@article {69660, title = {From the fly connectome to exact ring attractor dynamics}, journal = {bioRxiv}, year = {2024}, month = {2024 Nov 01}, abstract = {

A cognitive compass enabling spatial navigation requires neural representation of heading direction (HD), yet the neural circuit architecture enabling this representation remains unclear. While various network models have been proposed to explain HD systems, these models rely on simplified circuit architectures that are incompatible with empirical observations from connectomes. Here we construct a novel network model for the fruit fly HD system that satisfies both connectome-derived architectural constraints and the functional requirement of continuous heading representation. We characterize an ensemble of continuous attractor networks where compass neurons providing local mutual excitation are coupled to inhibitory neurons. We discover a new mechanism where continuous heading representation emerges from combining symmetric and anti-symmetric activity patterns. Our analysis reveals three distinct realizations of these networks that all match observed compass neuron activity but differ in their predictions for inhibitory neuron activation patterns. Further, we found that deviations from these realizations can be compensated by cell-type-specific rescaling of synaptic weights, which could be potentially achieved through neuromodulation. This framework can be extended to incorporate the complete fly central complex connectome and could reveal principles of neural circuits representing other continuous quantities, such as spatial location, across insects and vertebrates.

}, doi = {10.1101/2024.11.01.621596}, url = {https://doi.org/10.1101/2024.11.01.621596}, author = {Biswas, Tirthabir and Stanoev, Angel and Romani, Sandro and Fitzgerald, James E.} } @article {69624, title = {Leg compliance is required to explain the ground reaction force patterns and speed ranges in different gaits}, journal = {bioRxiv}, year = {2024}, month = {2024 Sep 24}, abstract = {

Two simple models, vaulting over stiff legs and rebounding over compliant legs, are employed to describe the mechanics of legged locomotion. It is agreed that compliant legs are necessary for describing running and that legs are compliant while walking. Despite this agreement, stiff legs continue to be employed to model walking. Here, we show that leg compliance is necessary to model walking and, in the process, identify the principles that underpin two important features of legged locomotion: First, at the same speed, step length, and stance duration, multiple gaits that differ in the number of leg contraction cycles are possible. Among them, humans and other animals choose a gait with M-shaped vertical ground reaction forces because it is energetically favored. Second, the transition from walking to running occurs because of the inability to redirect the vertical component of the velocity during the double stance phase. Additionally, we also examine the limits of double spring-loaded pendulum (DSLIP) as a quantitative model for locomotion, and conclude that DSLIP is limited as a model for walking. However, insights gleaned from the analytical treatment of DSLIP are general and will inform the construction of more accurate models of walking.

}, doi = {10.1101/2024.09.23.612940}, url = {https://doi.org/10.1101/2024.09.23.612940}, author = {Safa, Ali Tehrani and Biswas, Tirthabir and Ramakrishnan, Arun and Bhandawat, Vikas} } @article {65808, title = {A geometric framework to predict structure from function in neural networks}, journal = {Physical Review Research}, volume = {4}, year = {2022}, month = {2022 Jun 29}, pages = {023255}, abstract = {

Neural computation in biological and artificial networks relies on nonlinear synaptic integration. The structural connectivity matrix of synaptic weights between neurons is a critical determinant of overall network function. However, quantitative links between neural network structure and function are complex and subtle. For example, many networks can give rise to similar functional responses, and the same network can function differently depending on context. Whether certain patterns of synaptic connectivity are required to generate specific network-level computations is largely unknown. Here we introduce a geometric framework for identifying synaptic connections required by steady-state responses in recurrent networks of rectified-linear neurons. Assuming that the number of specified response patterns does not exceed the number of input synapses, we analytically calculate all feedforward and recurrent connectivity matrices that can generate the specified responses from the network inputs. We then use this analytical characterization to rigorously analyze the solution space geometry and derive certainty conditions guaranteeing a non-zero synapse between neurons. Numerical simulations of feedforward and recurrent networks verify our analytical results. Our theoretical framework could be applied to neural activity data to make anatomical predictions that follow generally from the model architecture. It thus provides novel opportunities for discerning what model features are required to accurately relate neural network structure and function.

}, doi = {10.1103/PhysRevResearch.4.023255}, url = {https://doi.org/10.1103/PhysRevResearch.4.023255}, author = {Biswas, Tirthabir and Fitzgerald, James E} } @article {66027, title = {Drosophila uses a tripod gait across all walking speeds, and the geometry of the tripod is important for speed control.}, journal = {eLife}, volume = {10}, year = {2021}, month = {2021 Feb 03}, abstract = {

Changes in walking speed are characterized by changes in both the animal{\textquoteright}s gait and the mechanics of its interaction with the ground. Here we study these changes in walking . We measured the fly{\textquoteright}s center of mass (CoM) movement with high spatial resolution and the position of its footprints. Flies predominantly employ a modified tripod gait that only changes marginally with speed. The mechanics of a tripod gait can be approximated with a simple model - angular and radial spring-loaded inverted pendulum (ARSLIP) - which is characterized by two springs of an effective leg that become stiffer as the speed increases. Surprisingly, the change in the stiffness of the spring is mediated by the change in tripod shape rather than a change in stiffness of the individual leg. The effect of tripod shape on mechanics can also explain the large variation in kinematics among insects, and ARSLIP can model these variations.

}, issn = {2050-084X}, doi = {10.7554/eLife.65878}, author = {Chun, Chanwoo and Biswas, Tirthabir and Bhandawat, Vikas} } @article {65856, title = {Theoretical principles for illuminating sensorimotor processing with brain-wide neuronal recordings.}, journal = {Current Opinion in Neurobiology}, volume = {65}, year = {2020}, month = {2020 Nov 25}, pages = {138-145}, abstract = {

Modern recording techniques now permit brain-wide sensorimotor circuits to be observed at single neuron resolution in small animals. Extracting theoretical understanding from these recordings requires principles that organize findings and guide future experiments. Here we review theoretical principles that shed light onto brain-wide sensorimotor processing. We begin with an analogy that conceptualizes principles as streetlamps that illuminate the empirical terrain, and we illustrate the analogy by showing how two familiar principles apply in new ways to brain-wide phenomena. We then focus the bulk of the review on describing three more principles that have wide utility for mapping brain-wide neural activity, making testable predictions from highly parameterized mechanistic models, and investigating the computational determinants of neuronal response patterns across the brain.

}, issn = {1873-6882}, doi = {10.1016/j.conb.2020.10.021}, author = {Biswas, Tirthabir and Bishop, William E and Fitzgerald, James E} }