation can be understood as the contextualization of sensory input by these persistent beliefs.

Spatiotemporal discrimination in attractor networks with short-term synaptic plasticity

We demonstrate that a randomly connected attractor network with dynamic synapses can discriminate between similar sequences containing multiple stimuli suggesting such networks provide a general basis for neural computations in the brain. The network contains units representing assemblies of pools of neurons, with preferentially strong recurrent excitatory connections rendering each unit bi-stable. Weak interactions between units leads to a multiplicity of attractor states, within which information can persist beyond stimulus offset. When a new stimulus arrives, the prior state of the network impacts the encoding of the incoming information, with short-term synaptic depression ensuring an itinerancy between sets of active units. We assess the ability of such a network to encode the identity of sequences of stimuli, so as to provide a template for sequence recall, or decisions based on accumulation of evidence. Across a range of parameters, such networks produce the primacy (better final encoding of the earliest stimuli) and recency (better final encoding of the latest stimuli) observed in human recall data and can retain the information needed to make a binary choice based on total number of presentations of a specific stimulus. Similarities and differences in the final states of the network produced by different sequences lead to predictions of specific errors that could arise when an animal or human subject generalizes from training data, when the training data comprises a subset of the entire stimulus repertoire. We suggest that such networks can provide the general purpose computational engines needed for us to solve many cognitive tasks.

Detecting single-cell stimulation in a large network of integrate-and-fire neurons.

Several experiments have shown that the stimulation of a single neuron in the cortex can influence the local network activity and even the behavior of an animal. From the theoretical point of view, it is not clear how stimulating a single cell in a cortical network can evoke a statistically significant change in the activity of a large population. Our previous study considered a random network of integrate-and-fire neurons and proposed a way of detecting the stimulation of a single neuron in the activity of a local network: a threshold detector biased toward a specific subset of neurons. Here, we revisit this model and extend it by introducing a second network acting as a readout. In the simplest scenario, the readout consists of a collection of integrate-and-fire neurons with no recurrent connections. In this case, the ability to detect the stimulus does not improve. However, a readout network with both feed-forward and local recurrent inhibition permits detection with a very small bias, if compared to the readout scheme introduced previously. The crucial role of inhibition is to reduce global input cross correlations, the main factor limiting detectability. Finally, we show that this result is robust if recurrent excitatory connections are included or if a different kind of readout bias (in the synaptic amplitudes instead of connection probability) is used.

Selective Strengthening of Intracortical Excitatory Input Leads to Receptive Field Refinement during Auditory Cortical Development.

In the primary auditory cortex (A1) of rats, refinement of excitatory input to layer (L)4 neurons contributes to the sharpening of their frequency selectivity during postnatal development. L4 neurons receive both feedforward thalamocortical and recurrent intracortical inputs, but how potential developmental changes of each component can account for the sharpening of excitatory input tuning remains unclear. By combining in vivo whole-cell recording and pharmacological silencing of cortical spiking in young rats of both sexes, we examined developmental changes at three hierarchical stages: output of auditory thalamic neurons, thalamocortical input and recurrent excitatory input to an A1 L4 neuron. In the thalamus, the tonotopic map matured with an expanded range of frequency representations, while the frequency tuning of output responses was unchanged. On the other hand, the tuning shape of both thalamocortical and intracortical excitatory inputs to a L4 neuron became sharpened. In particular, the intracortical input became better tuned than thalamocortical excitation. Moreover, the weight of intracortical excitation around the optimal frequency was selectively strengthened, resulting in a dominant role of intracortical excitation in defining the total excitatory input tuning. Our modeling work further demonstrates that the frequency-selective strengthening of local recurrent excitatory connections plays a major role in the refinement of excitatory input tuning of L4 neurons.SIGNIFICANCE STATEMENT During postnatal development, sensory cortex undergoes functional refinement, through which the size of sensory receptive field is reduced. In the rat primary auditory cortex, such refinement in layer (L)4 is mainly attributed to improved selectivity of excitatory input a L4 neuron receives. In this study, we further examined three stages along the hierarchical neural pathway where excitatory input refinement might occur. We found that developmental refinement takes place at both thalamocortical and intracortical circuit levels, but not at the thalamic output level. Together with modeling results, we revealed that the optimal-frequency-selective strengthening of intracortical excitation plays a dominant role in the refinement of excitatory input tuning.

Sparse recurrent excitatory connectivity in the microcircuit of the adult mouse and human cortex.

Generating a comprehensive description of cortical networks requires a large-scale, systematic approach. To that end, we have begun a pipeline project using multipatch electrophysiology, supplemented with two-photon optogenetics, to characterize connectivity and synaptic signaling between classes of neurons in adult mouse primary visual cortex (V1) and human cortex. We focus on producing results detailed enough for the generation of computational models and enabling comparison with future studies. Here, we report our examination of intralaminar connectivity within each of several classes of excitatory neurons. We find that connections are sparse but present among all excitatory cell classes and layers we sampled, and that most mouse synapses exhibited short-term depression with similar dynamics. Synaptic signaling between a subset of layer 2/3 neurons, however, exhibited facilitation. These results contribute to a body of evidence describing recurrent excitatory connectivity as a conserved feature of cortical microcircuits.

Signaling Pathways and Cellular Mechanisms Regulating Mossy Fiber Sprouting in the Development of Epilepsy.

The sprouting of hippocampal dentate granule cell axons, termed mossy fibers, into the dentate inner molecular layer is one of the most consistent findings in tissue from patients with mesial temporal lobe epilepsy. Decades of research in animal models have revealed that mossy fiber sprouting creates de novo recurrent excitatory connections in the hippocampus, fueling speculation that the pathology may drive temporal lobe epileptogenesis. Conducting definitive experiments to test this hypothesis, however, has been challenging due to the difficulty of dissociating this sprouting from the many other changes occurring during epileptogenesis. The field has been largely driven, therefore, by correlative data. Recently, the development of powerful transgenic mouse technologies and the discovery of novel drug targets has provided new tools to assess the role of mossy fiber sprouting in epilepsy. We can now selectively manipulate hippocampal granule cells in rodent epilepsy models, providing new insights into the granule cell subpopulations that participate in mossy fiber sprouting. The cellular pathways regulating this sprouting are also coming to light, providing new targets for pharmacological intervention. Surprisingly, many investigators have found that blocking mossy fiber sprouting has no effect on seizure occurrence, while seizure frequency can be reduced by treatments that have no effect on this sprouting. These results raise new questions about the role of mossy fiber sprouting in epilepsy. Here, we will review these findings with particular regard to the contributions of new granule cells to mossy fiber sprouting and the regulation of this sprouting by the mTOR signaling pathway.

Heteroassociative storage of hippocampal pattern sequences in the CA3 subregion.

BackgroundRecent research suggests that the CA3 subregion of the hippocampus has properties of both autoassociative network, due to its ability to complete partial cues, tolerate noise, and store associations between memories, and heteroassociative one, due to its ability to store and retrieve sequences of patterns. Although there are several computational models of the CA3 as an autoassociative network, more detailed evaluations of its heteroassociative properties are missing.MethodsWe developed a model of the CA3 subregion containing 10,000 integrate-and-fire neurons with both recurrent excitatory and inhibitory connections, and which exhibits coupled oscillations in the gamma and theta ranges. We stored thousands of pattern sequences using a heteroassociative learning rule with competitive synaptic scaling.ResultsWe showed that a purely heteroassociative network model can (i) retrieve pattern sequences from partial cues with external noise and incomplete connectivity, (ii) achieve homeostasis regarding the number of connections per neuron when many patterns are stored when using synaptic scaling, (iii) continuously update the set of retrievable patterns, guaranteeing that the last stored patterns can be retrieved and older ones can be forgotten.DiscussionHeteroassociative networks with synaptic scaling rules seem sufficient to achieve many desirable features regarding connectivity homeostasis, pattern sequence retrieval, noise tolerance and updating of the set of retrievable patterns.

Assessing the Role of Inhibition in Stabilizing Neocortical Networks Requires Large-Scale Perturbation of the Inhibitory Population.

Neurons within cortical microcircuits are interconnected with recurrent excitatory synaptic connections that are thought to amplify signals (Douglas and Martin, 2007), form selective subnetworks (Ko et al., 2011), and aid feature discrimination. Strong inhibition (Haider et al., 2013) counterbalances excitation, enabling sensory features to be sharpened and represented by sparse codes (Willmore et al., 2011). This balance between excitation and inhibition makes it difficult to assess the strength, or gain, of recurrent excitatory connections within cortical networks, which is key to understanding their operational regime and the computations that they perform. Networks that combine an unstable high-gain excitatory population with stabilizing inhibitory feedback are known as inhibition-stabilized networks (ISNs) (Tsodyks et al., 1997). Theoretical studies using reduced network models predict that ISNs produce paradoxical responses to perturbation, but experimental perturbations failed to find evidence for ISNs in cortex (Atallah et al., 2012). Here, we reexamined this question by investigating how cortical network models consisting of many neurons behave after perturbations and found that results obtained from reduced network models fail to predict responses to perturbations in more realistic networks. Our models predict that a large proportion of the inhibitory network must be perturbed to reliably detect an ISN regime robustly in cortex. We propose that wide-field optogenetic suppression of inhibition under promoters targeting a large fraction of inhibitory neurons may provide a perturbation of sufficient strength to reveal the operating regime of cortex. Our results suggest that detailed computational models of optogenetic perturbations are necessary to interpret the results of experimental paradigms.SIGNIFICANCE STATEMENT Many useful computational mechanisms proposed for cortex require local excitatory recurrence to be very strong, such that local inhibitory feedback is necessary to avoid epileptiform runaway activity (an “inhibition-stabilized network” or “ISN” regime). However, recent experimental results suggest that this regime may not exist in cortex. We simulated activity perturbations in cortical networks of increasing realism and found that, to detect ISN-like properties in cortex, large proportions of the inhibitory population must be perturbed. Current experimental methods for inhibitory perturbation are unlikely to satisfy this requirement, implying that existing experimental observations are inconclusive about the computational regime of cortex. Our results suggest that new experimental designs targeting a majority of inhibitory neurons may be able to resolve this question.

Short-Term Depression of Sprouted Mossy Fiber Synapses from Adult-Born Granule Cells.

Epileptic seizures potently modulate hippocampal adult neurogenesis, and adult-born dentate granule cells contribute to the pathologic retrograde sprouting of mossy fiber axons, both hallmarks of temporal lobe epilepsy. The characteristics of these sprouted synapses, however, have been largely unexplored, and the specific contribution of adult-born granule cells to functional mossy fiber sprouting is unknown, primarily due to technical barriers in isolating sprouted mossy fiber synapses for analysis. Here, we used DcxCreERT2 transgenic mice to permanently pulse-label age-defined cohorts of granule cells born either before or after pilocarpine-induced status epilepticus (SE). Using optogenetics, we demonstrate that adult-born granule cells born before SE form functional recurrent monosynaptic excitatory connections with other granule cells. Surprisingly, however, although healthy mossy fiber synapses in CA3 are well characterized "detonator" synapses that potently drive postsynaptic cell firing through their profound frequency-dependent facilitation, sprouted mossy fiber synapses from adult-born cells exhibited profound frequency-dependent depression, despite possessing some of the morphological hallmarks of mossy fiber terminals. Mature granule cells also contributed to functional mossy fiber sprouting, but exhibited less synaptic depression. Interestingly, granule cells born shortly after SE did not form functional excitatory synapses, despite robust sprouting. Our results suggest that, although sprouted mossy fibers form recurrent excitatory circuits with some of the morphological characteristics of typical mossy fiber terminals, the functional characteristics of sprouted synapses would limit the contribution of adult-born granule cells to hippocampal hyperexcitability in the epileptic hippocampus.SIGNIFICANCE STATEMENT In the hippocampal dentate gyrus, seizures drive retrograde sprouting of granule cell mossy fiber axons. We directly activated sprouted mossy fiber synapses from adult-born granule cells to study their synaptic properties. We reveal that sprouted synapses from adult-born granule cells have a diminished ability to sustain recurrent excitation in the epileptic hippocampus, which raises questions about the role of sprouting and adult neurogenesis in sustaining seizure-like activity.