Mathematical and Computational Biosciences Collective Colloquium - Spring 2026

For questions about the seminar schedule, please contact James MacLaurin or Kristina Wicke.

Zoom link for seminars: https://njit-edu.zoom.us/j/99264610662?pwd=zFOFhY2fJ0HMdedKu5aPROv3zaKJrg.1 

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February 04

Jorge Golowasch and Kristina Wicke, NJIT

Faculty Research Overview

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February 11

Danielle Bassett, University of Pennsylvania

How Costly is Your Brain's Activity Pattern

Neural systems in general—and the human brain in particular—are organized as networks of interconnected components. Across a range of spatial scales from single cells to macroscopic areas, biological neural networks are neither perfectly ordered nor perfectly random. Their heterogeneous organization supports---and simultaneously constrains---complex patterns of activity. How does the network constraint affect the cost of a specific brain's pattern? In this talk, I will use the formalism of network control theory to define a notion of network economy: the idea that a biological neural network’s organization partially determines the energetic cost of reaching a neural state, maintaining a neural state, and transitioning between neural states. Then, I will demonstrate how the principle of network economy can inform our study of neural system function in health and disease.

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February 18

James MacLaurin and Victor Matveev, NJIT

Faculty Research Overview

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March 4

Horacio Rotstein, NJIT

Discussion Group (Linking models and data: parameter estimation and other tools)

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March 11

Iris Yoon, Swarthmore College

How Topology Reveals Structure in Biology

In this talk, I will discuss recent developments in applied topology that study the structure of data. In particular, I will show how constructions in topology, such as Dowker complexes and path liftings to covering spaces, reveal interesting structures in data arising from cancer biology and neuroscience. I will also discuss recent efforts to generalize the application of Dowker complexes to more complex datasets. 

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March 25

Guillermo Cecchi, Icahn School of Medicine at Mount Sinai

Quantifying the Experience of Pain

Validated clinical instruments in the form of point-scale questionnaires are the backbone of psychiatric monitoring, yet their structured format imposes real costs: patient burden, clinician time, and an inherent ceiling on what can be captured. We investigated whether large language models could bypass these limitations by extracting clinical metrics directly from naturalistic patient speech. We applied LLM-based analysis to semi-structured interviews from two clinical populations, chronic low back pain (CLBP) and major depressive disorder, probing three hypotheses: (1) that automated metrics would discriminate meaningfully between diagnostic groups; (2) that LLM-derived scores would correlate with validated questionnaire measures; and (3) that the approach could surface novel constructs such as Narrative Fragmentation and Agency Deficit, theoretically relevant to pain and depression but not easily accessible to standard self-report. To test generalizability beyond the clinical setting, we applied the same framework to condition-specific Reddit communities, where individuals share experiences in free form. Results support the viability of LLM-based narrative analysis as a scalable, low-burden complement to traditional instruments, one that preserves the richness of patient experience rather than compressing it onto predetermined ordinal scales and can be extended to high-frequency (e.g., daily) interactions with patients across many mental health conditions.

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April 1

Noah Cowan, Johns Hopkins Whiting School of Engineering

Control and recalibration of path integration in the hippocampus

The hippocampus is thought to serve as a “cognitive map” wherein the events of an animal’s experience are encoded within a spatiotemporal framework. To continuously update the animal’s position and orientation on this internal map, the hippocampal system integrates self-motion signals over time. External landmarks provide feedback to correct the errors in the position estimate that would otherwise inevitably accumulate. Using a novel virtual reality apparatus, we discovered that if path integration is biased, such that the animal consistently under- or overestimates its movement through space, the landmarks (Jayakumar et al, Nature, 2019) or optic flow cues (Madhav et al, Nature Neuroscience 2024) in the environment can serve as a teaching signal for recalibration of the path integrator. Using a biophysically plausible attractor neural network model of path integration, we show that for landmark- based recalibration the path integration error, or its integral, must be encoded at the level of individual neurons in order to enable path integration recalibration (Secer et al, 2025, Nat Comm). Using this prediction, we turned back to the physiological data and discovered a rate code for error at the level of individual neurons. Finally, I’ll describe our team’s recent discovery that Area 29e, an understudied parahippocampal field, serves as specialized visuospatial hub that may carry landmark signals for anchoring hippocampal representations of space to the external world (Secer et al., bioRxiv, 2025).

This body work is the result of an equal collaboration with the lab of Prof. James Knierim, generously funded by the NIH and ARO.

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April 8

Evan Schaffer, Icahn School of Medicine at Mount Sinai

Stable and predictable geometry of neural representations is inevitable

In many brain regions, the stimulus tuning of neurons is both spatially disorganized and temporally unstable. For example, in mouse piriform cortex, neurons receive input from a random collection of glomeruli, resulting in odor representations that lack spatial organization. The piriform neurons responsive to a given odor are completely uncorrelated with those activated by the same odor two weeks later, a phenomenon often called ‘representational drift’. I will describe two general problems that emerge as properties of random connectivity and representational drift, respectively, and show how solutions to these two problems are related. First, random connectivity implies that odor representations are not only disorganized but different across individuals. How can individuals nevertheless agree on the qualities of an odor, such as how citrusy it is? We find that random representations can support consistent agreement about odor quality across a range of odors after only a single shared experience. Second, representational drift would seem to imply that piriform cortex, like other brain regions whose activity appears to drift, is useless for the retrieval of associative memories learned several weeks prior. However, we and others have found that stable decoding of drifting representations is possible. We offer a very general mathematical understanding of when and why stable decoding from drifting representations is possible and draw connections to both empirical results and experimentally testable predictions.

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April 15

Casey Diekman and Amitabha Bose, NJIT

Faculty Research Overview

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April 22

Jeff Sachs, Merck

Title/Abstract Forthcoming

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April 29

Laura Miller, University of Arizona

Title/Abstract Forthcoming

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Last updated: March 25, 2026