Topic: autoencoder

The natural world is full of noise, but the brain’s capacity for information transmission is severely limited. Therefore, discarding irrelevant information contained in sensory inputs while retaining salient features that are related to the input label, is key to survival. What are the salient features? And what are the underlying feature selection mechanisms? It is thought that the brain may implement information bottlenecks, which aim to optimise the trade-off between compression and preservation of salient information. However, information bottlenecks are notoriously difficult to implemen

Modern datasets in neuroscience enable unprecedented inquiries into the relationship between complex behaviors and the activity of many simultaneously recorded neurons. While latent variable models can successfully extract low-dimensional embeddings from such recordings, using them to generate realistic spiking data, especially in a behavior-dependent manner, still poses a challenge. Here, we present Latent Diffusion for Neural Spiking data (LDNS), a diffusion-based generative model with a low-dimensional latent space: LDNS employs an autoencoder with structured state-space (S4) layers to pro

Detecting a specific object in a visual scene is a task that the human visual system performs every day. To perform this target-specific object detection, it is necessary to filter out all unwanted objects during the processing, so that only the specific object can be detected. For this purpose, attention-related mechanisms along the visual pathway have been proposed to allow focusing on the desired object. Beuth and Hamker (2015) [1], using a biologically inspired recurrent model simulating lower and higher layers of the visual cortex, the frontal eye field, and the prefrontal cortex, showed

Modern datasets in neuroscience enable unprecedented inquiries into the relationship between complex behaviors and the activity of many simultaneously recorded neurons. While latent variable models can successfully extract low-dimensional embeddings from such recordings, using them to generate realistic spiking data, especially in a behavior-dependent manner, still poses a challenge. Here, we present Latent Diffusion for Neural Spiking data (LDNS), a diffusion-based generative model with a low-dimensional latent space: LDNS employs an autoencoder with structured state-space (S4) layers to proj

Sparse coding is an effective computational strategy for ensuring robust pattern storage and memory recall of correlated patterns. In the hippocampus, many neurons fire in localised receptive fields that tile an animal's physical environment, forming a sparse spatial population code. But the underlying computations and their functional purpose remain unclear. Various ANNs have been proposed to learn localised, manifold-tiling codes, purportedly due to explicit constraints on network activity levels or to specific task objectives, such as predicting future observations. Here, we show that in si