Mechanisms of Correlated Neuronal Activity in the Globus Pallidus

Neuronal Interactions in the Substantia Nigra Revealed by Statistical Analysis of Neuronal Spike Trains

Can we trust subthalamic local field potentials? Geometric and dynamical constraints on the interpretation of extracellular recordings

Network Dynamics and State-Dependent Effects of Electrical Stimulation in Recurrent Excitatory-Inhibitory Populations

Deep Brain Stimulation (DBS) is an established clinical treatment for a variety of neurological disorders, including Parkinson’s Disease where it has been shown to reduce motor symptoms as well as disrupt pathological beta oscillations in the basal ganglia. The mechanisms of action of DBS on the collective activity of neuronal circuits is not fully understood. We use a recurrently-connected excitatory-inhbitory network based on the Brunel network architecture that can produce activity in a variety of states. Using a model of DBS that can reproduce observed effects such as antidromic activation, local somatic suppression, and axonal activation, we characterize the effect of stimulation across the entire parameter space of the network. We show that the effects of stimulation are dependent on the baseline state of the network, with the level of beta suppression dependent on the level of inhibition and the external drive. Specifically, networks with higher inhibition and lower drive show greater disruption of beta oscillations. We further show that networks in different states are preferentially sensitive to different frequencies of stimulation, suggesting that alternative protocols to the clinically standard high-frequency stimulation may have therapeutic efficacy.

Estimation of the time course of excitatory and inhibitory conductance during oscillatory periods

Determining the amount of information a neuron receives per unit of time is key to understanding brain connectivity and how neural networks encode and transmit information. In particular, estimating this information flow by distinguishing between excitatory and inhibitory synaptic contributions is critical to understanding neural network function, as maintaining the excitation-inhibition (E/I) balance regulates neuronal excitability and circuit stability, whereas its disruption can lead to a plethora of brain disorders, including neurodegenerative and psychiatric conditions. However, because synaptic conductances cannot be measured directly, inverse methods are required to infer them from the membrane potential —a readily measurable quantity. Although partial solutions have been proposed, accurately estimating these conductances remains a significant challenge due to the complexity and diversity of the inputs. This is particularly true in the spiking regime, where neurons actively fire. In this work, we introduce a novel computational strategy that combines two critical metrics extracted from the time course of the membrane potential recording: the amplitude of the spike and the interspike interval. By using these quantities, the proposed method enables the accurate separation of excitatory and inhibitory contributions, yielding highly favorable results in the spiking regime. Author summary Quantifying the continuous stream of inputs a neuron receives is key to understanding brain connectivity. Inside the brain, individual cells must maintain a tight balance between excitation and inhibition (E/I) to process information correctly, as any disruption in this equilibrium can impair its functionality. However, directly measuring the underlying excitatory and inhibitory synaptic conductances is technically challenging, and existing mathematical tools often fail when neurons enter their active firing regime. In this work, we introduce a novel computational strategy designed to extract and separate these time-varying conductances directly from the neuron’s spiking activity. By dynamically tracking just two accessible metrics – the amplitude of the spikes and the time intervals between them – our algorithm estimates both conductance profiles with high precision. Furthermore, we demonstrate that this procedure is highly robust against realistic experimental noise and data variability, providing an accessible framework that does not require complex hardware or an unfeasible number of repetitive experimental trials. By tracking changes in the E/I ratio of the synaptic input, this method provides an efficient approach to detecting pathological imbalances and understanding how local connectivity shapes cellular functionality.

Tuft dendrite spikes are accompanied by selective input from distinct functional networks.

The tuft dendrites of layer 5 neurons can support regenerative events - dendritic spikes - that have been proposed to coordinate context-specific engagement and plasticity within cortical networks. However, it remains unclear whether tuft spikes are accompanied by input activity with dynamics that could support these network-level functions. To address this, glutamatergic synapses and postsynaptic calcium signals were simultaneously imaged in the tuft dendrites of layer 5 extratelencephalic neurons within the premotor cortex of mice of either sex performing a cued directional licking task. Trial-to-trial, the generation of tuft spikes was associated with a multiphasic elevation in synaptic activity spanning hundreds of milliseconds. This activity was highly specific to the dendrite in which a spike was detected, suggesting the concurrent activation of select subnetworks. Synapses that were strongly coupled to the overall population were the most synchronized with tuft spikes and preferentially encoded the transition between the preparation and action epochs of the task. Even among these strongly coupled synapses, increases in activity were largely specific to synapses located on the spiking dendrite. Surprisingly, among synapses with the poorest population coupling, a second population of coactive synapses was discovered that was also associated with tuft spikes and functionally selective for task-outcome. These results suggest that tuft spikes may be particularly driven by inputs from neurons that are both embedded in sparse subnetworks and synchronized through coupling to larger-scale functional networks.Significance Statement Flexible behavior and learning may depend on interactions between activity in different brain networks and dendritic spikes generated within the neurons that make up the output layer of the neocortex. The results of this study indicate that at the moment of spike generation, spikes in different dendrites are associated with the activation of very specific networks. Yet, across time, the inputs most associated with dendritic spikes are broadly coactive and share selectivity for similar features of behavior. This suggests that dendritic spikes may be particularly driven by the activation of "hub" neurons that coordinate communication between largescale and small-scale functional networks.

Diversity of Layer 3 Pyramidal Neuron Properties Across Areas of the Primate Neocortex.

Impaired activation of cortical circuits might contribute to working memory deficits in schizophrenia. In this disorder, layer 3 pyramidal neurons (L3PNs) of the prefrontal (PFC), primary visual (V1) and posterior parietal (PPC) cortices, three cortical areas essential for working memory, display alterations that may impair network activity. We review evidence suggesting that L3PN morphology and physiology differ significantly across PFC, PPC and V1 in primates. These differences are much less pronounced in rodents, suggesting a primate-enhanced regional variability that may be the substrate for area-specific L3PN vulnerability in schizophrenia. PFC L3PNs exhibit larger dendrites with higher spine density, thus substantially more excitatory synapses than V1 or PPC L3PNs. Furthermore, the PFC contains a unique stripe-like connectivity system mediated by the horizontal axon collaterals of L3PNs that might support robust recurrent excitation, and thus the mnemonic persistent activity thought to contribute to working memory storage. Physiologically, PFC L3PNs display higher spontaneous excitatory post-synaptic current (sEPSC) frequency and amplitude, indicating functionally more potent individual synapses in PFC than in V1 L3PNs. Although sEPSC differences are less pronounced between PPC and PFC L3PNs, the greater spine density in PFC suggests stronger excitatory drive in PFC L3PNs. We conclude by identifying open questions that are relevant for understanding schizophrenia pathophysiology: i) What are the sources of synaptic input on L3PNs in each area?, ii) What is the significance of dendritic spine density differences across areas?, and iii) Do NMDAR-mediated synaptic currents differ in strength between L3PNs in PFC, PPC, and V1?