Switchable slow cellular conductances determine robustness and tunability of network states.

Neuronal information processing is regulated by fast and localized fluctuations of brain states. Brain states reliably switch between distinct spatiotemporal signatures at a network scale even though they are composed of heterogeneous and variable rhythms at a cellular scale. We investigated the mechanisms of this network control in a conductance-based population model that reliably switches between active and oscillatory mean-fields. Robust control of the mean-field properties relies critically on a switchable negative intrinsic conductance at the cellular level. This conductance endows circuits with a shared cellular positive feedback that can switch population rhythms on and off at a cellular resolution. The switch is largely independent from other intrinsic neuronal properties, network size and synaptic connectivity. It is therefore compatible with the temporal variability and spatial heterogeneity induced by slower regulatory functions such as neuromodulation, synaptic plasticity and homeostasis. Strikingly, the required cellular mechanism is available in all cell types that possess T-type calcium channels but unavailable in computational models that neglect the slow kinetics of their activation.

Activation of D2 autoreceptors alters cocaine-induced locomotion and slows down local field oscillations in the rat ventral tegmental area.

Psychoactive substances affecting the dopaminergic system induce locomotor activation and, in high doses, stereotypies. Network mechanisms underlying the shift from an active goal-directed behavior to a "seemingly purposeless" stereotypic locomotion remain unclear. In the present study we sought to determine the relationships between the behavioral effects of dopaminergic drugs and their effects on local field potentials (LFPs), which were telemetrically recorded within the ventral tegmental area (VTA) of freely moving rats. We used the D2/D3 agonist quinpirole in a low, autoreceptor-selective (0.1 mg/kg, i.p.) and in a high (0.5 mg/kg, i.p.) dose, and a moderate dose of cocaine (10 mg/kg, i.p.). In the control group, power spectrum analysis revealed a prominent peak of LFP power in the theta frequency range during active exploration. Cocaine alone stimulated locomotion, but had no significant effect on the peak of the LFP power. In contrast, co-administration of low dose quinpirole with cocaine markedly altered the pattern of locomotion, from goal-directed exploratory behavior to recurrent motion resembling locomotor stereotypy. This behavioral effect was accompanied by a shift of the dominant theta power toward a significantly lower (by ∼15%) frequency. High dose quinpirole also provoked an increased locomotor activity with signs of behavioral stereotypies, and also induced a shift of the dominant oscillation frequency toward the lower range. These results demonstrate a correlation between the LFP oscillation frequency within the VTA and a qualitative aspect of locomotor behavior, perhaps due to a variable level of coherence of this region with its input or output areas.

Dynamic Input Conductances Shape Neuronal Spiking

Assessing the role of biophysical parameter variations in neuronal activity is critical to the understanding of modulation, robustness, and homeostasis of neuronal signalling. The paper proposes that this question can be addressed through the analysis of dynamic input conductances. Those voltage-dependent curves aggregate the concomitant activity of all ion channels in distinct timescales. They are shown to shape the current-voltage dynamical relationships that determine neuronal spiking. We propose an experimental protocol to measure dynamic input conductances in neurons. In addition, we provide a computational method to extract dynamic input conductances from arbitrary conductance-based models and to analyze their sensitivity to arbitrary parameters. We illustrate the relevance of the proposed approach for modulation, compensation, and robustness studies in a published neuron model based on data of the stomatogastric ganglion of the crab Cancer borealis.

A positive feedback at the cellular level promotes robustness and modulation at the circuit level.

This article highlights the role of a positive feedback gating mechanism at the cellular level in the robustness and modulation properties of rhythmic activities at the circuit level. The results are presented in the context of half-center oscillators, which are simple rhythmic circuits composed of two reciprocally connected inhibitory neuronal populations. Specifically, we focus on rhythms that rely on a particular excitability property, the postinhibitory rebound, an intrinsic cellular property that elicits transient membrane depolarization when released from hyperpolarization. Two distinct ionic currents can evoke this transient depolarization: a hyperpolarization-activated cation current and a low-threshold T-type calcium current. The presence of a slow activation is specific to the T-type calcium current and provides a slow positive feedback at the cellular level that is absent in the cation current. We show that this slow positive feedback is required to endow the network rhythm with physiological modulation and robustness properties. This study thereby identifies an essential cellular property to be retained at the network level in modeling network robustness and modulation.

Design and validation of a real-time spiking-neural-network decoder for brain-machine interfaces.

OBJECTIVE: Cortically-controlled motor prostheses aim to restore functions lost to neurological disease and injury. Several proof of concept demonstrations have shown encouraging results, but barriers to clinical translation still remain. In particular, intracortical prostheses must satisfy stringent power dissipation constraints so as not to damage cortex. APPROACH: One possible solution is to use ultra-low power neuromorphic chips to decode neural signals for these intracortical implants. The first step is to explore in simulation the feasibility of translating decoding algorithms for brain-machine interface (BMI) applications into spiking neural networks (SNNs). MAIN RESULTS: Here we demonstrate the validity of the approach by implementing an existing Kalman-filter-based decoder in a simulated SNN using the Neural Engineering Framework (NEF), a general method for mapping control algorithms onto SNNs. To measure this system's robustness and generalization, we tested it online in closed-loop BMI experiments with two rhesus monkeys. Across both monkeys, a Kalman filter implemented using a 2000-neuron SNN has comparable performance to that of a Kalman filter implemented using standard floating point techniques. SIGNIFICANCE: These results demonstrate the tractability of SNN implementations of statistical signal processing algorithms on different monkeys and for several tasks, suggesting that a SNN decoder, implemented on a neuromorphic chip, may be a feasible computational platform for low-power fully-implanted prostheses. The validation of this closed-loop decoder system and the demonstration of its robustness and generalization hold promise for SNN implementations on an ultra-low power neuromorphic chip using the NEF.

A Brain-Machine Interface Operating with a Real-Time Spiking Neural Network Control Algorithm.

Motor prostheses aim to restore function to disabled patients. Despite compelling proof of concept systems, barriers to clinical translation remain. One challenge is to develop a low-power, fully-implantable system that dissipates only minimal power so as not to damage tissue. To this end, we implemented a Kalman-filter based decoder via a spiking neural network (SNN) and tested it in brain-machine interface (BMI) experiments with a rhesus monkey. The Kalman filter was trained to predict the arm's velocity and mapped on to the SNN using the Neural Engineering Framework (NEF). A 2,000-neuron embedded Matlab SNN implementation runs in real-time and its closed-loop performance is quite comparable to that of the standard Kalman filter. The success of this closed-loop decoder holds promise for hardware SNN implementations of statistical signal processing algorithms on neuromorphic chips, which may offer power savings necessary to overcome a major obstacle to the successful clinical translation of neural motor prostheses.

Spiking Neural Network Decoder for Brain-Machine Interfaces.

We used a spiking neural network (SNN) to decode neural data recorded from a 96-electrode array in premotor/motor cortex while a rhesus monkey performed a point-to-point reaching arm movement task. We mapped a Kalman-filter neural prosthetic decode algorithm developed to predict the arm's velocity on to the SNN using the Neural Engineering Framework and simulated it using Nengo, a freely available software package. A 20,000-neuron network matched the standard decoder's prediction to within 0.03% (normalized by maximum arm velocity). A 1,600-neuron version of this network was within 0.27%, and run in real-time on a 3GHz PC. These results demonstrate that a SNN can implement a statistical signal processing algorithm widely used as the decoder in high-performance neural prostheses (Kalman filter), and achieve similar results with just a few thousand neurons. Hardware SNN implementations-neuromorphic chips-may offer power savings, essential for realizing fully-implantable cortically controlled prostheses.