Nonspiking neurons

History of Discovery

Animals

It is a well-known fact that there are an abundance of neurons that propagate messages via action potentials and the mechanics of this particular kind of transmission. Through studying these complex neural networks (spiking networks), a neuron that did not exhibit characteristic spiking behavior was discovered. These neurons exhibit a graded potential to transmit data and this method of transmission has a huge effect on the fidelity, strength, and lifetime of the signal. These were identified as a special kind of interneuron and function as an intermediary process for sensory-motor systems. Animals have become substantial models for understanding more about non-spiking neural networks and the role they play in an animal’s ability to process information and its overall function. Crustaceans and insects such as the crawfish have created many opportunities to learn about the modulatory role that these neurons have and how they can be modulated regardless of their lack of exhibiting an action potential. Most of the known information comes from animal models. Studies focus on neuromuscular joints and modulation of abdominal motor cells. Modulatory interneurons are literally neurons that are physically situated next to muscle fibers and innervate the fibers to allow for movement. ^[1] There have been advances in the study by furthering the delineation amongst different types of interneurons and how specific inhibitors allow these cells to be silenced indicating that the membrane exhibits some amount of specificity allowing the rules of competitive or steric hindrance to affect the overall function of these non-spiking neurons. Studies been done on the non-spiking qualities of specific non-spiking networks that exist in humans, e.g. retina amacrine cell of the eye. Animal models indicate that the interneurons modulate directional and posture creating behaviors. -^{[2] [3]}

Definition and Physiology

Definition

A non-spiking neuron is simply a neuron that exhibits transmission of a signal via graded potential which means that it is firing a signal regardless of any membrane potential threshold. They are primitive in that sense that they have no on or off switch, but will transmit any bit of noise that is may receive. Studies show that these neurons may offer a contribution to learning and modulation of motor neuron networks.

Spiking neurons and non-spiking neurons are usually integrated into the same neural network, but they possess specific characteristics. The major difference between these two neuron types is the manner in which encoded information as electricity is propagated along a length to the central nervous system or to some locus of interneurons. Non-spiking neurons propagate messages without eliciting an action potential. They function by administering graded potentials and serve as modulation points for some neuromuscular joints. The spiking neurons are noted as traditional action potential generating neurons.^[4]

“Interneurons” is a name used to indicate neurons that are neither sensory nor motor in nature, but function as an intermediary processing and transmission state for signals that have been received via dorsal root ganglia cells. A large amount of these interneurons seem to exhibit the non-spiking characteristic. To better define what it going on with the signal transduction, many experiments have been performed to qualify and quantify the fidelity, speed, and mechanic of signal transmission in non-spiking neurons. They are referred to as interneurons. There have been classifications based on the larger group “interneurons” where pre-motor nonspiking neurons are referred to as postlateral (PL) or anteriolateral (AL) interneurons, with AL interneurons divided into three types of interneurons based on staining. The intial differentiation between PL and AL interneurons are their responses to GABA, a neurotransmitter for muscle tone. ^[5]

Characteristics and Physiology

Some studies have indicated that considering the volatility of signal transmission with these particular neurons, they still perform very well with maintaining certain signal strength. Studies have indicated that the ratio of signal to noise of some signals are at least 1000 and upwards to 10000 over 5-7mm. ^[6]

These interneurons are connected to one another via synaptic junctions and a minority, approximately 15%, exhibit bidirectional capacity and were excitatory. 77% indicated a one-way mode of transmitting signals which were inhibitory in nature. These numbers were modeled from an arthropod as pre-motor elements in the motor control system. They were located in the abdominal region. Synapses are known as gaps between neurons which facilitate the spread of a message via neurotransmitters that may excite or depress the subsequent neuron through a complex cascade of electrochemical events. For the interneurons exhibiting one-way signaling, they would receive an excitatory stimulus, experimentally, and the post-synaptic cell was given an inhibitory signal. The interaction between the two cells was modulatory in which the pre-synaptic cell with the initial excitatory signal would mediate the postsynaptic cell even after being inhibited. Signal amplitude was used to determine the effects of the modulation on the signal transmission.^[7]

The speed of signal transmission at 200Hz, the most conserved bandwidth of signal transmission for non-spiking neurons, was approximately 2500 bits/second in which there was a 10-15% decrease in speed as the signal propagated down the axon. A spiking neuron compares at 200bits/ second, but reconstruction is greater and there is less influence by noise. There are other non-spiking neurons that exhibit conserved signal transmission at other bandwidths. ^[8]

While some non-spiking neurons specifically are involved in neuromuscular modulation, studying amacrine cells has opened the door for the discussion of neuroplasticity. Since amacrine cells, which are a type of non-spiking neurons, actually undergo a transformation from spiking to non-spiking cells, there have been many studies that try to identify the functional reasons for such a transformation. Starburst amacrine cells use action potentials during retinal development, and once the retina is mature, these cells transform into non-spiking neurons. The change from a cell that can generate action potentials to solely functioning off of a graded potential is drastic, and may provide insight into why the two kinds of neural networks exist. The cells lose sodium channels. The loss of the sodium channels is triggered by the opening of the eye correlating to the possibility of the environment playing a crucial role in determination of neural cell types. The rabbit animal model was used to develop this particular study. This transition is not quite understood but heavily concludes that the spiking and non-spiking statuses occupied by the starburst amacrine cells are vital to their maturation.^[9]

Cell Types

Many of the nonspiking neurons are found near neuromuscular joints and exist as long fibers that help to innervate certain motor nerves. They function in a modulatory role by helping to establish posture and directional behavior. This was intensely modeled in the crustacean and in insects showing how appendages are oriented via these nonspiking neural pathways. ^[10] Amacrine Cells are another major type of cell that is converted to a non-spiking neuron once the retina hits maturity. They are one of the first cells to differentiate during pre-natal development and upon the opening of the eyes, these cells begin to shed their sodium ion channels and become non-spiking neurons. It was hypothesized that the reason for its establishment as a spiking neuron was to help with the maturation of the retina by simply being able to create action potentials themselves, and not necessarily the information the action potential may carry. This may be due to synchronous firing done by the starburst amacrine cells during the initial stages of development. ^[11]

Applications

Modulation

By using known neurotransmitters that affect non-spiking neurons, modeled neural networks may be modified to either ease neuromuscular hyperactivity, or cells themselves may be transformed to be able to provide stronger signals. A calcium transporter study indicates the effect that protein channels have on the overall fidelity and firing capacity of the non-spiking neurons. Since most of the propagated messages are based on a proportionality constant, meaning, there is not a temporal or spatial significance to the presynaptic firing, these signals literally "repeat what they have been told" . When it comes down to chemical systems in the body, a non-spiking neural network is definitely an area of exploration. ^[12] The amacrine cell study poses new and exciting components to the study of altering the chemical and mechanical properties of the non-spiking neural networks.^[13]

Memory and Learning

Very little is known about the application of these networks to memory and learning. There are indications that spiking and nonspiking networks both play a vital role in their creation. ^[14][15] By studying how neurons transfer information, it becomes more possible to enhance those model neural networks and better define what clear information streams could be presented. Perhaps, by conjoining this study with the many neuro-trophic factors present, neural networks could be manipulated for optimal routing, and consequently optimal learning.

Device Production

By studying the nonspiking neuron, neuroscience has been able to create models that indicate how information is propagated through a neural network. This allows for the discussion of the factors that influence how networks work, and how they may be manipulated. Non-spiking neurons seem to be more sensitive to interference given that they exhibit graded potentials. So for non-spiking neurons, any stimulus will elicit a response, whereas spiking neurons exhibit action potentials which function as an "all or none" entity.^[16]

In biomedical engineering, it is a priority to understand the biological contributions to an overall system in order to understand how things may be improved of enhanced. Paul Bach y Rita was a famous believer of neuroplasticity and integrated the principles of device design in order to model what neurons were actually doing in the brain and create a device that simulated functions already prescribed by the biological system itself. Some special advances made in the medical field based on structured models of biological systems include the cochlear implant, practices encouraged by Dr. VS Ramachandran on phantom limbs and other optical applications, and other devices that simulate electrical impulses for sensory signal transduction. By continuing to achieve a workable model of the non-spiking neural network, its applications will become evident. ^[17]

Some research indicates that nonspiking neurons have a role in learning and memory.^[18]

References

1. ^ Namba, H (1994). "DESCENDING CONTROL OF NONSPIKING LOCAL INTERNEURONS IN THE TERMINAL ABDOMINAL-GANGLION OF THE CRAYFISH". Journal of Neurophysiology 72 (1): 235–247. 
2. ^ Hikosaka, R; Takahashi M, Takahata M (1996). "Variability and invariability in the structure of an identified nonspiking interneuron of crayfish as revealed by three-dimensional morphometry.". Zoological Science 13 (1): 69–78. 
3. ^ Brunn, DE (1998). "Cooperative mechanisms between leg joints of Carausius morosus - I. Nonspiking interneurons that contribute to interjoint coordination". Journal of Neurophysiology 79 (6): 2964–2976. 
4. ^ DiCaprio, Ralph (2004). "Information Transfer Rate of Nonspiking Afferent Neurons in the Crab". Journal or Neurophysiology 92: 302–310. 
5. ^ Nagayama, T; Namba H, Aonuma H (1997). "Distribution of GABAergic premotor nonspiking local interneurones in the terminal abdominal ganglion of the crayfish". Journal of Comparative Neurology 389 (1): 139–148. 
6. ^ DiCaprio, RA (2004). "Information Transfer Rate of Nonspiking Afferent Neurons in the Crab". J Neurophysiol 92: 302–310. 
7. ^ Namba, H; Nagayama T (2004). "Synaptic interactions between nonspiking local interneurones in the terminal abdominal ganglion of the crayfish". Journal of Comparative Physiology a-Neuroethology Sensory Neural and Behavioral Physiology 190 (8): 615–622. 
8. ^ DiCaprio, RA (2004). "Information Transfer Rate of Nonspiking Afferent Neurons in the Crab". J Neurophysiol 92: 302–310. 
9. ^ Zhou, ZJ; Cheney M, Fain GL (1996). "Starburst amacrine cells change from spiking to non-spiking neurons during visual development". Investigative Ophthalmology & Visual Science. 37 (3): 5263-5263. 
10. ^ Brunn, DE (1998). "Cooperative mechanisms between leg joints of Carausius morosus - I. Nonspiking interneurons that contribute to interjoint coordination". Journal of Neurophysiology 79 (6): 2964–2976. 
11. ^ Zhou, ZJ; Cheney M, Fain GL (1996). "Starburst amacrine cells change from spiking to non-spiking neurons during visual development". Investigative Ophthalmology & Visual Science. 37 (3): 5263-5263. 
12. ^ Hayashida, Y; Yagi T (2002). "Contribution of Ca2+ transporters to electrical response of a non-spiking retinal neuron". Neurocomputing 44: 7–12. 
13. ^ Zhou, ZJ; Cheney M, Fain GL (1996). "Starburst amacrine cells change from spiking to non-spiking neurons during visual development". Investigative Ophthalmology & Visual Science. 37 (3): 5263-5263. 
14. ^ Zipser, D; Kehoe B, Littlewort G, Fuster J (1993). "A SPIKING NETWORK MODEL OF SHORT-TERM ACTIVE MEMORY.". Journal of Neuroscience 13 (8): 3406–3420. 
15. ^ Christodoulou, C; Banfield G, Cleanthous A (2010). "Self-control with spiking and non-spiking neural networks playing games". Journal of Physiology-Paris 104 (3-4): 108–117. 
16. ^ DiCaprio, Ralph (2004). "Information Transfer Rate of Nonspiking Afferent Neurons in the Crab". Journal or Neurophysiology 92: 302–310. 
17. ^ Lo, JT-H (2011). "A low-order model of biological neural networks". Neural Computation 23 (10): 2626–2682. 
18. ^ Vassiliades, Vassilis; Cleanthous, Christodoulou (2011). "Multiagent Reinforcement Learning: Spiking and Nonspiking Agents In the Iterated Prisoner's Dilemma". Ieee Transactions on Neural Networks 22 (4): 639–653. 

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