|Title||Effect of network size on computational capacity|
|Publication Type||Conference Paper|
|Year of Publication||2016|
|Authors||Dura-Bernal, S., Menzies R. J., McLauchlan C., van Albada S. J., Kedziora D. J., Neymotin S., Lytton W. W., & Kerr C. C.|
|Conference Name||Computational Neuroscience Meeting (CNS 16')|
|Keywords||2016, BMC, BMC Neuroscience 2016, CNS|
There is exceptionally strong circumstantial evidence that organisms with larger nervous systems are capable of performing more complex computational tasks. Yet relatively few studies have investigated this effect directly, instead typically treating network size as a fixed property of a simulation while exploring the effects of other parameters. Recently, Diehl and Cook  found that network performance did increase modestly with network size; however, larger networks also required longer training times to achieve a given performance. In this work, we directly addresses the relationship between network size and computational capacity by using a biomimetic spiking network model of motor cortex to direct a virtual arm towards a target via reinforcement learning . The reaching task was performed by a two-joint virtual arm controlled by four muscles (flexor and extensor muscles for shoulder and elbow joints). These muscles were controlled by a neural model that consisted of excitatory and inhibitory Izhikevich neurons in three cortical populations: a proprioceptive population, which received input from the current arm position; a motor population, which was used to drive the arm muscles; and a sensory population, which served as the link between the proprioceptive and motor populations. The model was trained to reach the target using exploratory movements coupled with reinforcement learning and spike-timing dependent plasticity (STDP). The model was implemented using NEURON.