Krešimir Josić́

This paper introduces an information compass framework to model probabilistic reasoning in biological and artificial systems using information geometry.

This paper introduces a Bayesian method for inferring gene regulatory networks using a Chemical Langevin Equation model, enabling structure identification and kinetic parameter estimation without observing transient dynamics.

This study investigates how RNNs represent time and make decisions during delayed tasks, finding multiple dynamical solutions with similar behavior.

This study examines how humans use flexible decision rules to terminate evidence accumulation, finding that participants adjust their strategies to improve reward rate under varying conditions.

The paper constructs and tests synthetic circuits in E. coli to show that positive feedback-regulated secondary signals can propagate faster and farther than diffusion-limited signals.

This paper presents a method to control microbial consortium proportions using auxotrophic cross-feeding in continuous culture, demonstrating precise regulation through exogenous nutrient addition and mathematical modeling.

The paper shows hippocampal neurons encode meanings of spoken and heard words using shared geometric embeddings, enabling abstract cross-personal representations while preserving speaker identity binding.

Functionally similar neurons in mouse visual cortex connect preferentially across layers and areas, including feedback, and feature tuning, not receptive-field position, predicts synapses beyond axon-dendrite proximity.

The paper models how competition drives the evolution of multiple foraging strategies by balancing metabolic costs and foraging efficiency in a coevolutionary framework.

This study creates a synthetic genetic toggle switch that exhibits population bistability and generates spatial differentiation patterns in bacterial colonies.

This paper demonstrates how to tune gene circuit dynamics in a synthetic microbial consortium by adjusting strain fractions, creating a pulse generator with controllable amplitude.

The paper shows that recurrent neural circuits with Poisson spiking variability perform optimal Bayesian inference by sampling from posterior stimulus distributions.

The paper analyzes the hierarchical modular structure of the Drosophila connectome using community detection methods to identify organization across scales.

The paper develops a Bayesian MCMC method to infer gene regulatory network parameters from partial observations, demonstrating its ability to estimate delays and kinetic parameters in a two-step activation model.

This paper derives a normative decision model that uses adaptive thresholds to maximize reward in changing environments, showing better performance than constant-threshold models.

The paper investigates how working-memory limitations affect perceptual decision-making by examining strategy-dependent degradation of a continuous decision variable over time and capacity.

This paper introduces a stochastic neural network method for tracking bacterial cell motion in microscopy sequences, achieving high registration accuracy in both simulated and experimental data.

The paper introduces a hierarchical Bayesian framework to infer variability in gene regulatory processes with delays using both simulated and experimental data, showing improved parameter estimates.

The paper shows that humans use suboptimal inference strategies in rare-event tasks, inverting the bias-variance trade-off compared to optimal Bayesian strategies.

The paper shows that recurrent neural circuits can encode stimulus features through population codes, with variability aligned to stimulus directions and modulated by internal priors.

The paper proposes a hierarchical neural network model that simulates perceptual multistability with four stochastically alternating percepts, consistent with experimental observations.

This paper develops a theory of plasticity in balanced neural networks, showing how balance is maintained and how correlations affect synaptic weight evolution and learning.

This paper develops a method using differential Chapman-Kolmogorov equations to analyze dynamic decision-making models and compare them to empirical data using accuracy and divergence metrics.

This study investigates how trap size in microfluidic devices affects the spatiotemporal dynamics of synthetic microbial consortia, revealing a trade-off between signaling effectiveness and strain proportion stability.

The paper introduces a Bayesian framework to infer reaction rates and delay distributions in biochemical networks using single-cell data, showing robust parameter estimation with limited measurements.

The paper compares normative and approximate evidence accumulation models on a dynamic clicks task, showing parameter tuning differences and pitfalls in model fitting and interpretation.

The paper develops a drift-diffusion model for sequential decision-making, showing how prior information affects future decisions and optimizing reward rates in correlated trial sequences.

The paper develops a normative model for collective decision making in social networks, showing how agents integrate sensory and social evidence to optimize choices.

The paper links network connectivity statistics to spike time statistics in neuronal networks, showing how local connectivity features predict global activity patterns and how plasticity rules can coevolve connectivity and spiking statistics.

The paper presents an agent-based model to simulate mechanical interactions in growing rod-shaped bacterial populations and demonstrates its application in microfluidic traps.