Recurrent connections

Recurrent connections are a fundamental feature of neural circuits, enabling feedback loops that regulate network activity. Connectome datasets show strong recurrent connections in the fruit fly visual system, where motion information is processed. However, current models of motion detection focus on feedforward connectivity, leaving the role of recurrence largely unexplored. Here, we investigate the impact of recurrent connectivity on “ON” (bright stimuli) motion selectivity using connectome-constrained deep mechanistic networks (DMNs) of the fly visual system. DMNs feature a one-to-one corre

…The sequence contains both recurrent and feedforward connections (within and across assemblies, respect

…is characteristic for the underlying recurrent connectivity in the cortex that neurons with similar tuning predominantly excite each other. The reason for such an architecture is not clear. Using an analytically tractable model, we show that it can naturally arise from a cooperative coding scheme. In this scheme neurons with similar responses specifically support each other by sharing their computations to obtain the desired

…Recurrent connections among HD cells create a ring-attractor network that continuously encodes HD (Ajabi et al. 2023). Indeed, in the Drosophila HD system, three rings interact to linearly integrate angular head velocity (AHV), with two of the rings receiving countervailing velocity-dependent inputs (Turner-Evans et al. 2017). In contrast, for the desert locust an alternative mechanism has been proposed (Zittrell et al. 2023; Pabst et al. 2024). There, velocity modulates synaptic

Animals continuously process information from multiple sensory modalities to assess their environment and guide their behavior. For example, a predator may rely on both visual and auditory cues to track its prey. Over time, various algorithms have been developed to describe multisensory processing, including linear and nonlinear fusion. However, these algorithms typically treat each time step independently and fail to account for temporal dependencies in these signals. To address this limitation, we introduce a novel set of multisensory tasks that systematically incorporate controlled temporal

With the recent success of Transformers in the field of automatic natural language processing [1], it has become clear that we are able to create complex, high-performance neural network architectures that can extract, combine and transform information to meet a specific objective, such as predicting the next word in a text. However, to function, Transformers need to receive a context of all N elements of the past sequence (N = 2k to 32k tokens) at each time step, causing the number of computations to grow quadratically (N^2) with the length of the sequence [1]. This "perfect" memory of a co

Neural computations support stable behavior despite relying on many dynamically changing biological processes. One such process is representational drift (RD), which describes changes in neurons' response profile over the timescale of minutes to weeks. Across many brain areas, neurons change their tuning or even stop/start being active, while population encoding and behavior stays intact. Generally, RD is believed to be caused by changes in synaptic weights. These changes impact the population readout and consequently require adaptation of downstream areas to maintain stable function, a costly

…Here, recurrent connections learn to locally balance feedforward input in indivi

…Local recurrent connections between nearby neurons can potentially exert large effects on these transformations, but it has been unclear how recurrent connections influence input-output transformations in the cortex. Here we study recurrent influences in mouse V1 by experimentally stimulating excitatory neurons. To do this, we selectively express an excitatory opsin (stChrimsonR) in excitatory cells, and record activity using electrophysiology, 2-photon, and widefield calcium imaging

…synaptic plasticity, strengthening the recurrent connections within select groups of neurons that receive correlated stimulation. To remain stable in absence of such stimulation, these assemblies need to be self-reinforcing under the plasticity rule. Previous models of such assembly generation and maintenance required mechanisms of heterosynaptic competition or homeostatic plasticity often with biologic

A recent approach to understanding the mammalian visual system is to show correspondence between the sequential stages of processing in the ventral stream with layers in a deep convolutional neural network (DCNN), providing evidence that visual information is processed hierarchically, with successive stages containing ever higher-level information. However, correspondence is usually defined as shared variance between brain region and model layer. We propose that task-relevant variance is a stricter test: If a DCNN layer corresponds to a brain region, then substituting the model’s activity with