This study presents a comprehensive analysis of the Drosophila connectome, identifying previously uncharacterized axo-axonic synapses on descending neurons. These findings provide new insights into the circuit organization and computational properties of descending motor pathways, advancing our understanding of how neural circuits control behavior in this model organism.

This study presents a comprehensive analysis of the Drosophila connectome, identifying previously uncharacterized axo-axonic synapses on descending neurons. These findings provide new insights into the circuit organization and computational properties of descending motor pathways, advancing our understanding of how neural circuits control behavior in this model organism.

This collaborative benchmark study evaluates the capabilities of advanced artificial intelligence systems using expert-level academic questions across multiple domains. The research provides a comprehensive assessment framework for understanding current AI capabilities and limitations, with implications for future AI development and deployment. The work has been featured in major news outlets including The New York Times and Reuters for its significance in evaluating AI advancement.

This computational study investigates how deep brain stimulation can be optimized to induce target neural activity patterns using simulation-based inference. By leveraging detailed neural models and Bayesian inference techniques, the research demonstrates how in-silico approaches can inform DBS parameter selection and improve therapeutic outcomes for neurological conditions.

This research investigates how synaptic summation mechanisms interact with GABA-glutamate co-transmission to shape information transfer in neural circuits. The findings reveal how dual neurotransmitter signaling and nonlinear summation dynamics contribute to the computational properties of neurons and network-level information processing.

This study characterizes how postsynaptic neurons implement frequency filtering through the interaction of short-term synaptic plasticity and intrinsic cellular properties. The research demonstrates that these mechanisms work in concert to shape frequency-dependent responses, providing insights into how neural circuits selectively process information across different frequency bands.

This theoretical analysis compares Perceptual Control Theory (PCT) with the Free Energy Principle (FEP), two major frameworks for understanding biological organization and adaptive behavior. The comparison reveals complementary insights and differences in how these theories explain neural and behavioral phenomena, with implications for computational neuroscience and theoretical biology.

This study identifies the role of Frazzled/DCC in regulating gap junction formation at the giant synapse in Drosophila. The findings reveal molecular mechanisms controlling electrical synaptic connectivity, providing insights into how gap junctions are assembled and organized in neural circuits.

This study develops machine learning approaches to identify signatures of astrocytic influence on neuronal activity patterns. The research demonstrates how glial-neuronal interactions shape neural dynamics and provides tools for detecting these interactions in recorded neural data, highlighting the importance of considering glia in computational neuroscience.

This research reveals that neurons can exhibit distinct resonant properties in both single-spike and bursting modes. The findings demonstrate that neurons dynamically switch between different frequency-filtering regimes depending on their spiking pattern, providing new insights into how neural circuits encode and process information.

This work develops methods for selecting appropriate models to estimate functional connectivity between neurons with variable memory properties. The research provides statistical tools for identifying effective interactions in stochastic neural systems, improving the accuracy of functional connectivity estimates from neural recordings.

This study investigates how dendrites integrate synaptic inputs under conditions that mimic in vivo neural activity. The research characterizes different modes of dendritic integration and how they depend on the state of neural activity, revealing dynamic changes in neuronal computation during behavior.

This perspective article discusses how computational neuroscience bridges different levels of biological organization, from molecular and cellular processes to network and systems-level phenomena. The work addresses the challenges and opportunities for developing multi-scale computational models that capture brain function across organizational levels.

This work develops improved transfer entropy estimators for identifying effective connectivity in stochastic neural systems with variable memory properties. The research provides practical tools for analyzing information flow between neurons in complex neural networks, advancing methods for functional connectivity estimation from neural recordings.

This study investigates brain firing patterns, specifically the shift from regular spiking to burst firing in cortical neurons. It focuses on the role of potassium and calcium ion channels under various conditions. Findings suggest that slow potassium currents are vital for high-synchronous activities and spike-to-burst transitions. Bistable dynamics in neuronal networks underlie this transition, offering insights for potential pharmacological interventions to move epileptic brains from pathological bursting to healthier states.

We examined how hippocampal neurons switch between generating single spikes and bursts of spikes and whether they convey different information. We found that burst spikes are associated with encoding an animal’s position as it enters a place field, linked to theta rhythms, while single spikes are related to exiting a place field and connected to gamma rhythms. These spiking patterns are influenced by the properties of neurons and input frequency, suggesting they perform distinct computations for spatial behavior.

We investigate how neurons filter and code sensory inputs using temporal filters. Short-term plasticity (STP) and various processes influence these filters. We develop mathematical tools to understand their generation and extend the findings to complex networks.

We investigate how rapidly fluctuating signals interact with neuronal properties, generating varied and coherent responses. Using linear and non-linear models, we explore the effects of piecewise constant inputs. We find that sustained oscillations and variability arise from the interaction between inputs and intrinsic cell dynamics, independent of input stochasticity.

We analyze neuronal responses to oscillatory currents and synaptic-like inputs. Resonant cells exhibit selective resonance. Conductance-based inputs are attenuated compared to current-based inputs. Variability arises from different input orderings. Poisson-distributed synaptic inputs show attenuated responses. Our findings contribute to understanding oscillatory activity and network resonance.

WARs, genetically susceptible to sound-induced seizures, exhibit different GABAergic inhibitory postsynaptic current (IPSC) distributions compared to Wistar rats. Our computational model suggests two populations of GABAergic synapses in WARs, with reduced release frequency in a subset. This finding may contribute to their seizure susceptibility.

We study activity propagation in modular spiking networks. Optimal information flow occurs with increased synaptic strength or number of modules. Population-level and pairwise information propagation show different patterns. Increasing synaptic strength and modules enhance information transmission.

High-amplitude oscillatory currents induce asymmetric voltage responses in neurons due to nonlinearities in ionic currents and the voltage equation. Geometrical factors and time-scale separation contribute to these asymmetries. Surprising frequency-dependent patterns emerge in the gating variables of the currents. Our findings shed light on the ionic mechanisms involved in oscillatory information processing by neurons.

Sino-aortic denervation (SAD) in rats alters breathing patterns without affecting sympathetic activity or arterial pressure. We observed prolonged inspiration, increased variability in late-inspiratory neurons, and reduced frequency in pre-inspiratory/inspiratory neurons. These findings suggest a timing imbalance in the respiratory network of SAD rats, impacting presympathetic neuron modulation after baroreceptor afferent removal.

In our study on epilepsy, we examined the electrical properties and synaptic transmission in CA1 pyramidal neurons of the dorsal hippocampus in a genetic model, the Wistar Audiogenic Rat (WARs). We found reduced GABAergic neurotransmission in the dorsal hippocampus of WARs, leading to increased susceptibility to limbic seizures observed during audiogenic kindling.

We studied spontaneous cortical population activity in networks of spiking neurons. By adjusting inhibitory synaptic strength and synaptic noise, we identified different activity patterns. Noise transformed transient patterns into persistent ones. Networks with strong inhibition and moderate noise showed intermittent switches between oscillatory and quiescent states, resembling cortical states. Our findings reveal mechanisms underlying cortical activity at multiple scales.

Resonance in neurons with hyperpolarization-activated current (Ih) is determined by the biophysical properties of Ih, specifically the derivative conductance (GDer) and current activation kinetics (τh). The presence and frequency of resonance are controlled by GDer and τh, and their increase leads to enhanced resonance. Voltage dependence of GDer makes resonance voltage-dependent. These findings provide insights into predicting and explaining resonance properties in neurons with Ih.

We determine numerically from an iterative single-neuron simulation spontaneous cortical population activity in networks of spiking neurons with heterogeneous populations of different cellular and connection parameters.

The persistent sodium current (INaP) prolongs EPSP decay by increasing the membrane time constant. In the zero slope conductance region, non-decaying EPSPs are observed in CA1 hippocampal pyramidal cells. These EPSPs and delayed spikes are abolished by Na+ channel block and occur in response to small amplitude aEPSCs. This suggests that INaP enhances synaptic integration in distal dendritic regions.

In a diverse neuron network, global activity fluctuates between intense periods and low levels, resembling cortical “up” and “down” states. The distribution of membrane recovery variable plays a key role in initiating and stopping activity. Neuron behavior is more influenced by the presynaptic environment than their formal types.

The cerebral cortex exhibits spontaneous neural activity without external input. Using computer simulations, we studied the mechanisms behind this activity in hierarchical modular networks. Our models replicated experimental observations of irregular firing and oscillatory patterns. The activity duration depended on initial conditions and network characteristics, highlighting the impact of both structure and neuron types on cortical activity.