Narrow Results

The flow of information in the brain theorizes that each neuron in a network receives synaptic inputs and sends off its processed signals to neighboring neurons. Here, we model these synaptic inputs to understand how each neuron processes these inputs and transmits neurotransmitters to neighboring neurons. We use the NEURON simulation package to stimulate a neuron at multiple synaptic locations along its dendritic tree. Accumulation of multiple synaptic inputs causes changes in the neuron's membrane potential leading to firing of an action potential. Our simulations show that simultaneous synaptic stimulation approaches firing of an action potential at lesser inputs compared to sequential stimulation at multiple sites distributed along several dendritic branches.

In developing neuromimetic engineering systems, the choice of neuron-like element is critical, because this element determines the functional ability of the system. According to recent neuroscience research, a neuron is expected to perform sophisticated information processing, making use of the complex physiological properties of its dendrites. In this chapter, the computational function of neuronal dendrites is explored based on mathematical models. Theoretical analyses show that a passive dendrite is capable of complex logic operations by means of nonlinear synaptic interactions. Computer simulations show that active dendrites integrate synaptic inputs locally and then hierarchically according to synaptic organization and dendritic geometry. Based on these results, a formal neuron is proposed which simply and sufficiently describes the actual properties of the dendrites. This model is therefore suitable for constructing a large-scale network without the loss of the active dendrites' essential responsiveness. These modeling studies provide novel perspectives on the computation in a single neuron with a complex dendritic tree.