"The social rank of an animal is distinguished by its behavior relative to others in its community. Although social-status-dependent differences in behavior must arise because of differences in neural function, status-dependent differences in the underlying neural circuitry have only begun to be described. We report that dominant and subordinate crayfish differ in their behavioral orienting response to an unexpected unilateral touch, and that these differences correlate with functional differences in local neural circuits that mediate the responses. The behavioral differences correlate with simultaneously recorded differences in leg depressor muscle EMGs and with differences in the responses of depressor motor neurons recorded in reduced, in vitro preparations from the same animals. The responses of local serotonergic interneurons to unilateral stimuli displayed the same status-dependent differences as the depressor motor neurons. These results indicate that the circuits and their intrinsic serotonergic modulatory components are configured differently according to social status, and that these differences do not depend on a continuous descending signal from higher centers."

"Neurophysiology is increasingly focused on identifying coincident activity among neurons. Strong inferences about neural computation are made from the results of such studies, so it is important that these results be accurate. However, the preliminary step in the analysis of such data, the assignment of spike waveforms to individual neurons (“spike-sorting”), makes a critical assumption which undermines the analysis: that spikes, and hence neurons, are independent. We show that this assumption guarantees that coincident spiking estimates such as correlation coefficients are biased. We also show how to eliminate this bias. Our solution involves sorting spikes jointly, which contrasts with the current practice of sorting spikes independently of other spikes. This new “ensemble sorting” yields unbiased estimates of coincident spiking, and permits more data to be analyzed with confidence, improving the quality and quantity of neurophysiological inferences. These results should be of interest outside the context of neuronal correlations studies. Indeed, simultaneous recording of many neurons has become the rule rather than the exception in experiments, so it is essential to spike sort correctly if we are to make valid inferences about any properties of, and relationships between, neurons."

"Relational inference denotes the capacity to encode, flexibly retrieve, and integrate multiple memories to combine past experiences to update knowledge and improve decision-making in new situations. Although relational inference is thought to depend on the hippocampus and consciousness, we now show in young, healthy men that it may occur outside consciousness but still recruits the hippocampus. In temporally distinct and unique subliminal episodes, we presented word pairs that either overlapped (“winter–red”, “red–computer”) or not. Effects of unconscious relational inference emerged in reaction times recorded during unconscious encoding and in the outcome of decisions made 1 min later at test, when participants judged the semantic relatedness of two supraliminal words. These words were either episodically related through a common word (“winter–computer” related through “red”) or unrelated. Hippocampal activity increased during the unconscious encoding of overlapping versus nonoverlapping word pairs and during the unconscious retrieval of episodically related versus unrelated words. Furthermore, hippocampal activity during unconscious encoding predicted the outcome of decisions made at test. Hence, unconscious inference may influence decision-making in new situations."

Relations represented spatially?

I learned a lot from _The Computational Brain_ as a student, but avoided her philosophy. The new book though sounds worth trying.

"The mechanisms linking sensation and action during learning are poorly understood. Layer 2/3 neurons in the motor cortex might participate in sensorimotor integration and learning; they receive input from sensory cortex and excite deep layer neurons, which control movement. Here we imaged activity in the same set of layer 2/3 neurons in the motor cortex over weeks, while mice learned to detect objects with their whiskers and report detection with licking. Spatially intermingled neurons represented sensory (touch) and motor behaviours (whisker movements and licking). With learning, the population-level representation of task-related licking strengthened. In trained mice, population-level representations were redundant and stable, despite dynamism of single-neuron representations. The activity of a subpopulation of neurons was consistent with touch driving licking behaviour. Our results suggest that ensembles of motor cortex neurons couple sensory input to multiple, related motor programs during learning."

"Human brain functional networks are embedded in anatomical space and have topological properties—small-worldness, modularity, fat-tailed degree distributions—that are comparable to many other complex networks. Although a sophisticated set of measures is available to describe the topology of brain networks, the selection pressures that drive their formation remain largely unknown. Here we consider generative models for the probability of a functional connection (an edge) between two cortical regions (nodes) separated by some Euclidean distance in anatomical space. In particular, we propose a model in which the embedded topology of brain networks emerges from two competing factors: a distance penalty based on the cost of maintaining long-range connections; and a topological term that favors links between regions sharing similar input. We show that, together, these two biologically plausible factors are sufficient to capture an impressive range of topological properties of functional brain networks. Model parameters estimated in one set of functional MRI (fMRI) data on normal volunteers provided a good fit to networks estimated in a second independent sample of fMRI data. Furthermore, slightly detuned model parameters also generated a reasonable simulation of the abnormal properties of brain functional networks in people with schizophrenia. We therefore anticipate that many aspects of brain network organization, in health and disease, may be parsimoniously explained by an economical clustering rule for the probability of functional connectivity between different brain areas."

"Much of the information used for visual perception and visually guided actions is processed in complex networks of connections within the cortex. To understand how this works in the normal brain and to determine the impact of disease, mice are promising models. In primate visual cortex, information is processed in a dorsal stream specialized for visuospatial processing and guided action and a ventral stream for object recognition. Here, we traced the outputs of 10 visual areas and used quantitative graph analytic tools of modern network science to determine, from the projection strengths in 39 cortical targets, the community structure of the network. We found a high density of the cortical graph that exceeded that shown previously in monkey. Each source area showed a unique distribution of projection weights across its targets (i.e., connectivity profile) that was well fit by a lognormal function. Importantly, the community structure was strongly dependent on the location of the source area: outputs from medial/anterior extrastriate areas were more strongly linked to parietal, motor, and limbic cortices, whereas lateral extrastriate areas were preferentially connected to temporal and parahippocampal cortices. These two subnetworks resemble dorsal and ventral cortical streams in primates, demonstrating that the basic layout of cortical networks is conserved across species."

"We here report for the first time, to the best of our knowledge, rat motor cortex neurons predicting the behavior of the American stock market. We implanted the motor cortex of the brains of rats with silicon electrodes. Using the correlation technique, we monitored the activity of neurons in our rats while simultaneously tracking the activity of stocks in the U.S. stock market."

BBS target article.
Abstract: "An emerging class of theories concerning the functional structure of the brain takes the reuse of neural circuitry for various cognitive purposes to be a central organizational principle. According to these theories, it is quite common for neural circuits established for one purpose to be exapted (exploited, recycled, redeployed) during evolution or normal development, and be put to different uses, often without losing their original functions. Neural reuse theories thus differ from the usual understanding of the role of neural plasticity (which is, after all, a kind of reuse) in brain organization along the following lines: According to neural reuse, circuits can continue to acquire new uses after an initial or original function is established; the acquisition of new uses need not involve unusual circumstances such as injury or loss of established function; and the acquisition of a new use need not involve (much) local change to circuit structure (e.g., it might involve only the establishment of functional connections to new neural partners). Thus, neural reuse theories offer a distinct perspective on several topics of general interest, such as: the evolution and development of the brain, including (for instance) the evolutionary-developmental pathway supporting primate tool use and human language; the degree of modularity in brain organization; the degree of localization of cognitive function; and the cortical parcellation problem and the prospects (and proper methods to employ) for function to structure mapping. The idea also has some practical implications in the areas of rehabilitative medicine and machine interface design."

"Abstract: 20 years after the birth of neuroimaging, we have the exciting opportunity to review the accumulated evidence, and revisit some fundamental assumptions about the functional organization of the brain.  The current talk will focus on the issue of selectivity, and present evidence suggesting that local neural circuits are in fact used to support multiple tasks across diverse task categories–but that they cooperate with different neural partners in each category.
"Overall, the imaging data suggest a story about the evolution and development of the brain whereby new function emerges via the reuse and reconfiguration of existing neural machinery, leaving existing uses largely intact. In addition to reviewing the evidence from neuroimaging, I will discuss in some detail one specific instance of apparent reuse: the involvement of a local neural circuit in finger awareness, number representation, and other diverse functions.
"Specific implications for numerical cognition, and general implications for anatomical and functional modularity will be considered."
Unfortunately, I'm going to be missing the talk...

"Self-organized critical states are found in many natural systems, from earthquakes to forest fires, they have also been found in neural systems, particularly, in neuronal cultures. However, the presence of critical states in the awake brain remains controversial. Here, we compared avalanche analyses performed on different in vivo preparations during wakefulness, slow-wave sleep and REM sleep, in cat parietal cortex (8 electrodes), monkey motor cortex (64/96 electrodes) and human temporal cortex (96 electrodes) in epileptic patients. In neuronal avalanches defined from units (up to 152 single units), the size of avalanches never clearly scaled as power-law, but rather scaled exponentially or displayed intermediate scaling. We also analyzed the dynamics of local field potentials (LFPs) and in particular LFP negative peaks (nLFPs) among the different electrodes (up to 96 sites in temporal cortex or up to 128 sites in adjacent motor and pre-motor cortices). In this case, the avalanches defined from nLFPs displayed power-law scaling in double logarithmic representations, as reported previously in monkey. However, avalanche defined as positive LFP (pLFP) peaks, which are not related to neuronal firing, also displayed apparent power-law scaling. Closer examination of this scaling using the more reliable cumulative distribution function (CDF) and other rigorous statistical measures, did not confirm power-law scaling. The same pattern was seen for cats, monkey and human, as well as for different brain states of wakefulness and sleep. We also tested other alternative distributions. While simple exponentials yielded very good fits of the avalanche dynamics, the "sum of exponentials" provided the best fit to the data. Collectively, these results show no clear evidence for power-law scaling or self-organized critical states in the awake and sleeping brain of mammals, from cat to man."

Impressions from a quick scan: yes, those are not power laws (way too curved), but no, you cannot use R^2 like that --- and in fact we explained why, in that paper you cite. Oy.

"Information estimates such as the ``direct method'' of Strong et al. (1998) sidestep the difficult problem of estimating the joint distribution of response and stimulus by instead estimating the difference between the marginal and conditional entropies of the response. While this is an effective estimation strategy, it tempts the practitioner to ignore the role of the stimulus and the meaning of mutual information. We show here that, as the number of trials increases indefinitely, the direct (or ``plug-in'') estimate of marginal entropy converges (with probability 1) to the entropy of the time-averaged conditional distribution of the response, and the direct estimate of the conditional entropy converges to the time-averaged entropy of the conditional distribution of the response. Under joint stationarity and ergodicity of the response and stimulus, the difference of these quantities converges to the mutual information. When the stimulus is deterministic or non-stationary the direct estimate of information no longer estimates mutual information, which is no longer meaningful, but it remains a measure of variability of the response distribution across time."

"Functional connectivity networks have become a central focus in neuroscience because they reveal key higher-dimensional features of normal and abnormal nervous system physiology. Functional networks reflect activity-based coupling between brain regions that may be constrained by relatively static anatomical connections, yet these networks appear to support tremendously dynamic behaviors. Within this growing field, the stability and temporal characteristics of functional connectivity brain networks have not been well characterized. We evaluated the temporal stability of spontaneous functional connectivity networks derived from multi-day scalp encephalogram (EEG) recordings in five healthy human subjects. Topological stability and graph characteristics of networks derived from averaged data epochs ranging from 1 s to multiple hours across different states of consciousness were compared. We show that, although functional networks are highly variable on the order of seconds, stable network templates emerge after as little as ∼100 s of recording and persist across different states and frequency bands (albeit with slightly different characteristics in different states and frequencies). Within these network templates, the most common edges are markedly consistent, constituting a network “core.” Although average network topologies persist across time, measures of global network connectivity, density and clustering coefficient, are state and frequency specific, with sparsest but most highly clustered networks seen during sleep and in the gamma frequency band. These findings support the notion that a core functional organization underlies spontaneous cortical processing and may provide a reference template on which unstable, transient, and rapidly adaptive long-range assemblies are overlaid in a frequency-dependent manner."

"It is shown how to compute effective and functional connection matrices (eCMs and fCMs) from anatomical CMs (aCMs) and corresponding strength-of-connection matrices (sCMs) using propagator methods in which neural interactions play the role of scatterings. This analysis demonstrates how network effects dress the bare propagators (the sCMs) to yield effective propagators (the eCMs) that can be used to compute the covariances customarily used to define fCMs. The results incorporate excitatory and inhibitory connections, multiple structures and populations, asymmetries, time delays, and measurement effects. They can also be postprocessed in the same manner as experimental measurements for direct comparison with data and thereby give insights into the role of coarse-graining, thresholding, and other effects in determining the structure of CMs. The spatiotemporal results show how to generalize CMs to include time delays and how natural network modes give rise to long-range coherence at resonant frequencies. The results are demonstrated using tractable analytic cases via neural field theory of cortical and corticothalamic systems. These also demonstrate close connections between the structure of CMs and proximity to critical points of the system, highlight the importance of indirect links between brain regions and raise the possibility of imaging specific levels of indirect connectivity. Aside from the results presented explicitly here, the expression of the connections among aCMs, sCMs, eCMs, and fCMs in terms of propagators opens the way for propagator theory to be further applied to analysis of connectivity."

"New single-cell recordings show that humans do have mirror neurons, and in more brain regions than previously suspected. Some action-execution neurons were seen to be inhibited during observation, possibly preventing imitation and helping self/other discrimination."

"In this study, we assume that the brain uses a general-purpose pattern generator to transform static commands into basic movement segments. We hypothesize that this pattern generator includes an oscillator whose complete cycle generates a single movement segment. In order to demonstrate this hypothesis, we construct an oscillator-based model of movement generation. The model includes an oscillator that generates harmonic outputs whose frequency and amplitudes can be modulated by external inputs. The harmonic outputs drive a number of integrators, each activating a single muscle. The model generates muscle activation patterns composed of rectilinear and harmonic terms. We show that rectilinear and fundamental harmonic terms account for known properties of natural movements, such as the invariant bell-shaped hand velocity profile during reaching. We implement these dynamics by a neural network model and characterize the tuning properties of the neural integrator cells, the neural oscillator cells, and the inputs to the system. Finally, we propose a method to test our hypothesis that a neural oscillator is a central component in the generation of voluntary movement."

To be clear, "bad_data_analysis" applies to what Yarkoni is talking about, not to Yarkoni.

"Traditional theories have considered decision making as a separate neural process occurring before action planning. However, recent neurophysiological studies of spatial target selection have suggested that decision making and motor planning may be performed in an integrated manner. It was proposed that multiple potential plans are concurrently formed and the ultimately selected action simultaneously emerges within the same circuits (Shadlen and Newsome, 2001; Cisek and Kalaska, 2010). In the present study, we recorded from the parietal reach region (PRR) and dorsal area 5 (area 5d) in the posterior parietal cortex (PPC) while monkeys performed a nonspatial effector (saccade vs reach) choice task. The results show that PRR encodes potential and selected reach plans whereas area 5d encodes only selected reach plans, suggesting a serial visuomotor cortical circuitry for nonspatial effector decisions. Thus, there appears to be a different flow of processing for decisions and planning for spatial target selection, which is more integrated, and nonspatial effector decisions between eye and limb movements, which are more serial."

"In the cortex, the interactions among neurons give rise to transient coherent activity patterns that underlie perception, cognition, and action. Recently, it was actively debated whether the most basic interactions, i.e., the pairwise correlations between neurons or groups of neurons, suffice to explain those observed activity patterns. So far, the evidence reported is controversial. Importantly, the overall organization of neuronal interactions and the mechanisms underlying their generation, especially those of high-order interactions, have remained elusive. Here we show that higher-order interactions are required to properly account for cortical dynamics such as ongoing neuronal avalanches in the alert monkey and evoked visual responses in the anesthetized cat. A Gaussian interaction model that utilizes the observed pairwise correlations and event rates and that applies intrinsic thresholding identifies those higher-order interactions correctly, both in cortical local field potentials and spiking activities. This allows for accurate prediction of large neuronal population activities as required, e.g., in brain–machine interface paradigms. Our results demonstrate that higher-order interactions are inherent properties of cortical dynamics and suggest a simple solution to overcome the apparent formidable complexity previously thought to be intrinsic to those interactions."

"Female and male brains are different, thanks to hormones coursing through the brain before birth. That’s taught as fact in psychology textbooks, academic journals, and bestselling books. And these hardwired differences explain everything from sexual orientation to gender identity, to why there aren’t more women physicists or more stay-at-home dads.

In this compelling book, Rebecca Jordan-Young takes on the evidence that sex differences are hardwired into the brain. Analyzing virtually all published research that supports the claims of “human brain organization theory,” Jordan-Young reveals how often these studies fail the standards of science. Even if careful researchers point out the limits of their own studies, other researchers and journalists can easily ignore them because brain organization theory just sounds so right. But if a series of methodological weaknesses, questionable assumptions, inconsistent definitions, and enormous gaps between ambiguous findings and grand conclusions have accumulated through the years, then science isn’t scientific at all.

Elegantly written, this book argues passionately that the analysis of gender differences deserves far more rigorous, biologically sophisticated science. “The evidence for hormonal sex differentiation of the human brain better resembles a hodge-podge pile than a solid structure…Once we have cleared the rubble, we can begin to build newer, more scientific stories about human development.” "

Functional segregation and integration are fundamental characteristics of the human brain. Studying the connectivity among segregated regions and the dynamics of integrated brain networks has drawn increasing interest. A very controversial, yet fundamental issue in these studies is how to determine the best functional brain regions or ROIs (regions of interests) for individuals. Essentially, the computed connectivity patterns and dynamics of brain networks are very sensitive to the locations, sizes, and shapes of the ROIs. This paper presents a novel methodology to optimize the locations of an individual's ROIs in the working memory system. Our strategy is to formulate the individual ROI optimization as a group variance minimization problem, in which group-wise functional and structural connectivity patterns, and anatomic profiles are defined as optimization constraints. The optimization problem is solved via the simulated annealing approach. Our experimental results show that the optimized ROIs have significantly improved consistency in structural and functional profiles across subjects, and have more reasonable localizations and more consistent morphological and anatomic profiles.

"Motor learning changes the activity of cortical motor and subcortical areas of the brain, but does learning affect sensory systems as well? We examined in humans the effects of motor learning using fMRI measures of functional connectivity under resting conditions and found persistent changes in networks involving both motor and somatosensory areas of the brain. We developed a technique that allows us to distinguish changes in functional connectivity that can be attributed to motor learning from those that are related to perceptual changes that occur in conjunction with learning. Using this technique, we identified a new network in motor learning involving second somatosensory cortex, ventral premotor cortex, and supplementary motor cortex whose activation is specifically related to perceptual changes that occur in conjunction with motor learning. We also found changes in a network comprising cerebellar cortex, primary motor cortex, and dorsal premotor cortex that were linked to the motor aspects of learning. In each network, we observed highly reliable linear relationships between neuroplastic changes and behavioral measures of either motor learning or perceptual function. Motor learning thus results in functionally specific changes to distinct resting-state networks in the brain."

"Stress modulates instrumental action in favor of habit processes that encode the association between a response and preceding stimuli and at the expense of goal-directed processes that learn the association between an action and the motivational value of the outcome. Here, we asked whether this stress-induced shift from goal-directed to habit action is dependent on noradrenergic activation and may therefore be blocked by a β-adrenoceptor antagonist. To this end, healthy men and women were administered a placebo or the β-adrenoceptor antagonist propranolol before they underwent a stress or a control procedure. Shortly after the stress or control procedure, participants were trained in two instrumental actions that led to two distinct food outcomes. After training, one of the food outcomes was selectively devalued by feeding participants to satiety with that food. A subsequent extinction test indicated whether instrumental behavior was goal-directed or habitual. As expected, stress after placebo rendered participants' behavior insensitive to the change in the value of the outcome and thus habitual. After propranolol intake, however, stressed participants behaved, same as controls, goal-directed, suggesting that propranolol blocked the stress-induced bias toward habit behavior. Our findings show that the shift from goal-directed to habitual control of instrumental action under stress necessitates noradrenergic activation and could have important clinical implications, particularly for addictive disorders."

"Our present understanding of the neural mechanisms and sensorimotor transformations that govern the planning of arm and eye movements predominantly come from invasive parieto-frontal neural recordings in nonhuman primates. While functional MRI (fMRI) has motivated investigations on much of these same issues in humans, the highly distributed and multiplexed organization of parieto-frontal neurons necessarily constrain the types of intention-related signals that can be detected with traditional fMRI analysis techniques. Here we employed multivoxel pattern analysis (MVPA), a multivariate technique sensitive to spatially distributed fMRI patterns, to provide a more detailed understanding of how hand and eye movement plans are coded in human parieto-frontal cortex. Subjects performed an event-related delayed movement task requiring that a reach or saccade be planned and executed toward one of two spatial target positions. We show with MVPA that, even in the absence of signal amplitude differences, the fMRI spatial activity patterns preceding movement onset are predictive of upcoming reaches and saccades and their intended directions. Within certain parieto-frontal regions we show that these predictive activity patterns reflect a similar spatial target representation for the hand and eye. Within some of the same regions, we further demonstrate that these preparatory spatial signals can be discriminated from nonspatial, effector-specific signals. In contrast to the largely graded effector- and direction-related planning responses found with fMRI subtraction methods, these results reveal considerable consensus with the parieto-frontal network organization suggested from primate neurophysiology and specifically show how predictive spatial and nonspatial movement information coexists within single human parieto-frontal areas."

"Multiple measures have been developed to quantify the similarity between two spike trains. These measures have been used for the quantification of the mismatch between neuron models and experiments as well as for the classification of neuronal responses in neuroprosthetic devices and electrophysiological experiments. Frequently only a few spike trains are available in each class. We derive analytical expressions for the small-sample bias present when comparing estimators of the time-dependent firing intensity. We then exploit analogies between the comparison of firing intensities and previously used spike train metrics and show that improved spike train measures can be successfully used for fitting neuron models to experimental data, for comparisons of spike trains, and classification of spike train data. In classification tasks, the improved similarity measures can increase the recovered information. We demonstrate that when similarity measures are used for fitting mathematical models, all previous methods systematically underestimate the noise. Finally, we show a striking implication of this deterministic bias by reevaluating the results of the single-neuron prediction challenge."

"Neuronal responses in primary visual cortex have been found to be highly variable. This has led to the widespread notion that neuronal responses have to be averaged over large numbers of neurons to obtain suitably invariant responses that can be used to reliably encode or represent external stimuli. However, it is possible that the high variability of neuronal responses may result from the use of simple, artificial stimuli and that the visual cortex may respond differently to dynamic, naturalistic images. To investigate this question, we recorded the responses of primary visual cortical neurons in the anesthetized cat under stimulation with time-varying natural movies. We found that cortical neurons on the whole exhibited a high degree of spike count variability, but a surprisingly low degree of spike time variability. The spike count variability was further reduced when all but the first spike in a burst were removed. We also found that responses exhibiting low spike time variability exhibited low spike count variability, suggesting that rate coding and temporal coding might be more compatible than previously thought. In addition, we found the spike time variability to be significantly lower when stimulated by natural movies as compared with stimulation using drifting gratings. Our results indicate that response variability in primary visual cortex is stimulus dependent and significantly lower than previous measurements have indicated."

"Learning to navigate plays an integral role in the survival of humans and other animals. Research on human navigation has largely focused on how we deliberately map out our world. However, many of us also have experiences of navigating on “autopilot” or out of habit. Animal models have identified this cognitive mapping versus habit learning as two dissociable systems for learning a space—a hippocampal place-learning system and a striatal response-learning system. Here, we use this dichotomy in humans to understand variability in navigational style by demonstrating that brain activation during spatial encoding can predict where a person's behavior falls on a continuum from a more flexible cognitive map-like strategy to a more rigid creature-of-habit approach. These findings bridge the wealth of knowledge gained from animal models and the study of human behavior, opening the door to a more comprehensive understanding of variability in human spatial learning and navigation."

"...a survey of recent neuroscientific research on representational systems in animals and humans. Representational systems provide their owners with useful information about their environment and are shaped by the special informational needs of the organism with respect to its environment..."

"The amount of information encoded by networks of neurons critically depends on the correlation structure of their activity. Neurons with similar stimulus preferences tend to have higher noise correlations than others. In homogeneous populations of neurons, this limited range correlation structure is highly detrimental to the accuracy of a population code. Therefore, reduced spike count correlations under attention, after adaptation, or after learning have been interpreted as evidence for a more efficient population code. Here, we analyze the role of limited range correlations in more realistic, heterogeneous population models. We use Fisher information and maximum-likelihood decoding to show that reduced correlations do not necessarily improve encoding accuracy. In fact, in populations with more than a few hundred neurons, increasing the level of limited range correlations can substantially improve encoding accuracy. We found that this improvement results from a decrease in noise entropy that is associated with increasing correlations if the marginal distributions are unchanged. Surprisingly, for constant noise entropy and in the limit of large populations, the encoding accuracy is independent of both structure and magnitude of noise correlations."

WANT

"For more than 50 years, researchers—including many prominent scientists—have identified pheromones as the triggers for a wide range of mammalian behaviors and endocrine responses. In this provocative book, renowned olfaction expert Richard L. Doty rejects this idea and states bluntly that, in contrast to insects, mammals do not have pheromones.
Doty systematically debunks the claims and conclusions of studies that purport to reveal the existence of mammalian pheromones. He demonstrates that there is no generally accepted scientific definition of what constitutes a mammalian pheromone and that attempts to divide stimuli and complex behaviors into pheromonal and nonpheromonal categories have primarily failed. Doty's controversial assertion belies a continued fascination with the pheromone concept, numerous claims of its chemical isolation, and what he sees as the wasted expenditure of hundreds of millions of dollars by industry and government. "

I am sure it says something bad about me that the title immediately makes me think "neurobiological substrates of BDSM".

"It is shown that homogeneous, short-range, two-dimensional (2D) cortical connectivity, without modularity, hierarchy, or other specialized structure, reproduces key observed properties of cortical networks, including low path length, high clustering and modularity index, and apparent hierarchical block-diagonal structure in connection matrices. Geometry strongly influences connection matrices, implying that simple interpretations of connectivity measures as reflecting specialized structure can be misleading: Such apparent structure is seen in strictly uniform, locally connected architectures in 2D. Geometry is thus a proxy for function, modularity, and hierarchy and must be accounted for when structural inferences are made."

"Human memory is strikingly susceptible to social influences, yet we know little about the underlying mechanisms. We examined how socially induced memory errors are generated in the brain by studying the memory of individuals exposed to recollections of others. Participants exhibited a strong tendency to conform to erroneous recollections of the group, producing both long-lasting and temporary errors, even when their initial memory was strong and accurate. Functional brain imaging revealed that social influence modified the neuronal representation of memory. Specifically, a particular brain signature of enhanced amygdala activity and enhanced amygdala-hippocampus connectivity predicted long-lasting but not temporary memory alterations. Our findings reveal how social manipulation can alter memory and extend the known functions of the amygdala to encompass socially mediated memory distortions." - I will be pleasantly surprised if this is really sound.

"The revolution heralded by Eagleman's barrage of rhetorical questions and attacks on strawmen is a damp squib. If the neurosciences are going to make a genuine contribution to issues like this, the onus must be on us to engage with existing thought on complicated matters like criminal justice and provide detailed evidence of how neuroscience can inform these existing systems, rather than pretending that new findings in the lab can sweep away thousands of years of cultural and philosophical endeavour."

"Desert locusts, Schistocerca gregaria, show extreme phenotypic plasticity, transforming between a little-seen solitarious phase and the notorious swarming gregarious phase depending on population density. An essential tipping point in the process of swarm formation is the initial switch from strong mutual aversion in solitarious locusts to coherent group formation and greater activity in gregarious locusts. We show here that serotonin, an evolutionarily conserved mediator of neuronal plasticity, is responsible for this behavioral transformation, being both necessary if behavioral gregarization is to occur and sufficient to induce it. Our data demonstrate a neurochemical mechanism linking interactions between individuals to large-scale changes in population structure and the onset of mass migration."

Hey, they cite us positively!  By induction, the rest of the paper must be equally intelligent, learned and insightful.

"A ubiquitous feature of neuronal responses within a cortical area is their high degree of inhomogeneity. Even cells within the same functional column are known to have highly heterogeneous response properties when the same stimulus is presented. Whether the wide diversity of neuronal responses is an epiphenomenon or plays a role for cortical function is unknown. Here, we examined the relationship between the heterogeneity of neuronal responses and population coding. Contrary to our expectation, we found that the high variability of intrinsic response properties of individual cells changes the structure of neuronal correlations to improve the information encoded in the population activity. Thus, the heterogeneity of neuronal responses is in fact beneficial for sensory coding when stimuli are decoded from the population response."

Hmmm...

I will now venture some predictions before reading the article: (1) they did not employ the control of exposing subjects to a comparably arousing but non-threatening object (something disgusting, perhaps, or attractive). (2) they did not employ the control of something threatening but not an evolved threat (firecracker? live electrical hazard?) (3) There will be no more than twenty subjects, all recruited from their universities.  --- After reading: (1), check; (2), check; (3) N=20, subject pool unspecified. 

Aargh, aargh, aargh. Look, it's a cute story, and Fallon seems to be trying to do good, but the illogic! Take at face value that this "warrior gene" (barf) allele predisposes to violence, all else being equal. Do we know all the _other_ genes which contribute to this? No. For all we know he has the "big ol' teddy bear" gene (just look at him!), which is much stronger. Or again, if the function of such-and-such a region of the brain is suppressing violent impulses, _and_ this shows up as metabolic activity, _and_ that activity is absent, maybe it's because he doesn't have many impulses that need to be inhibited.

A little too pat, as fiction, but it makes the point.

We don't appear to subscribe, but presumably I could write some of them for a copy...  --- ETA: Received, thanks to T.D.

"This study used functional magnetic resonance imaging to investigate how assumptions about speakers' abilities changed the evoked BOLD response [changes in blood oxygenation indicating neural activity] in secular and Christian participants who received intercessory prayer. We find that recipients' assumptions about senders' charismatic abilities have important effects on their executive network. Most notably, the Christian participants deactivated the frontal network consisting of the medial and the dorsolateral prefrontal cortex bilaterally in response to speakers who they believed had healing abilities. An independent analysis across subjects revealed that this deactivation predicted the Christian participants' subsequent ratings of the speakers' charisma and experience of God's presence during prayer. These observations point to an important mechanism of authority that may facilitate charismatic influence..." !!!!!!

Watching MYL debunking tendentious appropriations of neuroscience _again_, the phrase "One must imagine Liberman happy" comes to mind...

That's an embedded EEG recording apparatus apparently.

"Electroencephalography, magnetoencephalography, or functional magnetic resonance imaging techniques provide functional connectivity patterns between different brain areas, and during different pathological and cognitive neuro-dynamical states. In this Tutorial we review novel complex networks approaches to unveil how brain networks can efficiently manage local processing and global integration for the transfer of information, while being at the same time capable of adapting to satisfy changing neural demands."

"Fifth International Workshop Statistical Analysis of Neuronal Data (SAND5): May 20-22, 2010 Pittsburgh, PA"

They mean models of cortical circuits implementing graphical models, but the abstract promises anatomical speculations.

Making of the poster.

Sounds like it should either be interesting or truly horrible.

Aaaaaaaaaaarrrrrrrrrrrrrrrrrrrrgh.

"The brain is widely assumed to be a paradigmatic example of a complex, self-organizing system. As such, it should exhibit the classic hallmarks of nonlinearity, multistability, and "nondiffusivity" (large coherent fluctuations). Surprisingly, at least at the very large scale of neocortical dynamics, there is little empirical evidence to support this... temporal fluctuations of power in human resting-state electroencephalograms ... strong evidence for bistability and nondiffusivity in key brain rhythms. Bistability is manifest as nonclassic bursting between high- and low-amplitude modes in the alpha rhythm. Nondiffusivity is expressed through the irregular appearance of high amplitude "extremal" events in beta rhythm power fluctuations. The statistical robustness of these observations was confirmed through comparison with Gaussian-rendered phase-randomized surrogate data."

If it were anyone but Dante...

This makes it sound like I'm presenting; but really I see my role as more that of "designated heckler".

EEG data sets.

They seem to have given up the struggle around 2007.

"This site provides information and Web links for researchers and others interested in the arthropod stomatogastric nervous system. Included are a basic overview of the stomatogastric nervous system, links to personal/professional web pages of researchers, links to bibliographic data, and links to other appropriate resources. Contributions and suggestions are welcome."

... by coincidence, a review paper on fMRI.