Contrastive learning

…include attention-guided alignment, contrastive learning for combined image-and-text retrieval, and evaluating images containing multiple people. The conversation connects identity leakage and attribute blending with the constraints of efficient on-device deployment.

The simple decision to seek and consume food requires integrating slowly changing information about internal state with rapid sensory processes indicating food availability. Whether fast and slow components unite in a single integrating location [2] or whether they are also integrated in a more distributed fashion remains unknown. Can neural population dynamics simultaneously encode multiple timescales? Could modeling such temporal multiplexing help us understand how the brain integrates temporal information to guide appetitive behavior? To examine these questions, we recorded neural populati

…We employ a contrastive learning approach over the Poincare ball in order to embed olfacto

To understand the neural basis of many cognitive processes, such as learning and memory, we need to track the millisecond, single-spike activity of neurons across days. However, compared to calcium imaging methods, tracking neurons in chronic electrophysiological recordings remains challenging. Recent studies have designed algorithms to do so, but mostly rely on engineered parameters that may not take advantages of the full complexity of the neuron’s electrophysiological waveform and miss or falsely match neurons. To go beyond these limitations, we developed DeepMatch, a deep neural network fr

…Here, we fill this gap with a novel contrastive learning method that jointly uses time-series d

Feedforward sensory processing in the brain is generally construed as proceeding through a hierarchy of layers, each constructing increasingly abstract and invariant representations of sensory inputs. This interpretation is at odds with the observation that activity in sensory processing layers is heavily modulated by contextual signals, such as cross modal information or internal mental states. While it is tempting to assume that such modulations bias the feedforward processing pathway towards detection of relevant input features given a context, this induces a dependence on the contextual st

…we hypothesize that a key part of this learning ability is the use of self-generated navigational information as a similarity label to drive a learning objective for self-supervised learning. The goal of this work is to exploit navigational information in a visual environment to provide performance in training that exceeds the state-of-the-art self-supervised training. Here, we show that using spatial and temporal information in the pretraining stage of contrastive learning can improve the performan

The 3D morphology of cortical neurons is highly complex with widely varying shapes and has long been used to classify neurons into cell types. Classification based on morphology has classically been done by either laborious expert analysis through visual inspection or by computing a set of predefined features that could be quantitatively measured. Both approaches are prone to bias and subjectivity. The classification of neurons by experts was shown to have a high variance, and becomes infeasible with larger datasets. Similarly, the definition of fixed morphological features for classification