Predictive processing offers a unifying view of neural computation, proposing that brains continuously anticipate sensory input and update internal models based on prediction errors. In this talk, I will present converging evidence for the computational mechanisms underlying this framework across human neuroscience and deep neural networks. I will begin with recent work showing that large-scale distributed prediction-error encoding in the human brain directly predicts how sensory representations reorganize through predictive learning. I will then turn to PredNet, a popular predictive coding inspired deep network that has been widely used to model real-world biological vision systems. Using dynamic stimuli generated with our Spatiotemporal Style Transfer algorithm, we demonstrate that PredNet relies primarily on low-level spatiotemporal structure and remains insensitive to high-level content, revealing limits in its generalization capacity. Finally, I will discuss new recurrent vision models that integrate top-down feedback connections with intrinsic neural variability, uncovering a dual mechanism for robust sensory coding in which neural variability decorrelates unit responses, while top-down feedback stabilizes network dynamics. Together, these results outline how prediction error signaling and top-down feedback pathways shape adaptive sensory processing in biological and artificial systems.

A postdoctoral position within the Computational Systems Neuroscience Group (CSNG) at Charles University, Prague, focusing on computational neuroscience and neuro-prosthetic system design. The group explores the intricacies of the visual system, sensory coding, and neuro-prosthetic solutions using computational approaches such as large-scale biologically detailed spiking network models, firing-rate models of development, and modern machine learning techniques. The team is dedicated to understanding visual perception and its restoration via neuro-prosthetic devices. Multiple project topics are available and can be adjusted to the interest and background of the applicant, including modeling electrical stimulation in a spiking model of the primary visual cortex, deep-neural networks in visual neuroscience, study of cortical dynamics in the visual cortex, and biologically detailed spiking large-scale models of early visual cortical pathway from Retina to V4.

Brain-organ communications play a crucial role in maintaining the body's physiological and psychological homeostasis, and are controlled by complex neural and hormonal systems, including the internal mechanosensory organs. However, the progress has been slow due to technical hurdles: the sensory neurons are deeply buried inside the body and are not readily accessible for direct observation, the projection patterns from different organs or body parts are complex rather than converging into dedicate brain regions, the coding principle cannot be directly adapted from that learned from conventional sensory pathways. Our lab apply the pipeline of "biophysics of receptors-cell biology of neurons-functionality of neural circuits-animal behaviors" to explore the molecular and neural mechanisms of self-perception. In the lab, we mainly focus on the following three questions: 1, The molecular and cellular basis for proprioception and interoception. 2, The circuit mechanisms of sensory coding and integration of internal and external information. 3, The function of interoception in regulating behavior homeostasis.

Statistics of natural scenes are not uniform - their structure varies dramatically from ground to sky. It remains unknown whether these non-uniformities are reflected in the large-scale organization of the early visual system and what benefits such adaptations would confer. By deploying an efficient coding argument, we predict that changes in the structure of receptive fields across visual space increase the efficiency of sensory coding. To test this experimentally, developed a simple, novel imaging system that is indispensable for studies at this scale. In agreement with our predictions, we could show that receptive fields of retinal ganglion cells change their shape along the dorsoventral axis, with a marked surround asymmetry at the visual horizon. Our work demonstrates that, according to principles of efficient coding, the panoramic structure of natural scenes is exploited by the retina across space and cell-types.

In this talk I will present an account of how an agent designed or evolved to be intelligent may come to enjoy subjective experiences. First, the agent is stipulated to be capable of (meta)representing subjective ‘qualitative’ sensory information, in the sense that it can easily assess how exactly similar a sensory signal is to all other possible sensory signals. This information is subjective in the sense that it concerns how the different stimuli can be distinguished by the agent itself, rather than how physically similar they are. For this to happen, sensory coding needs to satisfy sparsity and smoothness constraints, which are known to facilitate metacognition and generalization. Second, this qualitative information can under some specific circumstances acquire an ‘assertoric force’. This happens when a certain self-monitoring mechanism decides that the qualitative information reliably tracks the current state of the world, and informs a general symbolic reasoning system of this fact. I will argue that the having of subjective conscious experiences amounts to nothing more than having qualitative sensory information acquiring an assertoric status within one’s belief system. When this happens, the perceptual content presents itself as reflecting the state of the world right now, in ways that seem undeniably rational to the agent. At the same time, without effort, the agent also knows what the perceptual content is like, in terms of how subjectively similar it is to all other possible precepts. I will discuss the computational benefits of this architecture, for which consciousness might have arisen as a byproduct.

Neural responses are variable: even under identical experimental conditions, single neuron and population responses typically differ from trial to trial and across time. Recent work has demonstrated that this variability has predictable structure, can be modulated by sensory input and behaviour, and bears critical signatures of the underlying network dynamics and computations. However, current methods for characterising neural variability are primarily geared towards sensory coding in the laboratory: they require trials with repeatable experimental stimuli and behavioural covariates. In addition, they make strong assumptions about the parametric form of variability, rely on assumption-free but data-inefficient histogram-based approaches, or are altogether ill-suited for capturing variability modulation by covariates. Here we present a universal probabilistic spike count model that eliminates these shortcomings. Our method uses scalable Bayesian machine learning techniques to model arbitrary spike count distributions (SCDs) with flexible dependence on observed as well as latent covariates. Without requiring repeatable trials, it can flexibly capture covariate-dependent joint SCDs, and provide interpretable latent causes underlying the statistical dependencies between neurons. We apply the model to recordings from a canonical non-sensory neural population: head direction cells in the mouse. We find that variability in these cells defies a simple parametric relationship with mean spike count as assumed in standard models, its modulation by external covariates can be comparably strong to that of the mean firing rate, and slow low-dimensional latent factors explain away neural correlations. Our approach paves the way to understanding the mechanisms and computations underlying neural variability under naturalistic conditions, beyond the realm of sensory coding with repeatable stimuli.

A globally invasive form of the mosquito Aedes aegypti has evolved to specialize in biting humans, making it an efficient vector of dengue, yellow fever, Zika, and chikungunya. Host-seeking females identify humans primarily by smell, strongly preferring human odour over the odor of non-human animals. Exactly how they discriminate, however, is unclear. Human and animal odors are complex blends that share most of the same chemical components, presenting an interesting challenge in sensory coding. I will talk about recent work from the lab showing that (1) human and animal blends can be distinguished by the relative concentration of a diverse array of compounds and that (2) these complex chemical differences translate into a neural code for human odor that involves as few as two to three olfactory glomeruli in the mosquito brain. Our work demonstrates how organisms may evolve to discriminate complex odor stimuli of special biological relevance with a surprisingly simple combinatorial code and reveals novel targets for the design of next-generation mosquito control strategies.