Tinbergen’s challenge for the neuroscience of behavior

Nobel laureate Nikolaas Tinbergen provided clear criteria for declaring a neuroscience problem solved, criteria which despite the passage of more than 50 years and vastly expanded neuroscience tool kits remain applicable today. Tinbergen said for neuroscientists to claim that a behavior is understood, they must correspondingly understand its (i) development and its (ii) mechanisms and its (iii) function and its (iv) evolution. Now, all four of these domains represent hotbeds of current experimental work, each using arrays of new techniques which overlap only partly. Thus, as new methodologies come online, from single-nerve-cell RNA sequencing, for example, to smart FISH, large-scale calcium imaging from cortex and deep brain structures, computational ethology, and so on, one person, however smart, cannot master everything. Our response to the likely “fracturing” of neuroscience recognizes the value of ever larger consortia. This response suggests new kinds of problems for (i) funding and (ii) the fair distribution of credit, especially for younger scientists.

Localization of immunoreactivities for neuropeptides and neurotransmitter-synthesizing enzymes in the pterygopalatine ganglion of the pig

Study on the presence of the selected biologically active substances in nerve structures of the porcine pterygopalatine ganglion was performed with the use of immunofluorescence and RT-PCR. All neurons in the ganglion were ChAT-, VAChT-, NOS- and VIP- positive. However, some neurons displayed strong immunoreactivity, while in other neurons, immunoreactivity was moderate, or weak. Somatostatin (SOM) was present in approx. 11% of neurons. Tyrosine hydroxylase-positive (TH-positive) neurons were not detected, although in single nerve cell bodies, TH antibody revealed very weak staining which could be attributed to some residual TH immunoreactivity. Immunoreactivity to NPY was found in 25% of all neuronal perikarya while PACAP was present only in 2–3% of them. More numerous neurons (6%) contained immunoreactivity to GAL. No neurons stained for SP or CGRP. Numerous ChAT-, VAChT-, NOS-, VIP-, and PACAP-positive, scarce SP and CGRP-positive, single SOM-, NPY- and GAL-positive nerve fibers were observed throughout the ganglion. No TH immunoreactivity was found in the nerve fibres. RT-PCR detected strong signal of the transcripts of ChAT, SOM, NOS, VIP, NPY, PACAP, and GAL. Only very weak signal was observed in case of TH, SP and CGRP. No RT-PCR was performed for VAChT message.

Simplified Models of Individual Neurons

In the previous thirteen chapters, we met and described, sometimes in excruciating detail, the constitutive elements making up the neuronal hardware: dendrites, synapses, voltagedependent conductances, axons, spines and calcium. We saw how, different from electronic circuits in which only very few levels of organization exist, the nervous systems has many tightly interlocking levels of organization that codepend on each other in crucial ways. It is now time to put some of these elements together into a functioning whole, a single nerve cell. With such a single nerve cell model in hand, we can ask functional questions, such as: at what time scale does it operate, what sort of operations can it carry out, and how good is it at encoding information. We begin this Herculean task by (1) completely neglecting the dendritic tree and (2) replacing the conductance-based description of the spiking process (e.g., the Hodgkin- Huxley equations) by one of two canonical descriptions. These two steps dramatically reduce the complexity of the problem of characterizing the electrical behavior of neurons. Instead of having to solve coupled, nonlinear partial differential equations, we are left with a single ordinary differential equation. Such simplifications allow us to formally treat networks of large numbers of interconnected neurons, as exemplified in the neural network literature, and to simulate their dynamics. Understanding any complex system always entails choosing a level of description that retains key properties of the system while removing those nonessential for the purpose at hand. The study of brains is no exception to this. Numerous simplified single-cell models have been proposed over the years, yet most of them can be reduced to just one of two forms. These can be distinguished by the form of their output: spike or pulse models generate discrete, all-or-none impulses.