Neural Networks and Applications

Neural networks as decision trees: an analytical solution for learning and neural selectivity

This paper develops an analytical framework showing how nonlinear neural networks decompose tasks into local linear regressions and how this structure relates to neural selectivity and activation regions.

Neuromodulated Equilibrium Learning

This paper introduces Neuromodulated Equilibrium Learning (NEL), a single-equilibrium reward-based framework, and evaluates its performance on image datasets compared to multi-phase baselines.

NeuralRNN: a unified framework for recurrent neural network methods in cognitive neuroscience

NeuralRNN unifies RNN methods for cognitive neuroscience tasks by providing a single framework for model implementation, comparison, and extension.

Neural representational geometry of a joint code for stimulus category and category-independent features

The paper investigates how neural representations can jointly encode stimulus categories and category-independent features, showing that regression-relevant geometry can be optimized without compromising classification geometry.

Weight initialization shapes task organization in recurrent neural networks

This study shows that low initial weight variance in recurrent neural networks leads to overlapping weight subspaces for subtasks, while high variance results in more segregated subspaces.

Reward-Based Equilibrium Propagation

This paper introduces a reward-based variant of equilibrium propagation that uses sparse reward signals instead of full target vectors, achieving performance close to conventional EP on multiple datasets.

The Role of Training Paradigms in Shaping Connectivity and Dynamics: A Comparison of Gradient-Based and Evolutionary Methods in Recurrent Neural Network Models

This paper compares gradient-based and evolutionary training methods in RNNs, showing they produce distinct connectivity and dynamics, especially in working memory tasks.

Dendritic Wave Recurrent Neural Networks

This paper introduces the Dendritic Wave Recurrent Neural Network (DW-RNN), which improves upon wave recurrent neural networks by adding dendritic input integration, achieving better performance and stability on sequential tasks.

Signed-XOR Error and Sparse Coding in a Dale-Complaint Substrate for Sequence Memorization

The paper demonstrates a biologically plausible model that memorizes and recalls sequences using a sparse coding scheme and Dale's law constraints, achieving perfect recall with a modest latent size.

Interpretable compositional computation with recurrent neural networks

This paper develops a theory of interpretable compositional computation using low-rank recurrent neural networks, identifying shared latent components and their task-dependent usage.

Incorporation of single-neuron projectome-based connectivity motifs enhances the cortex-specific performance of artificial neural networks

This paper develops a connectome-informed neural network algorithm that improves RNN and LLM performance by incorporating natural cortical connectivity motifs.

Rank dependency of rescaled pruning in recurrent neural networks

The paper shows that synaptic rescaling preserves low-dimensional dynamics in low-rank RNNs but fails in high-rank networks during pruning.

Local gated-Hebbian learning of deep cerebellar networks with quadratic classification capacity

The paper shows that a two-hidden-layer network with local Hebbian learning can achieve quadratic classification capacity, supporting fast and efficient learning in cerebellum-like circuits.

Linking macroscale structure and function in brain-like recurrent neural networks

This paper introduces BrainRNN, a brain-like recurrent neural network architecture, and shows that structural constraints lead to functional organization similar to the human cortex.

Toward defining loss functions in neuroscience: an XOR-based neuronal mechanism for local learning

This paper investigates whether a signed error derived from an XOR-based neuronal mechanism can serve as a learning signal in a binary autoencoder, showing improved reconstruction accuracy compared to traditional methods.

Evolutionarily Optimized Network Topology as a Structural Prior for Data-Efficient Sparse Neural Classification

The paper tests if evolutionarily optimized network topologies can improve data efficiency in neural classifiers, finding that biologically derived sparse networks outperform synthetic alternatives with similar sparsity.

Individual differences in artificial neural networks capture individual differences in human behavior

This study shows that individual differences in artificial neural networks mirror human behavioral differences in accuracy, confidence, and response time across tasks.

Human-like individual differences emerge from random weight initializations in neural networks

This study shows that different random initializations of neural networks produce individual differences similar to humans, and using these differences improves predictions of human responses.

Partial recurrence enables robust and efficient computation

This paper introduces partially recurrent neural networks (pRNNs) and shows they can match or exceed fully recurrent networks in performance while using fewer parameters.

Diversity in the impact of heterogeneities on recurrent networks performing a cognitive task

This study assesses how different levels of heterogeneity affect the performance and dynamics of recurrent neural networks trained on a cognitive task.

Constructing Biologically Constrained RNNs via Dale's Backprop and Topologically-Informed Pruning

The paper develops RNNs that incorporate biological constraints like Dale's law and structured connectivity to better model cortical function and validate insights through mouse calcium imaging data.

Communication subspaces align with training in ANNs

This study examines if communication subspaces develop with training in ANNs and finds increased alignment between activations and weights in trained models.

Robustness in Hopfield neural networks with biased memory patterns

The paper studies how pattern bias affects robustness in Hopfield neural networks under different noise types and finds that higher pattern load favors more biased representations.

Brain-like learning with exponentiated gradients

This paper introduces exponentiated gradient (EG) as a learning algorithm for artificial neural networks that better aligns with biological principles than gradient descent.

Equivalence between representational similarity analysis, centered kernel alignment, and canonical correlations analysis

This paper shows that representational similarity analysis and centered kernel alignment are equivalent when mean-centering is applied to RSA, unifying two methods for comparing neural representations.

When and why modular representations emerge

This study shows that modular representations in neural networks emerge for complex behavior when inputs are low-dimensional, without anatomical constraints, and supports rapid learning and generalization.

Contribute to balance, wire in accordance: Emergence of backpropagation from a simple, bio-plausible neuroplasticity rule

This paper proposes a neuroplasticity rule that mathematically replicates backpropagation in neural networks without requiring biologically implausible assumptions.

Transition to chaos separates learning regimes and relates to measure of consciousness in recurrent neural networks

The paper shows that chaotic dynamics in recurrent neural networks separate learning regimes and correlate with a consciousness measure, suggesting a link between critical dynamics, learning, and consciousness.

AI without networks

This paper introduces a network-free AI framework based on data interpolation, demonstrating its effectiveness in generative modeling across ethology, control theory, and mathematics.

Iterative convergent computation may not be a useful inductive bias for residual neural networks

This paper investigates whether iterative convergent computation improves ResNets but finds it does not enhance classification accuracy on standard or challenging visual tasks.