Neuronal Network Model Research Articles

Theoretical framework for learning through structural plasticity

A growing body of research indicates that structural plasticity mechanisms are crucial for learning and memory consolidation. Starting from a simple phenomenological model, we exploit a mean-field approach to develop a theoretical framework of learning through this kind of plasticity, capable of taking into account several features of the connectivity and pattern of activity of biological neural networks, including probability distributions of neuron firing rates, selectivity of the responses of single neurons to multiple stimuli, probabilistic connection rules, and noisy stimuli. More importantly, it describes the effects of stabilization, pruning, and reorganization of synaptic connections. This framework is used to compute the values of some relevant quantities used to characterize the learning and memory capabilities of the neuronal network in training and testing procedures as the number of training patterns and other model parameters vary. The results are then compared with those obtained through simulations with firing-rate-based neuronal network models. Published by the American Physical Society 2024

Reconciliation of weak pairwise spike-train correlations and highly coherent local field potentials across space.

Multi-electrode arrays covering several square millimeters of neural tissue provide simultaneous access to population signals such as extracellular potentials and spiking activity of one hundred or more individual neurons. The interpretation of the recorded data calls for multiscale computational models with corresponding spatial dimensions and signal predictions. Multi-layer spiking neuron network models of local cortical circuits covering about $1\,{\text{mm}^{2}}$ have been developed, integrating experimentally obtained neuron-type-specific connectivity data and reproducing features of observed in-vivo spiking statistics. Local field potentials can be computed from the simulated spiking activity. We here extend a local network and local field potential model to an area of $4\times 4\,{\text{mm}^{2}}$, preserving the neuron density and introducing distance-dependent connection probabilities and conduction delays. We find that the upscaling procedure preserves the overall spiking statistics of the original model and reproduces asynchronous irregular spiking across populations and weak pairwise spike-train correlations in agreement with experimental recordings from sensory cortex. Also compatible with experimental observations, the correlation of local field potential signals is strong and decays over a distance of several hundred micrometers. Enhanced spatial coherence in the low-gamma band around $50\,\text{Hz}$ may explain the recent report of an apparent band-pass filter effect in the spatial reach of the local field potential.

In silico modeling of reservoir-based predictive coding in biological neuronal networks on microelectrode arrays

Reservoir computing and predictive coding together yield a computational model for exploring how neuronal dynamics in the mammalian cortex underpin temporal signal processing. Here, we construct an in-silico model of biological neuronal networks grown on microelectrode arrays and explore their computing capabilities through a sine wave prediction task in a reservoir-based predictive coding framework. Our results show that the time interval between stimulation pulses is a critical determinant of task performance. Additionally, under a fixed feedback latency, pulse amplitude modulation is a favorable encoding scheme for input signals. These findings provide practical guidelines for future implementation of the model in biological experiments.

Hippocampal storage and recall of neocortical "What"-"Where" representations.

A key question for understanding the function of the hippocampus in memory is how information is recalled from the hippocampus to the neocortex. This was investigated in a neuronal network model of the hippocampal system in which "What" and "Where" neuronal firing rate vectors were applied to separate neocortical modules, which then activated entorhinal cortex "What" and "Where" modules, then the dentate gyrus, then CA3, then CA1, then the entorhinal cortex, and then the backprojections to the neocortex. A rate model showed that the whole system could be trained to recall "Where" in the neocortex from "What" applied as a retrieval cue to the neocortex, and could in principle be trained up towards the theoretical capacity determined largely by the number of synapses onto any one neuron divided by the sparseness of the representation. The trained synaptic weights were then imported into an integrate-and-fire simulation of the same architecture, which showed that the time from presenting a retrieval cue to a neocortex module to recall the whole memory in the neocortex is approximately 100 ms. This is sufficiently fast for the backprojection synapses to be trained onto the still active neocortical neurons during storage of the episodic memory, and this is needed for recall to operate correctly to the neocortex. These simulations also showed that the long loop neocortex-hippocampus-neocortex that operates continuously in time may contribute to complete recall in the neocortex; but that this positive feedback long loop makes the whole dynamical system inherently liable to a pathological increase in neuronal activity. Important factors that contributed to stability included increased inhibition in CA3 and CA1 to keep the firing rates low; and temporal adaptation of the neuronal firing and of active synapses, which are proposed to make an important contribution to stabilizing runaway excitation in cortical circuits in the brain.

Investigating the Effects of Transcranial Alternating Current Stimulation on Cortical Oscillations and Network Dynamics.

Transcranial electrical brain stimulation techniques like transcranial direct current (tDCS) and transcranial alternating current (tACS) have emerged as potential tools for treating neurological diseases by modulating cortical excitability. These techniques deliver small electric currents to the brain non-invasively through electrodes on the scalp. tDCS uses constant direct current which weakly alters the membrane voltage of cortical neurons, while tACS utilizes alternating current to target and enhance cortical oscillations, though the underlying mechanisms are not fully understood more specifically. To elucidate how tACS perturbs endogenous network dynamics, we simulated spiking neuron network models. We identified distinct roles of the depolarizing and hyperpolarizing phases in driving network activity towards and away from the strong nonlinearity provided by pyramidal neurons. Exploring resonance effects, we found matching tACS frequency to the network's endogenous resonance frequency creates greater entrainment. Based on this, we developed an algorithm to determine the network's endogenous frequency, phase, and amplitude, then deliver optimized tACS to entrain network oscillations. Together, these computational results provide mechanistic insight into the effects of tACS on network dynamics and could inform future closed-loop tACS systems that dynamically tune stimulation parameters to ongoing brain activity.

Human tripartite cortical network model for temporal assessment of alpha-synuclein aggregation and propagation in Parkinson’s Disease

Previous studies have shown that aggregated alpha-synuclein (α-s) protein, a key pathological marker of Parkinson’s disease (PD), can propagate between cells, thus participating in disease progression. This prion-like propagation has been widely studied using in vivo and in vitro models, including rodent and human cell cultures. In this study, our focus was on temporal assessment of functional changes during α-s aggregation and propagation in human induced pluripotent stem cell (hiPSC)-derived neuronal cultures and in engineered networks. Here, we report an engineered circular tripartite human neuronal network model in a microfluidic chip integrated with microelectrode arrays (MEAs) as a platform to study functional markers during α-s aggregation and propagation. We observed progressive aggregation of α-s in conventional neuronal cultures and in the exposed (proximal) compartments of circular tripartite networks following exposure to preformed α-s fibrils (PFF). Furthermore, aggregated forms propagated to distal compartments of the circular tripartite networks through axonal transport. We observed impacts of α-s aggregation on both the structure and function of neuronal cells, such as in presynaptic proteins, mitochondrial motility, calcium oscillations and neuronal activity. The model enabled an assessment of the early, middle, and late phases of α-s aggregation and its propagation during a 13-day follow-up period. While our temporal analysis suggested a complex interplay of structural and functional changes during the in vitro propagation of α-s aggregates, further investigation is required to elucidate the underlying mechanisms. Taken together, this study demonstrates the technical potential of our introduced model for conducting in-depth analyses for revealing such mechanisms.

The vitals for steady nucleation maps of spontaneous spiking coherence in autonomous two-dimensional neuronal networks

Thin pancake-like neuronal networks cultured on top of a planar microelectrode array have been extensively tried out in neuroengineering, as a substrate for the mobile robot’s control unit, i.e., as a cyborg’s brain. Most of these attempts failed due to intricate self-organizing dynamics in the neuronal systems. In particular, the networks may exhibit an emergent spatial map of steady nucleation sites (“n-sites”) of spontaneous population spikes. Being unpredictable and independent of the surface electrode locations, the n-sites drastically change local ability of the network to generate spikes. Here, using a spiking neuronal network model with generative spatially-embedded connectome, we systematically show in simulations that the number, location, and relative activity of spontaneously formed n-sites (“the vitals”) crucially depend on the samplings of three distributions: (1) the network distribution of neuronal excitability, (2) the distribution of connections between neurons of the network, and (3) the distribution of maximal amplitudes of a single synaptic current pulse. Moreover, blocking the dynamics of a small fraction (about 4%) of non-pacemaker neurons having the highest excitability was enough to completely suppress the occurrence of population spikes and their n-sites. This key result is explained theoretically. Remarkably, the n-sites occur taking into account only short-term synaptic plasticity, i.e., without a Hebbian-type plasticity. As the spiking network model used in this study is strictly deterministic, all simulation results can be accurately reproduced. The model, which has already demonstrated a very high richness-to-complexity ratio, can also be directly extended into the three-dimensional case, e.g., for targeting peculiarities of spiking dynamics in cerebral (or brain) organoids. We recommend the model as an excellent illustrative tool for teaching network-level computational neuroscience, complementing a few benchmark models.

A lightweight data-driven spiking neuronal network model of Drosophila olfactory nervous system with dedicated hardware support.

Data-driven spiking neuronal network (SNN) models enable in-silico analysis of the nervous system at the cellular and synaptic level. Therefore, they are a key tool for elucidating the information processing principles of the brain. While extensive research has focused on developing data-driven SNN models for mammalian brains, their complexity poses challenges in achieving precision. Network topology often relies on statistical inference, and the functions of specific brain regions and supporting neuronal activities remain unclear. Additionally, these models demand huge computing facilities and their simulation speed is considerably slower than real-time. Here, we propose a lightweight data-driven SNN model that strikes a balance between simplicity and reproducibility. The model is built using a qualitative modeling approach that can reproduce key dynamics of neuronal activity. We target the Drosophila olfactory nervous system, extracting its network topology from connectome data. The model was successfully implemented on a small entry-level field-programmable gate array and simulated the activity of a network in real-time. In addition, the model reproduced olfactory associative learning, the primary function of the olfactory system, and characteristic spiking activities of different neuron types. In sum, this paper propose a method for building data-driven SNN models from biological data. Our approach reproduces the function and neuronal activities of the nervous system and is lightweight, acceleratable with dedicated hardware, making it scalable to large-scale networks. Therefore, our approach is expected to play an important role in elucidating the brain's information processing at the cellular and synaptic level through an analysis-by-construction approach. In addition, it may be applicable to edge artificial intelligence systems in the future.

A 3D neuronal network read-out interface with high recording performance using a neuronal cluster patterning on a microelectrode array

In recent years, in vitro three-dimensional (3D) neuronal network models utilizing extracellular matrices have been advancing. To understand the network activity from these models, attempts have been made to measure activity in multiple regions simultaneously using a microelectrode array (MEA). Although there hve been many attempts to measure the activity of 3D networks using 2-dimensional (2D) MEAs, the physical coupling between the 3D network and the microelectrodes was not stable and needed to be improved. In this study, we proposed a neuronal cluster interface that improves the active channel ratio of commercial 2D MEAs, enabling reliable measurement of 3D network activity. To achieve this, neuronal clusters, which consist of a small number of neurons, were patterned on microelectrodes and used as mediators to transmit the signal between the 3D network and the microelectrodes. We confirmed that the patterned neuronal clusters enhanced the active channel ratio and SNR(signal-to-noise-ratio) about 3D network recording and stimulation for a month. Our interface was able to functionally connect with 3D networks and measure the 3D network activity without significant alternation of activity characteristics. Finally, we demonstrated that our interface can be used to analyze the differences in the dynamics of 3D and 2D networks and to construct the 3D clustered network. This method is expected to be useful for studying the functional activity of various 3D neuronal network models, offering broad applications for the use of these models.

High angular resolution diffusion-weighted imaging and higher order tractography of the white matter tracts in the anterior thalamic area: Insights into deep brain stimulation targeting

BackgroundDeep brain stimulation of the anterior nucleus of the thalamus (ANT) is emerging EU/US-approved form of therapy for drug-resistant focal epilepsy. Its mechanism of action is not yet fully understood, and the patient outcomes in epilepsy appear less consistent compared to for instance movement disorders. Furthermore, very little anatomy-based information, such as tractography of relevant fiber systems, exists guiding DBS therapy at present. ObjectiveTo demonstrate ANT-related fiber systems based on histology in vivo employing multi-shell/multi-tissue constrained spherical deconvolution (MSMT-CSD) based deterministic and probabilistic tractography using a sophisticated scanning protocol of 3T high angular resolution diffusion-weighted imaging (HARDI) data in healthy volunteers. MethodHARDI data was acquired from five healthy volunteers in a 3T Siemens MAGNETOM Skyra Magnetic resonance imaging (MRI) machine using multiple b-values (1000, 2000, and 3000), 64 directions, and further preprocessed for tractography. MSMT-CSD-based deterministic and probabilistic tractography was performed from selected fiber systems based on existing literature. ResultsMultiple fiber systems were identified: The anterior thalamic radiation (ANT), the thalamo-cingulate tract, the inferior thalamic peduncle (with remote termination areas in the amygdala, the ventral tegmental area, and the occipital cortex), and the mammillothalamic tract. In addition, we observed three parallel connections to the hippocampus (via the cingulum bundle, the fornix, and the temporo-pulvinar pathway). Interestingly, different seed areas in ANT complex mimicking DBS contact locations resulted in visualization of different fiber systems. DiscussionThe connections of the ANT are complex and different stimulation sites are likely to affect different networks depending on lead locations and the selection of the active contact. ConclusionsIn-depth understanding of the network of anatomical structures related to the ANT is likely to influence therapy outcomes. A hypothetical model of neuronal networks affected by different DBS lead contact locations is proposed. A more profound understanding of neuroanatomic characteristics may guide stereotactic implantation and subsequent programming to optimize outcomes.

A ratiometric SERS strategy for the prediction of cancer cell proportion and guidance of glioma surgical resection

Rapid and accurate identification of tumor boundaries is critical for the cure of glioma, but it is difficult due to the invasive nature of glioma cells. This paper aimed to explore a rapid diagnostic strategy based on a label-free surface-enhanced Raman scattering (SERS) technique for the quantitative detection of glioma cell proportion intraoperatively. With silver nanoparticles as substrate, an in-depth SERS analysis was performed on simulated clinical samples containing normal brain tissue and different concentrations of patient-derived glioma cells. The results revealed two universal characteristic peaks of 655 and 717 cm−1, which strongly correlated with glioma cell proportion regardless of individual differences. Based on the intensity ratio of the two peaks, a ratiometric SERS strategy for the quantification of glioma cells was established by employing an artificial neuron network model and a polynomial regression model. Such a strategy accurately estimated the proportion of glioma cells in simulated clinical samples (R2 = 0.98) and frozen samples (R2 = 0.85). More importantly, it accurately facilitated the delineation of tumor margins in freshly obtained samples. Taken together, this SERS-based method ensured a rapid and more detailed identification of tumor margins during surgical resection, which could be beneficial for intraoperative decision-making and pathological evaluation.

A large-scale neuronal network modelling study: Stimulus size modulates gamma oscillations in the primary visual cortex by long-range connections.

Stimulus size modulation of neuronal firing activity is a fundamental property of the primary visual cortex. Numerous biological experiments have shown that stimulus size modulation is affected by multiple factors at different spatiotemporal scales, but the exact pathways and mechanisms remain incompletely understood. In this paper, we establish a large-scale neuronal network model of primary visual cortex withlayer 2/3 to study how gamma oscillation properties are modulated by stimulus size and especially how long-range connections affect the modulation as realistic neuronal properties and spatial distributions of synaptic connections are considered. It is shown that long-range horizontal synaptic connections are sufficient to produce dimensional modulation of firing rates and gamma oscillations. In particular, with increasing grating stimulus size, the firing rate increases and then decreases, the peak frequency of gamma oscillations decreases and the spectral power increases. These are consistent with biological experimental observations. Furthermore, we explain in detail how the number and spatial distribution of long-range connections affect the size modulation of gamma oscillations by using the analysis of neuronal firing activity and synaptic current fluctuations. Our results provide a mechanism explanation for size modulation of gamma oscillations in the primary visual cortex and reveal the important and unique role played by long-range connections, which contributes to a deeper understanding of the cognitive function of gamma oscillations in visual cortex.

Artificial neural network modelling of natural gas dehydration process

A multi-input–multi-output artificial neuron network (MIMO-ANN) model has been developed for process monitoring and improvement on a natural gas glycol dehydration process. The MIMO-ANN model was based on a steady-state process simulation model constructed in commercial software Aspen HYSYS. A set of training data was generated with the converged simulation model for the training of the MIMO-ANN model in Python. The process input of the model includes lean glycol recirculation rate and purity along with wet gas inlet pressure. On the other hand, the process output considered includes dehydrated gas water and aromatics content, dehydrated gas hydrate formation temperature and water dew point, stripping gas flow rate as well as reboiler duty. The overall mean squared error (MSE) of the MIMO-ANN model was calculated as 1.79. The best-fit line that highly overlaps with a 45° diagonal line is constructed with a correlation coefficient (R2) score of more than 0.999 for all studied process output testing datasets. The mean absolute percentage error (MAPE) of the predicted outputs is generally less than 1%, except for dehydrated gas water dew point (16.63%) and stripping gas flow rate (11.55%), due to error predictions attempted on pseudo-zero values. This successfully displays an exceptional predictive performance of the MIMO-ANN model developed in this work which can be further deployed as an online dashboard for real-time monitoring tool.

Spiral wave dynamics in a neuronal network model

Spiral waves are spatial-temporal patterns that can emerge in different systems as heart tissues, chemical oscillators, ecological networks and the brain. These waves have been identified in the neocortex of turtles, rats, and humans, particularly during sleep-like states. Although their functions in cognitive activities remain until now poorly understood, these patterns are related to cortical activity modulation and contribute to cortical processing. In this work, ,we construct a neuronal network layer based on the spatial distribution of pyramidal neurons. Our main goal is to investigate how local connectivity and coupling strength are associated with the emergence of spiral waves. Therefore, we propose a trustworthy method capable of detecting different wave patterns, based on local and global phase order parameters. As a result, we find that the range of connection radius (R) plays a crucial role in the appearance of spiral waves. For R < 20 µm, only asynchronous activity is observed due to small number of connections. The coupling strength ( gsyn ) greatly influences the pattern transitions for higher R, where spikes and bursts firing patterns can be observed in spiral and non-spiral waves. Finally, we show that for some values of R and gsyn bistable states of wave patterns are obtained.

Identifying Effective Connectivity between Stochastic Neurons with Variable-Length Memory Using a Transfer Entropy Rate Estimator.

Information theory explains how systems encode and transmit information. This article examines the neuronal system, which processes information via neurons that react to stimuli and transmit electrical signals. Specifically, we focus on transfer entropy to measure the flow of information between sequences and explore its use in determining effective neuronal connectivity. We analyze the causal relationships between two discrete time series, X:=Xt:t∈Z and Y:=Yt:t∈Z, which take values in binary alphabets. When the bivariate process (X,Y) is a jointly stationary ergodic variable-length Markov chain with memory no larger than k, we demonstrate that the null hypothesis of the test-no causal influence-requires a zero transfer entropy rate. The plug-in estimator for this function is identified with the test statistic of the log-likelihood ratios. Since under the null hypothesis, this estimator follows an asymptotic chi-squared distribution, it facilitates the calculation of p-values when applied to empirical data. The efficacy of the hypothesis test is illustrated with data simulated from a neuronal network model, characterized by stochastic neurons with variable-length memory. The test results identify biologically relevant information, validating the underlying theory and highlighting the applicability of the method in understanding effective connectivity between neurons.

Learning spiking neuronal networks with artificial neural networks: neural oscillations.

First-principles-based modelings have been extremely successful in providing crucial insights and predictions for complex biological functions and phenomena. However, they can be hard to build and expensive to simulate for complex living systems. On the other hand, modern data-driven methods thrive at modeling many types of high-dimensional and noisy data. Still, the training and interpretation of these data-driven models remain challenging. Here, we combine the two types of methods to model stochastic neuronal network oscillations. Specifically, we develop a class of artificial neural networks to provide faithful surrogates to the high-dimensional, nonlinear oscillatory dynamics produced by a spiking neuronal network model. Furthermore, when the training data set is enlarged within a range of parameter choices, the artificial neural networks become generalizable to these parameters, covering cases in distinctly different dynamical regimes. In all, our work opens a new avenue for modeling complex neuronal network dynamics with artificial neural networks.

Intensity- and frequency-specific effects of transcranial alternating current stimulation are explained by network dynamics

Objective. Transcranial alternating current stimulation (tACS) can be used to non-invasively entrain neural activity and thereby cause changes in local neural oscillatory power. Despite its increased use in cognitive and clinical neuroscience, the fundamental mechanisms of tACS are still not fully understood. Approach. We developed a computational neuronal network model of two-compartment pyramidal neurons (PY) and inhibitory interneurons, which mimic the local cortical circuits. We modeled tACS with electric field strengths that are achievable in human applications. We then simulated intrinsic network activity and measured neural entrainment to investigate how tACS modulates ongoing endogenous oscillations. Main results. The intensity-specific effects of tACS are non-linear. At low intensities (<0.3 mV mm−1), tACS desynchronizes neural firing relative to the endogenous oscillations. At higher intensities (>0.3 mV mm−1), neurons are entrained to the exogenous electric field. We then further explore the stimulation parameter space and find that the entrainment of ongoing cortical oscillations also depends on stimulation frequency by following an Arnold tongue. Moreover, neuronal networks can amplify the tACS-induced entrainment via synaptic coupling and network effects. Our model shows that PY are directly entrained by the exogenous electric field and drive the inhibitory neurons. Significance. The results presented in this study provide a mechanistic framework for understanding the intensity- and frequency-specific effects of oscillating electric fields on neuronal networks. This is crucial for rational parameter selection for tACS in cognitive studies and clinical applications.

Complete synchronization of three-layer Rulkov neuron network coupled by electrical and chemical synapses.

This paper analyzes the complete synchronization of a three-layer Rulkov neuron network model connected by electrical synapses in the same layers and chemical synapses between adjacent layers. The outer coupling matrix of the network is not Laplacian as in linear coupling networks. We develop the master stability function method, in which the invariant manifold of the master stability equations (MSEs) does not correspond to the zero eigenvalues of the connection matrix. After giving the existence conditions of the synchronization manifold about the nonlinear chemical coupling, we investigate the dynamics of the synchronization manifold, which will be identical to that of a synchronous network by fixing the same parameters and initial values. The waveforms show that the transient chaotic windows and the transient approximate periodic windows with increased or decreased periods occur alternatively before asymptotic behaviors. Furthermore, the Lyapunov exponents of the MSEs indicate that the network with a periodic synchronization manifold can achieve complete synchronization, while the network with a chaotic synchronization manifold can not. Finally, we simulate the effects of small perturbations on the asymptotic regimes and the evolution routes for the synchronous periodic and the non-synchronous chaotic network.

Application of Poisson’s ratio structures and decoupling algorithm for 3D force sensing

Flexible tactile electronic devices are extensively used in the fields of robotics, medical detection, and human-computer interaction. Monitoring contact parameters, including force magnitude, direction, and contact location, is particularly vital for skin-like tactile sensing devices. Herein, a 3D force sensor is designed based on porous structure with deliberately designed Poisson’s ratios. A genetic algorithm (GA) optimized back propagation neuronal network (BPNN) model is proposed to support the 3D force decoupling, which can greatly improve the decoupling accuracy. The introduction of the GA-BPNN significantly enhances decoupling accuracy compared to the initial neural network. Micro-porous structures with varied Poisson’s ratios are embedded into the sensing unit to achieve better sensibility. Significantly, this study underscores that the decoupling accuracy of the force components along the Z-axis can be further improved by substituting the solid unit with a designed porous structure unit featuring a specific Poisson’s ratio in an arrayed 3D force sensor.

Neural network approach for detecting spatial changes in catch probability of Engraulis ringens during El Niño‐Southern Oscillation events in northern Chile

AbstractEngraulis ringens (anchovy) is a small pelagic fish of the Family Engraulidae that inhabits the neretic‐coastal zone from northern Peru to south‐central Chile. It is the main resource species of industrial fishing of northern Chile, representing 80% of the annual landings of the purse seine fleet. The history of this fishery (1985–2023) shows a strong decrease in annual industrial landings, especially during extreme El Niño–Southern Oscillation (ENSO) events. The greatest decrease in landings in two decades occurred in 2020, coinciding with a cold La Niña event, which had not been observed in previous La Niña events. We evaluated whether the current decrease in annual landings of E. ringens is associated with oceanographic changes in northern Chile during El Niño or La Niña events. We applied a neuronal network model to identify the spatial and temporal distribution of E. ringens using the catch probability of each boat of the industrial purse seine fleet. The selected oceanographic variables (sea surface temperature, salinity, depth of the mixed layer, sea height and currents) for the 2003–2020 period were obtained from the Copernicus Marine Environment Monitoring Service (CMEMS program) and used as predictor variables of the monthly landings of E. ringens. The neural network model explained 97% of the monthly variability of catch probability of E. ringens by the industrial purse seine fleet. The spatial distribution of catch probability of E. ringens was analyzed independently for El Niño (2015), La Niña (2007, 2013, and 2020), and Neutral (2004) years. We found that catch probability extended further west during La Niña events (except for 2020), occupying a greater area, but were limited to a 10 nautical mile coastal strip during the El Niño event. The spatial distribution of catch probability in the Neutral condition was near the coast, although not as restricted as during the El Niño event. The higher catch probabilities in the La Niña event of 2020 were near the coast, in contrast to the previous La Niña events of 2007 and 2013, due to the restriction of the optimal habitat of E. ringens in response to changes in oceanographic conditions. The application of the results of this study will allow understanding and probably anticipating the consequences that extreme ENSO events could have on the catch probability of the industrial anchovy purse seine fleet in northern Chile.