bioRxiv Science⌕ Search

Biology subjects

Carnevale, N. T.

Publications and source records attributed to Carnevale, N. T..

4 recordsLinked to original sources

Neuronal phase shifts differ for excitation vs. inhibition: a computer modeling study

Rhythmic activity is ubiquitous in neural systems, and impedance analysis has been widely used to examine frequency-dependent responses of neuronal membranes to rhythmic inputs. Impedance analysis assumes the neuronal membrane is a linear system, requiring the use of small signals to stay in a near-linear regime. However, postsynaptic potentials are often large and trigger nonlinear mechanisms. We therefore augmented impedance analysis to evaluate membrane responses in this nonlinear domain, analyzing responses to injected current for subthreshold membrane voltage (Vmemb), suprathreshold spike-blocked Vmemb, and spiking in a validated neocortical pyramidal neuron computer model. Responses in these output regimes were asymmetrical, with different phase shifts during hyperpolarizing and depolarizing half-cycles. Suprathreshold chirp stimulation gave equivocal results due to nonstationarity of response, requiring us to use fixed-frequency sinusoids. Sinusoidal inputs produced phase retreat: action potentials occurred progressively later in cycles of the input stimulus, resulting from adaptation. Conversely, sinusoidal current with increasing amplitude over cycles produced a pattern of phase advance: action potentials occurred progressively earlier. Phase retreat was dependent on Ih and IAHP currents; phase advance was modulated by these currents. Our results suggest differential responses of cortical neurons depending on the frequency of oscillatory input in the delta - beta range, which will play a role in neuronal responses to shifts in network state. We hypothesize that intrinsic cellular properties complement network properties and contribute to in vivo phase-shift phenomena such as phase precession, seen in place and grid cells, and phase roll, observed in hippocampal CA1 neurons. New & NoteworthyWe augmented electrical impedance analysis to characterize phase shifts between large amplitude current stimuli and nonlinear, asymmetric membrane potential responses. We predict different frequency-dependent phase shifts in response excitation versus inhibition, as well as shifts in spike timing over multiple input cycles, in resonant pyramidal neurons. We hypothesize that these effects contribute to navigation-related phenomena like phase precession and phase roll. Our neuron-level hypothesis complements, rather than falsifies, prior network-level explanations of these phenomena.

neuroscience↗

Modernizing the NEURON Simulator for Sustainability, Portability, and Performance

AO_SCPLOWBSTRACTC_SCPLOWThe need for reproducible, credible, multiscale biological modeling has led to the development of standardized simulation platforms, such as the widely-used NEURON environment for computational neuroscience. Developing and maintaining NEURON over several decades has required attention to the competing needs of backwards compatibility, evolving computer architectures, the addition of new scales and physical processes, accessibility to new users, and efficiency and flexibility for specialists. In order to meet these challenges, we have now substantially modernized NEURON, providing continuous integration, an improved build system and release workflow, and better documentation. With the help of a new source-to-source compiler of the NMODL domain-specific language we have enhanced NEURONs ability to run efficiently, via the CoreNEURON simulation engine, on a variety of hardware platforms, including GPUs. Through the implementation of an optimized in-memory transfer mechanism this performance optimized backend is made easily accessible to users, providing training and model-development paths from laptop to workstation to supercomputer and cloud platform. Similarly, we have been able to accelerate NEURONs reaction-diffusion simulation performance through the use of just-in-time compilation. We show that these efforts have led to a growing developer base, a simpler and more robust software distribution, a wider range of supported computer architectures, a better integration of NEURON with other scientific workflows, and substantially improved performance for the simulation of biophysical and biochemical models.

neuroscience↗

Effects of Ih and TASK-like shunting current on dendritic impedance in layer 5 pyramidal-tract neurons

Pyramidal neurons in neocortex have complex input-output relationships that depend on their morphologies, ion channel distributions, and the nature of their inputs, but which cannot be replicated by simple integrate-and-fire models. The impedance properties of their dendritic arbors, such as resonance and phase shift, shape neuronal responses to synaptic inputs and provide intraneuronal functional maps reflecting their intrinsic dynamics and excitability. Experimental studies of dendritic impedance have shown that neocortical pyramidal tract neurons exhibit distance-dependent changes in resonance and impedance phase with respect to the soma. We therefore investigated how well several biophysically-detailed multi-compartment models of neocortical layer 5 pyramidal tract neurons reproduce the location-dependent impedance profiles observed experimentally. Each model tested here exhibited location-dependent impedance profiles, but most captured either the observed impedance amplitude or phase, not both. The only model that captured features from both incorporates HCN channels and a shunting current, like that produced by Twik-related acid-sensitive K+(TASK) channels. TASK-like channel activity in this model was dependent on local peak HCN channel conductance (Ih). We found that while this shunting current alone is insufficient to produce resonance or realistic phase response, it modulates all features of dendritic impedance, including resonance frequencies, resonance strength, synchronous frequencies, and total inductive phase. We also explored how the interaction of Ih and a TASK-like shunting current shape synaptic potentials and produce degeneracy in dendritic impedance profiles, wherein different combinations of Ih and shunting current can produce the same impedance profile. New & NoteworthyWe simulated chirp current stimulation in the apical dendrites of 5 biophysically-detailed multi-compartment models of neocortical pyramidal tract neurons and found that a combination of HCN channels and TASK-like channels produced the best fit to experimental measurements of dendritic impedance. We then explored how HCN and TASK-like channels can shape the dendritic impedance as well as the voltage response to synaptic currents.

neuroscience↗

Human Neocortical Neurosolver (HNN): A new software tool for interpreting the cellular and network origin of human MEG/EEG data

Magneto- and electro-encephalography (MEG/EEG) non-invasively record human brain activity with millisecond resolution providing reliable markers of healthy and disease states. Relating these macroscopic signals to underlying cellular- and circuit-level generators is a limitation that constrains using MEG/EEG to reveal novel principles of information processing or to translate findings into new therapies for neuropathology. To address this problem, we built Human Neocortical Neurosolver (HNN, https://hnn.brown.edu) software. HNN has a graphical user interface designed to help researchers and clinicians interpret the neural origins of MEG/EEG. HNNs core is a neocortical circuit model that accounts for biophysical origins of electrical currents generating MEG/EEG. Data can be directly compared to simulated signals and parameters easily manipulated to develop/test hypotheses on a signals origin. Tutorials teach users to simulate commonly measured signals, including event related potentials and brain rhythms. HNNs ability to associate signals across scales makes it a unique tool for translational neuroscience research.

neuroscience↗