Topic: spiking neural network

It is common for animals to use self-generated movements to actively sense the surrounding environment. For instance, rodents rhythmically move their whiskers to explore the space close to their body. The mouse whisker system has become a standard model to study active sensing and sensorimotor integration through feedback loops. In 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

Depth estimation is an important computer vision task, useful in particular for navigation in autonomous vehicles, or for object manipulation in robotics. Here we solved it using an end-to-end neuromorphic approach, combining 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

Odor memories are exceptionally robust and essential for the survival of many species. In rodents, the olfactory cortex shows 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

The brain continuously encodes new information as memories. At the synaptic level, memory formation is enabled by adjusting connections between activated excitatory neurons in the cortex, with mirroring inhibitory activity ensuring stable storage. Particularly, synaptic plasticity occurs as memory reactivation takes place during sleep, contributing to increased memory stability. However, the mechanism by which reactivation 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