Biological Basis of the Code

The provided code is designed to simulate and analyze the activity of layer 2 (L2) cortical neurons under various conditions, specifically focusing on evoked activity in the presence or absence of layer 1 (L1) principal whisker (PW) neurons. This simulation has a direct grounding in computational neuroscience, where mathematical models and simulations are used to understand the behavior of neurons and neural networks.

Key Biological Concepts

Cortical Layers and Neurons

• Cortical Layers: The mammalian cerebral cortex is organized into six layers, with each layer having distinct types of neurons and connectivity patterns. Layer 2 (L2) is a superficial layer containing predominantly pyramidal neurons, which play a role in integrating inputs from various sources.
• Layer 1 (L1) Neurons: L1 is the most superficial layer of the cortex and contains mainly dendrites of neurons from other layers as well as a small number of inhibitory interneurons. The code investigates how activity from L1 neurons affects L2 neuron responses.

Evoked and Ongoing Activity

• Evoked Activity: This refers to the neural response to external stimuli, such as sensory input. In this code, the evoked activity is evaluated under different synaptic input conditions, simulating how L2 neurons might respond to a stimulus in the presence or absence of activity from L1 neurons.
• Ongoing Activity: This represents spontaneous neural activity that occurs in the absence of intentional stimuli, often characterized by alternating high (up-states) and low (down-states) activity levels. The model includes parameters for ongoing activity to simulate more realistic neural dynamics.

Synaptic Inputs and States

• The model simulates both up-states and down-states to reflect the changes in synaptic inputs L2 neurons might experience during ongoing activity. The interplay between these states and different forms of evoked activity helps understand how neurons transition between states in response to stimuli.

Neuron Model and Parameters

• NEURON Simulator: The code utilizes the NEURON simulation environment, widely used for computational modeling of individual neurons and networks.
• Synapses and Networks: Synaptic properties are mapped to the model, affecting how neurons within the network respond to inputs. Synapse dynamics are crucial for capturing the complexity of neuronal activity and adjusting parameters like synaptic strength, latency, and timing.

Morphological and Electrophysiological Parameters

• Dendritic Scaling: The code includes a process for scaling dendritic diameters, possibly representing different levels of synaptic input integration depending on the physical structure of the dendrites.
• Spike Threshold: The model uses a threshold of -38 mV, which reflects the typical voltage at which an action potential is triggered in neurons, based on previous empirical studies.

Conclusion

This code is an example of how computational models can be used to explore the function and dynamics of neurons in the cortex. By simulating the evoked and ongoing activities of L2 neurons with and without L1 PW neuron involvement, researchers can gain insights into the interaction between different cortical layers and the influence of synaptic inputs on neuronal responsiveness. This approach allows for detailed exploration of neuronal function and helps bridge the gap between experimental neuroscience and theoretical models.