Faculty Bibliography

Technological advances in electrode construction and digital signal processing now allow recording simultaneous extracellular action potential discharges from many single neurons, with the potential to revolutionize understanding of the neural codes for sensory, motor, and cognitive variables. Such studies have revealed the importance of ensemble neural codes, encoding information in the dynamic relationships among the action potential spike trains of multiple single neurons. Although the success of this research depends on the accurate classification of extracellular action potentials to individual neurons, there are no widely used quantitative methods for assessing the quality of the classifications. Here we describe information theoretic measures of action potential waveform isolation applicable to any dataset that have an intuitive, universal interpretation, that are not dependent on the methods or choice of parameters for single-unit isolation, and that have been validated using a dataset of simultaneous intracellular and extracellular neuronal recordings from Sprague Dawley rats.

Cognitive and psychiatric comorbidities are common and clinically important in medial temporal lobe epilepsy and are likely caused by ongoing abnormalities in brain activity. In addition, it is unclear how the dynamics of interictal brain activity in medial temporal lobe epilepsy contributes to the generation of seizures. To investigate these issues, the authors evaluated multisite interictal EEG from a perinatal excitotoxic, hippocampal lesion rat model of medial temporal lobe epilepsy. Sample entropy, an information theoretical measure, demonstrated decreased complexity at different time scales and across all channels in epileptic animals. However, higher-order multiarea measures showed evidence of increased variability in population correlation measures. This apparent paradox was resolved by noting that although the EEG from epileptic animals was overall more stereotyped, there were frequent periods where two or more brain areas "broke off" from ongoing brain activity in epileptic animals, producing decorrelations between areas. These decorrelations were particularly apparent across the midline, suggesting impairments of interhemispheric coordination, a form of interhemispheric diaschisis. Both the observed alterations could contribute to a reduction in brain functionality: an overall reduction in complexity and a failure of interhemispheric brain coordination, suggesting a breakdown in communication between hemispheres. The authors speculate that any tendency of areas to lose communication or break away from coordinated brain activity might predispose to seizures in these areas.

In an effort to design a simulation environment that is more similar to that of neurophysiology, we introduce a virtual slice setup in the NEURON simulator. The virtual slice setup runs continuously and permits parameter changes, including changes to synaptic weights and time course and to intrinsic cell properties. The virtual slice setup permits shocks to be applied at chosen locations and activity to be sampled intra- or extracellularly from chosen locations. By default, a summed population display is shown during a run to indicate the level of activity and no states are saved. Simulations can run for hours of model time, therefore it is not practical to save all of the state variables. These, in any case, are primarily of interest at discrete times when experiments are being run: the simulation can be stopped momentarily at such times to save activity patterns. The virtual slice setup maintains an automated notebook showing shocks and parameter changes as well as user comments. We demonstrate how interaction with a continuously running simulation encourages experimental prototyping and can suggest additional dynamical features such as ligand wash-in and wash-out-alternatives to typical instantaneous parameter change. The virtual slice setup currently uses event-driven cells and runs at approximately 2 min/h on a laptop.

The scale of large neuronal network simulations is memory limited due to the need to store connectivity information: connectivity storage grows as the square of neuron number up to anatomically relevant limits. Using the NEURON simulator as a discrete-event simulator (no integration), we explored the consequences of avoiding the space costs of connectivity through regenerating connectivity parameters when needed: just in time after a presynaptic cell fires. We explored various strategies for automated generation of one or more of the basic static connectivity parameters: delays, postsynaptic cell identities, and weights, as well as run-time connectivity state: the event queue. Comparison of the JitCon implementation to NEURON's standard NetCon connectivity method showed substantial space savings, with associated run-time penalty. Although JitCon saved space by eliminating connectivity parameters, larger simulations were still memory limited due to growth of the synaptic event queue. We therefore designed a JitEvent algorithm that added items to the queue only when required: instead of alerting multiple postsynaptic cells, a spiking presynaptic cell posted a callback event at the shortest synaptic delay time. At the time of the callback, this same presynaptic cell directly notified the first postsynaptic cell and generated another self-callback for the next delay time. The JitEvent implementation yielded substantial additional time and space savings. We conclude that just-in-time strategies are necessary for very large network simulations but that a variety of alternative strategies should be considered whose optimality will depend on the characteristics of the simulation to be run.