The code provided is part of a computational model used to simulate and analyze neural network dynamics and synaptic plasticity mechanisms, likely within cortical or hippocampal circuits. Here’s a breakdown of the biological basis of this code:
Neuron Types: The network is composed of two types of neurons: excitatory (NE) and inhibitory (NI), each consisting of 500 neurons. This distinction reflects the well-known categorization of cortical neurons, where a majority are excitatory pyramidal cells, and a smaller fraction are inhibitory interneurons.
Synaptic Weights: The connections between neurons (synaptic weights) are adjusted according to specified parameters (e.g., JEE, JEI, JIE, JII). These weights reflect the strength of synapses between different neuron types. The tuning of these weights may mirror synaptic scaling processes that maintain excitation-inhibition balance in biological circuits.
Plasticity Mechanism: The code includes a simulation of synaptic weight changes (dw), indicative of synaptic plasticity such as long-term potentiation (LTP) or long-term depression (LTD). Plasticity is likely induced via perturbations applied to a subset of neurons, simulating learning and memory processes.
Perturbation Simulation: Neuronal ensembles are subjected to perturbations (pert_ids), which could be interpreted as targeted stimuli or learning events that lead to specific synaptic changes. This models how specific experiences can lead to strengthening or weakening of certain synaptic connections, a key process in memory formation.
po_exc, po_inh), suggesting that the network may be modeling a cortical area involved in sensory processing, such as the visual cortex, where neurons respond preferentially to specific orientations of stimuli.r, v) are investigated, reflecting how neuronal activation patterns evolve over time. This incorporates key biophysical properties such as the synaptic time constant (tau), likely representing the temporal dynamics of synaptic integration and response.Induction Curves: The code evaluates induction curves, comparing the effect of ensemble size on network responses. This could relate biologically to how groups of neurons co-active during learning affect synaptic and neural network plasticity.
Specificity of Induction: The specificity of synaptic changes, assessing differences between within-ensemble and out-of-ensemble induction, highlights the importance of neural circuitry specificity in learning processes, reflecting the precision of synaptic plasticity governing neural adaptations.
The code aims to explore how specific neural activation patterns (representing learning events) can induce changes in synaptic strength, thereby altering network dynamics in ways that model real neural processes such as memory encoding or sensory processing. The simulated effects of ensemble size and duration on plasticity provide insights into synaptic integration at the network level, which are essential for understanding brain function, particularly under conditions where plasticity plays a crucial role. Bio-inspired parameters such as neuron orientation preference and synaptic excitation/inhibition dynamics underline the attempt to closely replicate biological neurons’ behavior in the cortical microcircuit.