Memory-electroluminescence for multiple action-potentials combination in bio-inspired afferent nerves.

The development of optoelectronics mimicking the functions of the biological nervous system is important to artificial intelligence. This work demonstrates an optoelectronic, artificial, afferent-nerve strategy based on memory-electroluminescence spikes, which can realize multiple action-potentials combination through a single optical channel. The memory-electroluminescence spikes have diverse morphologies due to their history-dependent characteristics and can be used to encode distributed sensor signals. As the key to successful functioning of the optoelectronic, artificial afferent nerve, a driving mode for light-emitting diodes, namely, the non-carrier injection mode, is proposed, allowing it to drive nanoscale light-emitting diodes to generate a memory-electroluminescence spikes that has multiple sub-peaks. Moreover, multiplexing of the spikes can be obtained by using optical signals with different wavelengths, allowing for a large signal bandwidth, and the multiple action-potentials transmission process in afferent nerves can be demonstrated. Finally, sensor-position recognition with the bio-inspired afferent nerve is developed and shown to have a high recognition accuracy of 98.88%. This work demonstrates a strategy for mimicking biological afferent nerves and offers insights into the construction of artificial perception systems.

Dynamic corticothalamic modulation of the somatosensory thalamocortical circuit during wakefulness.

The feedback projections from cortical layer 6 (L6CT) to the sensory thalamus have long been implicated in playing a primary role in gating sensory signaling but remain poorly understood. To causally elucidate the full range of effects of these projections, we targeted silicon probe recordings to the whisker thalamocortical circuit of awake mice selectively expressing Channelrhodopsin-2 in L6CT neurons. Through optogenetic manipulation of L6CT neurons, multi-site electrophysiological recordings, and modeling of L6CT circuitry, we establish L6CT neurons as dynamic modulators of ongoing spiking in the ventral posteromedial nucleus of the thalamus (VPm), either suppressing or enhancing VPm spiking depending on L6CT neurons' firing rate and synchrony. Differential effects across the cortical excitatory and inhibitory sub-populations point to an overall influence of L6CT feedback on cortical excitability that could have profound implications for regulating sensory signaling across a range of ethologically relevant conditions.

Analysis of hippocampal local field potentials by diffusion mapped delay coordinates.

Spatial navigation through novel spaces and to known goal locations recruits multiple integrated structures in the mammalian brain. Within this extended network, the hippocampus enables formation and retrieval of cognitive spatial maps and contributes to decision making at choice points. Exploration and navigation to known goal locations produce synchronous activity of hippocampal neurons resulting in rhythmic oscillation events in local networks. Power of specific oscillatory frequencies and numbers of these events recorded in local field potentials correlate with distinct cognitive aspects of spatial navigation. Typically, oscillatory power in brain circuits is analyzed with Fourier transforms or short-time Fourier methods, which involve assumptions about the signal that are likely not true and fail to succinctly capture potentially informative features. To avoid such assumptions, we applied a method that combines manifold discovery techniques with dynamical systems theory, namely diffusion maps and Takens' time-delay embedding theory, that avoids limitations seen in traditional methods. This method, called diffusion mapped delay coordinates (DMDC), when applied to hippocampal signals recorded from juvenile rats freely navigating a Y-maze, replicates some outcomes seen with standard approaches and identifies age differences in dynamic states that traditional analyses are unable to detect. Thus, DMDC may serve as a suitable complement to more traditional analyses of LFPs recorded from behaving subjects that may enhance information yield.

Electrophysiology of Ctenophore Smooth Muscle.

Unlike in the Cnidaria, where muscle cells are coupled together into an epithelium, ctenophore muscles are single, elongated, intramesogleal structures resembling vertebrate smooth muscle. Under voltage-clamp, these fibers can be separated into different classes with different sets of membrane ion channels. The ion channel makeup is related to the muscle's anatomical position and specific function. For example, Beroe ovata radial fibers, which are responsible for maintaining the rigidity of the body wall, generate sequences of brief action potentials whereas longitudinal fibers, which are concerned with mouth opening and body flexions, often produce single longer duration action potentials.Beroe muscle contractions depend on the influx of Ca2+. During an action potential the inward current is carried by Ca2+, and the increase in intracellular Ca2+ concentration generated can be monitored in FLUO-3-loaded cells. Confocal microscopy in line scan mode shows that the Ca2+ spreads from the outer membrane into the core of the fiber and is cleared from there relatively slowly. The rise in intracellular Ca2+ is linked to an increase in a Ca2+-activated K+ conductance (KCa), which can also be elicited by iontophoretic Ca2+ injection. Near the cell membrane, Ca2+ clearance monitored using FLUO3, matches the decline in the KCa conductance. For light loads, Ca2+ is cleared rapidly, but this fast system is insufficient when Ca2+ influx is maintained. Action potential frequency may be regulated by the slowly developing KCa conductance.

Ionic plasmon-polariton in application to neurosignaling.

The diffusive ion current is insufficient to explain the fast saltatory conduction observed in myelinated axons and in pain-sensing C fibers in the human nervous system, where the stimulus signal exhibits a velocity two orders of magnitude greater than the upper limit of ion diffusion velocity, even when the diffusion is accelerated by myelin, as in the discrete cable model including the Hodgkin-Huxley mechanism. The agreement with observations has been achieved in a wave-type model of stimulus signal kinetics via synchronized ion local density oscillations propagating as a wave in axons periodically corrugated by myelin segments in myelinated axons, or by periodically distributed rafts with clusters of Na^{+} channels in C fibers. The resulting so-called plasmon-polariton model for saltatory conduction reveals also the specific role of myelin, which is different from what was previously thought. This can be important for identifying a new target for the future treatment of demyelination diseases.

A modular organic neuromorphic spiking circuit for retina-inspired sensory coding and neurotransmitter-mediated neural pathways.

Signal communication mechanisms within the human body rely on the transmission and modulation of action potentials. Replicating the interdependent functions of receptors, neurons and synapses with organic artificial neurons and biohybrid synapses is an essential first step towards merging neuromorphic circuits and biological systems, crucial for computing at the biological interface. However, most organic neuromorphic systems are based on simple circuits which exhibit limited adaptability to both external and internal biological cues, and are restricted to emulate only specific the functions of an individual neuron/synapse. Here, we present a modular neuromorphic system which combines organic spiking neurons and biohybrid synapses to replicate a neural pathway. The spiking neuron mimics the sensory coding function of afferent neurons from light stimuli, while the neuromodulatory activity of interneurons is emulated by neurotransmitters-mediated biohybrid synapses. Combining these functions, we create a modular connection between multiple neurons to establish a pre-processing retinal pathway primitive.

Approximating Nonlinear Functions With Latent Boundaries in Low-Rank Excitatory-Inhibitory Spiking Networks.

Deep feedforward and recurrent neural networks have become successful functional models of the brain, but they neglect obvious biological details such as spikes and Dale's law. Here we argue that these details are crucial in order to understand how real neural circuits operate. Towards this aim, we put forth a new framework for spike-based computation in low-rank excitatory-inhibitory spiking networks. By considering populations with rank-1 connectivity, we cast each neuron's spiking threshold as a boundary in a low-dimensional input-output space. We then show how the combined thresholds of a population of inhibitory neurons form a stable boundary in this space, and those of a population of excitatory neurons form an unstable boundary. Combining the two boundaries results in a rank-2 excitatory-inhibitory (EI) network with inhibition-stabilized dynamics at the intersection of the two boundaries. The computation of the resulting networks can be understood as the difference of two convex functions and is thereby capable of approximating arbitrary non-linear input-output mappings. We demonstrate several properties of these networks, including noise suppression and amplification, irregular activity and synaptic balance, as well as how they relate to rate network dynamics in the limit that the boundary becomes soft. Finally, while our work focuses on small networks (5-50 neurons), we discuss potential avenues for scaling up to much larger networks. Overall, our work proposes a new perspective on spiking networks that may serve as a starting point for a mechanistic understanding of biological spike-based computation.

Sparse Firing in a Hybrid Central Pattern Generator for Spinal Motor Circuits.

Central pattern generators are circuits generating rhythmic movements, such as walking. The majority of existing computational models of these circuits produce antagonistic output where all neurons within a population spike with a broad burst at about the same neuronal phase with respect to network output. However, experimental recordings reveal that many neurons within these circuits fire sparsely, sometimes as rarely as once within a cycle. Here we address the sparse neuronal firing and develop a model to replicate the behavior of individual neurons within rhythm-generating populations to increase biological plausibility and facilitate new insights into the underlying mechanisms of rhythm generation. The developed network architecture is able to produce sparse firing of individual neurons, creating a novel implementation for exploring the contribution of network architecture on rhythmic output. Furthermore, the introduction of sparse firing of individual neurons within the rhythm-generating circuits is one of the factors that allows for a broad neuronal phase representation of firing at the population level. This moves the model toward recent experimental findings of evenly distributed neuronal firing across phases among individual spinal neurons. The network is tested by methodically iterating select parameters to gain an understanding of how connectivity and the interplay of excitation and inhibition influence the output. This knowledge can be applied in future studies to implement a biologically plausible rhythm-generating circuit for testing biological hypotheses.

T-wave inversion through inhomogeneous voltage diffusion within the FK3V cardiac model.

The heart beats are due to the synchronized contraction of cardiomyocytes triggered by a periodic sequence of electrical signals called action potentials, which originate in the sinoatrial node and spread through the heart's electrical system. A large body of work is devoted to modeling the propagation of the action potential and to reproducing reliably its shape and duration. Connection of computational modeling of cells to macroscopic phenomenological curves such as the electrocardiogram has been also intense, due to its clinical importance in analyzing cardiovascular diseases. In this work, we simulate the dynamics of action potential propagation using the three-variable Fenton-Karma model that can account for both normal and damaged cells through a the spatially inhomogeneous voltage diffusion coefficient. We monitor the action potential propagation in the cardiac tissue and calculate the pseudo-electrocardiogram that reproduces the R and T waves. The R-wave amplitude varies according to a double exponential law as a function of the (spatially homogeneous, for an isotropic tissue) diffusion coefficient. The addition of spatial inhomogeneity in the diffusion coefficient by means of a defected region representing damaged cardiac cells may result in T-wave inversion in the calculated pseudo-electrocardiogram. The transition from positive to negative polarity of the T-wave is analyzed as a function of the length and the depth of the defected region.

Enhanced Release Probability without Changes in Synaptic Delay during Analogue-Digital Facilitation.

Neuronal timing with millisecond precision is critical for many brain functions such as sensory perception, learning and memory formation. At the level of the chemical synapse, the synaptic delay is determined by the presynaptic release probability (Pr) and the waveform of the presynaptic action potential (AP). For instance, paired-pulse facilitation or presynaptic long-term potentiation are associated with reductions in the synaptic delay, whereas paired-pulse depression or presynaptic long-term depression are associated with an increased synaptic delay. Parallelly, the AP broadening that results from the inactivation of voltage gated potassium (Kv) channels responsible for the repolarization phase of the AP delays the synaptic response, and the inactivation of sodium (Nav) channels by voltage reduces the synaptic latency. However, whether synaptic delay is modulated during depolarization-induced analogue-digital facilitation (d-ADF), a form of context-dependent synaptic facilitation induced by prolonged depolarization of the presynaptic neuron and mediated by the voltage-inactivation of presynaptic Kv1 channels, remains unclear. We show here that despite Pr being elevated during d-ADF at pyramidal L5-L5 cell synapses, the synaptic delay is surprisingly unchanged. This finding suggests that both Pr- and AP-dependent changes in synaptic delay compensate for each other during d-ADF. We conclude that, in contrast to other short- or long-term modulations of presynaptic release, synaptic timing is not affected during d-ADF because of the opposite interaction of Pr- and AP-dependent modulations of synaptic delay.

A robust balancing mechanism for spiking neural networks.

Dynamical balance of excitation and inhibition is usually invoked to explain the irregular low firing activity observed in the cortex. We propose a robust nonlinear balancing mechanism for a random network of spiking neurons, which works also in the absence of strong external currents. Biologically, the mechanism exploits the plasticity of excitatory-excitatory synapses induced by short-term depression. Mathematically, the nonlinear response of the synaptic activity is the key ingredient responsible for the emergence of a stable balanced regime. Our claim is supported by a simple self-consistent analysis accompanied by extensive simulations performed for increasing network sizes. The observed regime is essentially fluctuation driven and characterized by highly irregular spiking dynamics of all neurons.

Fractional order memcapacitive neuromorphic elements reproduce and predict neuronal function.

There is an increasing need to implement neuromorphic systems that are both energetically and computationally efficient. There is also great interest in using electric elements with memory, memelements, that can implement complex neuronal functions intrinsically. A feature not widely incorporated in neuromorphic systems is history-dependent action potential time adaptation which is widely seen in real cells. Previous theoretical work shows that power-law history dependent spike time adaptation, seen in several brain areas and species, can be modeled with fractional order differential equations. Here, we show that fractional order spiking neurons can be implemented using super-capacitors. The super-capacitors have fractional order derivative and memcapacitive properties. We implemented two circuits, a leaky integrate and fire and a Hodgkin-Huxley. Both circuits show power-law spiking time adaptation and optimal coding properties. The spiking dynamics reproduced previously published computer simulations. However, the fractional order Hodgkin-Huxley circuit showed novel dynamics consistent with criticality. We compared the responses of this circuit to recordings from neurons in the weakly-electric fish that have previously been shown to perform fractional order differentiation of their sensory input. The criticality seen in the circuit was confirmed in spontaneous recordings in the live fish. Furthermore, the circuit also predicted long-lasting stimulation that was also corroborated experimentally. Our work shows that fractional order memcapacitors provide intrinsic memory dependence that could allow implementation of computationally efficient neuromorphic devices. Memcapacitors are static elements that consume less energy than the most widely studied memristors, thus allowing the realization of energetically efficient neuromorphic devices.

Adaptive myelination causes slow oscillations in recurrent neural loops.

The brain is known to be plastic, i.e., capable of changing and reorganizing as it develops and accumulates experience. Recently, a novel form of brain plasticity was described which is activity-dependent myelination of nerve fibers. Since the speed of propagation of action potentials along axons depends significantly on their degree of myelination, this process leads to adaptive change of axonal delays depending on the neural activity. To understand the possible influence of the adaptive delays on the behavior of neural networks, we consider a simple setup, a neuronal oscillator with delayed feedback. We show that introducing the delay plasticity into this circuit can lead to the occurrence of slow oscillations which are impossible with a constant delay.

Cortical cell assemblies and their underlying connectivity: An in silico study.

Recent developments in experimental techniques have enabled simultaneous recordings from thousands of neurons, enabling the study of functional cell assemblies. However, determining the patterns of synaptic connectivity giving rise to these assemblies remains challenging. To address this, we developed a complementary, simulation-based approach, using a detailed, large-scale cortical network model. Using a combination of established methods we detected functional cell assemblies from the stimulus-evoked spiking activity of 186,665 neurons. We studied how the structure of synaptic connectivity underlies assembly composition, quantifying the effects of thalamic innervation, recurrent connectivity, and the spatial arrangement of synapses on dendrites. We determined that these features reduce up to 30%, 22%, and 10% of the uncertainty of a neuron belonging to an assembly. The detected assemblies were activated in a stimulus-specific sequence and were grouped based on their position in the sequence. We found that the different groups were affected to different degrees by the structural features we considered. Additionally, connectivity was more predictive of assembly membership if its direction aligned with the temporal order of assembly activation, if it originated from strongly interconnected populations, and if synapses clustered on dendritic branches. In summary, reversing Hebb's postulate, we showed how cells that are wired together, fire together, quantifying how connectivity patterns interact to shape the emergence of assemblies. This includes a qualitative aspect of connectivity: not just the amount, but also the local structure matters; from the subcellular level in the form of dendritic clustering to the presence of specific network motifs.

Computational models of compound nerve action potentials: Efficient filter-based methods to quantify effects of tissue conductivities, conduction distance, and nerve fiber parameters.

BACKGROUND: Peripheral nerve recordings can enhance the efficacy of neurostimulation therapies by providing a feedback signal to adjust stimulation settings for greater efficacy or reduced side effects. Computational models can accelerate the development of interfaces with high signal-to-noise ratio and selective recording. However, validation and tuning of model outputs against in vivo recordings remains computationally prohibitive due to the large number of fibers in a nerve. METHODS: We designed and implemented highly efficient modeling methods for simulating electrically evoked compound nerve action potential (CNAP) signals. The method simulated a subset of fiber diameters present in the nerve using NEURON, interpolated action potential templates across fiber diameters, and filtered the templates with a weighting function derived from fiber-specific conduction velocity and electromagnetic reciprocity outputs of a volume conductor model. We applied the methods to simulate CNAPs from rat cervical vagus nerve. RESULTS: Brute force simulation of a rat vagal CNAP with all 1,759 myelinated and 13,283 unmyelinated fibers in NEURON required 286 and 15,860 CPU hours, respectively, while filtering interpolated templates required 30 and 38 seconds on a desktop computer while maintaining accuracy. Modeled CNAP amplitude could vary by over two orders of magnitude depending on tissue conductivities and cuff opening within experimentally relevant ranges. Conduction distance and fiber diameter distribution also strongly influenced the modeled CNAP amplitude, shape, and latency. Modeled and in vivo signals had comparable shape, amplitude, and latency for myelinated fibers but not for unmyelinated fibers. CONCLUSIONS: Highly efficient methods of modeling neural recordings quantified the large impact that tissue properties, conduction distance, and nerve fiber parameters have on CNAPs. These methods expand the computational accessibility of neural recording models, enable efficient model tuning for validation, and facilitate the design of novel recording interfaces for neurostimulation feedback and understanding physiological systems.

A voltage-based Event-Timing-Dependent Plasticity rule accounts for LTP subthreshold and suprathreshold for dendritic spikes in CA1 pyramidal neurons.

Long-term potentiation (LTP) is a synaptic mechanism involved in learning and memory. Experiments have shown that dendritic sodium spikes (Na-dSpikes) are required for LTP in the distal apical dendrites of CA1 pyramidal cells. On the other hand, LTP in perisomatic dendrites can be induced by synaptic input patterns that can be both subthreshold and suprathreshold for Na-dSpikes. It is unclear whether these results can be explained by one unifying plasticity mechanism. Here, we show in biophysically and morphologically realistic compartmental models of the CA1 pyramidal cell that these forms of LTP can be fully accounted for by a simple plasticity rule. We call it the voltage-based Event-Timing-Dependent Plasticity (ETDP) rule. The presynaptic event is the presynaptic spike or release of glutamate. The postsynaptic event is the local depolarization that exceeds a certain plasticity threshold. Our model reproduced the experimentally observed LTP in a variety of protocols, including local pharmacological inhibition of dendritic spikes by tetrodotoxin (TTX). In summary, we have provided a validation of the voltage-based ETDP, suggesting that this simple plasticity rule can be used to model even complex spatiotemporal patterns of long-term synaptic plasticity in neuronal dendrites.

Coupling of Sharp Wave Events between Zebrafish Hippocampal and Amygdala Homologs.

The mammalian hippocampus exhibits spontaneous sharp wave events (1-30 Hz) with an often-present superimposed fast ripple oscillation (120-220 Hz) to form a sharp wave ripple (SWR) complex. During slow-wave sleep or quiet restfulness, SWRs result from the sequential spiking of hippocampal cell assemblies initially activated during learned or imagined experiences. Additional cortical/subcortical areas exhibit SWR events that are coupled to hippocampal SWRs, and studies in mammals suggest that coupling may be critical for the consolidation and recall of specific memories. In the present study, we have examined juvenile male and female zebrafish and show that SWR events are intrinsically generated and maintained within the telencephalon and that their hippocampal homolog, the anterodorsolateral lobe (ADL), exhibits SW events with ∼9% containing an embedded ripple (SWR). Single-cell calcium imaging coupled to local field potential recordings revealed that ∼10% of active cells in the dorsal telencephalon participate in any given SW event. Furthermore, fluctuations in cholinergic tone modulate SW events consistent with mammalian studies. Moreover, the basolateral amygdala (BLA) homolog exhibits SW events with ∼5% containing an embedded ripple. Computing the SW peak coincidence difference between the ADL and BLA showed bidirectional communication. Simultaneous coupling occurred more frequently within the same hemisphere, and in coupled events across hemispheres, the ADL more commonly preceded BLA. Together, these data suggest conserved mechanisms across species by which SW and SWR events are modulated, and memories may be transferred and consolidated through regional coupling.

Mathematical generation of data-driven hippocampal CA1 pyramidal neurons and interneurons copies via A-GLIF models for large-scale networks covering the experimental variability range.

Efficient and accurate large-scale networks are a fundamental tool in modeling brain areas, to advance our understanding of neuronal dynamics. However, their implementation faces two key issues: computational efficiency and heterogeneity. Computational efficiency is achieved using simplified neurons, whereas there are no practical solutions available to solve the problem of reproducing in a large-scale network the experimentally observed heterogeneity of the intrinsic properties of neurons. This is important, because the use of identical nodes in a network can generate artifacts which can hinder an adequate representation of the properties of a real network. To this aim, we introduce a mathematical procedure to generate an arbitrary large number of copies of simplified hippocampal CA1 pyramidal neurons and interneurons models, which exhibit the full range of firing dynamics observed in these cells - including adapting, non-adapting and bursting. For this purpose, we rely on a recently published adaptive generalized leaky integrate-and-fire (A-GLIF) modeling approach, leveraging on its ability to reproduce the rich set of electrophysiological behaviors of these types of neurons under a variety of different stimulation currents. The generation procedure is based on a perturbation of model's parameters related to the initial data, firing block, and internal dynamics, and suitably validated against experimental data to ensure that the firing dynamics of any given cell copy remains within the experimental range. A classification procedure confirmed that the firing behavior of most of the pyramidal/interneuron copies was consistent with the experimental data. This approach allows to obtain heterogeneous copies with mathematically controlled firing properties. A full set of heterogeneous neurons composing the CA1 region of a rat hippocampus (approximately 1.2 million neurons), are provided in a database freely available in the live paper section of the EBRAINS platform. By adapting the underlying A-GLIF framework, it will be possible to extend the numerical approach presented here to create, in a mathematically controlled manner, an arbitrarily large number of non-identical copies of cell populations with firing properties related to other brain areas.

Hypothalamic Paraventricular Stimulation Inhibits Nociceptive Wide Dynamic Range Trigeminocervical Complex Cells via Oxytocinergic Transmission.

Oxytocinergic transmission blocks nociception at the peripheral, spinal, and supraspinal levels through the oxytocin receptor (OTR). Indeed, a neuronal pathway from the hypothalamic paraventricular nucleus (PVN) to the spinal cord and trigeminal nucleus caudalis (Sp5c) has been described. Hence, although the trigeminocervical complex (TCC), an anatomical area spanning the Sp5c, C1, and C2 regions, plays a role in some pain disorders associated with craniofacial structures (e.g., migraine), the role of oxytocinergic transmission in modulating nociception at this level has been poorly explored. Hence, in vivo electrophysiological recordings of TCC wide dynamic range (WDR) cells sensitive to stimulation of the periorbital or meningeal region were performed in male Wistar rats. PVN electrical stimulation diminished the neuronal firing evoked by periorbital or meningeal electrical stimulation; this inhibition was reversed by OTR antagonists administered locally. Accordingly, neuronal projections (using Fluoro-Ruby) from the PVN to the WDR cells filled with Neurobiotin were observed. Moreover, colocalization between OTR and calcitonin gene-related peptide (CGRP) or OTR and GABA was found near Neurobiotin-filled WDR cells. Retrograde neuronal tracers deposited at the meningeal (True-Blue, TB) and infraorbital nerves (Fluoro-Gold, FG) showed that at the trigeminal ganglion (TG), some cells were immunopositive to both fluorophores, suggesting that some TG cells send projections via the V1 and V2 trigeminal branches. Together, these data may imply that endogenous oxytocinergic transmission inhibits the nociceptive activity of second-order neurons via OTR activation in CGRPergic (primary afferent fibers) and GABAergic cells.

Ndnf Interneuron Excitability Is Spared in a Mouse Model of Dravet Syndrome.

Dravet syndrome (DS) is a neurodevelopmental disorder characterized by epilepsy, developmental delay/intellectual disability, and features of autism spectrum disorder, caused by heterozygous loss-of-function variants in SCN1A encoding the voltage-gated sodium channel α subunit Nav1.1. The dominant model of DS pathogenesis is the "interneuron hypothesis," whereby GABAergic interneurons (INs) express and preferentially rely on Nav1.1-containing sodium channels for action potential (AP) generation. This has been shown for three of the major subclasses of cerebral cortex GABAergic INs: those expressing parvalbumin (PV), somatostatin, and vasoactive intestinal peptide. Here, we define the function of a fourth major subclass of INs expressing neuron-derived neurotrophic factor (Ndnf) in male and female DS (Scn1a+/-) mice. Patch-clamp electrophysiological recordings of Ndnf-INs in brain slices from Scn1a+/â mice and WT controls reveal normal intrinsic membrane properties, properties of AP generation and repetitive firing, and synaptic transmission across development. Immunohistochemistry shows that Nav1.1 is strongly expressed at the axon initial segment (AIS) of PV-expressing INs but is absent at the Ndnf-IN AIS. In vivo two-photon calcium imaging demonstrates that Ndnf-INs in Scn1a+/â mice are recruited similarly to WT controls during arousal. These results suggest that Ndnf-INs are the only major IN subclass that does not prominently rely on Nav1.1 for AP generation and thus retain their excitability in DS. The discovery of a major IN subclass with preserved function in the Scn1a+/â mouse model adds further complexity to the "interneuron hypothesis" and highlights the importance of considering cell-type heterogeneity when investigating mechanisms underlying neurodevelopmental disorders.