About NeuronBank
NeuronBank project is an online reference source and informatics tool for exploring our vast knowledge of neurons and the circuits that they form. NeuronBank is still early in its development.
Nervous systems from different species will be represented in different branches. Users can search within one branch or search across branches for features of neurons and neural circuits.
Join the list serve for users and developers to get updates about NeuronBank and to participate in discussions about its development. http://mailbox.gsu.edu/mailman/listinfo/neuronbank
What is the Neurome?
Just as the collection of all genes in an organism is its genome, the collection of all neurons is its neurome. Similarly the study of the neurome is the field of neuromics. The function of the nervous system is to process information. This is ccomplished with networks of interconnected neurons. In order to understand how the brain and the nervous system process information, it would be useful to have a wiring diagram with all of the neurons and connections. It is unlikely that we will ever have a complete wiring diagram for any nervous system except the most simple organisms. However, we will be able to develop a partial wiring diagram for many complex organisms including mammals. Therefore, it is extremely useful to develop a means to catalog and curate our knowledge about neurons and synaptic connectivity.
Why is NeuronBank Needed?
Despite the fundamental value of identifying neurons and mapping circuits, progress seems to have slowed in recent years. For example, the Aplysia abdominal ganglion has proven invaluable for basic research on learning and memory, yet only 247 of its estimated 1600 neurons (Coggeshall, 1976) have been identified, and no new identifications have been made in the past 10 years (full review is here). Similarly, only 324 of an estimated 8000 neurons have been identified in the Tritonia central ganglia, with nearly 300 neurons identified in the initial two publications (Getting, 1976; Willows et al., 1973).
Consolidate our Current Knowledge. Neuroscience has produced a wealth of data on identified neurons and the circuits they form, but this data is distributed over decades of journal articles. It is thus difficult to organize and increasingly fragmented over time. For example, a researcher interested in the siphon-withdrawal circuit in Aplysia californica would have to delve into 40 years of publications, some of which were subsequently shown to contain inaccuracies and nomenclature inconsistencies. Clearly, journal-based representation of neural circuitry is untenable. To facilitate progress, we need to develop systems for entering, storing, and mining information about identified neuron types and their connections. The NeuronBank project seeks to fill this need.
Unpublishable data. The impact of new neuron identifications diminishes in proportion to the number already known and has generally fallen below the level for journal-based publication. NeuronBank will help alleviate this problem by serving as a repository for new observations of identified neurons and their synaptic connections. Importantly, new observations will be uniquely citable and credited to the reporting lab.
Informatics tool for Identifying New Neurons. Identifying new neurons is the tedious task of ensuring that the suite of characteristics that describe a neuron is unique and will enable definitive identification for future experiments. NeuronBank will allow users to rapidly check the uniqueness of an observation and even will be able to suggest diagnostics for further clarifying if a neuron is a known type or novel. For new cell types, NeuronBank can compare neurons with neighboring identified types and suggest the qualities that would provide the most distinctive markers of identity. Thus, NeuronBank will function not only as a knowledge base but also as an innovative informatics approach to identifying new neuron types. This functionality could be particularly fruitful, as researchers regularly come across apparently novel cell types in the course of experimentation. Without a quick way to cross-reference the observation against other known types or a means of publishing the observation, these novel cells are usually abandoned and ignored. The availability of NeuronBank could help transform these un-utilized observations into a rapid expansion in the state of knowledge.
Coggeshall RE (1967) A light and electron microscope study of the abdominal ganglion of Aplysia californica. Journal of Neurophysiology 30:1263-1287.
Getting PA (1976) Afferent neurons mediating escape-swimming of the marine molusk, Tritonia diomedia. Journal of comparative physiology. A, Sensory, neural, and behavioral physiology . 110:271-386.
Willows AO, Dorsett DA, Hoyle G (1973) The neuronal basis of behavior in Tritonia. I. Functional organization of the central nervous system. Journal of Neurobiology 4:207-237.
What is an identified neuron?
Neurons can be reliably sorted into classes based on their morphology, synaptic connectivity, physiological profile, and molecular contents. In invertebrate nervous systems, neuron classification can be so fine-grained as to reach the level of individually identifiable neurons–single neurons can be reliably found in every animal and distinguished from all other neurons in the nervous system (Bullock, 2000; Comer and Robertson, 2001; Leonard, 2000).
Classifying and identifying neurons allows neuroscientists to trace neural circuits, providing fundamental insight into information flow and processing in a nervous system. Over the past 40 years, mapped circuits of identified neurons have proven an invaluable framework for studying all aspects of nerual function. Examples of important invertebrate model circuits include the central pattern generator (CPG) for swimming in the mollusc Tritonia (Getting, 1989) , the gill and siphon withdrawal circuit and feeding circuitry in the mollusc Aplysia (Hawkins et al., 1993), circuits for heartbeat control, local bending, and swimming in the leech (Brodfuehrer et al., 1995; Brodfuehrer and Thorogood, 2001; Calabrese et al., 1995; Kristan, Jr. et al., 1995), visual circuits in insects (Borst and Haag, 2002), circuits for foregut movements in lobsters and crabs (Nusbaum and Beenhakker, 2002), and escape responses in crayfish (Edwards et al., 1999).
Identified Neurons in the Abdominal Ganglion of Aplysia californica
There are an estimated 1600 neurons in the adult Aplysia abdominal ganglion (Coggeshal, 1967). Approximately 78 classes of neurons have been identified in this ganglion, encompassing 247 individual neurons. Thus, nearly 16% of the ganglion has been elucidated. A complete list of cell types with references is here. Note that these totals do not include the bag cells.
The rate of discovery can be seen in the following table, which shows the total number of cells identified by year in 5-year blocks. The number of new identifications has dwindled, and no new cells have been identified since 1995 (Hickie and Walters, 1995)