arXiv subjects

Akke Mats Houben

Publications and source records attributed to Akke Mats Houben.

8 recordsLinked to original sources

arXiv2026

Analysis of Evolving Cortical Neuronal Networks Using Visual Informatics

Understanding how neuronal population activity changes during development and after stimulation is essential for studying neuronal network dynamics. This work examines how visual informatics can summarize high-dimensional spiking activity while retaining information that is biologically interpretable. We develop a framework based on Minimum-Distortion Embedding (MDE), and compare it with Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE). In addition to evaluating the embeddings by visual separation, we quantify whether they preserve the cosine-shape radius within each condition and the pairwise distances between condition centroids. Our \emph{in silico} experiments show that MDE with a cosine metric captures the trajectory of simulated network maturation and preserves the contraction of the activity cloud as connectivity increases. Complementary \emph{in vitro} experiments on human cortical cultures show a coherent developmental trajectory from Day In VITRO 23 (DIV23) to DIV64. We also study weak and strong stimulation in simulation, and long-term potentiation stimulation in primary cortical cultures. In the stimulation experiments, MDE separates activity phases more clearly than PCA and preserves transient changes in within-phase variability that are missed by PCA. These results show that metric selection is central to dimensionality reduction of neuronal data. In particular, cosine distance between population activity vectors provides embeddings that better reflect changes in population activity patterns than Euclidean distance. The proposed framework provides a quantitative way to visualize network development and stimulation-induced changes in neuronal activity.

q-bio.NC↗

arXiv2025

Amplitude equations of associative memory patterns in spatially distributed systems

Evolution equations are derived for the amplitudes of associative memories: heterogeneous states stored in the connectivity of distributed systems with non-local interactions. The resulting coupled amplitude equations describe the spatio-temporal dynamics of memory recall. They capture pattern completion and selection, and show that short-range connections can sustain spatio-temporal memory pattern dynamics in the form of propagating patterning fronts. The derived amplitude equations are of the same form as those describing classical pattern-forming instabilities, indicating a universality of the dynamics of memory recall and pattern formation in non-equilibrium systems.

q-bio.NC↗

arXiv2025

Role of connectivity anisotropies in the dynamics of cultured neuronal networks

Laboratory-grown, engineered living neuronal networks in vitro have emerged in the last years as an experimental technique to understand the collective behavior of neuronal assemblies in relation to their underlying connectivity. An inherent obstacle in the design of such engineered systems is the difficulty to predict the dynamic repertoire of the emerging network and its dependence on experimental variables. To fill this gap, and inspired on recent experimental studies, here we present a numerical model that aims at, first, replicating the anisotropies in connectivity imprinted through engineering, to next realize the collective behavior of the neuronal network and make predictions. We use experimentally measured, biologically-realistic data combined with the Izhikevich model to quantify the dynamics of the neuronal network in relation to tunable structural and dynamical parameters. These parameters include the synaptic noise, strength of the imprinted anisotropies, and average axon lengths. The latter are involved in the simulation of the development of neurons in vitro. We show that the model captures the behavior of engineered neuronal cultures, in which a rich repertoire of activity patterns emerge but whose details are strongly dependent on connectivity details and noise. Results also show that the presence of connectivity anisotropies substantially improves the capacity of reconstructing structural connectivity from activity data, an aspect that is important in the quest for understanding the structure-to-function relationship in neuronal networks. Our work provides the in silico basis to assist experimentalists in the design of laboratory in vitro networks and anticipate their outcome, an aspect that is particularly important in the effort to conceive reliable brain-on-a-chip circuits and explore key aspects such as input-output relationships or information coding.

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arXiv2024

Integrated Information Decomposition Unveils Major Structural Traits of $In$ $Silico$ and $In$ $Vitro$ Neuronal Networks

The properties of complex networked systems arise from the interplay between the dynamics of their elements and the underlying topology. Thus, to understand their behaviour, it is crucial to convene as much information as possible about their topological organization. However, in a large systems such as neuronal networks, the reconstruction of such topology is usually carried out from the information encoded in the dynamics on the network, such as spike train time series, and by measuring the Transfer Entropy between system elements. The topological information recovered by these methods does not necessarily capture the connectivity layout, but rather the causal flow of information between elements. New theoretical frameworks, such as Integrated Information Decomposition ($Φ$-ID), allow to explore the modes in which information can flow between parts of a system, opening a rich landscape of interactions between network topology, dynamics and information. Here, we apply $Φ$-ID on $in$ $silico$ and $in$ $vitro$ data to decompose the usual Transfer Entropy measure into different modes of information transfer, namely synergistic, redundant or unique. We demonstrate that the unique information transfer is the most relevant measure to uncover structural topological details from network activity data, while redundant information only introduces residual information for this application. Although the retrieved network connectivity is still functional, it captures more details of the underlying structural topology by avoiding to take into account emergent high-order interactions and information redundancy between elements, which are important for the functional behavior, but mask the detection of direct simple interactions between elements constituted by the structural network topology.

q-bio.NC↗

arXiv2024

Inferring Structure of Cortical Neuronal Networks from Firing Data: A Statistical Physics Approach

Understanding the relation between cortical neuronal network structure and neuronal activity is a fundamental unresolved question in neuroscience, with implications to our understanding of the mechanism by which neuronal networks evolve over time, spontaneously or under stimulation. It requires a method for inferring the structure and composition of a network from neuronal activities. Tracking the evolution of networks and their changing functionality will provide invaluable insight into the occurrence of plasticity and the underlying learning process. We devise a probabilistic method for inferring the effective network structure by integrating techniques from Bayesian statistics, statistical physics and principled machine learning. The method and resulting algorithm allow one to infer the effective network structure, identify the excitatory and inhibitory nature of its constituents, and predict neuronal spiking activities by employing the inferred structure. We validate the method and algorithm's performance using synthetic data, spontaneous activity of an in silico emulator and realistic in vitro neuronal networks of modular and homogeneous connectivity, demonstrating excellent structure inference and activity prediction. We also show that our method outperforms commonly used existing methods for inferring neuronal network structure. Inferring the evolving effective structure of neuronal networks will provide new insight into the learning process due to stimulation in general and will facilitate the development of neuron-based circuits with computing capabilities.

physics.soc-ph↗

arXiv2021

Matched transient and steady-state approximation of first-passage-time distributions of coloured noise driven leaky neurons

The first-passage-time distribution of a leaky integrate-and-fire neuron driven by a characteristically coloured noise is approximated by matching a transient and a steady-state solution of the membrane voltage distribution. These approximations follow from a simple manipulation, made possible by the specific `eigen' colouring of the noise, which allows to express the membrane potential as a Gaussian diffusion process on top of a deterministic exponential movement. Following, the presented method is extended to the case of an arbitrarily coloured noise driving by factoring out the `eigen' noise and replacing the residue with an equivalent Gaussian process. It is shown that the obtained expressions agree well with numerical simulations for different values of the neuron parameters and noise colouring.

q-bio.NC↗

arXiv2021

Signal anticipation and delay in excitable media: group delay of the FitzHugh-Nagumo model

An expression for the group delay of the FitzHugh-Nagumo model in response to low amplitude input is obtained by linearisation of the cubic term of the voltage equation around its stable fixed-point. It is found that a negative group delay exists for low frequencies, indicating that the evolution of slowly fluctuating signals are anticipated by the voltage dynamics. The effects of the group delay for different types of signals are shown numerically for the non-linearised FitzHugh-Nagumo model, and some observations on the signal aspects that are anticipated are stated.

q-bio.NC↗

arXiv2020

Frequency selectivity of neural circuits with heterogeneous transmission delays

Neurons are connected to other neurons by axons and dendrites that conduct signals with finite velocities, resulting in delays between the firing of a neuron and the arrival of the resultant impulse at other neurons. Since delays greatly complicate the analytical treatment and interpretation of models, they are usually neglected or taken to be uniform, leading to a lack in the comprehension of the effects of delays in neural systems. This paper shows that heterogeneous transmission delays make small groups of neurons respond selectively to inputs with differing frequency spectra. By studying a single integrate-and-fire neuron receiving correlated time-shifted inputs, it is shown how the frequency response is linked to both the strengths and delay times of the afferent connections. The results show that incorporating delays alters the functioning of neural networks, and changes the effect that neural connections and synaptic strengths have.

q-bio.NC↗