The basolateral nucleus of the amygdala (BL) is thought to support numerous emotional behaviors through specific microcircuits. These are often thought to be comprised of feedforward networks of principal cells (PNs) and interneurons. Neither well-understood nor often considered are recurrent and feedback connections, which likely engender oscillatory dynamics within BL. Indeed, oscillations in the gamma frequency range (40 − 100 Hz) are known to occur in the BL, and yet their origin and effect on local circuits remains unknown. To address this, we constructed a biophysically and anatomically detailed model of the rat BL and its local field potential (LFP) based on the physiological and anatomical literature, along with in vivo and in vitro data we collected on the activities of neurons within the rat BL. Remarkably, the model produced intermittent gamma oscillations (∼50 − 70 Hz) whose properties matched those recorded in vivo, including their entrainment of spiking. BL gamma-band oscillations were generated by the intrinsic circuitry, depending upon reciprocal interactions between PNs and fast-spiking interneurons (FSIs), while connections within these cell types affected the rhythm’s frequency. The model allowed us to conduct experimentally impossible tests to characterize the synaptic and spatial properties of gamma. The entrainment of individual neurons to gamma depended on the number of afferent connections they received, and gamma bursts were spatially restricted in the BL. Importantly, the gamma rhythm synchronized PNs and mediated competition between ensembles. Together, these results indicate that the recurrent connectivity of BL expands its computational and communication repertoire.

Competitive synaptic interactions between principal neurons (PNs) with differing intrinsic excitability were recently shown to determine which dorsal lateral amygdala (LAd) neurons are recruited into a fear memory trace. Here, we explored the contribution of these competitive interactions in determining the stimulus specificity of conditioned fear associations. To this end, we used a realistic biophysical computational model of LAd that included multi-compartment conductance-based models of 800 PNs and 200 interneurons. To reproduce the continuum of spike frequency adaptation displayed by PNs, the model included three subtypes of PNs with high, intermediate, and low spike frequency adaptation. In addition, the model network integrated spatially differentiated patterns of excitatory and inhibitory connections within LA, dopaminergic and noradrenergic inputs, extrinsic thalamic and cortical tone afferents to simulate conditioned stimuli as well as shock inputs for the unconditioned stimulus. Last, glutamatergic synapses in the model could undergo activity-dependent plasticity. Our results suggest that plasticity at both excitatory (PN–PN) and di-synaptic inhibitory (PN–ITN and, particularly, ITN–PN) connections are major determinants of the synaptic competition governing the assignment of PNs to the memory trace. The model also revealed that training-induced potentiation of PN–PN synapses promotes, whereas that of ITN–PN synapses opposes, stimulus generalization. Indeed, suppressing plasticity of PN–PN synapses increased, whereas preventing plasticity of interneuronal synapses decreased the CS specificity of PN recruitment. Overall, our results indicate that the plasticity configuration imprinted in the network by synaptic competition ensures memory specificity. Given that anxiety disorders are characterized by tendency to generalize learned fear to safe stimuli or situations, understanding how plasticity of intrinsic LAd synapses regulates the specificity of learned fear is an important challenge for future experimental studies.

The lateral (LA) and basolateral (BL) nuclei of the amygdala regulate emotional behaviors. Despite their dissimilar extrinsic connectivity, they are often combined, perhaps because their cellular composition is similar to that of the cerebral cortex, including excitatory principal cells reciprocally connected with fast-spiking interneurons (FSIs). In the cortex, this microcircuitry produces gamma oscillations that support information processing and behavior. We tested whether this was similarly the case in the rat (males) LA and BL using extracellular recordings, biophysical modeling, and behavioral conditioning. During periods of environmental assessment, both nuclei exhibited gamma oscillations that stopped upon initiation of active behaviors. Yet, BL exhibited more robust spontaneous gamma oscillations than LA. The greater propensity of BL to generate gamma resulted from several microcircuit differences, especially the proportion of FSIs and their interconnections with principal cells. Furthermore, gamma in BL but not LA regulated the efficacy of excitatory synaptic transmission between connected neurons. Together, these results suggest fundamental differences in how LA and BL operate. Most likely, gamma in LA is externally driven, whereas in BL it can also arise spontaneously to support ruminative processing and the evaluation of complex situations.

Recent experimental and modeling studies on the lateral amygdala (LA) have implicated intrinsic excitability and competitive synaptic interactions among principal neurons (PNs) in the formation of auditory fear memories. The present modeling studies, conducted over an expanded range of intrinsic excitability in the network, revealed that only excitable PNs that received tone inputs participate in the competition. Strikingly, the number of model PNs integrated into the fear memory trace remained constant despite the much larger range considered, and model runs highlighted several conditioning-induced tone responsive characteristics of the various PN populations. Furthermore, these studies showed that although excitation was important, disynaptic inhibition among PNs is the dominant mechanism that keeps the number of plastic PNs stable despite large variations in the network's excitability. Finally, we found that the overall level of inhibition in the model network determines the number of projection cells integrated into the fear memory trace.

Numerous intrinsic currents are known to collectively shape neuronal membrane potential dynamics, or neuronal signatures. Although how sets of currents shape specific signatures such as spiking characteristics or oscillations has been studied individually, it is less clear how a neuron’s suite of currents jointly shape its entire set of signatures. Biophysical conductance based models of neurons represent a viable tool to address this important question. We hypothesized that currents are grouped into distinct modules that shape specific neuronal characteristics or signatures, such as resting potential, sub-threshold oscillations, and spiking waveforms, for several classes of neurons. For such a grouping to occur, the currents within one module should have minimal functional interference with currents belonging to other modules. This condition is satisfied if the gating functions of currents in the same module are grouped together on the voltage axis; in contrast, such functions are segregated along the voltage axis for currents belonging to different modules. We tested this hypothesis using four published example case models and found it to be valid for these classes of neurons. This insight into the neurobiological organization of currents also suggests an intuitive, systematic, and robust methodology to develop biophysical single cell models with multiple biological characteristics applicable for both hand- and automated- tuning approaches. We illustrate the methodology using two example case rodent pyramidal neurons, from the lateral amygdala and the hippocampus. The methodology also helped reveal that a single core compartment model could capture multiple neuronal properties. Such biophysical single compartment models have potential to improve the fidelity of large network models.

Images are an important carrier for emotional expression. Human can understand emotions in image easily and quickly, whereas it is a very challenging task for machines to extract accurate emotions. In this study, we propose a novel spatial and channel-wise attention-based emotion prediction model, SCEP, to assist computers in recognizing the emotions of images more accurately. SCEP integrates both spatial attention and channel-wise weight mechanisms into a classical convolutional neural network (CNN) layer structure to predict image emotions, on the grounds that the spatial attention mechanism can enhance the contrast between salient regions and potentially irrelevant regions, and that the channel-wise weight mechanism can emphasize informative features while suppressing less useful features. The SCEP model outputs emotion values in a continuous 2-D valence and arousal space, so that more emotions can be expressed than by simply discretely classifying emotions. To validate the effectiveness of our model, we use an existing image dataset with a widespread emotion distribution for testing. Extensive experiments show that when compared to base models (i.e. VGG and ResNet) without spatial attention or channel-wise mechanisms, SCEP can improve the accuracy of emotion prediction (evaluated by concordance correlation coefficient) by ~3%-5% in the arousal domain, and by ~3-6% in the valence domain. Therefore, we conclude that using SCEP can bring higher accuracy in emotion prediction.

Background Duchenne muscular dystrophy (DMD) is an X-linked lethal muscle disease. Dystrophic dogs are excellent models to test novel therapies for DMD. However, the use of the dog model has been hindered by the lack of an effective method to evaluate whole-body mobility. We recently showed that night activity is a good indicator of dog mobility. However, our published method relies on frame-by-frame manual processing of a 12-hour video for each dog. This labor-intensive and time-consuming approach makes it unrealistic to use this assay as a routine outcome measurement. Objective To solve this problem, we developed an automatic video-capturing/imaging processing system. The new system reduces the data analysis time over 1,000 fold and also provides a more detailed activity profile of the dog. Methods Using the new system, we analyzed more than 120 twelve-hour recordings from 12 normal and 22 affected dogs. Results We observed similar activity profiles during repeated recording of the same dog. Throughout the night, normal dogs were in motion 10.4 ± 0.9% of the time while affected dogs were in motion 4.6 ± 0.2% of the time (p < 0.0001). Further, normal dogs made significantly more movements (p < 0.0001) while affected dogs rested significantly longer (p < 0.0001) during the period of recording (from 6 pm to 6 am next day). Importantly, statistical significance persisted irrespective of the coat color, gender and mutation type. Conclusions Our results suggest that night activity reduction is a robust, quantitative physiological biomarker for dystrophic dogs. The new system may be applicable to study mobility in other species.

Knowledge graph (KG) contains a large number of real-world knowledge and has become an invaluable aid to assist the application of artificial intelligence. Knowledge graph completion (KGC) is the task to complete the missing triple in KG database. Our goal in this study is to enhance the performance of KGC tasks based on CNN model. To do this, we first investigated the effect of adding multiple filters of different shapes into the pioneer model. The obscure improvement leads us to seek other approaches. Our second proposed model, termed DP-ConvKB, which is a deep convolution-neural-network-based model, outperforms state-of-the-art models on several metrics. Our study provides supporting evidence that, by cooperating deep pyramid network structure into models, it can significantly improve the KGC performances.