The EU project METATOOL aims to provide a computational model of synthetic awareness to enhance adaptation and achieve tool invention. This will enable a robot to monitor and self-evaluate its performance, ground and reuse this information for adapting to new circumstances, and finally unlock the possibility of creating new tools. Under the predictive account of awareness, and based on both neuroscientific and archeological evidence, we will develop a novel computational model of metacognition based on predictive processing (metaprediction) and validate its utility in real robots in two use case scenarios: conditional sequential tasks and tool creation. At Humboldt-Universität zu Berlin, we will develop and investigate computational models for tool-use and tool invention based on predictive processing paradigms. The models will be evaluated and implemented in robots interacting with tools in a real physical environment.

Two postdoctoral research positions and three PhD positions are available at the Sussex Centre for Consciousness Science (SCCS). The postdoc positions are funded for 2 years with a possible extension for another 2, starting as soon as suitable candidates are identified. The PhD positions are fully funded for 3.5 years, starting in September 2024, with an earlier start being negotiable. The ERC-funded positions will focus on computational (neuro)phenomenology, exploring different applications of a predictive processing view of conscious perception. One of the ERC PhD positions will specifically focus on perceptual diversity, analyzing and extending the Perception Census project. The SCCS PhD position is flexible regarding project focus and supervisor, inviting proposals with a computational/informatics element.

Two postdoctoral research positions and three PhD positions are now available at the Sussex Centre for Consciousness Science (SCCS). The postdoc positions are funded for 2 years with a possible extension for another 2, starting as soon as suitable candidates are identified. The PhD positions are fully funded for 3.5 years, starting in September 2024 (earlier start may be negotiable). The ERC positions are within the broad remit of computational (neuro)phenomenology, exploring different applications of a predictive processing view of conscious perception. One of the ERC PhD positions will focus on perceptual diversity, analysing and extending the Perception Census project. The SCCS PhD is flexible in terms of project focus and supervisor, inviting PhD proposals that have a computational/informatics element.

For the NWO project ‘DBI2’ we are looking for a PhD candidate to study predictive error responses in the auditory cortex. The main goal is to design an experimental approach to distinguish between two alternative theories of predictive coding and processing. Predictive coding and predictive processing are compelling theories to explain brain function. The idea that the brain continually maintains and updates an internal model of the outside world, and compares the incoming input with the expectations generated by this model, can explain many phenomena including adaptive behaviour and sensory effects such as oddball responses. However, until now there is no consensus on how such predictive coding could be implemented in real neural tissue. Importantly, there are two alternative theories on how error signals in predictive processing could be coded in neural signals: either as (1) top down signals from ‘higher order’ brain areas (hierarchical predictive coding, [2]) or (2) local signals, resulting in membrane potentials reflecting error signals ([3], for a review, see [1]). The goal of the research presented here, under the primary supervision of Dr Zeldenrust, is to design an experimental approach to distinguish between these two theoretical approaches. Measuring error signals in neural tissue is experimentally challenging. Therefore, a direct exchange between theory and experiment is needed, so that hypotheses and specific predictions about which neurons to record from and stimulate and the results expected can be quickly updated for the design of optimal experiments. The student will work in close collaboration with the Englitz lab, so that there is a direct link between modelling, data analysis and experiment. As a PhD candidate you will use data on oddball paradigms [4,5], which provide the ability to directly observe predictions and distinguish them from prediction errors. The data are a combined approach of widefield imaging of the entire auditory cortex with local and layer-specific imaging using 2-photon recordings in the same animals. To directly test the top-down hypothesis, neurons in subareas of the prefrontal cortex will be transfected with an inhibitory opsin (eNpHR3.0) to modulate their top-down influence. You will develop a model of the hierarchical interaction between the auditory cortex and the prefrontal cortex, in which error signals are either coded as top-down (theory 1) or local (theory 2). You will use this model to formulate testable predictions, distinguishing theory 1 from theory 2. These predictions will be tested by both analysing existing data from the Englitz lab and formulating new experimental paradigms that are suitable to distinguish between the local and top-down hypothesis. [1] N’dri, A. W., Gebhardt, W., Teulière, C., Zeldenrust, F., Rao, R. P. N., Triesch, J., & Ororbia, A. (2024). Predictive Coding with Spiking Neural Networks: A Survey (arXiv:2409.05386). arXiv. https://doi.org/10.48550/arXiv.2409.05386 [2] Rao, R. P. N., & Ballard, D. H. (1999). Predictive coding in the visual cortex: A functional interpretation of some extra-classical receptive-field effects. Nature Neuroscience, 2(1), 79–87. [3] Zeldenrust, F., Gutkin, B., & Denéve, S. (2021). Efficient and robust coding in heterogeneous recurrent networks. PLOS Computational Biology, 17(4), e1008673. https://doi.org/10.1371/journal.pcbi.1008673 [4] Nieto-Diego, J. & Malmierca, M. S. Topographic Distribution of Stimulus-Specific Adaptation across Auditory Cortical Fields in the Anesthetized Rat. (2016) PLOS Biol. 14, e1002397 [5] Lao-Rodríguez, A. B. ... Englitz B, (2023) Neuronal responses to omitted tones in the auditory brain: A neuronal correlate for predictive coding. Sci. Adv. 9, eabq8657 * We will give you a temporary employment contract (1.0 FTE) of 1.5 years, after which your performance will be evaluated. If the evaluation is positive, your contract will be extended by 2.5 years (4-year contract). * You will receive a starting salary of €2,901 gross per month based on a 38-hour working week, which will increase to €3,707 in the fourth year (salary scale P). * You will receive an 8% holiday allowance and an 8,3% end-of-year bonus. * We offer Dual Career Coaching. The Dual Career Coaching assists your partner via support, tools, and resources to improve their chances of independently finding employment in the Netherlands. * You will receive extra days off. With full-time employment, you can choose between 30 or 41 days of annual leave instead of the statutory 20.

My lab is looking for a PhD candidate in Modelling Predictive Error Responses: https://www.ru.nl/en/working-at/job-opportunities/phd-position-in-computational-neuroscience-modelling-predictive-error-responses For the NWO project ‘DBI2’ we are looking for a PhD candidate to study predictive error responses in the auditory cortex. The main goal is to design an experimental approach to distinguish between two alternative theories of predictive coding and processing. Predictive coding and predictive processing are compelling theories to explain brain function. The idea that the brain continually maintains and updates an internal model of the outside world, and compares the incoming input with the expectations generated by this model, can explain many phenomena including adaptive behaviour and sensory effects such as oddball responses. However, until now there is no consensus on how such predictive coding could be implemented in real neural tissue. Importantly, there are two alternative theories on how error signals in predictive processing could be coded in neural signals: either as (1) top down signals from ‘higher order’ brain areas (hierarchical predictive coding, [2]) or (2) local signals, resulting in membrane potentials reflecting error signals ([3], for a review, see [1]). The goal of the research presented here, under the primary supervision of Dr Zeldenrust, is to design an experimental approach to distinguish between these two theoretical approaches. Measuring error signals in neural tissue is experimentally challenging. Therefore, a direct exchange between theory and experiment is needed, so that hypotheses and specific predictions about which neurons to record from and stimulate and the results expected can be quickly updated for the design of optimal experiments. The student will work in close collaboration with the Englitz lab, so that there is a direct link between modelling, data analysis and experiment. As a PhD candidate you will use data on oddball paradigms [4,5], which provide the ability to directly observe predictions and distinguish them from prediction errors. The data are a combined approach of widefield imaging of the entire auditory cortex with local and layer-specific imaging using 2-photon recordings in the same animals. To directly test the top-down hypothesis, neurons in subareas of the prefrontal cortex will be transfected with an inhibitory opsin (eNpHR3.0) to modulate their top-down influence. You will develop a model of the hierarchical interaction between the auditory cortex and the prefrontal cortex, in which error signals are either coded as top-down (theory 1) or local (theory 2). You will use this model to formulate testable predictions, distinguishing theory 1 from theory 2. These predictions will be tested by both analysing existing data from the Englitz lab and formulating new experimental paradigms that are suitable to distinguish between the local and top-down hypothesis. [1] N’dri, A. W., Gebhardt, W., Teulière, C., Zeldenrust, F., Rao, R. P. N., Triesch, J., & Ororbia, A. (2024). Predictive Coding with Spiking Neural Networks: A Survey (arXiv:2409.05386). arXiv. https://doi.org/10.48550/arXiv.2409.05386 [2] Rao, R. P. N., & Ballard, D. H. (1999). Predictive coding in the visual cortex: A functional interpretation of some extra-classical receptive-field effects. Nature Neuroscience, 2(1), 79–87. [3] Zeldenrust, F., Gutkin, B., & Denéve, S. (2021). Efficient and robust coding in heterogeneous recurrent networks. PLOS Computational Biology, 17(4), e1008673. https://doi.org/10.1371/journal.pcbi.1008673 [4] Nieto-Diego, J. & Malmierca, M. S. Topographic Distribution of Stimulus-Specific Adaptation across Auditory Cortical Fields in the Anesthetized Rat. (2016) PLOS Biol. 14, e1002397 [5] Lao-Rodríguez, A. B. ... Englitz B, (2023) Neuronal responses to omitted tones in the auditory brain: A neuronal correlate for predictive coding. Sci. Adv. 9, eabq8657 We offer * We will give you a temporary employment contract (1.0 FTE) of 1.5 years, after which your performance will be evaluated. If the evaluation is positive, your contract will be extended by 2.5 years (4-year contract). * You will receive a starting salary of €2,901 gross per month based on a 38-hour working week, which will increase to €3,707 in the fourth year (salary scale P). * You will receive an 8% holiday allowance and an 8,3% end-of-year bonus. * We offer Dual Career Coaching. The Dual Career Coaching assists your partner via support, tools, and resources to improve their chances of independently finding employment in the Netherlands. * You will receive extra days off. With full-time employment, you can choose between 30 or 41 days of annual leave instead of the statutory 20.

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.

Predictive processing is a computational framework that aims to explain how the brain processes sensory information by making predictions about the environment and minimizing prediction errors. It can also be used to explain some of the key symptoms of psychotic disorders such as schizophrenia. In my talk, I will provide an overview of our progress in this endeavor.

There has been increasing interest in the neurocomputational mechanisms underlying psychotic disorders in recent years. One promising approach is based on the theoretical framework of predictive processing, which proposes that inferences regarding the state of the world are made by combining prior beliefs with sensory signals. Delusions and hallucinations are the core symptoms of psychosis and often co-occur. Yet, different predictive-processing alterations have been proposed for these two symptom dimensions, according to which the relative weighting of prior beliefs in perceptual inference is decreased or increased, respectively. I will present recent behavioural, neuroimaging, and computational work that investigated perceptual decision-making under uncertainty and ambiguity to elucidate the changes in predictive processing that may give rise to psychotic experiences. Based on the empirical findings presented, I will provide a more nuanced predictive-processing account that suggests a common mechanism for delusions and hallucinations at low levels of the predictive-processing hierarchy, but still has the potential to reconcile apparently contradictory findings in the literature. This account may help to understand the heterogeneity of psychotic phenomenology and explain changes in symptomatology over time.

Despite the increasing recognition of predictive processing in circuit neuroscience, little is known about how it may be implemented in cortical circuits. We set out to develop and characterise a biological model system with layer 5 pyramidal cells in the centre. We aim to gain access to prediction and internal model generating processes by controlling, understanding or monitoring everything else: the sensory environment, feed-forward and feed-back inputs, integrative properties, their spiking activity and output. I’ll show recent work from the lab establishing such a model system both in terms of biology as well as tool development.

The theory of predictive processing posits that the brain computes expectations to process information predictively. Empirical evidence in support of this theory, however, is scarce and largely limited to sensory areas. Here, we report a precise and adaptive mechanism in the frontal cortex of non-human primates consistent with predictive processing of temporal events. We found that the speed of neural dynamics is precisely adjusted according to the average time of an expected stimulus. This speed adjustment, in turn, enables neurons to encode stimuli in terms of deviations from expectation. This lawful relationship was evident across multiple experiments and held true during learning: when temporal statistics underwent covert changes, neural responses underwent predictable changes that reflected the new mean. Together, these results highlight a precise mathematical relationship between temporal statistics in the environment and neural activity in the frontal cortex that may serve as a mechanism for predictive temporal processing.

Interest in psychedelic compounds is growing due to their remarkable potential for understanding altered neural states and their breakthrough status to treat various psychiatric disorders. However, there are major knowledge gaps regarding how psychedelics affect the brain. The Computational Neuroscience Laboratory at the Turner Institute for Brain and Mental Health, Monash University, uses multimodal neuroimaging to test hypotheses of the brain’s functional reorganisation under psychedelics, informed by the accounts of hierarchical predictive processing, using dynamic causal modelling (DCM). DCM is a generative modelling technique which allows to infer the directed connectivity among brain regions using functional brain imaging measurements. In this webinar, Associate Professor Adeel Razi and PhD candidate Devon Stoliker will showcase a series of previous and new findings of how changes to synaptic mechanisms, under the control of serotonin receptors, across the brain hierarchy influence sensory and associative brain connectivity. Understanding these neural mechanisms of subjective and therapeutic effects of psychedelics is critical for rational development of novel treatments and for the design and success of future clinical trials. Associate Professor Adeel Razi is a NHMRC Investigator Fellow and CIFAR Azrieli Global Scholar at the Turner Institute of Brain and Mental Health, Monash University. He performs cross-disciplinary research combining engineering, physics, and machine-learning. Devon Stoliker is a PhD candidate at the Turner Institute for Brain and Mental Health, Monash University. His interest in consciousness and psychiatry has led him to investigate the neural mechanisms of classic psychedelic effects in the brain.

Sensory stimuli are often predictable consequences of one’s actions, and behavior exerts a correspondingly strong influence over sensory responses in the brain. Closed-loop experiments with the ability to control the sensory outcomes of specific animal behaviors have revealed that neural responses to self-generated sounds are suppressed in the auditory cortex, suggesting a role for prediction in local sensory processing. However, it is unclear whether this phenomenon derives from a precise movement-based prediction or how it affects the neural representation of incoming stimuli. We address these questions by designing a behavioral paradigm where mice learn to expect the predictable acoustic consequences of a simple forelimb movement. Neuronal recordings from auditory cortex revealed suppression of neural responses that was strongest for the expected tone and specific to the time of the sound-associated movement. Predictive suppression in the auditory cortex was layer-specific, preceded by the arrival of movement information, and unaffected by behavioral relevance or reward association. These findings illustrate that expectation, learned through motor-sensory experience, drives layer-specific predictive processing in the mouse auditory cortex.

At the foundation of human conscious experience lie basic embodied experiences of selfhood – experiences of simply ‘being alive’. In this talk, I will make the case that this central feature of human existence is underpinned by predictive regulation of the interior of the body, using the framework of predictive processing, or active inference. I start by showing how conscious experiences of the world around us can be understood in terms of perceptual predictions, drawing on examples from psychophysics and virtual reality. Then, turning the lens inwards, we will see how the experience of being an ‘embodied self’ rests on control-oriented predictive (allostatic) regulation of the body’s physiological condition. This approach implies a deep connection between mind and life, and provides a new way to understand the subjective nature of consciousness as emerging from systems that care intrinsically about their own existence. Contrary to the old doctrine of Descartes, we are conscious because we are beast machines.

According to the theory of predictive processing, expectations modulate neural activity so as to optimize the processing of sensory inputs expected in the current environment. While there is accumulating evidence that the brain indeed operates under this principle, most of the attention has been placed on mechanisms that rely on static coding properties of neurons. The potential contribution of dynamical features, such as those reflected in the evolution of neural population dynamics, has thus far been overlooked. In this talk, I will present evidence for a novel mechanism for predictive processing in the temporal domain which relies on neural population dynamics. I will use recordings from the frontal cortex of macaques trained on a time interval reproduction task and show how neural dynamics can be directly related to animals’ temporal expectations, both in a stationary environment and during learning.