Dynamical Systems ePosters

A range of work has been done analyzing the effect of specific pruning rules on the dynamics of recurrent neural networks (RNNs) with a given connectivity structure. However, a more comprehensive study that explains the effect of different, biologically plausible pruning rules across a variety of structures has yet to…

Ongoing neural activity unfolds over intrinsic timescales that vary widely across the brain. The increase in timescales along the sensory-transmodal cortical axis parallels the cortical hierarchy, while diversity of timescales within each area may reflect functional specialization of individual neurons. However,…

Latent dynamical system (LDS) models have been widely used to characterize the dynamics of high-dimensional neural population activity. However, these models typically ignore the fact that the brain contains multiple cell types. This limits their ability to capture the functional roles of distinct cell classes, and…

Higher brain regions are thought to use the computational advantages of sitting closer to a critical point to support more complex cognitive functions. Finding evidence for this hypothesis from neural data is challenging, requiring robust and accurate measures of the distance to criticality (DTC). Many existing…

Neural activity in the cortex exhibits a wide range of timescales, allowing animals to respond to rapidly changing sensory stimuli while maintaining information in working memory. Recent studies show how top-down inputs representing locomotive state modulates these timescales differently across visual areas in mice.…

Cortical neuronal networks exhibit a rich dynamical repertoire and are capable of performing many different computations, for instance the generation of multiple distinct dynamical patterns in motor cortex. However, we do not know what connectivity structures support this capability, nor the mechanism for selecting…

Animals use knowledge of the environment to guide efficient foraging decisions. It is unclear how this knowledge is accumulated or maintained by recurrently connected circuits in the brain. In order to guide the study of how structures of knowledge are accumulated and maintained to support optimal foraging decisions,…

Reconciling theoretical models with experimental data is a major challenge in neuroscience. We investigate how neurons that exhibit disorderly properties individually can produce orderly collective dynamics. Focusing on continuous-attractor networks, which model representations of continuous variables such as head…

Recent findings have shown that the brain’s internal representation of the external world shifts over days, a phenomenon termed representational drift [Driscoll et al., 2017, Rule et al., 2020]. Thus, it becomes crucial to account for such non-stationarities when investigating the roles of circuits in the brain. While…

Neuronal responses to sensory stimuli are strongly modulated by an animal’s brain state. Three distinct subtypes of inhibitory interneurons, parvalbumin (PV), somatostatin (SOM), and vasoactive intestinal peptide (VIP) expressing cells, have been identified as key players in flexibly modulating sensory responses [1].…

Deep feedforward and recurrent networks have become popular choices for models of neural computation, but they do not provide a clear link to the details of the underlying biophysical processes. Here we argue that the reason this link has been difficult to establish is because biological circuits operate in a…

The synaptic connection strengths in the brain largely follow a heavy-tailed distribution, whereas most theoretical frameworks assume a Gaussian distribution due to the mathematical complexity involved. Understanding how these heavy-tailed distributions influence neural dynamics is crucial for developing more…

Sensory, cognitive, and motor functions rely on transmission/integration of signals across multiple spatiotemporal scales which are fundamentally constrained by neuroanatomy. Uncovering connectivity patterns that shape signal propagation is thus of central importance to understanding brain computations. An…

Visual gamma entrainment using sensory stimuli (vGENUS) is a promising non-invasive therapeutic approach for Alzheimer's disease (AD), showing efficacy in improving memory function and physiological biomarkers levels in mice and, in perspective, also human patients. However, its mechanisms of action remain poorly…

Complex movements, such as riding a bike, require rich, coordinated, self-sustaining patterns of neural activity. Experimental studies show that such activity patterns are often controlled by brief, intermittent inputs. In contrast, computational models usually rely on constant external inputs or generate only…

The critical brain hypothesis proposes that neural networks operate near criticality to reap the computational benefits of accessing a wide range of timescales [1]. Proponents of this viewpoint highlight the presence of heavy-tailed (power-law) spatiotemporal correlations as markers of criticality in the brain [2].…

Spontaneous, naturalistic behaviors comprise most of an animal’s lifetime. However, understanding how the brain encodes complex naturalistic behavior remains a fundamental challenge in neuroscience. Decades of research has shown that performance on overtrained, task-based, goal-oriented behaviors can be decoded from…

Memories are thought to be stored in synapses and retrieved through the reactivation of neural ensembles. However, neural representations exhibit significant dynamics even in stable environments, a phenomenon called representational drift. Theoretical studies suggest that multiple neural representations can correspond…

A central goal of modern neuroscience is to relate structure, dynamics, and computation in recurrent neural networks (RNNs), in particular those that can be created via biological learning rules. To date, most work on RNN training has focused on either efficient but non-biological rules (e.g., backprop, FORCE) or…

Brains can process sensory information from different modalities at astonishing speed; this is surprising as already the integration of inputs through the membrane causes a delayed response. Experiments reveal a possible explanation for the fast processing, showing that neurons can advance their output firing rate…

Grid cells in the medial entorhinal cortex (MEC) of mammalian brains show hexagonal firing fields and are thought to encode the animal’s current location in the environment. As the animal moves, grid cells integrate self-motion cues to track the current position relative to a starting reference location. Recent…

Recent technological advances have enabled the measurement of the ‘connectome,’ or synaptic wiring diagram, of neurons across large brain regions or entire brains. Theoretical work has long sought to link connectivity features of large neural networks to their emergent dynamics. Nevertheless, how to integrate…

Cognitive flexibility is characterized by the ability to adapt behavior in response to changing environments. When the environment changes, previously appropriate behavior can lead to negative feedback. Surprising negative feedback – failure to receive a reward when strongly expected – may signal a change in the…

Mounting evidence suggests that traveling waves are a key dynamical motif in biological neural information processing systems [Muller et al., 2018], yet their precise computational role remains under investigation. While the Transformer architecture [Vaswani et al., 2023] has achieved state-of-the-art performance…