Stochastic Ion Channel Gating in Dendritic Neurons: Morphology Dependence and Probabilistic Synaptic Activation of Dendritic Spikes

Neuronal activity is mediated through changes in the probability of stochastic transitions between open and closed states of ion channels. While differences in morphology define neuronal cell types and may underlie neurological disorders, very little is known about influences of stochastic ion channel gating in neurons with complex morphology. We introduce and validate new computational tools that enable efficient generation and simulation of models containing stochastic ion channels distributed across dendritic and axonal membranes. Comparison of five morphologically distinct neuronal cell types reveals that when all simulated neurons contain identical densities of stochastic ion channels, the amplitude of stochastic membrane potential fluctuations differs between cell types and depends on sub-cellular location. For typical neurons, the amplitude of membrane potential fluctuations depends on channel kinetics as well as open probability. Using a detailed model of a hippocampal CA1 pyramidal neuron, we show that when intrinsic ion channels gate stochastically, the probability of initiation of dendritic or somatic spikes by dendritic synaptic input varies continuously between zero and one, whereas when ion channels gate deterministically, the probability is either zero or one. At physiological firing rates, stochastic gating of dendritic ion channels almost completely accounts for probabilistic somatic and dendritic spikes generated by the fully stochastic model. These results suggest that the consequences of stochastic ion channel gating differ globally between neuronal cell-types and locally between neuronal compartments. Whereas dendritic neurons are often assumed to behave deterministically, our simulations suggest that a direct consequence of stochastic gating of intrinsic ion channels is that spike output may instead be a probabilistic function of patterns of synaptic input to dendrites

Differential Effects of Cocaine on Dopamine Neuron Firing in Awake and Anesthetized Rats

Cocaine (benzoylmethylecgonine), a natural alkaloid, is a powerful psychostimulant and a highly addictive drug. Unfortunately, the relationships between its behavioral and electrophysiological effects are not clear. We investigated the effects of cocaine on the firing of midbrain dopaminergic (DA) neurons, both in anesthetized and awake rats, using pre-implanted multielectrode arrays and a recently developed telemetric recording system. In anesthetized animals, cocaine (10 mg/kg, intraperitoneally) produced a general decrease of the firing rate and bursting of DA neurons, sometimes preceded by a transient increase in both parameters, as previously reported by others. In awake rats, however, injection of cocaine led to a very different pattern of changes in firing. A decrease in firing rate and bursting was observed in only 14% of DA neurons. Most of the other DA neurons underwent increases in firing rate and bursting: these changes were correlated with locomotor activity in 52% of the neurons, but were uncorrelated in 29% of them. Drug concentration measurements indicated that the observed differences between the two conditions did not have a pharmacokinetic origin. Taken together, our results demonstrate that cocaine injection differentially affects the electrical activity of DA neurons in awake and anesthetized states. The observed increases in neuronal activity may in part reflect the cocaine-induced synaptic potentiation found ex vivo in these neurons. Our observations also show that electrophysiological recordings in awake animals can uncover drug effects, which are masked by general anesthesia

Transistor analogs of emergent iono-neuronal dynamics

Neuromorphic analog metal-oxide-silicon (MOS) transistor circuits promise compact, low-power, and high-speed emulations of iono-neuronal dynamics orders-of-magnitude faster than digital simulation. However, their inherently limited input voltage dynamic range vs power consumption and silicon die area tradeoffs makes them highly sensitive to transistor mismatch due to fabrication inaccuracy, device noise, and other nonidealities. This limitation precludes robust analog very-large-scale-integration (aVLSI) circuits implementation of emergent iono-neuronal dynamics computations beyond simple spiking with limited ion channel dynamics. Here we present versatile neuromorphic analog building-block circuits that afford near-maximum voltage dynamic range operating within the low-power MOS transistor weak-inversion regime which is ideal for aVLSI implementation or implantable biomimetic device applications. The fabricated microchip allowed robust realization of dynamic iono-neuronal computations such as coincidence detection of presynaptic spikes or pre- and postsynaptic activities. As a critical performance benchmark, the high-speed and highly interactive iono-neuronal simulation capability on-chip enabled our prompt discovery of a minimal model of chaotic pacemaker bursting, an emergent iono-neuronal behavior of fundamental biological significance which has hitherto defied experimental testing or computational exploration via conventional digital or analog simulations. These compact and power-efficient transistor analogs of emergent iono-neuronal dynamics open new avenues for next-generation neuromorphic, neuroprosthetic, and brain-machine interface applications