The provided code is part of a computational neuroscience model that simulates the orientation selectivity in the early visual system, specifically in the primary visual cortex (V1) of the brain. Below is a description of the biological basis underlying the code. ### Biological Basis #### 1. **Objective and Context** The code is designed to explore how neurons in the V1 area of the brain can develop orientation selectivity, a fundamental feature of visual processing where neurons respond preferentially to edges of a particular orientation. The model uses a framework based on spike-timing dependent plasticity (STDP) and relative spike timing coding. #### 2. **Spike-Timing Dependent Plasticity (STDP)** - **STDP is a biological learning rule** that modifies the strength of synapses based on the precise timing of spikes. It observed changes in synaptic strength due to the relative timing of pre- and postsynaptic spikes. - **Parameters:** - `PARAM.stdp_t_pos` and `PARAM.stdp_t_neg`: Time constants for the positive and negative phases of STDP, respectively. These reflect the temporal windows during which STDP operates. - `PARAM.stdp_a_pos` and `PARAM.stdp_a_neg`: Amplitude factors for synaptic potentiation and depression, respectively. They control the magnitude of the synaptic changes. #### 3. **Excitatory Postsynaptic Potential (EPSP)** - **EPSP represents the postsynaptic potential change** associated with a presynaptic spike, contributing to neuron firing if it reaches a threshold. - **Parameters:** - `PARAM.tm` (membrane time constant) and `PARAM.ts` (synapse time constant): These reflect the time constants that determine how long postsynaptic potentials last. - `PARAM.epspKernel`: A kernel that models the time course of the EPSP. The code implements a bi-exponential decay function for this purpose, which is common in biological neurons. #### 4. **Inhibitory Postsynaptic Potential (IPSP)** - The code models an inhibitory postsynaptic potential, typically resulting in the hyperpolarization of the neuron, counteracting EPSPs. - **Parameters:** - `PARAM.ipspKernel`: Aims to represent the time course of the IPSP. Its scaling ensures that it can influence the firing threshold as specified by `PARAM.inhibStrength`. #### 5. **Neuron Model** - The neurons in this model are configured to simulate their threshold behavior and refractory periods, which are critical for realistic neuronal firing patterns. - Neurons follow a leaky integrate-and-fire (LIF) model: They accumulate charge over time and fire when a threshold is crossed. - **Parameters:** - `PARAM.threshold`: Represents the membrane potential level at which the neuron fires an action potential. - `PARAM.refractoryPeriod`: The period after firing during which the neuron cannot fire again, reflecting real neuronal behavior. #### 6. **Orientation Selectivity** - The simulation within the code is designed to show how neurons develop preference for specific orientations over time due to STDP, mimicking the plasticity observed in biological visual cortical areas in response to visual experiences. This code represents a simplified yet powerful model aimed at capturing the essence of visual feature learning in a computational framework. It uses biophysically inspired parameters and rules like STDP to simulate biological phenomena observed in the mammalian visual system.