Threshold Neurons Research Articles

Modelling a pathological GSX2 variant that selectively alters DNA binding reveals hypomorphic mouse basal ganglia and hindbrain defects.

Gsx2 is a homeodomain transcription factor critical for development of the ventral telencephalon and hindbrain of the mouse. Loss of Gsx2 function results in severe basal ganglia dysgenesis as well as defects in the nucleus tractus solitarius (nTS) of the hindbrain together with respiratory failure at birth. De Mori et al. (2019) reported two patients with severe dystonia and basal ganglia dysgenesis that encode distinct recessive GSX2 variants, including a missense mutation within the homeodomain (GSX2Q251R). Hence, we modelled the homologous Gsx2 mutation (i.e. Gsx2Q252R) in mouse and our biochemical analysis revealed this variant selectively altered DNA binding. Moreover, mice carrying the Gsx2Q252R allele exhibited basal ganglia dysgenesis, albeit to a lesser extent than Gsx2 null mice. A notable difference between Gsx2Q252R and Gsx2 null mice was that Gsx2Q252R mice survive, and hindbrain analysis revealed relative sparing of the glutamatergic nTS neurons and catecholaminergic A1/C1, A2/C2 groups. Thus, the Gsx2Q252R variant is a hypomorph that compromises a subset of Gsx2-dependent neuronal subtypes and highlights a critical role for distinct thresholds of catecholaminergic and/or glutamatergic nTS neurons for viability.

Enabling Electric Field Model of Microscopically Realistic Brain.

Modeling brain stimulation at the microscopic scale may reveal new paradigms for various stimulation modalities. We present the largest map to date of extracellular electric field distributions within a layer L2/L3 mouse primary visual cortex brain sample. This was enabled by the automated analysis of serial section electron microscopy images with improved handling of image defects, covering a volume of 250 × 140 × 90 μm³. The map was obtained by applying a uniform brain stimulation electric field at three different polarizations and accurately computing microscopic field perturbations using the boundary element fast multipole method. We used the map to identify the effect of microscopic field perturbations on the activation thresholds of individual neurons. Previous relevant studies modeled a macroscopically homogeneous cortical volume. Our result shows that the microscopic field perturbations - an 'electric field spatial noise' with a mean value of zero - only modestly influence the macroscopically predicted stimulation field strengths necessary for neuronal activation. The thresholds do not change by more than 10% on average. Under the stated limitations and assumptions of our method, this result essentially justifies the conventional theory of "invisible" neurons embedded in a macroscopic brain model for transcranial magnetic and transcranial electrical stimulation. However, our result is solely sample-specific and is only relevant to this relatively small sample with 396 neurons. It largely neglects the effect of the microcapillary network. Furthermore, we only considered the uniform impressed field and a single-pulse stimulation time course.

Kv4 channels improve the temporal processing of auditory neurons in the cochlear nucleus.

Kv4 channels generate A-type current known to regulate neuronal excitability. Its role in processing timing information is understudied, especially in the auditory system where temporal information is crucial for hearing. In the cochlear nucleus, principal bushy neurons are specialized for temporal processing with distinct biophysical properties owing to their expression of various voltage-gated ion channels. Previous studies reported conflicting information regarding the expression and potential role of Kv4 channels in these neurons. We explored these questions using electrophysiology in CBA/CaJ mice of either sex. A-type current was isolated from 88% of bushy neurons using Kv4 channel-selective blocker Jingzhaotoxin-X (JZ-X), which increased the intrinsic excitability of bushy neurons without altering their synaptic input. During high-rate activity, JZ-X treatment significantly increased the spike jitter and reduced the firing threshold of bushy neurons. In old mice, A-type current in bushy neurons reduced in magnitude but maintained current density, accompanied by decreased membrane surface area. In contrast, TEA-sensitive Kv3 current reduced in both magnitude and current density, indicative of a greater contribution to the altered biophysical properties of bushy neurons during ageing. Our findings suggest that Kv4 channels play significant roles in regulating neuronal excitability and improving the temporal processing of bushy neurons. Such function is likely retained with age and is not the primary mechanism driving compromised temporal processing under age-related hearing loss. KEY POINTS: Most bushy neurons of the cochlear nucleus exhibit Kv4-mediated A-type current. A-type current regulates neuronal excitability of bushy neurons without contributing to the synaptic transmission at the endbulb of Held. A-type current increases the firing threshold and improves the temporal precision of spikes in bushy neurons during high-rate activity. A-type current reduces peak amplitude in bushy neurons during ageing but maintains current density. Decreased Kv3 current, rather than Kv4 current, likely play more significant roles in altering the biophysical properties of bushy neurons during ageing, contributing to compromised temporal processing during age-related hearing loss.

Enabling Electric Field Model of Microscopically Realistic Brain.

This study is arguably the first attempt to model brain stimulation at the microscopic scale, enabled by automated analysis of modern scanning electron microscopy images of the brain. It concentrates on modeling microscopic perturbations of the extracellular electric field caused by the physical cell structure and is applicable to any type of brain stimulation. Post-processed cell CAD models (383, stl format), microcapillary CAD models (34, stl format), post-processed neuron morphologies (267, swc format), extracellular electric field and potential distributions at different polarizations (267x3, MATLAB format), *.ses projects files for biophysical modeling with Neuron software (267x2, Neuron format), and computed neuron activating thresholds at different conditions (267x8, Excel tables, without the sample polarization correction from Section 2.8) are made available online through BossDB , a volumetric open-source database for 3D and 4D neuroscience data.

Statistical method accounts for microscopic electric field distortions around neurons when simulating activation thresholds.

Notwithstanding advances in computational models of neuromodulation, there are mismatches between simulated and experimental activation thresholds. Transcranial Magnetic Stimulation (TMS) of the primary motor cortex generates motor evoked potentials (MEPs). At the threshold of MEP generation, whole-head models predict macroscopic (at millimeter scale) electric fields (50-70 V/m) which are considerably below conventionally simulated cortical neuron thresholds (200-300 V/m). We hypothesize that this apparent contradiction is in part a consequence of electrical field warping by brain microstructure. Classical neuronal models ignore the physical presence of neighboring neurons and microstructure and assume that the macroscopic field directly acts on the neurons. In previous work, we performed advanced numerical calculations considering realistic microscopic compartments (e.g., cells, blood vessels), resulting in locally inhomogeneous (micrometer scale) electric field and altered neuronal activation thresholds. Here we combine detailed neural threshold simulations under homogeneous field assumptions with microscopic field calculations, leveraging a novel statistical approach. We show that, provided brain-region specific microstructure metrics, a single statistically derived scaling factor between microscopic and macroscopic electric fields can be applied in predicting neuronal thresholds. For the cortical sample considered, the statistical methods match TMS experimental thresholds. Our approach can be broadly applied to neuromodulation models, where fully coupled microstructure scale simulations may not be practical.

Magnetoelectric nanodiscs enable wireless transgene-free neuromodulation

Deep brain stimulation with implanted electrodes has transformed neuroscience studies and treatment of neurological and psychiatric conditions. Discovering less invasive alternatives to deep brain stimulation could expand its clinical and research applications. Nanomaterial-mediated transduction of magnetic fields into electric potentials has been explored as a means for remote neuromodulation. Here we synthesize magnetoelectric nanodiscs (MENDs) with a core–double-shell Fe3O4–CoFe2O4–BaTiO3 architecture (250 nm diameter and 50 nm thickness) with efficient magnetoelectric coupling. We find robust responses to magnetic field stimulation in neurons decorated with MENDs at a density of 1 µg mm−2 despite individual-particle potentials below the neuronal excitation threshold. We propose a model for repetitive subthreshold depolarization that, combined with cable theory, supports our observations in vitro and informs magnetoelectric stimulation in vivo. Injected into the ventral tegmental area or the subthalamic nucleus of genetically intact mice at concentrations of 1 mg ml−1, MENDs enable remote control of reward or motor behaviours, respectively. These findings set the stage for mechanistic optimization of magnetoelectric neuromodulation towards applications in neuroscience research.

Giant cell arteritis (GCA) as a risk factor for seizures: a cohort study

ABSTRACT Objectives The objective of this study was to assess the risk of seizures in Giant Cell Arteritis (GCA) patients in a large cohort of Israeli subjects, in comparison to matched controls. Methods Patients diagnosed with GCA between 2002 and 2017 were included. Controls were matched based on sex, age, socioeconomic status, country of birth, diabetes mellitus, and hypertension in a 4:1 ratio. Patients with seizure records prior to the study period were excluded. Hazard ratios for seizures was obtained by cox regression models. Results The study cohort was composed by 8,103 GCA patients and 32,412 matched controls. The GCA group included 5,535 women (68%), 2,644 patients born in Israel (33%), and 2,888 patients with low socioeconomic status (36%). The median age of this group was 71. During the followed cumulative person-years of 54,641 and 222,537 in the GCA and control group, respectively, 15.92 cases per 10,000 person-years was found in the GCA group, compared to 9.62 per 10,000 person-years in the controls. GCA was associated with seizures in the unadjusted (HR = 1.66, 95% CI [1.29 to 2.13]) and adjusted (HR = 1.67, 95% CI [1.3 to 2.14]) models. GCA was also associated with seizures after controlling for strokes (HR = 1.55, 95% CI [1.16 to 2.07]). Conclusion According to this study, individuals with GCA are at a higher risk of developing seizures when compared to the general population. This increased risk is independent of their predisposition for stroke. One proposed mechanism is that the GCA pro-inflammatory state may decrease the neuronal threshold for depolarization.

Neuronal threshold functions: Determining symptom onset in neurological disorders

Synaptic networks determine brain function. Highly complex interconnected brain synaptic networks provide output even under fluctuating or pathological conditions. Relevant to the treatment of brain disorders, understanding the limitations of such functional networks becomes paramount. Here we use the example of Parkinson’s Disease (PD) as a system disorder, with PD symptomatology emerging only when the functional reserves of neurons, and their interconnected networks, are unable to facilitate effective compensatory mechanisms. We have denoted this the “threshold theory” to account for how PD symptoms develop in sequence. In this perspective, threshold functions are delineated in a quantitative, synaptic, and cellular network context. This provides a framework to discuss the development of specific symptoms. PD includes dysfunction and degeneration in many organ systems and both peripheral and central nervous system involvement. The threshold theory accounts for and explains the reasons why parallel gradually emerging pathologies in brain and peripheral systems generate specific symptoms only when functional thresholds are crossed, like tipping points. New and mounting evidence demonstrate that PD and related neurodegenerative diseases are multisystem disorders, which transcends the traditional brain-centric paradigm. We believe that representation of threshold functions will be helpful to develop new medicines and interventions that are specific for both pre- and post-symptomatic periods of neurodegenerative disorders.

Memristive leaky integrate-and-fire neuron and learnable straight-through estimator in spiking neural networks.

Compared to artificial neural networks (ANNs), spiking neural networks (SNNs) present a more biologically plausible model of neural system dynamics. They rely on sparse binary spikes to communicate information and operate in an asynchronous, event-driven manner. Despite the high heterogeneity of the neural system at the neuronal level, most current SNNs employ the widely used leaky integrate-and-fire (LIF) neuron model, which assumes uniform membrane-related parameters throughout the entire network. This approach hampers the expressiveness of spiking neurons and restricts the diversity of neural dynamics. In this paper, we propose replacing the resistor in the LIF model with a discrete memristor to obtain the heterogeneous memristive LIF (MLIF) model. The memristance of the discrete memristor is determined by the voltage and flux at its terminals, leading to dynamic changes in the membrane time parameter of the MLIF model. SNNs composed of MLIF neurons can not only learn synaptic weights but also adaptively change membrane time parameters according to the membrane potential of the neuron, enhancing the learning ability and expression of SNNs. Furthermore, since the proper threshold of spiking neurons can improve the information capacity of SNNs, a learnable straight-through estimator (LSTE) is proposed. The LSTE, based on the straight-through estimator (STE) surrogate function, features a learnable threshold that facilitates the backward propagation of gradients through neurons firing spikes. Extensive experiments on several popular static and neuromorphic benchmark datasets demonstrate the effectiveness of the proposed MLIF and LSTE, especially on the DVS-CIFAR10 dataset, where we achieved the top-1 accuracy of 84.40 .

Whisker deprivation triggers a distinct form of cortical homeostatic plasticity that is impaired in the Fmr1 KO.

Mouse models of Fragile X Syndrome (FXS) have demonstrated impairments in excitatory and inhibitory sensory-evoked neuronal firing. Homeostatic plasticity, which encompasses a set of mechanisms to stabilize baseline activity levels, does not compensate for these changes in activity. Previous work has shown that impairments in homeostatic plasticity are observed in FXS, including deficits in synaptic scaling and intrinsic excitability. Here, we aimed to examine how homeostatic plasticity is altered in vivo in an Fmr1 KO mouse model following unilateral whisker deprivation (WD). We show that WD in the wild type leads to an increase in the proportion of L5/6 somatosensory neurons that are recruited, but this does not occur in the KO. In addition, we observed a change in the threshold of excitatory neurons at a later developmental stage in the KO. Compromised homeostatic plasticity in development could influence sensory processing and long-term cortical organization.

Novel Hybrid Optimization Technique for Solar Photovoltaic Output Prediction Using Improved Hippopotamus Algorithm

This paper introduces a novel hybrid optimization technique aimed at improving the prediction accuracy of solar photovoltaic (PV) outputs using an Improved Hippopotamus Optimization Algorithm (IHO). The IHO enhances the traditional Hippopotamus Optimization (HO) algorithm by addressing its limitations in search efficiency, convergence speed, and global exploration. The IHO algorithm used Latin hypercube sampling (LHS) for population initialization, significantly enhancing the diversity and global search potential of the optimization process. The integration of the Jaya algorithm further refines solution quality and accelerates convergence. Additionally, a combination of unordered dimensional sampling, random crossover, and sequential mutation is employed to enhance the optimization process. The effectiveness of the proposed IHO is demonstrated through the optimization of weights and neuron thresholds in the extreme learning machine (ELM), a model known for its rapid learning capabilities but often affected by the randomness of initial parameters. The IHO-optimized ELM (IHO-ELM) is tested against benchmark algorithms, including BP, the traditional ELM, the HO-ELM, LCN, and LSTM, showing significant improvements in prediction accuracy and stability. Moreover, the IHO-ELM model is validated in a different region to assess its generalization ability for solar PV output prediction. The results confirm that the proposed hybrid approach not only improves prediction accuracy but also demonstrates robust generalization capabilities, making it a promising tool for predictive modeling in solar energy systems.

Do Not Let the Beginning Trap you! On Inhibition, Associative Creative Chains, and Hopfield Neural Networks

ABSTRACTCreative thinking stems from the cognitive process that fosters the creation of new ideas and problem‐solving solutions. Artificial intelligence systems and neural network models can reduce the intricacy of understanding creative cognition. For instance, the generation of ideas could be symbolized as patterns of binary code in which clusters of neurons synchronize their firing and store information inside a neural network, forming connections based on correlation. The Hopfield neural network (HNN) is a simple model known for its biological plausibility in storing and retrieving neuron patterns. We implemented certain modifications to HNN as a step toward the larger framework of creative thinking‐based association. These modifications included introducing pattern weights control, which provides a robust representation for content addressable memory and conceptual links in stored data. We identified two mechanisms controlling the transition from analytical to associative‐based thinking. The first mechanism refers to the activation threshold of neurons, which acts as an on/off switch for the network. The second was the inhibition of stored concepts, similar to an on/off switch that guides the network to search for associative links and when to stop. Our findings suggest that neurons step back from the contextual focus and find alternatives when analytical thinking is insufficient. These alternatives are linked to seemingly unrelated ideas, using inhibition as an analogy to the hyperparameters. Using hyperparameters to inhibit the stored patterns, we could control the creation of associative links.

A computational model elucidating mechanisms and variability in theta burst stimulation responses.

Theta burst stimulation (TBS) is a form of repetitive transcranial magnetic stimulation (rTMS) with unknown underlying mechanisms and highly variable responses across subjects. To investigate these issues, we developed a simple computational model. Our model consisted of two neurons linked by an excitatory synapse that incorporates two mechanisms: short-term plasticity (STP) and spike-timing-dependent plasticity (STDP). We applied a variable-amplitude current through I-clamp with a TBS time pattern to the pre- and post-synaptic neurons, simulating synaptic plasticity. We analyzed the results and provided an explanation for the effects of TBS, as well as the variability of responses to it. Our findings suggest that the interplay of STP and STDP mechanisms determines the direction of plasticity, which selectively affects synapses in extended neurons and underlies functional effects. Our model describes how the timing, number, and intensity of pulses delivered to neurons during rTMS contribute to induced plasticity. This not only successfully explains the different effects of intermittent TBS (iTBS) and continuous TBS (cTBS), but also predicts the results of other protocols such as 10 Hz rTMS. We propose that the variability in responses to TBS can be attributed to the variable span of neuronal thresholds across individuals and sessions. Our model suggests a biologically plausible mechanism for the diverse responses to TBS protocols and aligns with experimental data on iTBS and cTBS outcomes. This model could potentially aid in improving TBS and rTMS protocols and customizing treatments for patients, brain areas, and brain disorders.

Memristor-based Bayesian spiking neural network for IBD diagnosis

Traditional deep learning methods, such as deep neural networks, have achieved remarkable success in image processing, natural language processing, graph computation, and other fields. However, in the diagnosis of Inflammatory Bowel Disease (IBD), conventional deep learning methods are highly susceptible to generating overconfident results, which can lead to great loss of life and property if it results in misdiagnosis of the patient. In this work, we propose a memristor-based IBD diagnostic system inspired by the idea of variational inference based on Bayes' law. The memristors in our system are used for both synaptic weight storage and entropy sources so that an efficient Bayesian inference system can be realized. Our memristor-based true random number generator can produce probability-tunable bitstreams required for Bayesian inference with a mean-square error that is reduced by 10.9 × than that of a conventional pseudo-random generator. The proposed system consists of a coarse-grained and a fined-grained spiking neural network, and an optimized strategy that trims the neuron's threshold is proposed for maintaining the appropriate model sparsity and reducing information loss. With Bayesian inference, our work achieves lower diagnostic error rates, which are reduced by 10.79 % and 0.49 % for the coarse-grained and fine-grained networks, respectively. The proposed system is easy to hardware implement and presents a novel solution for community-wide IBD diagnosis.

Nociceptor-immune interactomes reveal insult-specific immune signatures of pain

Inflammatory pain results from the heightened sensitivity and reduced threshold of nociceptor sensory neurons due to exposure to inflammatory mediators. However, the cellular and transcriptional diversity of immune cell and sensory neuron types makes it challenging to decipher the immune mechanisms underlying pain. Here we used single-cell transcriptomics to determine the immune gene signatures associated with pain development in three skin inflammatory pain models in mice: zymosan injection, skin incision and ultraviolet burn. We found that macrophage and neutrophil recruitment closely mirrored the kinetics of pain development and identified cell-type-specific transcriptional programs associated with pain and its resolution. Using a comprehensive list of potential interactions mediated by receptors, ligands, ion channels and metabolites to generate injury-specific neuroimmune interactomes, we also uncovered that thrombospondin-1 upregulated by immune cells upon injury inhibited nociceptor sensitization. This study lays the groundwork for identifying the neuroimmune axes that modulate pain in diverse disease contexts.

A Synapse-Threshold Synergistic Learning Approach for Spiking Neural Networks

Spiking neural networks (SNNs) have demonstrated excellent capabilities in various intelligent scenarios. Most existing methods for training SNNs are based on the concept of synaptic plasticity; however, learning in the realistic brain also utilizes intrinsic non-synaptic mechanisms of neurons. The spike threshold of biological neurons is a critical intrinsic neuronal feature that exhibits rich dynamics on a millisecond timescale and has been proposed as an underlying mechanism that facilitates neural information processing. In this study, we develop a novel synergistic learning approach that involves simultaneously training synaptic weights and spike thresholds in SNNs. SNNs trained with synapse-threshold synergistic learning (STL-SNNs) achieve significantly superior performance on various static and neuromorphic datasets than SNNs trained with two degenerated single-learning models. During training, the synergistic learning approach optimizes neural thresholds, providing the network with stable signal transmission via appropriate firing rates. Further analysis indicates that STL-SNNs are robust to noisy data and exhibit low energy consumption for deep network structures. Additionally, the performance of STL-SNN can be further improved by introducing a generalized joint decision framework. Overall, our findings indicate that biologically plausible synergies between synaptic and intrinsic non-synaptic mechanisms may provide a promising approach for developing highly efficient SNN learning methods.

Deep spiking neural networks with integrate and fire neuron using steep switching device

Deep learning has shown impressive capabilities in tasks like speech recognition and image classification. However, modern deep neural networks often demand a significant number of weights and extensive computational resources, creating efficiency challenges for applications on edge devices. To address these issues, researchers have introduced deep spiking neural networks (DSNNs) that leverage specialized hardware for synapses and neurons. DSNNs offer a potential solution by improving efficiency in edge-device applications. In this paper, the hardware based DSNN with integrate and fire neuron using steep switching device was investigated. We propose integrate and fire neuron using steep switching device to implement rate coding as input encoding method. Because the steep switching device has double-gate, the threshold voltage of the neuron circuits can be adaptively controlled, which changes the rates of input pulse. Hence, the adjustment of the threshold of neuron can be employed to mitigate the accuracy deterioration resulting from the transformation from deep neural networks (DNNs) to DSNNs. In addition, the off-current of proposed integrate and fire neuron circuit decreases significantly as the steep switching device has steep subthreshold swing. A system simulation of a hardware based DSNN shows that the adjustable threshold of the neuron circuit can achieve a high inference accuracy of 98.36 % which is comparable to that obtained with software based DNN.

Spiking Tucker Fusion Transformer for Audio-Visual Zero-Shot Learning.

The spiking neural networks (SNNs) that efficiently encode temporal sequences have shown great potential in extracting audio-visual joint feature representations. However, coupling SNNs (binary spike sequences) with transformers (float-point sequences) to jointly explore the temporal-semantic information still facing challenges. In this paper, we introduce a novel Spiking Tucker Fusion Transformer (STFT) for audio-visual zero-shot learning (ZSL). The STFT leverage the temporal and semantic information from different time steps to generate robust representations. The time-step factor (TSF) is introduced to dynamically synthesis the subsequent inference information. To guide the formation of input membrane potentials and reduce the spike noise, we propose a global-local pooling (GLP) which combines the max and average pooling operations. Furthermore, the thresholds of the spiking neurons are dynamically adjusted based on semantic and temporal cues. Integrating the temporal and semantic information extracted by SNNs and Transformers are difficult due to the increased number of parameters in a straightforward bilinear model. To address this, we introduce a temporal-semantic Tucker fusion module, which achieves multi-scale fusion of SNN and Transformer outputs while maintaining full second-order interactions. Our experimental results demonstrate the effectiveness of the proposed approach in achieving state-of-the-art performance in three benchmark datasets. The harmonic mean (HM) improvement of VGGSound, UCF101 and ActivityNet are around 15.4%, 3.9%, and 14.9%, respectively.

Ethanol’s interaction with BK channel α subunit residue K361 does not mediate behavioral responses to alcohol in mice

Large conductance potassium (BK) channels are among the most sensitive molecular targets of ethanol and genetic variations in the channel-forming α subunit have been nominally associated with alcohol use disorders. However, whether the action of ethanol at BK α influences the motivation to drink alcohol remains to be determined. To address this question, we first tested the effect of systemically administered BK channel modulators on voluntary alcohol consumption in C57BL/6J males. Penitrem A (blocker) exerted dose-dependent effects on moderate alcohol intake, while paxilline (blocker) and BMS-204352 (opener) were ineffective. Because pharmacological manipulations are inherently limited by non-specific effects, we then sought to investigate the behavioral relevance of ethanol’s direct interaction with BK α by introducing in the mouse genome a point mutation known to render BK channels insensitive to ethanol while preserving their physiological function. The BK α K361N substitution prevented ethanol from reducing spike threshold in medial habenula neurons. However, it did not alter acute responses to ethanol in vivo, including ataxia, sedation, hypothermia, analgesia, and conditioned place preference. Furthermore, the mutation did not have reproducible effects on alcohol consumption in limited, continuous, or intermittent access home cage two-bottle choice paradigms conducted in both males and females. Notably, in contrast to previous observations made in mice missing BK channel auxiliary β subunits, the BK α K361N substitution had no significant impact on ethanol intake escalation induced by chronic intermittent alcohol vapor inhalation. It also did not affect the metabolic and locomotor consequences of chronic alcohol exposure. Altogether, these data suggest that the direct interaction of ethanol with BK α does not mediate the alcohol-related phenotypes examined here in mice.

Binge-like ethanol exposure during the brain growth spurt disrupts the function of retrosplenial cortex-projecting anterior thalamic neurons in adolescent mice

Ethanol (EtOH) exposure during late pregnancy leads to enduring impairments in learning and memory that may stem from damage to components of the posterior limbic memory system, including the retrosplenial cortex (RSC) and anterior thalamic nuclei (ATN). In rodents, binge-like EtOH exposure during the first week of life (equivalent to the third trimester of human pregnancy) triggers apoptosis in these brain regions. We hypothesized that this effect induces long-lasting alterations in the function of RSC-projecting ATN neurons. To test this hypothesis, vesicular GABA transporter-Venus mice (expressing fluorescently tagged GABAergic interneurons) were subjected to binge-like EtOH vapor exposure on postnatal day (P) 7. This paradigm activated caspase 3 in the anterodorsal (AD), anteroventral (AV), and reticular thalamic nuclei at P7 but did not reduce neuronal density in these areas at P60-70. At P40-60, we injected red retrobeads into the RSC and performed patch-clamp slice electrophysiological recordings from retrogradely labeled neurons in the AD and AV nuclei 3–4 days later. We found significant effects of treatment on instantaneous action potential (AP) frequency and AP overshoot, as well as sex × treatment interactions for AP threshold and overshoot in AD neurons. A sex × treatment interaction was detected for AP number in AV neurons. EtOH exposure also reduced the frequency and amplitude of spontaneous excitatory postsynaptic currents and increased the charge transfer of spontaneous inhibitory postsynaptic currents. These results highlight a novel cellular mechanism that could contribute to the lasting learning and memory deficits associated with developmental EtOH exposure.