Category Archives: anatomy

Ancient Origins – a great book

Ancient Origins – a great book

I have just read a book by Feinberg and Mallatt, The Ancient Origins of Consciousness – How the Brain Created Experience. It may turn out to be one of those classic books that cause a big change in accepted science. They tackle the ‘mystery of consciousness’ in a new way, a very biological way. The book ends with, “a satisfying and complete explanation of primary consciousness requires a confluence of points of view, necessarily including neurobiological, evolutionary, and philosophical arguments, each contributing important answers to the ‘hard question’. Perhaps one reason no one has solved it before is that it requires all three perspectives, including what happened over half a billion years ago.” I assume there will be many who find the book’s theory wanting because of they view neurobiological naturalism is impossible and believe normal science cannot explain consciousness. The authors brand of neurobiological naturalism has three postulates which the book documents:

1. “sensory consciousness can be explained by known neurobiological principles

2. “sensory consciousness is ancient and widespread in the animal kingdom, and diverse neural architectures can create it

3. “the philosophical issues of ontological subjectivity, neuroontological irreducibility, and the ‘hard problem’ can be explained by the nondissociable confluence of neurobiological and adaptive neuroevolutionary events.

The book has changed my ideas in a number of ways. First to fall was my attitude to the idea of ’emergent properties’. I have viewed it as a hedge, a cope-out, and even a way to bring dualism back in disguise. This book describes emergence in a way that makes sense. In a layered hierarchy each layer is created from the layer below but is more complex with novel elements which are labeled as emergent. But the external constraints act primarily on the top layer which constrains the layers beneath it. Thus there is both control and innovation by both bottom-up effects and top-down effects. Yes, this arrangement does need its own name and is a typical situation in living organisms. “In living systems such as the human body, cells constrain their subunits (organelles) to work together, the tissues and organs constrain their cells to cooperate, and the entire body constrains its organs to team up, all to perform the many physiological functions needed for the body to survive. If the constraints were to fail at any level, the body would disassemble and die.” A particular type of layered hierarchy, nested maps of the sensory organs such as the retina, is the basis of consciousness.

My second change of thinking was about the nature of the Cambrian explosion. It had seemed to me that the changes between geological periods were caused by changes to the environment like a meteor strikes which kill off the dominant animals and plants and allowed the others to flourish. But the book makes a case of a change to some animals being the cause and not the result of the abrupt explosion 560 – 520 million years ago. The result was new lines of animals which have populated the earth ever since. Predators appeared for the first time and this resulted in an arms race between predators and prey. There were many adaptations, and among them, improved distance sensing: vision, hearing and smell. Anthropoids and vertebrates in particular evolved high mobility and brains that improved sensory processing. A key change was image forming eyes. These allowed topographical maps of the retina. The other senses in vertebrates (except smell) re-evolved from a new cell line, on the pattern of the eye and its mapping in brain. In the resulting hierarchy of topographical maps for the senses, consciousness evolved.

I had assumed that the source of consciousness was lower in the brain than the cerebrum but was surprised by the location. The book documents it arising first in the optical tectum (superior colliculus in humans) and later extending to the thalamus and cerebrum, 220 years ago in mammals. This move not only added more layers to the existing hierarchies and put the top layer in close proximity to the sense of smell and its related memory in the cerebrum. This was a major advancement for consciousness for mammals and later for birds. Again I had to change my view as I had thought that memory and consciousness were always tightly bound.

The book also traces the evolution of affective consciousness (feelings and emotions), just as old as sensory consciousness. What was news to me was the intermingling of interoceptive bodily senses and affective limbic feelings giving three strains of consciousness.

The authors point out that the experience that the brain creates is embodied, personal, and does not include information about its creation – and therefore is wholly subjective and unique to each being. How this is done, the mechanism, is available to objective investigation. The subjective cannot see the objective and objective cannot see the subjective. There is a gap and it cannot be removed but neurobiological naturalism can ‘bridge’ it. This conclusion was not new to me as I have always been suspicious of whether the ‘hard question’ was really a question at all.

It’s a great book.

 

 

To see others as we see ourselves

In psychology there is a theory about the ‘fundamental attribution error’, the error in how we attribute causes to actions. When we look at our own actions, they are caused by our cognition in the circumstances in which we are deciding what to do. When we look at the actions of others, they are caused by their personality or character traits. So we do not really take into consideration the circumstances of others when we judge their actions. Nor do we consider the fixed patterns of our own behavior that do not enter into our conscious thoughts when we judge our own actions. We just do what is reasonable at the time and they just do what they always do. I can be too busy to help while they can be too thoughtless. This is a problem for us but at least we can understand the problem and occasionally overcome it. (My way to deal with it is to just assume that people are intelligent and well-meaning most of the time. If they do something that seems dumb or nasty, I look at the circumstances to see if there is a reasonable explanation. There very often is. I realize that this view of my own behaviour is somewhat ironic in its internal attribution – well nothing is perfect.)

But this problem with attribution is much greater than human social interaction. We do the same thing with animals. Elephants were tested for self recognition with the mirror test. If they recognize a black spot appearing on their forehead then it is clear that they know it is their forehead. Elephants failed the test and so they were said to not have a sense of self. It turned out that the mirrors used were too small. The elephants could not make out that it was an elephant in the mirror let alone themselves. If we start out underestimating an animals intelligence, and either not test that assumption or test it in a way that is inappropriate for the animal – then we are making a big attribution error.

There is an assumption on the part of many that vertebrate brains are quite different in the various sorts of vertebrates. This is not true! All animals with a spine have the same brain pattern with the same regions. All vertebrates have seven parts and no more or less: accessory olfactory bulb; cerebellum; cerebral hemispheres; medulla oblongata; olfactory bulb; optic tectum; and pituitary gland. There are differences in size, details and subdivisions, but there are no missing parts. (R.G. Northcutt; Understanding Vertebrate Brain Evolution; Integr. Comp. Biol. 2002 42(4) 743-756). There is every reason to believe that the brain works in fundamentally the same way in mammals, birds, reptiles, amphibians and fish. And by and large, this same pattern of brain has the same functions – to move, find/eat food, escape enemies and so on. It is obvious that animals have motor control and sensory perception.

What evidence is there that other animals have emotions, memory, or consciousness? Can they be automatons with no mental life? The reports trickle in year after year that add to the evidence that animals have a mental life similar to ours.

Reptiles probably dream. Most animal species sleep, from invertebrates to primates. However, neuroscientists have until now only actively recorded the sleeping brains of birds and mammals. Shein-Idelson et al. now describe the electrophysiological hallmarks of sleep in reptiles. Recordings from the brains of Australian dragons revealed the typical features of slow-wave sleep and rapid eye movement (REM) sleep. These findings indicate that the brainstem circuits responsible for slow-wave and REM sleep are not only very ancient but were already involved in sleep dynamics in reptiles.(Shein-Idelson, Ondracek, Liaw, Reiter, Laurent; Slow waves, sharp waves, ripples, and REM in sleeping dragons; Science 2016 Vol 352 (6285) 590-596) These wave types in sleep also are evidence for a memory system similar to ours.

Fish don’t make noise or wave their fins to show emotion but that does not mean they don’t have emotions. “Whether fishes are sentient beings remains an unresolved and controversial question. Among characteristics thought to reflect a low level of sentience in fishes is an inability to show stress-induced hyperthermia (SIH), a transient rise in body temperature shown in response to a variety of stressors. This is a real fever response, so is often referred to as ‘emotional fever’. It has been suggested that the capacity for emotional fever evolved only in amniotes (mammals, birds and reptiles), in association with the evolution of consciousness in these groups. According to this view, lack of emotional fever in fishes reflects a lack of consciousness. We report here on a study in which six zebrafish groups with access to a temperature gradient were either left as undisturbed controls or subjected to a short period of confinement. The results were striking: compared to controls, stressed zebrafish spent significantly more time at higher temperatures, achieving an estimated rise in body temperature of about 2–48C. Thus, zebrafish clearly have the capacity to show emotional fever. While the link between emotion and consciousness is still debated, this finding removes a key argument for lack of consciousness in fishes.” (Rey, Huntingford, Boltana, Vargas, Knowles, Mackenzie; Fish can show emotional fever: stress-induced hyperthermia in zebrafish; 2015 Proc. R. Soc. B 282: 20152266)

One of the problems with comparing the brains of different vertebrates is that they have been named differently. When development is followed through the embryos, many differently named regions should really have a single name. Parts of the tectum are the same as our superior colliculus and they have been found to act in the same way. They integrate sensory stimuli from various senses. They can register whether events are simultaneous. For example in tadpoles the tectum can tell if a sight and vibration stimulus are simultaneous. That is the same function with the same development in the same part of the brain in an amphibian and a mammal. (Felch, Khakhalin, Aizenmen; Multisensory integration in the developing tectum is constrained by the balance of excitation and inhibition. 2016 eLife 5)

We should be assuming that other vertebrates think like we do to a large extent – just as we should assume that other people do – and try to understand their actions without an attribution error.

Language in the left hemisphere

Here is the posting mentioned in the last post. A recent paper (Harvey M. Sussman; Why the Left Hemisphere Is Dominant for Speech Production: Connecting the Dots; Biolinguistics Vol 9 Dec 2015), deals with the nature of language processing in the left hemisphere and why it is that in right-handed people with split brains only the left cortex can talk although both sides can listen. There is a lot of interesting information in this paper (especially for someone like me who is left-handed and dyslexic). He has a number of ‘dots’ and he connects them.

Dot 1 is infant babbling. The first language-like sounds babies make are coos and these have a very vowel-like quality. Soon they babble consonant-vowel combinations in repetitions. By noting the asymmetry of the mouth it can be shown that babbling comes from the left hemisphere, non-babbling noises from both, and smiles from the right hemisphere. A speech sound map is being created by the baby and it is formed at the dorsal pathway’s projection in the frontal left articulatory network.

Dot 2 is the primacy of the syllable. Syllables are the unit of prosodic events. A person’s native language syllable constraints are the origin of the types of errors that happen in second language pronunciation. Also syllables are the units of transfer in language play. Early speech sound networks are organized in syllable units (vowel and associated consonants) in the left hemisphere of right-handers.

Dot 3 is the inability for the right hemisphere to talk in split brain people. When language tasks are directed at the right hemisphere the stimulus exposure must be longer (greater than 150 msec) than when directed to the left. The right hemisphere can comprehend language but does not evoke a sound image from seen objects and words although the meaning of the objects and words is understood by that hemisphere. The right hemisphere cannot recognize if two words rhyme from seeing illustations of the words. So the left hemisphere (in right-handers) has the only language neural network with sound images. This network serves as the neural source for generating speech, therefore in a split brain only the left side can speak.

Dot 4 deals with the problems of DAS, Development Apraxia of Speech. I am going to skip this.

Dot 5 is the understanding of speech errors. The ‘slot-segment’ hypothesis is based on analysis of speech errors. Two thirds of errors are the type where phonemes are substituted, omitted, transposed or added. The picture is of a two-tiered neural ‘map’ with syllable slots serially ordered as one tier, and an independent network of consonant sounds in the other tier. The tiers are connected together. The vowel is the heart of the syllable in the nucleus slot. Forms are built around it with consonants (CV, CVC, CCV etc.). Spoonerisms are restricted to consonants exchanging with consonants and vowels exchanging with vowels; and, exchanges occurring between the same syllable positions – first with first, last with last etc.

Dot 6 is Hawkin’s model, “the neo-cortex uses stored memories to produce behaviors.” Motor memories are used sequentially and operate in an auto-associative way. Each memory elicits the next in order (think how hard it is to do things backwards). Motor commands would be produced in a serial order, based on syllables – learned articulatory behaviors linked to sound equivalents.

Dot 7 is experiments that showed representations of sounds in human language at the neural level. For example there is a representation of a generic ‘b’ sound, as well as representations of various actual ‘b’s that differ from one another. This is why we can clearly hear a ‘b’ but have difficulty identifying a ‘b’ when the sound pattern is graphed.

Here is the abstract:

Evidence from seemingly disparate areas of speech/language research is reviewed to form a unified theoretical account for why the left hemisphere is specialized for speech production. Research findings from studies investigating hemispheric lateralization of infant babbling, the primacy of the syllable in phonological structure, rhyming performance in split-brain patients, rhyming ability and phonetic categorization in children diagnosed with developmental apraxia of speech, rules governing exchange errors in spoonerisms, organizational principles of neocortical control of learned motor behaviors, and multi-electrode recordings of human neuronal responses to speech sounds are described and common threads highlighted. It is suggested that the emergence, in developmental neurogenesis, of a hard-wired, syllabically-organized, neural substrate representing the phonemic sound elements of one’s language, particularly the vocalic nucleus, is the crucial factor underlying the left hemisphere’s dominance for speech production.

Close but not quite

I wonder how often we are almost right but not quite. It seems to be a fairly common trap in biology.

Motor somatoIt has been thought for many years (140+ years) that the primary motor cortex (lying across the top of the head) mapped the muscles of the body and controlled their contractions. From this we got the comical homunculus with its huge lips and hands on a spindly little body. Each small area on this map was supposed to activate one muscle.

A recent paper by Graziano, Ethological Action Maps: A Paradigm Shift for the Motor Cortex (here), argues that this is not as it appears. What is being mapped are actions and not muscles. Here is the abstract:

The map of the body in the motor cortex is one of the most iconic images in neuroscience. The map, however, is not perfect. It contains overlaps, reversals, and fractures. The complex pattern suggests that a body plan is not the only organizing principle. Recently a second organizing principle was discovered: an action map. The motor cortex appears to contain functional zones, each of which emphasizes an ethologically relevant category of behavior. Some of these complex actions can be evoked by cortical stimulation. Although the findings were initially controversial, interest in the ethological action map has grown. Experiments on primates, mice, and rats have now confirmed and extended the earlier findings with a range of new methods.

Trends – For nearly 150 years, the motor cortex was described as a map of the body. Yet the body map is overlapping and fractured, suggesting that it is not the only organizing principle. In the past 15 years, a second fundamental organizing principle has been discovered: a map of complex, meaningful movements. Different zones in the motor cortex emphasize different actions from the natural movement repertoire of the animal. These complex actions combine multiple muscles and joints. The ‘action map’ organization has now been demonstrated in primates, prosimians, and rodents with various stimulation, lesion, and neuronal recording methods. The action map was initially controversial due to the use of electrical stimulation. The best argument that the action map is not an artifact of one technique is the growing confirming evidence from other techniques.”

Even settled science when it is neuroscience should be taken with a grain of salt. Any part of it could be something similar but not the same.

The brain’s gateway

There have been a few papers lately on the function of the thalamic reticular nucleus (TRN) that characterize it as a filter, a sieve, and a switchboard. The citations and abstracts of 4 of these papers are below. Francis Crick suggested this function for the TRN many years ago but it was not possible until recently to demonstrate it because of the anatomy of the TRN.

The thalamus sits at the center of the brain and is connected to the brain stem and spinal cord below, the cerebral hemispheres above and the basal ganglia to the sides. The thalamus is part of almost all the functional processing loops in the brain. In particular, almost all sensory information enters the cortex from the thalamus, and every corner of the cortex sends signals back to the thalamus. When this traffic, the thalamo-cerebral loops, shut down, so does consciousness.

The TRN is a thin layer of neurons that almost entirely covers the thalamus. Because it is so thin and so deep in the brain, it has been difficult to study. New methods have overcome some of these problems.

In effect all the traffic between the cortex and the thalamus is carried by axons that pass through the TRN and the axons have little branches that make contact with TRN neurons. In other words the TRN gets a smell of all the passing signals – it does not interfere with the axons but just spies on them. The TRN neurons are inhibitory, so when a passing signals activates one of them, it will suppress the neuron in the thalamus that is sending or receiving the signal. This action keeps most activity at a low level. During sleep the thalamo-cerebral loops are effectively turned off and sensory information does not reach the cortex. During attention (and multitasking) the TRN reduces distracting signals but not the attended ones. It also seems to control the type of sleep by controlling types of brain waves in the cortex during sleep. The executive functions of the prefrontal cortex seems to act through the TRN rather than directly on areas of the cortex, to control attention (steer the spotlight of attention).

Here are the abstracts and citations:

Sandra Ahrens, Santiago Jaramillo, Kai Yu, Sanchari Ghosh, Ga-Ram Hwang, Raehum Paik, Cary Lai, Miao He, Z Josh Huang, Bo Li. ErbB4 regulation of a thalamic reticular nucleus circuit for sensory selection. Nature Neuroscience, 2014; DOI: 10.1038/nn.3897

Selective processing of behaviorally relevant sensory inputs against irrelevant ones is a fundamental cognitive function whose impairment has been implicated in major psychiatric disorders. It is known that the thalamic reticular nucleus (TRN) gates sensory information en route to the cortex, but the underlying mechanisms remain unclear. Here we show in mice that deficiency of the Erbb4 gene in somatostatin-expressing TRN neurons markedly alters behaviors that are dependent on sensory selection. Whereas the performance of the Erbb4-deficient mice in identifying targets from distractors was improved, their ability to switch attention between conflicting sensory cues was impaired. These behavioral changes were mediated by an enhanced cortical drive onto the TRN that promotes the TRN-mediated cortical feedback inhibition of thalamic neurons. Our results uncover a previously unknown role of ErbB4 in regulating cortico-TRN-thalamic circuit function. We propose that ErbB4 sets the sensitivity of the TRN to cortical inputs at levels that can support sensory selection while allowing behavioral flexibility.

Ralf D. Wimmer, L. Ian Schmitt, Thomas J. Davidson, Miho Nakajima, Karl Deisseroth, Michael M. Halassa. Thalamic control of sensory selection in divided attention. Nature, 2015; DOI: 10.1038/nature15398

How the brain selects appropriate sensory inputs and suppresses distractors is unknown. Given the well-established role of the prefrontal cortex (PFC) in executive function, its interactions with sensory cortical areas during attention have been hypothesized to control sensory selection. To test this idea and, more generally, dissect the circuits underlying sensory selection, we developed a cross-modal divided-attention task in mice that allowed genetic access to this cognitive process. By optogenetically perturbing PFC function in a temporally precise window, the ability of mice to select appropriately between conflicting visual and auditory stimuli was diminished. Equivalent sensory thalamocortical manipulations showed that behaviour was causally dependent on PFC interactions with the sensory thalamus, not sensory cortex. Consistent with this notion, we found neurons of the visual thalamic reticular nucleus (visTRN) to exhibit PFC-dependent changes in firing rate predictive of the modality selected. visTRN activity was causal to performance as confirmed by bidirectional optogenetic manipulations of this subnetwork. Using a combination of electrophysiology and intracellular chloride photometry, we demonstrated that visTRN dynamically controls visual thalamic gain through feedforward inhibition. Our experiments introduce a new subcortical model of sensory selection, in which the PFC biases thalamic reticular subnetworks to control thalamic sensory gain, selecting appropriate inputs for further processing.

Laura D Lewis, Jakob Voigts, Francisco J Flores, Lukas I Schmitt, Matthew A Wilson, Michael M Halassa, Emery N Brown. Thalamic reticular nucleus induces fast and local modulation of arousal state. eLife, October 2015 DOI: 10.7554/eLife.08760

During low arousal states such as drowsiness and sleep, cortical neurons exhibit rhythmic slow wave activity associated with periods of neuronal silence. Slow waves are locally regulated, and local slow wave dynamics are important for memory, cognition, and behaviour. While several brainstem structures for controlling global sleep states have now been well characterized, a mechanism underlying fast and local modulation of cortical slow waves has not been identified. Here, using optogenetics and whole cortex electrophysiology, we show that local tonic activation of thalamic reticular nucleus (TRN) rapidly induces slow wave activity in a spatially restricted region of cortex. These slow waves resemble those seen in sleep, as cortical units undergo periods of silence phase-locked to the slow wave. Furthermore, animals exhibit behavioural changes consistent with a decrease in arousal state during TRN stimulation. We conclude that TRN can induce rapid modulation of local cortical state.

Michael M. Halassa, Zhe Chen, Ralf D. Wimmer, Philip M. Brunetti, Shengli Zhao, Basilis Zikopoulos, Fan Wang, Emery N. Brown, Matthew A. Wilson. State-Dependent Architecture of Thalamic Reticular Subnetworks. Cell, 2014; 158 (4): 808 DOI: 10.1016/j.cell.2014.06.025

Behavioral state is known to influence interactions between thalamus and cortex, which are important for sensation, action, and cognition. The thalamic reticular nucleus (TRN) is hypothesized to regulate thalamo-cortical interactions, but the underlying functional architecture of this process and its state dependence are unknown. By combining the first TRN ensemble recording with psychophysics and connectivity-based optogenetic tagging, we found reticular circuits to be composed of distinct subnetworks. While activity of limbic-projecting TRN neurons positively correlates with arousal, sensory-projecting neurons participate in spindles and show elevated synchrony by slow waves during sleep. Sensory-projecting neurons are suppressed by attentional states, demonstrating that their gating of thalamo-cortical interactions is matched to behavioral state. Bidirectional manipulation of attentional performance was achieved through subnetwork-specific optogenetic stimulation. Together, our findings provide evidence for differential inhibition of thalamic nuclei across brain states, where the TRN separately controls external sensory and internal limbic processing facilitating normal cognitive function.

Islands and ocean of memory

Episodic memories are tagged with information about time and place. If we remember an event then it is almost certain we will remember where it happened and where it lies in the temporal sequence of events. Research has shown that an activity pattern in a part of the brain involved in memory, the entorhinal cortex, feeds where and when information to the hippocampus which forms the new memory.

The research is reported in a recent paper: Takashi Kitamura, Chen Sun, Jared Martin, Lacey J. Kitch, Mark J. Schnitzer, Susumu Tonegawa. Entorhinal Cortical Ocean Cells Encode Specific Contexts and Drive Context-Specific Fear Memory. Neuron, 2015; DOI: 10.1016/j.neuron.2015.08.036.

The entorhinal area involved has been likened to an ocean of context specific ‘where’ cells with islands of ‘when’ cells. The ocean cells signal the CA3 cells of the hippocampus and the island cells signal the CA1 cells. If ocean cells are blocked, animals cannot learn to connect fear with a particular environment. Island cells seem to react to the speed an animal is moving at and manipulating their signals changed the gap between events being linked in an animals memory. This is probably one of many ingredients in the processing of time-and-space.

Absract: “Forming distinct representations and memories of multiple contexts and episodes is thought to be a crucial function of the hippocampal-entorhinal cortical network. The hippocampal dentate gyrus (DG) and CA3 are known to contribute to these functions, but the role of the entorhinal cortex (EC) is poorly understood. Here, we show that Ocean cells, excitatory stellate neurons in the medial EC layer II projecting into DG and CA3, rapidly form a distinct representation of a novel context and drive context-specific activation of downstream CA3 cells as well as context-specific fear memory. In contrast, Island cells, excitatory pyramidal neurons in the medial EC layer II projecting into CA1, are indifferent to context-specific encoding or memory. On the other hand, Ocean cells are dispensable for temporal association learning, for which Island cells are crucial. Together, the two excitatory medial EC layer II inputs to the hippocampus have complementary roles in episodic memory.

The thalamus revisited

For a few decades, I have had the opinion that to understand how the brain works it is important to look at more than the neocortex, but also look to the other areas of the brain that may modify, control or even drive the activity of the cortex. Because of my special interest in consciousness, the thalamus was always interesting in this respect. Metaphorically the cortex seemed to be the big on-line computer run by the thalamus.

A recent paper makes another connection between the cortex and the thalamus, to add to many others – (F. Alcaraz, A. R. Marchand, E. Vidal, A. Guillou, A. Faugere, E. Coutureau, M. Wolff. Flexible Use of Predictive Cues beyond the Orbitofrontal Cortex: Role of the Submedius Thalamic Nucleus. Journal of Neuroscience, 2015; 35 (38): 13183 DOI: 10.1523/JNEUROSCI.1237-15.2015).

The various parts of the thalamus are connected to incoming sensory signals, all parts of the cortex, the hippocampus, the mid-brain areas, the spinal cord and the brain stem. It is one of the ‘hubs’ of the brain and its activity is essential for consciousness. However, the particular bit of the thalamus that is implicated in this particular function (adaptive decision making flexibility) appears to have been mainly studied in relationship to pain and control of pain. There is a lot to learn about the thalamus!

Here is the abstract: “The orbitofrontal cortex (OFC) is known to play a crucial role in learning the consequences of specific events. However, the contribution of OFC thalamic inputs to these processes is largely unknown. Using a tract-tracing approach, we first demonstrated that the submedius nucleus (Sub) shares extensive reciprocal connections with the OFC. We then compared the effects of excitotoxic lesions of the Sub or the OFC on the ability of rats to use outcome identity to direct responding. We found that neither OFC nor Sub lesions interfered with the basic differential outcomes effect. However, more specific tests revealed that OFC rats, but not Sub rats, were disproportionally relying on the outcome, rather than on the discriminative stimulus, to guide behavior, which is consistent with the view that the OFC integrates information about predictive cues. In subsequent experiments using a Pavlovian contingency degradation procedure, we found that both OFC and Sub lesions produced a severe deficit in the ability to update Pavlovian associations. Altogether, the submedius therefore appears as a functionally relevant thalamic component in a circuit dedicated to the integration of predictive cues to guide behavior, previously conceived as essentially dependent on orbitofrontal functions.

SIGNIFICANCE STATEMENT: In the present study, we identify a largely unknown thalamic region, the submedius nucleus, as a new functionally relevant component in a circuit supporting the flexible use of predictive cues. Such abilities were previously conceived as largely dependent on the orbitofrontal cortex. Interestingly, this echoes recent findings in the field showing, in research involving an instrumental setup, an additional involvement of another thalamic nuclei, the parafascicular nucleus, when correct responding requires an element of flexibility (Bradfield et al., 2013a). Therefore, the present contribution supports the emerging view that limbic thalamic nuclei may contribute critically to adaptive responding when an element of flexibility is required after the establishment of initial learning.

What meets where

A paper looking at the newly re-found nerve bundle, the vertical occipital fasciculus, connects it with “The Man who Mistook his Wife for a Hat”. Why would someone reach for an object in a different place than the object was? ScienceDaily has a report (here), “Scientists chart a lost highway in the brain”.

The ‘what’ and ‘where’ visual pathways have been studied for some time, but there appeared to be little connecting them until they had passed out of the early visual area. A nerve tract that was known over a hundred years ago and was lost to the anatomy texts until recently, connects the ‘what’ and ‘where’ maps. “Our new study shows that the VOF may provide the fundamental white matter connection between two parts of the visual system: that which identifies objects, words and faces and that which orients us in space. … The structure forms a ‘highway’ between the lower, ventral part of the visual system, which processes the properties of faces, words and objects, and the upper, dorsal parietal regions, which orients attention to an object’s spatial location.

This long flat nerve tract was described long ago and then mysteriously disappeared from view. Why? “The answer may be scientific rivalry. In their earlier paper, Pestilli and collaborators attributed the VOF’s disappearance to competing beliefs among 19th-century neuroanatomists. In contrast to Wernicke, Theodor Meynert, another prominent scientist in Germany, never accepted the new structure due to his belief that all white matter pathways ran horizontally. Over time, the VOF faded into obscurity.

Are other structures being overlooked because they are up and down rather than side to side or back to front, or in some other way are counter to orthodoxy?

Here is the abstract (H. Takemura, A. Rokem, J. Winawer, J. D. Yeatman, B. A. Wandell, F. Pestilli. A Major Human White Matter Pathway Between Dorsal and Ventral Visual Cortex. Cerebral Cortex, 2015; DOI: 10.1093/cercor/bhv064): “Human visual cortex comprises many visual field maps organized into clusters. A standard organization separates visual maps into 2 distinct clusters within ventral and dorsal cortex. We combined fMRI, diffusion MRI, and fiber tractography to identify a major white matter pathway, the vertical occipital fasciculus (VOF), connecting maps within the dorsal and ventral visual cortex. We use a model-based method to assess the statistical evidence supporting several aspects of the VOF wiring pattern. There is strong evidence supporting the hypothesis that dorsal and ventral visual maps communicate through the VOF. The cortical projection zones of the VOF suggest that human ventral (hV4/VO-1) and dorsal (V3A/B) maps exchange substantial information. The VOF appears to be crucial for transmitting signals between regions that encode object properties including form, identity, and color and regions that map spatial information.

 

The learning of concepts

I once tried to learn a simple form of a Bantu language and failed (not surprising as I always fail to learn a new language). One of the problems with this particular attempt was classes of nouns. There were 10 or so classes, each with their own rules. Actually it works like the gender of nouns in most European languages, but it is much more complex and unlike gender it is less arbitrary. The nouns are grouped in somewhat descriptive groups like animals, people, places, tools etc. Besides the Bantu languages there are a number of other groups that have extensive noun classes, twenty or more.

Years ago I found the noun classes inexplicable. Why did they exist? But there has been a number of hints that it is a quite natural way for concepts to be stored in the brain – faces stored here, tools stored there, places stored somewhere else.

A recent paper (Andrew James Bauer, Marcel Adam Just. Monitoring the growth of the neural representations of new animal concepts. Human Brain Mapping, 2015; DOI: 10.1002/hbm.22842) studies how and where new concepts are stored.

Their review of previous finds illustrates the idea. “Research to date has revealed that object concepts (such as the concept of a hammer) are neurally represented in multiple brain regions, corresponding to the various brain systems that are involved in the physical and mental interaction with the concept. The concept of a hammer entails what it looks like, what it is used for, how one holds and wields it, etc., resulting in a neural representation distributed over sensory, motor, and association areas. There is a large literature that documents the responsiveness (activation) of sets of brain regions to the perception or contemplation of different object concepts, including animals (animate natural objects), tools, and fruits and vegetables. For example, fMRI research has shown that nouns that refer to physically manipulable objects such as tools elicit activity in left premotor cortex in right-handers, and activity has also been observed in a variety of other regions to a lesser extent. Clinical studies of object category-specific knowledge deficits have uncovered results compatible with those of fMRI studies. For example, damage to the inferior parietal lobule can result in a relatively selective knowledge deficit about the purpose and the manner of use of a tool. The significance of such findings is enhanced by the commonality of neural representations of object concepts across individuals. For example, pattern classifiers of multi-voxel brain activity trained on the data from a set of participants can reliably predict which object noun a new test participant is contemplating. Similarity in neural representation across individuals may indicate that there exist domain-specific brain networks that process information that is important to survival, such as information about food and eating or about enclosures that provide shelter.

Their study is concerned with how new concepts are formed (they have a keen interest in education). Collectively, the results show that before instruction about a feature, there were no stored representations of the new feature knowledge; and after instruction, the feature information had been acquired and stored in the critical brain regions. The activation patterns in the regions that encode the semantic information that was taught (habitat and diet) changed, reflecting the specific new concept knowledge. This study provides a novel form of evidence (i.e. the emergence of new multi-voxel representations) that newly acquired concept knowledge comes to reside in brain regions previously shown to underlie a particular type of knowledge. Furthermore, this study provides a foundation for brain research to trace how a new concept makes its way from the words and graphics used to teach it, to a neural representation of that concept in a learner’s brain.

This is a different type of learning. It is conceptual knowledge learning rather than learning an intellectual skill such as reading or a motor skill such as juggling.

The storage of conceptual knowledge appears to be quite carefully structured rather than higgly piggly.

Here is the abstract. “Although enormous progress has recently been made in identifying the neural representations of individual object concepts, relatively little is known about the growth of a neural knowledge representation as a novel object concept is being learned. In this fMRI study, the growth of the neural representations of eight individual extinct animal concepts was monitored as participants learned two features of each animal, namely its habitat (i.e., a natural dwelling or scene) and its diet or eating habits. Dwelling/scene information and diet/eating-related information have each been shown to activate their own characteristic brain regions. Several converging methods were used here to capture the emergence of the neural representation of a new animal feature within these characteristic, a priori-specified brain regions. These methods include statistically reliable identification (classification) of the eight newly acquired multivoxel patterns, analysis of the neural representational similarity among the newly learned animal concepts, and conventional GLM assessments of the activation in the critical regions. Moreover, the representation of a recently learned feature showed some durability, remaining intact after another feature had been learned. This study provides a foundation for brain research to trace how a new concept makes its way from the words and graphics used to teach it, to a neural representation of that concept in a learner’s brain.

Simplifying assumptions

There is an old joke about a group of horse betters putting out a tender to scientists for a plan to predict the results of races. A group of biologists submitted a plan to genetically breed a horse that would always win. It would take decades and cost billions. A group of statisticians submitted a plan to devise a computer program to predict races. It would cost millions and would only predict a little over chance. But a group of physicists said they could do it for a few thousand. They would be able to have the program finished in just a few weeks. The betters wanted to know how they could be so quick and cheap. “Well, we have equations for how the race variables interact. It’s a complex equation but we have made simplifying assumptions. First we said let each horse be a perfect rolling sphere. Then…

For over 3 decades ideas have appeared about how the brain must work from studies of electronic neural nets. These studies usually make a lot of assumptions. First, they assume that the only active cells in the brain are the neurons. Second, the neurons are simple (they have inputs which can be weighted and if the sum of the weighted inputs is over a threshold, the neuron fires its output signals) and there is only one type (or a very, very few different types). Third, the connections between the neurons are only structured in very simple and often statistically driven nets. There is only so much that can be learned about the real brain from this model.

But on the basis of electronic neural nets and information theory with, I believe, only a small input from the physiology of real brains, it became accepted that the brain used a ‘sparse coding’. What does this mean? At one end of a spectrum, the information held in a network depends on the state of just one neuron. This coding is sometimes referred to as grandmother cells because one and only one neuron would code for your grandmother. If the information depends on the state of all the neurons or in other words your grandmother would be coded by a particular pattern of activity that includes the states of all the neurons, that is the other end of the spectrum. Sparse coding uses only a few neurons so is near the grandmother cell end of the spectrum.

Since the 1980s it has generally been accepted that the brain uses sparse coding. But experiments with actual brains have been showing that it may not be the case. A recent paper (Anton Spanne, Henrik Jörntell. Questioning the role of sparse coding in the brain. Trends in Neurosciences, 2015; DOI: 10.1016/j.tins.2015.05.005) argues that it may not be sparse after all.

It was assumed that the brain would use the coding system that gives the lowest total activity without losing functionality. But that is not what the brain actually does. It has higher activity that it theoretically needs. This is probably because the brain sits in a fairly active state even at rest (a sort of knife edge) where it can quickly react to situations.

If sparse coding were to apply, it would entail a series of negative consequences for the brain. The largest and most significant consequence is that the brain would not be able to generalize, but only learn exactly what was happening on a specific occasion. Instead, we think that a large number of connections between our nerve cells are maintained in a state of readiness to be activated, enabling the brain to learn things in a reasonable time when we search for links between various phenomena in the world around us. This capacity to generalize is the most important property for learning.

Here is the abstract:

Highlights

  • Sparse coding is questioned on both theoretical and experimental grounds.
  • Generalization is important to current brain models but is weak under sparse coding.
  • The beneficial properties ascribed to sparse coding can be achieved by alternative means.

Coding principles are central to understanding the organization of brain circuitry. Sparse coding offers several advantages, but a near-consensus has developed that it only has beneficial properties, and these are partially unique to sparse coding. We find that these advantages come at the cost of several trade-offs, with the lower capacity for generalization being especially problematic, and the value of sparse coding as a measure and its experimental support are both questionable. Furthermore, silent synapses and inhibitory interneurons can permit learning speed and memory capacity that was previously ascribed to sparse coding only. Combining these properties without exaggerated sparse coding improves the capacity for generalization and facilitates learning of models of a complex and high-dimensional reality.