A common rule governing differentiation kinetics of mouse cortical progenitors.

The balance between proliferation and differentiation of stem cells and progenitors determines the size of an adult brain region. While the molecular mechanisms regulating proliferation and differentiation of cortical progenitors have been intensively studied, an analysis of the kinetics of progenitor choice between self-renewal and differentiation in vivo is, due to the technical difficulties, still unknown. Here we established a descriptive mathematical model to estimate the probability of self-renewal or differentiation of cortical progenitor behaviors in vivo, a variable we have termed the expansion coefficient. We have applied the model, one which depends only on experimentally measured parameters, to the developing mouse cortex where the expansive neuroepithelial cells and neurogenic radial glial progenitors are coexisting. Surprisingly, we found that the expansion coefficients of both neuroepithelium cells and radial glial progenitors follow the same developmental trajectory during cortical development, suggesting a common rule governing self-renewal/differentiation behaviors in mouse cortical progenitor differentiation.

Conserved features of the primate face code.

A recent paper demonstrated that the pattern of firing rates across ∼100 neurons in the anterior medial face patch is closely related to which human face (of 2,000) had been presented to a monkey [Chang L, Tsao DY (2017) Cell 169:1013-1028]. In addition, the firing rates for these neurons can be predicted for a novel human face. Although it is clear from this work that the firing rates of these face patch neurons encode faces, the properties of the face code have not yet been fully described. Based on an analysis of 98 neurons responding to 2,000 faces, I conclude that the anterior medial face patch uses a combinatorial rate code, one with an exponential distribution of neuron rates that has a mean rate conserved across faces. Thus, the face code is maximally informative (technically, maximum entropy) and is very similar to the code used by the fruit fly olfactory system.

A neural data structure for novelty detection.

Novelty detection is a fundamental biological problem that organisms must solve to determine whether a given stimulus departs from those previously experienced. In computer science, this problem is solved efficiently using a data structure called a Bloom filter. We found that the fruit fly olfactory circuit evolved a variant of a Bloom filter to assess the novelty of odors. Compared with a traditional Bloom filter, the fly adjusts novelty responses based on two additional features: the similarity of an odor to previously experienced odors and the time elapsed since the odor was last experienced. We elaborate and validate a framework to predict novelty responses of fruit flies to given pairs of odors. We also translate insights from the fly circuit to develop a class of distance- and time-sensitive Bloom filters that outperform prior filters when evaluated on several biological and computational datasets. Overall, our work illuminates the algorithmic basis of an important neurobiological problem and offers strategies for novelty detection in computational systems.

Deep(er) Learning.

Animals successfully thrive in noisy environments with finite resources. The necessity to function with resource constraints has led evolution to design animal brains (and bodies) to be optimal in their use of computational power while being adaptable to their environmental niche. A key process undergirding this ability to adapt is the process of learning. Although a complete characterization of the neural basis of learning remains ongoing, scientists for nearly a century have used the brain as inspiration to design artificial neural networks capable of learning, a case in point being deep learning. In this viewpoint, we advocate that deep learning can be further enhanced by incorporating and tightly integrating five fundamental principles of neural circuit design and function: optimizing the system to environmental need and making it robust to environmental noise, customizing learning to context, modularizing the system, learning without supervision, and learning using reinforcement strategies. We illustrate how animals integrate these learning principles using the fruit fly olfactory learning circuit, one of nature's best-characterized and highly optimized schemes for learning. Incorporating these principles may not just improve deep learning but also expose common computational constraints. With judicious use, deep learning can become yet another effective tool to understand how and why brains are designed the way they are.

A statistical property of fly odor responses is conserved across odors.

I have reanalyzed the data presented by Hallem and Carlson [Hallem EA, Carlson JR (2006) Cell 125(1):143-160] and shown that the combinatorial odor code supplied by the fruit fly antenna is a very simple one in which nearly all odors produce, statistically, the same neuronal response; i.e., the probability distribution of sensory neuron firing rates across the population of odorant sensory neurons is an exponential for nearly all odors and odor mixtures, with the mean rate dependent on the odor concentration. Between odors, then, the response differs according to which sensory neurons are firing at what individual rates and with what mean population rate, but not in the probability distribution of firing rates. This conclusion is independent of adjustable parameters, and holds both for monomolecular odors and complex mixtures. Because the circuitry in the antennal lobe constrains the mean firing rate to be the same for all odors and concentrations, the odor code is what is known as maximum entropy.

Novel neural circuit mechanism for visual edge detection.

The primary visual cortex is organized in a way that assigns a specific collection of neurons the job of providing the rest of the brain with all of the information it needs about each small part of the image present on the retina: Neighboring patches of the visual cortex provide the information about neighboring patches of the visual world. Each one of these cortical patches--often identified as a "pinwheel"--contains thousands of neurons, and its corresponding image patch is centered on a particular location in the retina. For stimuli within their image patch, neurons respond selectively to lines or edges with a particular slope (orientation tuning) and to regions of the patch of different sizes (known as spatial frequency tuning). The same number of neurons is devoted to reporting each possible slope (orientation). For the cells that cover different-sized regions of their image patch, however, the number of neurons assigned depends strongly on their preferred region size. Only a few neurons report on large and small parts of the image patch, but many neurons report visual information from medium-sized areas. I show here that having different numbers of neurons responsible for image regions of different sizes actually carries out a computation: Edges in the image patch are extracted. I also explain how this edge-detection computation is done.

What the fly's nose tells the fly's brain.

The fly olfactory system has a three-layer architecture: The fly's olfactory receptor neurons send odor information to the first layer (the encoder) where this information is formatted as combinatorial odor code, one which is maximally informative, with the most informative neurons firing fastest. This first layer then sends the encoded odor information to the second layer (decoder), which consists of about 2,000 neurons that receive the odor information and "break" the code. For each odor, the amplitude of the synaptic odor input to the 2,000 second-layer neurons is approximately normally distributed across the population, which means that only a very small fraction of neurons receive a large input. Each odor, however, activates its own population of large-input neurons and so a small subset of the 2,000 neurons serves as a unique tag for the odor. Strong inhibition prevents most of the second-stage neurons from firing spikes, and therefore spikes from only the small population of large-input neurons is relayed to the third stage. This selected population provides the third stage (the user) with an odor label that can be used to direct behavior based on what odor is present.

Predicting visual acuity from the structure of visual cortex.

Three decades ago, Rockel et al. proposed that neuronal surface densities (number of neurons under a square millimeter of surface) of primary visual cortices (V1s) in primates is 2.5 times higher than the neuronal density of V1s in nonprimates or many other cortical regions in primates and nonprimates. This claim has remained controversial and much debated. We replicated the study of Rockel et al. with attention to modern stereological precepts and show that indeed primate V1 is 2.5 times denser (number of neurons per square millimeter) than many other cortical regions and nonprimate V1s; we also show that V2 is 1.7 times as dense. As primate V1s are denser, they have more neurons and thus more pinwheels than similar-sized nonprimate V1s, which explains why primates have better visual acuity.

Structural uniformity of neocortex, revisited.

The number of neurons under a square millimeter of cortical surface has been reported to be the same across five cortical areas and five species [Rockel et al. (1980) Brain 103(2):221-244] despite differences in cortical thickness between the areas. Although the accuracy of this result has been the subject of sharp debate since its publication approximately 30 y ago, the experiments of Rockel et al. have never been directly replicated with modern stereological methods. We have replicated these experiments and confirm the accuracy of the original report. In addition, we have observed that the number of glial cells under a square millimeter of cortical surface depends on cortical thickness, but not on cortical area or species.

Control of organ size: development, regeneration, and the role of theory in biology.

How organs grow to be the right size for the animal is one of the central mysteries of biology. In a paper in BMC Biology, Khammash et al. propose a mechanism for escaping from the deficiencies of feedback control of growth as a mechanism.

Short-term plasticity constrains spatial organization of a hippocampal presynaptic terminal.

Although the CA3-CA1 synapse is critically important for learning and memory, experimental limitations have to date prevented direct determination of the structural features that determine the response plasticity. Specifically, the local calcium influx responsible for vesicular release and short-term synaptic facilitation strongly depends on the distance between the voltage-dependent calcium channels (VDCCs) and the presynaptic active zone. Estimates for this distance range over two orders of magnitude. Here, we use a biophysically detailed computational model of the presynaptic bouton and demonstrate that available experimental data provide sufficient constraints to uniquely reconstruct the presynaptic architecture. We predict that for a typical CA3-CA1 synapse, there are ~70 VDCCs located 300 nm from the active zone. This result is surprising, because structural studies on other synapses in the hippocampus report much tighter spatial coupling. We demonstrate that the unusual structure of this synapse reflects its functional role in short-term plasticity (STP).

NSF workshop report: discovering general principles of nervous system organization by comparing brain maps across species.

Efforts to understand nervous system structure and function have received new impetus from the federal Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative. Comparative analyses can contribute to this effort by leading to the discovery of general principles of neural circuit design, information processing, and gene-structure-function relationships that are not apparent from studies on single species. We here propose to extend the comparative approach to nervous system 'maps' comprising molecular, anatomical, and physiological data. This research will identify which neural features are likely to generalize across species, and which are unlikely to be broadly conserved. It will also suggest causal relationships between genes, development, adult anatomy, physiology, and, ultimately, behavior. These causal hypotheses can then be tested experimentally. Finally, insights from comparative research can inspire and guide technological development. To promote this research agenda, we recommend that teams of investigators coalesce around specific research questions and select a set of 'reference species' to anchor their comparative analyses. These reference species should be chosen not just for practical advantages, but also with regard for their phylogenetic position, behavioral repertoire, well-annotated genome, or other strategic reasons. We envision that the nervous systems of these reference species will be mapped in more detail than those of other species. The collected data may range from the molecular to the behavioral, depending on the research question. To integrate across levels of analysis and across species, standards for data collection, annotation, archiving, and distribution must be developed and respected. To that end, it will help to form networks or consortia of researchers and centers for science, technology, and education that focus on organized data collection, distribution, and training. These activities could be supported, at least in part, through existing mechanisms at NSF, NIH, and other agencies. It will also be important to develop new integrated software and database systems for cross-species data analyses. Multidisciplinary efforts to develop such analytical tools should be supported financially. Finally, training opportunities should be created to stimulate multidisciplinary, integrative research into brain structure, function, and evolution.

The fraction of cortical GABAergic neurons is constant from near the start of cortical neurogenesis to adulthood.

Approximately one in five neurons is GABAergic in many neocortical areas and species, forming a critical balance between inhibition and excitation in adult circuits. During development, cortical GABAergic neurons are generated in ventral telencephalon and migrate up to developing cortex where the excitatory glutamatergic neurons are born. We ask here: when during development is the adult GABAergic/glutamatergic neuron ratio first established? To answer this question, we have determined the fraction of all neocortical GABAergic neurons that will become inhibitory (GAD67(+)) in mice from embryonic day 10.5 (E10.5) to postnatal day 28 (P28). We find that this fraction is close to 1/5, the adult value, starting from early in corticogenesis (E14.5, when GAD67(+) neurons are still migrating tangentially to the cortex) and continuing at the same 1/5 value throughout the remainder of brain development. Thus our data indicate the one-in-five fraction of GABAergic neurons is already established during their neuronal migration and well before significant synapse formation.

Development regulates a switch between post- and presynaptic strengthening in response to activity deprivation.

In response to decreased activity, neurons make global compensatory increases in excitatory synaptic strength. However, how neuronal maturity affects this process is unclear. We silenced cultured hippocampal neurons with TTX at 7 days in vitro, during rapid synaptogenesis, and at 14 days, when major synaptogenesis is complete. For each age, we have explored the effects of short (1 day) and longer (2 days) periods of silencing. We have confirmed that the changes in synaptic strength depend on 2 main mechanisms, one presynaptic and the other postsynaptic. The presynaptic mechanism involves an increase in the probability of neurotransmitter release, mostly arising through an increase in the number of synaptic vesicles available for release. The postsynaptic mechanism operates through an increase in the number of postsynaptic receptors for the excitatory neurotransmitter glutamate. When neurons are silenced for 1 day, young neurons employ the postsynaptic mechanism, whereas more mature neurons increase their strength through the presynaptic mechanism. The postsynaptic strengthening in young neurons does not depend on gene transcription, whereas the presynaptic mechanism does. If neurons are silenced for 2 days, younger and older neurons employ both pre and postsynaptic mechanisms for synaptic strengthening. We also found evidence for 2 additional mechanisms that increased the effective synaptic coupling between neurons after 2 days of silencing: an increase in the number of synapses, and an increase in the electrotonic length of dendrites. These results expand our basic understanding of neuronal homeostasis, and reveal the developmental regulation of its expression mechanisms.

Predictable irregularities in retinal receptive fields.

Understanding how the nervous system achieves reliable performance using unreliable components is important for many disciplines of science and engineering, in part because it can suggest ways to lower the energetic cost of computing. In vision, retinal ganglion cells partition visual space into approximately circular regions termed receptive fields (RFs). Average RF shapes are such that they would provide maximal spatial resolution if they were centered on a perfect lattice. However, individual shapes have fine-scale irregularities. Here, we find that irregular RF shapes increase the spatial resolution in the presence of lattice irregularities from approximately 60% to approximately 92% of that possible for a perfect lattice. Optimization of RF boundaries around their fixed center positions reproduced experimental observations neuron-by-neuron. Our results suggest that lattice irregularities determine the shapes of retinal RFs and that similar algorithms can improve the performance of retinal prosthetics where substantial irregularities arise at their interface with neural tissue.

Robustness and fault tolerance make brains harder to study.

Brains increase the survival value of organisms by being robust and fault tolerant. That is, brain circuits continue to operate as the organism needs, even when the circuit properties are significantly perturbed. Kispersky and colleagues, in a recent paper in Neural Systems & Circuits, have found that Granger Causality analysis, an important method used to infer circuit connections from the behavior of neurons within the circuit, is defeated by the mechanisms that give rise to this robustness and fault tolerance.

The role of presynaptic dynamics in processing of natural spike trains in hippocampal synapses.

Short-term plasticity (STP) represents a key neuronal mechanism of information processing. In excitatory hippocampal synapses, STP serves as a high-pass filter optimized for the transmission of information-carrying place-field discharges. This STP filter enables synapses to perform a highly nonlinear, switch-like operation permitting the passage and amplification of signals with place-field-like characteristics. Because of the complexity of interactions among STP processes, the synaptic mechanisms underlying this filtering paradigm remain poorly understood. Here, we describe a simple mechanistic model of STP, derived in large part from basic principles of synaptic function, that reproduces this highly nonlinear synaptic behavior. The model, formulated in terms of release probability, considers the interactions between calcium-dependent forms of presynaptic enhancement and their impact on vesicle pool dynamics, which is described using a two-pool model of vesicle recruitment. By considering the interdependency between release probability and various forms of STP, the model attempts to provide a realistic coupling among major presynaptic processes. The model parameters are first determined using synaptic dynamics during constant-frequency stimulation. The model then accurately reproduces all major characteristics of the synaptic filtering paradigm during natural stimulus patterns without free parameters. An elimination approach is then used to identify the contribution of each STP component to synaptic dynamics. Based on this analysis, the model predicts strong calcium dependence of synaptic filtering properties, which is verified experimentally in rat hippocampal slices. This simple model may thus offer a useful framework to further investigate the role of STP in neural computations.

Probing synaptic vesicle fusion by altering mechanical properties of the neuronal surface membrane.

Because synaptic vesicle exocytosis is a nano-mechanical process, it should be influenced by the mechanical properties of the cell membrane to which the vesicle fuses. By dissolving surfactants at various concentrations in the neuronal membrane, we have perturbed mechanical properties of the membrane and have found that dissolved surfactants lower the probability that a synaptic vesicle will open its fusion pore when the fusion machinery of the vesicle is activated by binding calcium. By using standard theories from the physics and chemistry of surfaces, we can account for this decrease in fusion probability and can infer that a vesicle, when activated, opens its fusion pore approximately 3 times out of 4 and that the area of the fusion pore is approximately 4 nm(2).