Mathematical Biology ePosters

Many animals rely on olfactory cues to navigate their environments. For example, insects are particularly adept at locating odor sources. The navigational algorithms of insects have often been modeled as simple sensorimotor reflexes based on instantaneous cues: surging upwind when encountering the odor and casting…

Despite the diversity of neural responses, the brain is highly structured both at anatomical and functional levels. Neurons in different brain areas exhibit different response profiles (large-scale anatomical organization), and within brain areas, the neurons can sometimes be grouped together into specialized…

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…

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.…

The brain navigates mazes using the entorhinal grid and hippocampal place codes. While specific grid codes can represent maze structures independent of behavioral policy, little is known about how these task structure-specific grid codes generalize to novel tasks requiring different structures and policies.…

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…

To survive, organisms must make rapid inferences about their surroundings. For example, to successfully escape approaching predators, prey must estimate the direction of approach from noisy stimuli [1]. Such rapid inferences are particularly challenging because there is little time to gather sensory information, yet…

To resolve the fundamental question of how the mammalian neocortex learns to create complex hierarchical representations, two main competing theories that intersect ideas from neuroscience and machine learning are currently debated. The first theory draws inspiration from artificial neural networks, suggesting the…

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…

A change in pairwise spike train correlations is a well-established neural correlate of a shift in brain state, and as such has been the topic of intense study. However, while the explosion in population recording has provided a wealth of data to measure spike train correlations and their state dependence, there…

Recent studies have shown that synaptic spines undergo spontaneous size fluctuations, a phenomenon hypothesized to be critical for understanding synaptic plasticity and neural network stability. In this work, we develop a model for spine size dynamics using a simple stochastic process based on minimal biological…

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].…

Imagine predicting the precise responses to arbitrary sensory stimuli for a neuron probed deeply within a brain. We developed a robust characterization technique called spike-triggered descent (STD) which optimally recovers n-order kernels for the very general cumulative spike response model (CSRM). This works by…

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…

It is beyond doubt that cognitive phenomena such as language and reasoning are the direct product of the activity of neurons and synapses. Making progress on this open question, often called the bridging problem, was identified by Richard Axel as the most important challenge in neuroscience today. The research…

The mammalian olfactory system shows an exceptional ability for rapid and accurate decoding of both the identity and concentration of odorants. In a given scene, only a handful out of millions of possible odorants are present. Yet, the mammals can effectively distinguish these odorants using the dimensionally-reduced…

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…

Perception balances what we sense with what we expect. To maintain accuracy, our brains must continuously update sensory expectations when predictions don't match reality. To investigate this, we trained mice on an auditory detection task with hidden switches in stimulus probability across trials. Mice dynamically…

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…

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…

Stable neural representations are essential for consistent behavior and learning, but recent findings on representational drift challenge our understanding of how individual neurons contribute to stable brain computations over time. While representational drift has been observed across multiple brain regions, its…

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…