Biology subjects

Isomura, T.

Publications and source records attributed to Isomura, T..

15 recordsLinked to original sources

bioRxiv2026

Facial Skin Blood Flow Enhances the Human Likeness of Artificial Agents

Recent studies have shown that implementing explicit social cues, such as gaze, facial expressions, and gestures, in artificial agents can improve impressions of these agents. However, humans may also use implicit physiological cues, such as facial coloration and cardiac information, in social perception. The present study examined whether subtle skin color changes reflecting pulse signals enhance the perceived human likeness of artificial agents, and whether this effect depends on agent type, signal type, observers interoceptive sensibility, and their awareness of the skin color changes. Participants observed morphed face stimuli created from artificial agents and human faces and judged whether each stimulus appeared human-like or robot-like. In Experiment 1, skin color changes based on human-derived pulse wave signals enhanced perceived human likeness for a highly human-like agent, but not for a less human-like agent. In Experiment 2, perceived human likeness was enhanced not only by pulse-based skin color changes but also by sinusoidal skin color changes matched to the pulse wave signal in terms of mean amplitude and number of peaks. In addition, participants with higher scores on some subscales of the Multidimensional Assessment of Interoceptive Awareness (MAIA), a subjective measure of interoceptive sensibility, tended to notice the skin color changes. However, neither observers interoceptive sensibility nor their awareness of skin color changes directly explained the enhancement of perceived human likeness induced by skin color changes. These results suggest that subtle skin color changes reflecting pulse wave information may function as implicit dynamic cues signaling embodiment or biologicalness in artificial agents, thereby contributing to perceived human likeness.

physiology↗

bioRxiv2025

Transferring active inference to newly encountered prism-shifted environments

Active inference has been proposed as a unified explanation of perception and action. Previous work has shown that canonical neural networks that minimize shared Helmholtz energy are cast as performing active inference of the external environment. However, how animals flexibly adapt to newly encountered environments remains to be fully addressed. To investigate the brains generalizability and adaptability to new environments, this work develops canonical neural networks that employ multiple policy matrices in parallel. We demonstrate that the proposed model can recapitulate the prism adaptation--a form of visuomotor adaptation--under an arm-reaching task. Using policy matrices pretrained under various target positions, these networks could transfer previous experiences and exhibit faster adaptation to the prism-shifted environment than the naive networks. Furthermore, after-effects were observed following the removal of simulated prism glasses. These results suggest the biological plausibility and utility of the proposed model, providing insights into the adaptive capabilities of the brain.

neuroscience↗

bioRxiv2025

Predicting individual learning trajectories in zebrafish via the free-energy principle

The free-energy principle has been proposed as a unified theory of brain function, and recent evidence from in vitro experiments supports its validity. However, its empirical application to in vivo neuronal dynamics during active decision-making remains limited. This work reverse-engineered generative models--cast as canonical neural networks--from large-scale calcium imaging data of zebrafish performing a visually guided go/no-go task in a virtual-reality environment. Leveraging the formal equivalence between neural network dynamics and variational Bayesian inference, we constructed biologically plausible synthetic agents capable of active inference. These agents recapitulated individual variability in zebrafish neuronal dynamics and behaviour by identifying subject-specific prior and posterior beliefs. Additionally, they enabled quantitative predictions of long-term changes in neural activity, effective synaptic connectivity, and behavioural performance, including task accuracy after training. Our results demonstrate a powerful framework of active inference for modelling in vivo neuronal self-organisation and highlight the predictive validity of the free-energy principle in behaving animals.

neuroscience↗

bioRxiv2025

Behavioural and physiological evidence for the development of cardiac-exteroceptive integration during the first year of life

Continuous integration of environmental exteroceptive and internal interoceptive signals is fundamental for perception and adaptive behaviours, yet its developmental trajectory remains poorly understood. Here, we introduced a modified iBEATs paradigm, based on prior work, to investigate cardiac-exteroceptive integration in 3-8-month-old infants. Using behavioural measures of looking time and physiological measures of pupillometry, we found that the ability to detect cardiac-exteroceptive synchrony emerges during the first year of life. Critically, this was evident only when stimuli coincided with systole, the baroreceptor-active phase of the cardiac cycle, supporting central multimodal integration interpretable within a predictive coding framework. Furthermore, individual differences in behavioural sensitivity were accounted for by the degree of autonomic maturation. These findings provide the first evidence for the developmental emergence and mechanisms of cardiac-exteroceptive integration, and establish the modified iBEATs paradigm as a promising tool for assessing interoceptive development in early life.

neuroscience↗

bioRxiv2025

Cardiac-phase gating of eye movements: evidence from a gaze-contingent "search-light" paradigm

Humans actively sample their environment by alternating rapid saccades for exploration with fixations for detailed perception, as high-resolution vision is restricted to the fovea. Efficient visual sampling depends on the precise coordination of these eye movements. Here, we show that this coordination is gated by the cardiac cycle. Using a gaze-contingent "searchlight" paradigm, we limited visibility to a foveal window--thereby inducing frequent saccades and clearly dissociating the roles of saccades and fixations during natural-scene exploration--while simultaneously recording ECG. We found that saccades occur predominantly during systole, when baroreceptors are active, whereas fixations predominate during diastole, when baroreceptor-mediated afferent signals are quiescent. These findings identify the heartbeat as an internal clock that choreographs exploration and perception, revealing a mechanism through which interoceptive signals organize spontaneous perception-action cycles.

physiology↗

bioRxiv2025

Heartfelt gaze: Cardiac afferent signals and vagal tone affect gaze perception

Perceiving others gaze direction is an essential aspect of social interactions. The cone of direct gaze (CoDG) refers to the range within which an observer perceives gaze as looking directly at them. Previous research has demonstrated that self-relevant exteroceptive cues can widen CoDG. However, the effect of self-relevant interoceptive information on the CoDG remains unknown. In the present study, we used a modified gaze discrimination task to synchronize face stimuli with various gaze directions to specific phases of the cardiac cycle. The results showed that participants with higher heart rate variability (HRV) exhibited a wider CoDG during cardiac systole (when cardiac signals are maximally represented in the brain), but not during cardiac diastole (when these signals are quiescent). Moreover, this effect was independent of individual differences in anxiety levels and autistic traits. We interpret these findings as evidence that individuals with greater cardiac vagal control are more sensitive to cardiac afferent signals during systole, which in turn leads to a stronger self-directed perception of others gaze under transient and ambiguous gaze perception conditions. Our findings highlight the self-referential role of cardiac interoceptive signals in gaze perception, expanding our knowledge of interoceptive influences on social judgment.

physiology↗

bioRxiv2024

Synaptic pruning facilitates online Bayesian model selection

Identifying appropriate structures for generative or world models is essential for both biological organisms and machines. This work shows that synaptic pruning facilitates efficient statistical structure learning. We extend previously established canonical neural networks to derive a synaptic pruning scheme that is formally equivalent to an online Bayesian model selection. The proposed scheme, termed Bayesian synaptic model pruning (BSyMP), utilizes connectivity parameters to switch between the presence (ON) and absence (OFF) of synaptic connections. Mathematical analyses reveal that these parameters converge to zero for uninformative connections, thus providing reliable and efficient model reduction. This enables the identification of a plausible structure for the environmental model, particularly when the environment is characterized by sparse likelihood and transition matrices. Through causal inference and rule learning simulations, we demonstrate that BSyMP achieves model reduction more efficiently than the conventional Bayesian model reduction scheme. These findings indicate that synaptic pruning could be a neuronal substrate underlying structure learning and generalizability in the brain.

neuroscience↗

bioRxiv2022

Experimental validation of the free-energy principle with in vitro neural networks

Empirical applications of the free-energy principle are not straightforward because they entail a commitment to a particular process theory, especially at the cellular and synaptic levels. Using a recently established reverse engineering technique, we confirm the quantitative predictions of the free-energy principle using in vitro networks of rat cortical neurons that perform causal inference. Upon receiving electrical stimuli--generated by mixing two hidden sources--neurons self-organised to selectively encode the two sources. Pharmacological up- and downregulation of network excitability disrupted the ensuing inference, consistent with changes in prior beliefs about hidden sources. As predicted, changes in effective synaptic connectivity reduced variational free energy, where the connection strengths encoded parameters of the generative model. In short, we show that variational free energy minimisation can quantitatively predict the self-organisation of neuronal networks, in terms of their responses and plasticity. These results demonstrate the applicability of the free-energy principle to in vitro neural networks and establish its predictive validity in this setting.

neuroscience↗

bioRxiv2020

Canonical neural networks perform active inference

This work considers a class of canonical neural networks comprising rate coding models, wherein neural activity and plasticity minimise a common cost function--and plasticity is modulated with a certain delay. We show that such neural networks implicitly perform active inference and learning to minimise the risk associated with future outcomes. Mathematical analyses demonstrate that this biological optimisation can be cast as maximisation of model evidence, or equivalently minimisation of variational free energy, under the well-known form of a partially observed Markov decision process model. This equivalence indicates that the delayed modulation of Hebbian plasticity--accompanied with adaptation of firing thresholds--is a sufficient neuronal substrate to attain Bayes optimal control. We corroborated this proposition using numerical analyses of maze tasks. This theory offers a universal characterisation of canonical neural networks in terms of Bayesian belief updating and provides insight into the neuronal mechanisms underlying planning and adaptive behavioural control.

neuroscience↗

bioRxiv2019

Reverse engineering neural networks to characterise their cost functions

This work considers a class of biologically plausible cost functions for neural networks, where the same cost function is minimised by both neural activity and plasticity. We show that such cost functions can be cast as a variational bound on model evidence under an implicit generative model. Using generative models based on Markov decision processes (MDP), we show, analytically, that neural activity and plasticity perform Bayesian inference and learning, respectively, by maximising model evidence. Using mathematical and numerical analyses, we then confirm that biologically plausible cost functions--used in neural networks--correspond to variational free energy under some prior beliefs about the prevalence of latent states that generate inputs. These prior beliefs are determined by particular constants (i.e., thresholds) that define the cost function. This means that the Bayes optimal encoding of latent or hidden states is achieved when, and only when, the networks implicit priors match the process that generates the inputs. Our results suggest that when a neural network minimises its cost function, it is implicitly minimising variational free energy under optimal or sub-optimal prior beliefs. This insight is potentially important because it suggests that any free parameter of a neural networks cost function can itself be optimised--by minimisation with respect to variational free energy.

neuroscience↗

bioRxiv2019

Future state prediction errors guide active avoidance behavior by adult zebrafish.

Human predicts future. To ask if fish also has this capacity, we established the virtual reality training system with live imaging of the telencephalic neurons of adult zebrafish in the active avoidance and found that, at the onset of the trial, learned fish conceives two future conditions as the favorable status on its way to the safe goal, i.e. one with the backwardly moving landscape and the other with the color of the safe goal. And the two different neural ensembles monitor the discrepancy between these predictions and the perceived real external status. Once fish reaches the goal, another ensemble is set to work to monitor whether fish keeps staying in the safe goal. The manipulation to artificially enhance these prediction errors elevated the activities of these ensembles and induced fish to behave to correct errors, revealing that fish sets behavioral strategy to actively realize these predictions.

neuroscience↗

bioRxiv2018

Multi-context blind source separation by error-gated Hebbian rule

Animals need to adjust their inferences according to the context they are in. This is required for the multi-context blind source separation (BSS) task, where an agent needs to infer hidden sources from their context-dependent mixtures. The agent is expected to invert this mixing process for all contexts. Here, we show that a neural network that implements the error-gated Hebbian rule (EGHR) with sufficiently redundant sensory inputs can successfully learn this task. After training, the network can perform the multi-context BSS without further updating synapses, by retaining memories of all experienced contexts. Finally, if there is a common feature shared across contexts, the EGHR can extract it and generalize the task to even inexperienced contexts. This demonstrates an attractive use of the EGHR for dimensionality reduction by extracting common sources across contexts. The results highlight the utility of the EGHR as a model for perceptual adaptation in animals.

neuroscience↗

bioRxiv2018

In vitro neural networks minimise variational free energy

In this work, we address the neuronal encoding problem from a Bayesian perspective. Specifically, we ask whether neuronal responses in an in vitro neuronal network are consistent with ideal Bayesian observer responses under the free energy principle. In brief, we stimulated an in vitro cortical cell culture with stimulus trains that had a known statistical structure. We then asked whether recorded neuronal responses were consistent with variational message passing (i.e., belief propagation) based upon free energy minimisation (i.e., evidence maximisation). Effectively, this required us to solve two problems: first, we had to formulate the Bayes-optimal encoding of the causes or sources of sensory stimulation, and then show that these idealised responses could account for observed electrophysiological responses. We describe a simulation of an optimal neural network (i.e., the ideal Bayesian neural code) and then consider the mapping from idealised in silico responses to recorded in vitro responses. Our objective was to find evidence for functional specialisation and segregation in the in vitro neural network that reproduced in silico learning via free energy minimisation. Finally, we combined the in vitro and in silico results to characterise learning in terms of trajectories in a variational information plane of accuracy and complexity.

neuroscience↗

bioRxiv2018

Social intelligence model with multiple internal models

To exhibit social intelligence, animals have to recognize who they are communicating with. One way to make this inference is to select among multiple internal generative models of each conspecific. This induces an interesting problem: when receiving sensory input generated by a particular conspecific, how does an animal know which internal model to update? We consider a theoretical and neurobiologically plausible solution that enables inference and learning under multiple generative models by integrating active inference and (online) Bayesian model selection. This scheme fits sensory inputs under each generative model. Model parameters are then updated in proportion to the probability it could have generated the current input (i.e., model evidence). We show that a synthetic bird who employs the proposed scheme successfully learns and distinguishes (real zebra finch) birdsongs generated by several different birds. These results highlight the utility of having multiple internal models to make inferences in complicated social environments.

neuroscience↗

bioRxiv2017

Common features in plastic changes rather than constructed structures in recurrent neural network prefrontal cortex models

We have flexible control over our cognition depending on the context or surrounding environments. The prefrontal cortex (PFC) controls this cognitive flexibility; however, the detailed underlying mechanisms remain unclear. Recent developments in machine learning techniques have allowed simple recurrent neural network PFC models to perform human- or animal-like behavioral tasks. These systems allow us to acquire parameters, which we could not in biological experiments, for performing the tasks. We compared four models, in which a flexible cognition task, called context-dependent integration task, was performed; subsequently, we searched for common features. In all the models, we observed that high plastic synapses were concentrated in the small neuronal population and the more concentrated neuronal units contributed further to the performance. However, there were no common properties in the constructed structures. These results suggest that plastic changes can be more general and important to accomplish cognitive tasks than features of the constructed structures.

neuroscience↗