Wide propagation of graded signals in nonspiking neurons

Integrating Non-spiking Interneurons in Spiking Neural Networks

Researchers working with neural networks have historically focused on either non-spiking neurons tractable for running on computers or more biologically plausible spiking neurons typically requiring special hardware. However, in nature homogeneous networks of neurons do not exist. Instead, spiking and non-spiking neurons cooperate, each bringing a different set of advantages. A well-researched biological example of such a mixed network is a sensorimotor pathway, responsible for mapping sensory inputs to behavioral changes. This type of pathway is also well-researched in robotics where it is applied to achieve closed-loop operation of legged robots by adapting amplitude, frequency, and phase of the motor output. In this paper we investigate how spiking and non-spiking neurons can be combined to create a sensorimotor neuron pathway capable of shaping network output based on analog input. We propose sub-threshold operation of an existing spiking neuron model to create a non-spiking neuron able to interpret analog information and communicate with spiking neurons. The validity of this methodology is confirmed through a simulation of a closed-loop amplitude regulating network inspired by the internal feedback loops found in insects for posturing. Additionally, we show that non-spiking neurons can effectively manipulate post-synaptic spiking neurons in an event-based architecture. The ability to work with mixed networks provides an opportunity for researchers to investigate new network architectures for adaptive controllers, potentially improving locomotion strategies of legged robots.

A tale of two leeches: Toward the understanding of the evolution and development of behavioral neural circuits

In the animal kingdom, behavioral traits encompass a broad spectrum of biological phenotypes that have critical roles in adaptive evolution, but an EvoDevo approach has not been broadly used to study behavior evolution. Here, we propose that, by integrating two leech model systems, each of which has already attained some success in its respective field, it is possible to take on behavioral traits with an EvoDevo approach. We first identify the developmental changes that may theoretically lead to behavioral evolution and explain why an EvoDevo study of behavior is challenging. Next, we discuss the pros and cons of the two leech model species, Hirudo, a classic model for invertebrate neurobiology, and Helobdella, an emerging model for clitellate developmental biology, as models for behavioral EvoDevo research. Given the limitations of each leech system, neither is particularly strong for behavioral EvoDevo. However, the two leech systems are complementary in their technical accessibilities, and they do exhibit some behavioral similarities and differences. By studying them in parallel and together with additional leech species such as Haementeria, it is possible to explore the different levels of behavioral development and evolution.

Spike-induced ordering: Stochastic neural spikes provide immediate adaptability to the sensorimotor system

The functional advantages of using a stochastically spiking neural network (sSNN) instead of a nonspiking neural network (NS-NN) have remained largely unknown. We developed an architecture which enabled the parametric adjustment of the spikiness (i.e., impulsive dynamics and stochasticity) of the sSNN output and observed that stochastic spikes instantaneously induced the ordered motion of a dynamical system. We demonstrated the benefits of sSNNs using a musculoskeletal bipedal walker and, moreover, showed that the decrease in the spikiness of motor neuron output leads to a reduction in adaptability. Stochastic spikes may aid the adaptation of a biological system to sudden perturbations or environmental changes. Our architecture can easily be connected to the conventional NS-NN and may superimpose the on-site adaptability.

Most biological neurons exhibit stochastic and spiking action potentials. However, the benefits of stochastic spikes versus continuous signals other than noise tolerance and energy efficiency remain largely unknown. In this study, we provide an insight into the potential roles of stochastic spikes, which may be beneficial for producing on-site adaptability in biological sensorimotor agents. We developed a platform that enables parametric modulation of the stochastic and discontinuous output of a stochastically spiking neural network (sSNN) to the rate-coded smooth output. This platform was applied to a complex musculoskeletal–neural system of a bipedal walker, and we demonstrated how stochastic spikes may help improve on-site adaptability of a bipedal walker to slippery surfaces or perturbation of random external forces. We further applied our sSNN platform to more general and simple sensorimotor agents and demonstrated four basic functions provided by an sSNN: 1) synchronization to a natural frequency, 2) amplification of the resonant motion in a natural frequency, 3) basin enlargement of the behavioral goal state, and 4) rapid complexity reduction and regular motion pattern formation. We propose that the benefits of sSNNs are not limited to musculoskeletal dynamics. Indeed, a wide range of the stability and adaptability of biological systems may arise from stochastic spiking dynamics.

Graded boosting of synaptic signals by low-threshold voltage-activated calcium conductance

Low-threshold voltage-activated calcium conductances (LT-VACCs) play a substantial role in shaping the electrophysiological attributes of neurites. We have investigated how these conductances affect synaptic integration in a premotor nonspiking (NS) neuron of the leech nervous system. These cells exhibit an extensive neuritic tree, do not fire Na(+)-dependent spikes, but express an LT-VACC that was sensitive to 250 μM Ni(2+) and 100 μM NNC 55-0396 (NNC). NS neurons responded to excitation of mechanosensory pressure neurons with depolarizing responses for which amplitude was a linear function of the presynaptic firing frequency. NNC decreased these synaptic responses and abolished the concomitant widespread Ca(2+) signals. Coherent with the interpretation that the LT-VACC amplified signals at the postsynaptic level, this conductance also amplified the responses of NS neurons to direct injection of sinusoidal current. Synaptic amplification thus is achieved via a positive feedback in which depolarizing signals activate an LT-VACC that, in turn, boosts these signals. The wide distribution of LT-VACC could support the active propagation of depolarizing signals, turning the complex NS neuritic tree into a relatively compact electrical compartment.

Recurrent inhibition in motor systems, a comparative analysis

The review proposes a comparison between recurrent inhibition in motor systems of vertebrates and the leech nervous system, where a detailed cellular and functional analysis has been accomplished. A comparative study shows that recurrent inhibition is a conserved property in motor systems of phylogenetically distant species. Recurrent inhibition has been extensively characterized in the spinal cord of mammals, where Renshaw cells receive excitatory synaptic inputs from motoneurons (MNs) and, in turn, exert an inhibitory effect on the MNs. In the leech, a recurrent inhibitory circuit has been described, centered around a pair of nonspiking (NS) neurons. NS are linked to every excitatory MN through rectifying electrical junctions. And, in addition, the MNs are linked to the NS neurons through hyperpolarizing chemical synapses. Functional analysis of this leech circuit showed that heteronymous MNs in the leech are electrically coupled and this coupling is modulated by the membrane potential of NS neurons. Like Renshaw cells, the membrane potential of NS neurons oscillates in phase with rhythmic motor patterns. Functional analysis performed in the leech shows that NS influences the activity of MNs in the course of crawling suggesting that the recurrent inhibitory circuit modulates the motor performance.