Topic: neuron types

Liset de la Prida discusses hippocampal sharp-wave ripples and their relationship to replay and memory. The conversation connects different ripple patterns and neuron types to population activity, asking how particular cells help shape the lower-dimensional dynamics used to describe neural computation.

A central question in neuroscience is how the structure of brain circuits determines their activity and function. To explore this systematically, we developed a 230,000-neuron model of mouse primary visual cortex (area V1). The model integrates a broad array of experimental data:Distribution and morpho-electric properties of different neuron types in V1.

Spike Timing Dependent Plasticity (STDP) is argued to modulate synaptic strength depending on the timing of pre- and postsynaptic spikes. Physiological experiments identified a variety of temporal kernels: Hebbian, anti-Hebbian and symmetrical LTP/LTD. In this work we present a novel plasticity model, the Voltage-Dependent Pathway Model (VDP), which is able to replicate those distinct kernel types and intermediate versions with varying LTP/LTD ratios and symmetry features. In addition, unlike previous models it retains these characteristics for different neuron models, which allows for compari

Contrary to typical artificial neural network (ANN) design, biological neurons are not identical. Neurons differ substantially in their physiological properties. Heterogeneity has been hypothesized to increase the dimensionality of the neural dynamics, which improves the encoding properties of a network [1], promotes robustness and stability [2], and maximizes information flow in large networks [3]. We aim to show the functional effect of heterogeneity in rate-based recurrent neural networks. To vary the degree of heterogeneity, we introduce neuron types, with each neuron type having its own

Mapping and understanding the functional differences between neuron types is crucial for advancing our knowledge of neural circuits and brain function. However, classifying neuron types in vivo remains challenging due to the difficulty of collecting reliable labels and the complexity of deciphering how neuronal activity varies across different conditions and stimuli. Even when labeled data is available, it is often hard to identify subtle functional differences between neuron classes. To address these challenges, we introduce a novel spatiotemporal Transformer-based architecture that learns a