Spiking neural network

…methods to perform inference, where neural activity reflects samples from the posterior probability distri

…information while enhancing the neural representation of relevant stimuli. This phenomenon is well-explained by the theory of biased competition, which posits that attention biases neural processing towards behaviorally relevant stimuli when multiple objects are present. However, the mechanism of attention remains under debate. We show how biased competition can be so

The cerebellum, a long-studied brain structure, has recently experienced significant advances in our understanding of its functions in many processes. Although its influence on emotions is still being studied, the cerebellum has been found to influence emotional control. This has important implications for understanding emotional disorders and neuropsychiatric conditions, contributing to a broader understanding of brain function in emotions.Our project consists of developing a bottom-up model, addressing the microscale, mesoscale, and large scale. This approach seeks to capture the structure,

…mediates plasticity to balance network currents for stable memory storage remains unclear. Here, we investigated whether a newly proposed form of codependent plasticity coul

Neuronal ensembles encoding a specific memory have been shown to be distributed across multiple brain regions. However, the computational principles behind the distributed nature of such memory engrams are unknown. Here we propose that distributed engrams support functional flexibility and versatility. We investigated whether distributed engrams can differentially regulate memory discrimination and generalization, opposing and complementary computations that must be balanced for adaptive memory-guided behavior. For instance, while animals need to discriminate between threat-predictive and neut

…the sparse information carrier in the neural network. The proposed method, focused on spikes, consists

Elements of neural networks (NNs), both biological and artificial, can be described by selectivity to specific cognitive features. Revealing such features is important for understanding NNs' inner working mechanisms. For living systems, where the neural response to a stimulus is not a known function, one needs to build a feedback loop of exposing it to a set of different stimuli. The properties of such stimuli should be iteratively varied aiming in the direction of neuronal maximal response. To utilize such a loop for a biological network, one should run it quickly and efficiently, reaching th

How changes in synaptic connections underlie learning and memory is a central question in Neuroscience. Previous modeling efforts focused on biologically realistic learning rules and dynamics, but these rules usually produce bimodal weight distributions that are not aligned with experimentally observed lognormal distributions of synaptic weights. How biological learning rules preserve such unimodal distributions and at the same time retain information, i.e., learn, remains unknown. Here, we take a game-theoretical approach, asking whether learning is possible under the constraint of retaining

The claustrum (CLA) is a densely connected brain region that is associated with attention, motor planning, anxiety and sleep. Majority of the research studies focus on its connections to the cortex. Its connections with the subcortical areas are needed to gain more insights about its function. This study investigates the CLA focusing on its connections with the basolateral amygdala (BLA) and prefrontal brain region. Using ex-vivo patch clamp, optogenetics, and in-vivo electrophysiology, we map the BLA's inputs to the CLA and its outputs to the prefrontal cortex. Findings reveal that the CLA re

…Despite ample knowledge about neural representations of space, the mechanisms of RL in spatial navigation remain understudied. To fill this gap, we extended an existing closed-loop simulator of spatial navigation and learning (Ghazinouri et al. 2023). The model is based on a spiking neural network that captures features of the hippocampus, specifically place cells and boundary cells. These cells feed inputs into action selection neurons, whose activity

…study, we used both a rate model and a spiking neural network to investigate the mechanisms responsible for the generations of beta oscillations. Results show that using a rate model with realistic time constants, the classical candidate - a rec

…this work, we developed a bioinspired spiking neural network model of the sensorimotor peripheral whisker system, modelling trigeminal ganglion, trigeminal nuclei, facial nuclei, and central pattern generator neuronal populations. This network was embedded in a virt

…two event-based cameras and a Spiking Neural Network (SNN) with a slightly modified U-Net-like encoder-decoder architecture, that we named StereoSpike. More specifically, we used the Multi Vehicle Stereo Event Camera Dataset (MVSEC). It provides a depth ground-truth, which was used to train StereoSpike in a supervised manner, using surrogate gradient descent. We propose a

…features of an autoassociative memory network and plays a key role in the retrieval of olfactory memories (Meissner-Bernard et al., 2019). Interestingly, the telencephalic area Dp, the zebrafish homolog of olfactory cortex, transiently enters a state of precise balance during the presentation of an odor (Rupprecht and Friedrich, 2018). This state is characterized by large synaptic conductances (relative to the resting conductance) and by co-tuning of excitation and inhi