Neural Computation

Recurrent neural networks (RNNs) have proven to be highly successful in emulating human-like cognitive functions such as working memory. In recent years, RNNs are evolving to incorporate more biophysical realism to produce more plausible predictions on how cognitive tasks are solved in real neural circuits. However, there are major challenges in constructing and training networks with the complex and nonlinear properties of real neurons. A major component of the success of RNNs is that they share the same mathematical base as deep neural networks, permitting highly efficient optimization of model parameters using standard deep learning techniques. To do so, they use abstract representations of neurons which fail to capture the impact of cell-level biophysical and morphologic properties that may benefit network-level function. Expanding task-trained RNNs with biophysical properties such as dendrites and active ionic currents poses substantial challenges, as it moves these models away from the validated training regimes known to be highly effective for RNNs. To address this gap, we developed a biophysically detailed reservoir computing (BRC) framework with the goal of extracting mechanistic insights from biophysical neural models, and propose that these insights can be used to guide model choices that will work for specific categories of cognitive tasks. The BRC network was constructed with synaptically coupled excitatory and inhibitory cells, in which the excitatory cells include multicompartment biophysically active dendrites; motivated by empirical studies suggesting dendrites have desirable computational benefits (e.g. pattern classification and coincidence detection). We trained the BRC network to do a simplified working memory task where it had to maintain the representation of an extrinsic "cue" input. We studied the impact of extrinsic input time constants (fast AMPA vs slow NMDA) and location (dendrite vs soma) on the ability of a network to solve the task. Our results revealed that cue inputs through NMDA receptors are particularly efficient for solving the working memory task. Further, the properties of NMDA receptors are uniquely suited for cue inputs delivered at the dendrite, as networks trained with dendritic AMPA cue inputs failed to solve the task. Detailed examination of the cell and network dynamics that solve the task reveals distinct local network configurations and computing principles for the different types of extrinsic input. Overall, much like the body of mechanistic insights that have underpinned the success of training RNNs, this study lays the groundwork for applying the BRC framework to train biophysically detailed neural models to solve complex human-like cognitive tasks.

Neural decoding is a widely-used machine learning technique for investigating how behavior, perception and cognition are represented in neural activity. However without careful application data leakage can occur, where information from the test set contaminates the training set, leading to biased estimates of decoding performance and potentially invalidating biological conclusions. Here we use simulated and biological datasets to demonstrate how both supervised and unsupervised data preprocessing, including dimensionality reduction, can introduce leakage in neural decoding studies. We reveal that in some cases leakage can paradoxically decrease decoding performance relative to unbiased estimates, and we provide theoretical analyses explaining how this occurs. We demonstrate that, for autocorrelated neural time series, standard k-fold cross-validation can dramatically overstate performance. Finally we provide detailed recommendations for avoiding data leakage in neural decoding.

The predictive coding framework offers a compelling model for temporal signal processing in the cortex. Recent studies explored its implementation in spiking architectures using Hebbian plasticity rules or offline learning; however, a biologically inspired model that enables gradient-based minimization of prediction errors remains an open challenge. In this work, we demonstrate that the predictive coding objective can be optimized using the online and local nature of the e-prop learning algorithm in recurrent spiking neural networks, creating the Predictive E-prop model. We demonstrate that the model is capable of learning complex time-series signals purely from self-supervised learning, using only its own prediction error as input, maintaining self-sustaining activity and reproducing the targets underlying dynamics even in the absence of external stimuli. Furthermore, Predictive E-prop shows robust signal reconstruction abilities, effectively filtering noise and successfully interpolating sparse data. A comparative study against a backpropagation-based approach reveals that the two achieve comparable performance after training, confirming the viability of our model for timeseries generation tasks. These findings are particularly relevant for future developments in neuromorphic hardware, offering a purely self-supervised, gradient-based model that could provide significant advantages in power efficiency and computational ability.

AO_SCPLOWBSTRACTC_SCPLOWOne of the most compelling ideas for bridging neuroscience and artificial neural networks is the establishment of a framework based on three main components: network architecture, optimization mechanism, and loss (or objective) function to be minimized. While the first two components have been extensively explored, the definition of a loss or objective function in neuroscience has been addressed less thoroughly, often from perspectives such as predictive coding. In this work, we propose an elementary loss function grounded in the comparison of neuronal responses to two signals: an external one, used for learning, and an internal one, reflecting the acquired knowledge. The loss function is thus simply the basic difference between the two, which, in terms of logical signals, corresponds to a well-known non-linearly separable function: the XOR function. We illustrate with a computational example how a binarized image recognition algorithm can be straightforwardly implemented in an autoencoder, and we show how a neuronal motif organized around an inhibitory neuron could implement such XOR operation and provide a feedback signal that makes optimization possible.

Collective neural phenomena, such as oscillations and avalanches, are high-level neural signatures observed in aggregated spiking neuronal activity which have been consistently associated with a range of cognitive functions. However, it is often hard to elucidate whether such phenomena are mere epiphenomena or have causal or informational relevance. In this work, we investigate this question by leveraging recent information-theoretic tools to identify emergent phenomena between relevant scales of neural activity. For this, we propose a computational framework combining information-theoretic and network science principles, which we use to investigate emergence in both in-vivo datasets and in-silico simulations. Our approach enables characterisation of emergence phenomena, identifies the relevant scales at which they take place, and elucidates the network-level mechanisms that underpin them. Results show that in-vivo neuronal oscillations show substantial emergent behaviour for smaller prediction delays, while avalanches maintain their emergent nature for larger timescales. These results are supported by in-silico simulations, which show that the emergent signature of oscillations is facilitated by the network structure and interneuronal time-delays. Overall, these results highlight the role of network-level interactions between groups of neuronal assemblies as the key driver of emergent population activity in the brain.

Many existing models of computation in recurrent neural networks assume dense, unconstrained initial connectivity, where any pair of neurons may be coupled to generate the rich dynamics needed for learning complex temporal patterns. Inspired by invertebrate circuits that often exhibit ring-like connectivity, we show that computation can occur in ultra-sparse spiking and rate reservoirs that are initially coupled as simple unidirectional rings. In contrast to standard recurrent networks, the total number of network parameters in these ring networks scales only linearly with network size, while still producing rich feature sets. We demonstrate that such networks can successfully reproduce a range of dynamical systems tasks, including oscillations, multi-stable switches, and low-dimensional chaotic attractors. Our findings show that structured spatio-temporal dynamics naturally arising from large ring topologies, often observed in invertebrate circuits, are a sufficient mechanism for learning different types of attractors.

Generalization is a fundamental criterion for evaluating learning effectiveness, a domain where biological intelligence excels yet artificial intelligence continues to face challenges. In biological learning and memory, the well-documented spacing effect shows that appropriately spaced intervals between learning trials can significantly improve behavioral performance. While multiple theories have been proposed to explain its underlying mechanisms, one compelling hypothesis is that spaced training promotes integration of input and innate variations, thereby enhancing generalization to novel but related scenarios. Here we examine this hypothesis by introducing a bio-inspired spacing effect into artificial neural networks, integrating input and innate variations across spaced intervals at the neuronal, synaptic, and network levels. These spaced ensemble strategies yield significant performance gains across various benchmark datasets and network architectures. Biological experiments on Drosophila further validate the complementary effect of appropriate variations and spaced intervals in improving generalization, which together reveal a convergent computational principle shared by biological learning and machine learning.

Latent Factor Analysis via Dynamical Systems (LFADS) is a powerful variational autoencoder for inferring neural population dynamics from spike train data. However, LFADS suffers from pos-terior collapse, where the learned posterior collapses to the prior, eliminating meaningful latent representations. Current solutions require computationally expensive Population-Based Training (PBT) to dynamically tune regularization hyperparameters. Here, we demonstrate that Large Lan-guage Model (LLM)-based program evolution can discover regularization schedules that prevent posterior collapse without PBT. Using FunSearch, an evolutionary algorithm that uses LLMs to generate and refine Python functions, we evolved adaptive regularization schedules that respond to training dynamics. Our best evolved schedule prevents posterior collapse across all tested conditions, maintaining KL divergence 6.5x higher than baseline schedules at 50 epochs (n = 10 seeds each, p < 0.001) and stable above 0.09 through 500 epochs across three Neural Latents Benchmark datasets, while preserving reconstruction quality. This work represents the first application of LLM-based program synthesis to variational autoencoder hyperparameter scheduling, offering a computationally efficient alternative to population-based optimization.

Human behavior differs substantially across individuals. While artificial neural networks (ANNs) are regarded as promising models of human perception, they are often assumed to lack such individual differences. Here, we demonstrate that multiple instances of the same ANN architecture exhibit substantial individual differences in behavior that mimic those observed in humans. We trained and tested 60 ANN instances from three architectures on a digit recognition task and found notable individual differences in overall accuracy, confidence, and response time (RT). Critically, these individual differences in ANN instances mapped consistently onto the individual differences produced by 60 humans performing the same task, with the mapping strength often approaching the human-to-human benchmark across all three behavioral metrics (accuracy, confidence, RT). The mapping generalized even across behavioral metrics: an ANN instance that aligned with an individual human on accuracy also aligned with the same individual on confidence and RT. These findings generalized to a more complex, 10-choice blurry object recognition task, though the human-ANN mapping was generally less robust than the human-human benchmark. Overall, these findings open the possibility of using ANN ensembles as computational proxies for probing the mechanisms underlying human variability.

The brain faces the feature binding problem: how are multiple stimulus features and variables combined into coherent representations that support flexible behavior? A key finding from neuroscience is that some brain regions employ factorized representations, where distinct features are encoded in neural state space in such a way that enables independent readout and robust generalization. Various algebraic operations have been proposed to model multi-variable representations, but despite extensive study of their theoretical properties (e.g., capacity, noise robustness), it remains unclear which operations produce the representational geometries observed in neural recordings. We systematically evaluate six binding operations implemented in recurrent spiking neural networks performing a working memory task. We find that only superposition and binding with slot-filler structure produce factorized geometry with favorable scaling, while the alternatives do not. These results provide a taxonomy linking algebraic binding operations to neural representational signatures, offering guidance for both computational modelers and experimentalists.

Point process generalized linear models (GLMs) have been a major tool for studying coordinated activity across populations of neurons. These models typically quantify how the spiking of a single neuron depends on the past activity of other neurons at multiple time lags, and the resulting neuron-to-neuron interactions are then aggregated to obtain population-coupling effects. However, when neurons within the same population exhibit similar spiking patterns, explicitly modeling individual interactions can be redundant and can unnecessarily increase model complexity. In such cases, population-level formulations may offer a more efficient alternative. For example, biophysical population models often characterize circuit dynamics using the average firing rate across neurons within a population, and recent data-driven approaches have similarly demonstrated the utility of population-level statistics for capturing cross-population interactions. Motivated by this consideration, we reformulate the GLM framework to operate directly at the population level. The resulting model, which we call pop-GLM, provides a computationally efficient method for estimating coupling between populations. In a simulated dataset, we show that pop-GLM achieves greater sensitivity in detecting coupling effects and can account for trial-to-trial variation in stimulus drive, which would otherwise introduce bias. We also note that moving from single-neuron to population-level modeling requires a specific modification of the traditional GLM framework. We then apply pop-GLM to real data and find reduced functional connectivity from primary visual cortex (V1) to a higher visual area during locomotion, a change not detected by single-neuron GLMs. Author summaryA central goal of systems neuroscience is to understand how multiple populations of neurons across different brain areas interact as a coordinated circuit to produce perception and behavior. We formulated and investigated a new method for estimating functional interactions between two populations of spiking neurons, and we show that it can be more sensitive and robust than previous approaches. To illustrate, we discovered decreased interaction between two mouse visual areas during locomotion, a result that previous techniques did not detect. The method should aid investigators in searching for important functional relationships across populations of neurons, with precise time scale resolution.

Neurons in the cerebral cortex are organized topographically. In the primate visual cortex, neighboring neurons often respond to similar stimulus parameters, such as receptive field position, orientation, color, and spatial frequency. Preferred stimulus parameters change smoothly across the cortical surface. If such topographic organization plays an important role in computation, it is likely to emerge in artificial neural networks. In this study, a multistream convolutional neural network was constructed in which filters in the first convolutional layer were arranged in a two-dimensional filter matrix according to their output connections. The network was trained using supervised learning for image classification. Although adjacent filters in the filter matrix can develop any structure in principle, they acquire similar degrees of orientation and color selectivity. Moreover, they prefer similar orientations, hues, and spatial frequency. The similarity decreases with distance between filters in the matrix. Furthermore, neural-network model instances that have a strong relationship between filter distance and filter-property similarity performed better than those with a weak relationship. These results suggest that topographic organization emerges spontaneously in an artificial neural network and plays an important role in model performance, suggesting the importance of topographic organization for computations performed by artificial and biological neural networks.

Inhibitory synapses can control a neurons firing rate and also control supralinear dendritic integration. It is not known how inhibitory synapses can learn to perform these functions using only signals available locally at the synaptic site. We study an inhibitory plasticity rule based on the Bienenstock-Cooper-Munro theory in multicompartment models of striatal projection neurons, and show that it can perform these two functions. The rule uses local voltage-gated calcium concentration in the dendrites to regulate inhibitory synaptic strength. We show that, for rate-coded inputs, the rule can achieve precise control of neuronal firing rate after changes in excitatory input rate or excitatory synaptic strength. Additionally, for sparsely-coded inputs that activate localized synaptic clusters in dendrites, the rule can either allow or inhibit the supralinear dendritic response evoked by the clustered excitatory synapses, or equalize the dendritic response arising from different clusters. Finally, we demonstrate the use of learning to inhibit supralinear dendritic integration for solving the nonlinear feature binding problem (NFBP), in tandem with a simple excitatory plasticity rule. We conclude by discussing why the collateral inhibitory synapses between striatal projection neurons could contribute to solving the NFBP with this plasticity rule. Author summaryNeurons are the main cells in the nervous system that process information. They receive signals from the bodys senses--both external and internal--and use them to guide actions such as muscle movement and the regulation of bodily functions. A neuron becomes active when incoming signals excite it strongly enough. But for neurons to work timely, precisely, and reliably, their activity needs to be shaped, modified and controlled. This is done by inhibition, which comes from specialized inhibitory neurons. In this article we study how inhibition can learn to do two of its most basic roles in the nervous system. The first is to help neurons stay responsive across a wide range of input strengths--from very weak to very strong stimulation. For example, neurons in the retina allow vision both in dim starlight and in bright sunlight, even though these conditions differ in brightness by a trillion-fold. Inhibition contributes to handling this huge range by preventing overstimulation of the neurons in bright light. The second role of inhibition is to control strong, local excitations that occur on specific dendritic branches of a neuron. These local excitations can suddenly push a neuron into activity, and inhibition controls whether such excitations are allowed or suppressed. We use a learning mechanism that is already known to exist for excitatory synapses, but here we apply it to inhibition to explore what it could achieve. The results show that if inhibitory synapses used this same learning rule, they could support the two fundamental roles of inhibition in the nervous system described above.

A mean field model (MFM) is a mesoscopic description of neuronal population dynamics that can reduce the complexity of neural microcircuits into equations preserving key functional properties. The generation of a MFM is a complex mathematical process that starts with the incorporation of single neuron input/output relationships and local connectivity. Once neuron electroresponsiveness and synaptic properties are defined, in principle, the process can be automatized. Here we develop a tool for automatic MFM derivation from biophysically grounded spiking networks (Auto-MFM) by performing micro-to-mesoscale parameter remapping, estimating input/output relationships specific for different neuronal populations (i.e., transfer functions), and optimizing transfer function parameters. Auto-MFM was tested using a spiking cerebellar circuit as a generative model. The cerebellar MFM derived with Auto-MFM accurately reproduced cerebellar population dynamics of the corresponding spiking network, matching mean and time-varying firing rates across a wide range of stimulation patterns. Auto-MFM allowed us to model and explore physiological and pathological circuit variants; indeed, it was used to map ataxia-related structural connectivity alterations of the cerebellar network, in which Purkinje cells with simplified dendritic structure altered the cerebellar connectivity. Furthermore, Auto-MFM was used to create a library of cerebellar MFMs by sweeping the level of the excitatory conductance at mossy fiber - granule cell synapse, which is altered in several neuropathologies. Auto-MFM is thus proving a flexible and powerful tool to generate region-specific MFMs of healthy and pathological brain networks to be embedded in brain digital models.

Convolutional neural networks (CNNs) are a class of artificial neural networks (ANNs). Since the early 2010s, they have been widely adopted as models of primate vision and classifiers of neuroimaging data, becoming relevant for a wealth of neuroscientific fields. However, the majority of neuroscience researchers come from soft-science backgrounds (like medicine, biology, or psychology) and do not have enough quantitative skills to understand the inner workings of A/CNNs. To avoid undesirable black boxes, neuroscientists should acquire some rudiments of computational neuroscience and machine learning. However, most researchers do not have the time nor the resources to make big learning investments, and self-study materials are hardly tailored to people with little mathematical background. This paper aims to fill this gap by providing a concise but accurate introduction to CNNs and their use in neuroscience -- using the minimum required mathematics, neuroscientific analogies, and Python code examples. A companion Jupyter Notebook guides readers through code examples, translating theory into practice and providing visual outputs. The paper is organised in three sections: The Concepts, The Implementation, and The Biological Plausibility of A/CNNs. The three sections are largely independent, so readers can either go through the entire paper or select a section of interest.

Understanding neural dynamics is crucial for uncovering how the brain processes information and controls behavior. Linear dynamical systems (LDS) are widely used for modeling neural data due to their simplicity and effectiveness in capturing latent dynamics. However, LDS assumes a stable mapping from the latent states to neural activity, limiting its ability to capture representational drift--gradual changes in the brains representation of the external world. To address this, we introduce the Stiefel Manifold Dynamical System (SMDS), a new class of model designed to account for drift in neural representations across trials. In SMDS, emission matrices are constrained to be orthonormal and evolve smoothly over trials on the Stiefel manifold--the space of all orthonormal matrices--while the dynamics parameters are shared. This formulation allows SMDS to leverage data across trials while accounting for non-stationarity, thus capturing the underlying neural dynamics more accurately compared to an LDS. We apply SMDS to both simulated datasets and neural recordings across species. Our results consistently show that SMDS outperforms LDS in terms of log-likelihood and requires fewer latent dimensions to capture the same activity. Moreover, SMDS provides a powerful framework for quantifying and interpreting representational drift. It reveals a gradual drift over the course of minutes in the neural recordings and uncovers varying drift rates across dimensions, with slower drift in behaviorally and neurally significant dimensions.

BackgroundFlexibility and robustness of neuronal function are closely linked to degeneracy, the ability of distinct structural or parametric configurations to produce similar functional outcomes. At the cellular level, this often manifests as ion-channel degeneracy, in which multiple combinations of intrinsic conductances yield comparable electrophysiological phenotypes. MethodologyWe used a population-based, data-driven modelling framework to generate large ensembles of biophysically detailed CA1 pyramidal neuron models constrained by somatic electrophysiological features extracted from patch-clamp recordings in acute slices from early-birth rats. 10 reconstructed morphologies were incorporated, and model populations were analyzed using parameter correlation analysis, principal component analysis, and generalization tests to assess robustness, degeneracy, and morphology dependence of intrinsic properties. ConclusionsAcross the model population, similar somatic firing behaviours emerged from widely different combinations of intrinsic parameters, demonstrating robust two-level ion channel degeneracy both within and across morphologies. Each morphology occupied a distinct region of parameter space, indicating morphology-specific compensatory effects, while weak pairwise parameter correlations suggested distributed compensation rather than tight parameter dependencies. Even with a fixed morphology, multiple parameter subspaces supported comparable electrophysiological phenotypes. Generalization across morphologies was structure-dependent and non-reciprocal, with successful parameter similarity occurring preferentially between structurally similar neurons. Interestingly, to accurately simulate spike-frequency adaptation, it was important to retain some kinetic properties of the ion channel models as free parameters during optimization. Together, these findings show that dendrite morphology shapes the valid parameter space, and similar electrophysiology of CA1 pyramidal neurons arises from the interplay between structural variability and ion-channel diversity. This work highlights the importance of population-based modelling for capturing biological variability and provides insights into how neuronal robustness might be maintained despite substantial heterogeneity, and offers a scalable pipeline for generating biophysically realistic CA1 neuron populations for use in network simulations. Author summaryNeurons must reliably process information even though their internal components, such as ion channels and cellular shape, can vary widely from cell to cell. How stable behaviour emerges from such variability is a fundamental question in neuroscience. In this study, we explored this problem using detailed computer models of early-birth rat hippocampal CA1 pyramidal neurons, a cell type that plays a central role in learning and memory. Instead of building a single "average" neuron model, we created large populations of models that all reproduced key experimental recordings but differed in their internal parameters. We found that neurons with different shapes and different combinations of ion channels could nevertheless generate similar electrical activity. This phenomenon, known as ion channel degeneracy, allows neurons to remain functional despite biological variability or perturbations. Our results show that neuronal shape strongly influences which parameter combinations are viable, but that multiple solutions exist even for the same morphology. The population of models we provide offers a resource for future studies of early-birth CA1 pyramidal cell function and dysfunction.

Human decisions under uncertainty are shaped by experience, but the computations that translate expectation and experience into choice remain debated in neural and cognitive science. Prior studies highlight reinforcement learning (RL) as a unifying framework, yet it is unclear whether human behavior under risk is better captured by symmetric updating from outcomes or by asymmetric learning that weights reward and loss differently. This work examines which learning strategies better explain trial-by-trial choices given contextual uncertainty and manipulations of outcome distributions. Our results show that a Risk Sensitive (RS) model with asymmetric learning rates best explains human behavior in our novel decision-making task. Fitting candidate models to individual trial histories yielded value signals that predicted both choice and response time. These results highlight that RS model, as an asymmetric learning provides a concise and identifiable account of behavior in decision-making under risk tasks.

Artificial recurrent networks are powerful models for studying neural dynamics and representations underlying complex cognitive tasks. However, the impact of neural-circuit heterogeneities on learning, dynamics, robustness, and generalization in these networks remains poorly understood. Here, we systematically investigated the impact of graded intrinsic heterogeneities in artificial recurrent networks trained on different cognitive tasks using reward- modulated Hebbian learning. Across networks trained with distinct hyperparameters and different levels of intrinsic heterogeneity, we observed pronounced network-to-network and task-to-task variability in training convergence, error dynamics during training, and task performance. These effects were strongly task dependent, with memory-dependent tasks exhibiting greater sensitivity to heterogeneity than memoryless tasks. We assessed these networks for robustness to multiple forms of graded post-training perturbations. Perturbations to intrinsic time constant distributions altered network dynamics, but had limited impact on final task accuracy in most cases. In contrast, perturbations to initial conditions, exploratory activity impulses, or task epoch durations strongly affected memory-dependent tasks. Among all perturbations, synaptic jitter was consistently the most detrimental, impairing performance across all tasks and heterogeneity levels. Importantly, despite such pronounced impact of heterogeneities, none of the metrics (spanning training, performance, dynamics, and robustness) varied monotonically with the level of training heterogeneity, instead showing additional dependencies on task demands, network configuration, and perturbation type. Finally, networks trained on a single task were able to perform structurally related untrained tasks, but failed on fundamentally distinct tasks. Strikingly, similar task performances emerged from divergent activity trajectories across networks and training conditions, together revealing pronounced functional degeneracy in network dynamics. Collectively, our findings establish that heterogeneous recurrent networks operate in a complex systems regime, where robust function emerges from non-unique, task-specific interactions among hyperparameters, dynamics, and heterogeneities. Our analyses emphasize the need for population- of-networks approaches that focus on interactions among multiple forms of neural heterogeneities in shaping learning and computation.

1ObjectiveThe complexity of neural data changes as the brain processes information during events. Universal lossless compression algorithms, which are broadly applicable and grounded in information theory, identify and exploit redundancies in data in order to compress it to essentially-optimal sizes regardless of underlying statistics. These algorithms may be used to conveniently and efficiently estimate a given signals Shannon entropy rate, a biologically relevant measure of the complexity of a signal. It is therefore natural to explore their effectiveness in the analysis of spiking neural data. ApproachThis work focuses on using compression to analyze recordings (96-channel Utah arrays) taken from motor cortex of animals performing reaching tasks for three days before and three days after administering electrolytic lesions (Subject U: 4 lesions, H: 3). In particular, we use the inverse compression ratio (ICR), which compares the sizes of compressed and uncompressed data to estimate the amount of statistically unique information. We calculate ICR with temporally-independent lossless compression (gzip) and temporally-dependent lossy compression (H.264, MPEG-2). Compression-based ICR was compared to single-neuron measures used to understand spiking data, such as average firing rates and Fano factor. Compression is also compared to common dimensionality reduction techniques, principal component analysis (PCA) and factor analysis (FA). Main ResultsStatistical tests on aggregate data comparing each metric before and after lesioning reveal that ICR is able to significantly (Mann-Whitney U test, p < 0.01) detect lesions with higher accuracy than single-neuron metrics, but not dimensionality reduction (ICR methods: 85.7%, single-neuron methods: 78.6%, dimensionality reduction: 100%). Additionally, statistical results on the same data show that ICR metrics remain more stable than single-neuron methods after lesion. The bitrate parameter of lossy compression algorithms is swept to better understand the effect of information rates and "optimal" compression on lesion detection performance. Our conclusions are confirmed by the same analyses performed on several different simulated neural datasets. SignificanceThese results suggest that compression algorithms may be a useful tool to detect and better understand perturbations to the underlying structure of neural data. Information-theoretic analyses may complement techniques like dimensionality reduction and firing rate tuning as a convenient and useful tool to characterize neural data.