Biology subjects

Josic, K.

Publications and source records attributed to Josic, K..

29 records · Page 2Linked to original sources

bioRxiv2020

Normative decision asymmetries with symmetric priors but asymmetric evidence

Solutions to challenging inference problems are often subject to a fundamental trade-off between bias (being systematically wrong) that is minimized with complex inference strategies and variance (being oversensitive to uncertain observations) that is minimized with simple inference strategies. However, this trade-off is based on the assumption that the strategies being considered are optimal for their given complexity and thus has unclear relevance to the frequently suboptimal inference strategies used by humans. We examined inference problems involving rare, asymmetrically available evidence, which a large population of human subjects solved using a diverse set of strategies that were suboptimal relative to the Bayesian ideal observer. These suboptimal strategies reflected an inversion of the classic bias-variance trade-off: subjects who used more complex, but imperfect, Bayesian-like strategies tended to have lower variance but high bias because of incorrect tuning to latent task features, whereas subjects who used simpler heuristic strategies tended to have higher variance because they operated more directly on the observed samples but displayed weaker, near-normative bias. Our results yield new insights into the principles that govern individual differences in behavior that depends on rare-event inference, and, more generally, about the information-processing trade-offs that are sensitive to not just the complexity, but also the optimality of the inference process.

neuroscience↗

bioRxiv2020

Recurrent circuit based neural population codes for stimulus representation and inference

A large part of the synaptic input received by cortical neurons comes from local cortico-cortical connectivity. Despite their abundance, the role of local recurrent connections in cortical function is unclear, and in simple coding schemes it is often the case that a circuit with no recurrent connections performs optimally. We consider a recurrent excitatory-inhibitory circuit model of a cortical hypercolumn which performs sampling-based Bayesian inference to infer latent hierarchical stimulus features. We show that local recurrent connections can store an internal model of the correlations between stimulus features that are present in the external world. When the resulting recurrent input is combined with feedforward input it produces a population code from which the posterior over the stimulus features can be linearly read out. Internal Poisson spiking variability provides the proper fluctuations for the population to sample stimulus features, yet the resultant population variability is aligned along the stimulus feature direction, producing differential correlations. Importantly, the amplitude of these internally generated differential correlations is determined by the associative prior in the model stored in the recurrent connections. This provides experimentally testable predictions for how population connectivity and response variability are related to the structure of latent external stimuli.

neuroscience↗

bioRxiv2020

Synaptic Plasticity in Correlated Balanced Networks

The dynamics of local cortical networks are irregular, but correlated. Dynamic excitatory- inhibitory balance is a plausible mechanism that generates such irregular activity, but it remains unclear how balance is achieved and maintained in plastic neural networks. In particular, it is not fully understood how plasticity induced changes in the network affect balance, and in turn, how correlated, balanced activity impacts learning. How does the dynamics of balanced networks change under different plasticity rules? How does correlated spiking activity in recurrent networks change the evolution of weights, their eventual magnitude, and structure across the network? To address these questions, we develop a general theory of plasticity in balanced networks. We show that balance can be attained and maintained under plasticity induced weight changes. We find that correlations in the input mildly, but significantly affect the evolution of synaptic weights. Under certain plasticity rules, we find an emergence of correlations between firing rates and synaptic weights. Under these rules, synaptic weights converge to a stable manifold in weight space with their final configuration dependent on the initial state of the network. Lastly, we show that our framework can also describe the dynamics of plastic balanced networks when subsets of neurons receive targeted optogenetic input.

neuroscience↗

bioRxiv2019

A hierarchical model of perceptual multistability involving interocular grouping

Ambiguous visual images can generate dynamic and stochastic switches in perceptual interpretation known as perceptual rivalry. Such dynamics have primarily been studied in the context of rivalry between two percepts, but there is growing interest in the neural mechanisms that drive rivalry between more than two percepts. In recent experiments, we showed that split images presented to each eye lead to subjects perceiving four stochastically alternating percepts (Jacot-Guillarmod et al., 2017): two single eye images and two interocularly grouped images. Here we propose a hierarchical neural network model that exhibits dynamics consistent with our experimental observations. The model consists of two levels, with the first representing monocular activity, and the second representing activity in higher visual areas. The model produces stochastically switching solutions, whose dependence on task parameters is consistent with four generalized Levelt Propositions. Our neuromechanistic model also allowed us to probe the roles of inter-actions between populations at the network levels. Stochastic switching at the lower level representing alternations between single eye percepts dominated, consistent with experiments.

neuroscience↗

bioRxiv2019

Bayesian inference of distributed time delay in transcriptional and translational regulation

MotivationAdvances in experimental and imaging techniques have allowed for unprecedented insights into the dynamical processes within individual cells. However, many facets of intracellular dynamics remain hidden, or can be measured only indirectly. This makes it challenging to reconstruct the regulatory networks that govern the biochemical processes underlying various cell functions. Current estimation techniques for inferring reaction rates frequently rely on marginalization over unobserved processes and states. Even in simple systems this approach can be computationally challenging, and can lead to large uncertainties and lack of robustness in parameter estimates. Therefore we will require alternative approaches to efficiently uncover the interactions in complex biochemical networks.\n\nResultsWe propose a Bayesian inference framework based on replacing uninteresting or unobserved reactions with time delays. Although the resulting models are non-Markovian, recent results on stochastic systems with random delays allow us to rigorously obtain expressions for the likelihoods of model parameters. In turn, this allows us to extend MCMC methods to efficiently estimate reaction rates, and delay distribution parameters, from single-cell assays. We illustrate the advantages, and potential pitfalls, of the approach using a birth-death model with both synthetic and experimental data, and show that we can robustly infer model parameters using a relatively small number of measurements. We demonstrate how to do so even when only the relative molecule count within the cell is measured, as in the case of fluorescence microscopy.

systems biology↗

bioRxiv2019

Spatiotemporal dynamics of synthetic microbial consortia in microfluidic devices

Synthetic microbial consortia consist of two or more engineered strains that grow together and share the same resources. When intercellular signaling pathways are included in the engineered strains, close proximity of the microbes can generate complex dynamic behaviors that are difficult to obtain using a single strain. However, when a consortium is not cultured in a well-mixed environment the constituent strains passively compete for space as they grow and divide, complicating cell-cell signaling. Here, we explore the temporal dynamics of the spatial distribution of consortia co-cultured in microfluidic devices. To do this, we grew two different strains of Escherichia coli in microfluidic devices with cell-trapping regions (traps) of several different designs. We found that the size and shape of the traps are critical determinants of spatiotemporal dynamics. In small traps, cells can easily signal one another but the relative proportion of each strain within the trap can fluctuate wildly. In large traps, the relative ratio of strains is stabilized, but intercellular signaling can be hindered by distances between cells. This presents a trade-off between the trap size and the effectiveness of intercellular signaling, which can be mitigated by controlling the initial seeding of cells in the large trap. These results show how synthetic microbial consortia behave in microfluidic traps and provide a method to help remedy the spatial heterogeneity inherent to different trap geometries.

synthetic biology↗

bioRxiv2019

Analyzing dynamic decision-making models using Chapman-Kolmogorov equations

Decision-making in dynamic environments typically requires adaptive evidence accumulation that weights new evidence more heavily than old observations. Recent experimental studies of dynamic decision tasks require subjects to make decisions for which the correct choice switches stochastically throughout a single trial. In such cases, an ideal observers belief is described by an evolution equation that is doubly stochastic, reflecting stochasticity in the both observations and environmental changes. In these contexts, we show that the probability density of the belief can be represented using differential Chapman-Kolmogorov equations, allowing efficient computation of ensemble statistics. This allows us to reliably compare normative models to near-normative approximations using, as model performance metrics, decision response accuracy and Kullback-Leibler divergence of the belief distributions. Such belief distributions could be obtained empirically from subjects by asking them to report their decision confidence. We also study how response accuracy is affected by additional internal noise, showing optimality requires longer integration timescales as more noise is added. Lastly, we demonstrate that our method can be applied to tasks in which evidence arrives in a discrete, pulsatile fashion, rather than continuously.

neuroscience↗

bioRxiv2019

Performance of normative and approximate evidence accumulation on the dynamic clicks task

The aim of a number of psychophysics tasks is to uncover how mammals make decisions in a world that is in flux. Here we examine the characteristics of ideal and near-ideal observers in a task of this type. We ask when and how performance depends on task parameters and design, and, in turn, what observer performance tells us about their decision-making process. In the dynamic clicks task subjects hear two streams (left and right) of Poisson clicks with different rates. Subjects are rewarded when they correctly identify the side with the higher rate, as this side switches unpredictably. We show that a reduced set of task parameters defines regions in parameter space in which optimal, but not near-optimal observers, maintain constant response accuracy. We also show that for a range of task parameters an approximate normative model must be finely tuned to reach near-optimal performance, illustrating a potential way to distinguish between normative models and their approximations. In addition, we show that using the negative log-likelihood and the 0/1-loss functions to fit these types of models is not equivalent: the 0/1-loss leads to a bias in parameter recovery that increases with sensory noise. These findings suggest ways to tease apart models that are hard to distinguish when tuned exactly, and point to general pitfalls in experimental design, model fitting, and interpretation of the resulting data.

neuroscience↗

bioRxiv2018

Optimal evidence accumulation on social networks

A fundamental question in biology is how organisms integrate sensory and social evidence to make decisions. However, few models describe how both these streams of information can be combined to optimize choices. Here we develop a normative model for collective decision making in a network of agents performing a two-alternative forced choice task. We assume that rational (Bayesian) agents in this network make private measurements, and observe the decisions of their neighbors until they accumulate sufficient evidence to make an irreversible choice. As each agent communicates its decision to those observing it, the flow of social information is described by a directed graph. The decision-making process in this setting is intuitive, but can be complex. We describe when and how the absence of a decision of a neighboring agent communicates social information, and how an agent must marginalize over all unobserved decisions. We also show how decision thresholds and network connectivity affect group evidence accumulation, and describe the dynamics of decision making in social cliques. Our model provides a bridge between the abstractions used in the economics literature and the evidence accumulator models used widely in neuroscience and psychology.

neuroscience↗

bioRxiv2018

Optimizing sequential decisions in the drift-diffusion model

To make decisions organisms often accumulate information across multiple timescales. However, most experimental and modeling studies of decision-making focus on sequences of independent trials. On the other hand, natural environments are characterized by long temporal correlations, and evidence used to make a present choice is often relevant to future decisions. To understand decision-making under these conditions we analyze how a model ideal observer accumulates evidence to freely make choices across a sequence of correlated trials. We use principles of probabilistic inference to show that an ideal observer incorporates information obtained on one trial as an initial bias on the next. This bias decreases the time, but not the accuracy of the next decision. Furthermore, in finite sequences of trials the rate of reward is maximized when the observer deliberates longer for early decisions, but responds more quickly towards the end of the sequence. Our model also explains experimentally observed patterns in decision times and choices, thus providing a mathematically principled foundation for evidence-accumulation models of sequential decisions.

neuroscience↗

bioRxiv2018

Boundary-Driven Emergent Spatiotemporal Order in Growing Microbial Colonies

We introduce a tractable stochastic spatial Moran model to explain experimentally-observed patterns of rod-shaped bacteria growing in rectangular microfluidic traps. Our model shows that spatial patterns can arise as a result of a tug-of-war between boundary effects and modulations of growth rate due to cell-cell interactions. Cells align parallel to the long side of the trap when boundary effects dominate. However, when the magnitude of cell-cell interactions exceeds a critical value, cells align orthogonally to the traps long side. Our model is analytically tractable, and completely solvable under a mean-field approximation. This allows us to elucidate the mechanisms that govern the formation of population-level patterns. The model can be easily extended to examine various types of interactions that can shape the collective behavior in bacterial populations.

biophysics↗