Neural coding with spikes and bursts: characterizing neurons and networks with noisy input

Neural coding with spikes and bursts: characterizing neurons and networks with noisy input

Neural coding with spikes and bursts: characterizing neurons and networks with noisy input

Learning in spiking neural networks with a calcium-based Hebbian rule for spike timing-dependent plasticity

This work presents a Hebbian local learning rule that models synaptic modification as a function of calcium traces tracking neuronal activity and demonstrates how spike timing and rate can be complementary in their role of shaping the connectivity of spiking neural networks.

Neural coding with spikes and bursts: characterizing neurons and networks with noisy input

Multiplexing of functionally distinct inputs by intrinsic modulation of spike timing in a monoaminergic nucleus.

Monoaminergic nuclei such as the serotonergic dorsal raphe nucleus (DRN) receive functionally distinct streams of synaptic inputs, yet whether and how these are transformed into distinct population codes remains unknown. Combining electrophysiology and computational modeling, we show that DRN 5-HT neurons exhibit separable spiking representations of two major excitatory inputs-those from the lateral habenula (LHb) and the medial prefrontal cortex (mPFC). Dual-color opsin experiments revealed that 5-HT neurons received convergent input from both regions. Subthreshold events were similar between inputs, but suprathreshold spiking showed input-specific latencies and dispersion. We found a low-threshold calcium conductance that selectively boosts slow excitatory inputs and shapes membrane noise to generate input-specific variation of spike latency and jitter. Stochastic simulations confirmed that these cell-intrinsic properties are sufficient to generate distinct output spike patterns. This emergent population spike synchrony code provides a means for the DRN to widely broadcast these streams as a multiplexed signal.

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.