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But this kind of transgenerational epigenetic effect on behav ior should not be confused with the transmission of learned information medications jfk was on order 75 mcg synthroid with amex. A demonstration of the epigenetic transfer of memory would require evidence that a specific memory formed by individuals is transferred to their offspring. The standard Darwinian paradigm can certainly account for the origin of innate behav iors, but it is very slow and requires that the mutant behavioral trait be of a fortuitous advantage to individuals in their population. If a mutant phenotype does not improve the fitness of an organism in its environment, then it is unlikely to come to fixation in the population. On the other hand, the epigenetic paradigm is attractive from a naïve perspective because it would mean that our own learning might directly influence the behav ior of our offspring and help to direct the evolution of our species. Such speculations go back at least as far as Lamarck and tend to resurface whenever a new biological mechanism is characterized that can be imagined to somehow carry learned information from the brain of an individual animal to its germ cells, through the developing offspring, and into their brains, ultimately resulting in very specific effects on brain development. This idea is at odds with the essential stability of innate behav iors across generations and the fact of conservation of similar instincts across related species. Here I present a more parsimonious working hypothesis for how instincts may be evolutionarily descendant from memories, not through direct epigenetic transfer of a molecular substrate but by imitation of the informational content of an ancestral memory by an independently formed instinct. Over a century ago, the Baldwin effect described how learned behav iors may facilitate the evolution of similar innate behav iors by creating an environment or niche where hard-wired versions of those behav iors would have a selective advantage (Baldwin, 1896; Morgan, 1896; Osborn, 1896). Without such niche construction, a random 2 While such proposals are radical, there have been striking reports of olfactory conditioning in mice promoting glomerular plasticity for similar odors in mice offspring (Dias & Ressler, 2014). While such cases may be valid, they seem to represent the exception rather than the rule. But if that innate behav ior can substitute for a valuable or necessary learned behav ior that already exists in the population, then the new instinct will instill a competitive advantage on the mutant individuals relative to their wild-type peers in that ecosystem (figure 18. Learning is hard work-it is imperfect and acquiring the information by experience is fraught with risk. Mutant organisms born with genet ically encoded instincts will outcompete their less privileged peers who must learn the information for themselves. While more biologically plausible than Lamarckian inheritance and supported by computational analysis (Hinton & Nowlan, 1987), the Baldwin effect has not been empirically demonstrated, and no concrete mechanism has been proposed. However, a conceptual synthesis of recent research in the neurobiology of memories and instincts may provide novel evidence for a continuity between memory and instinct. In neurobiology it is understood that instinct is information embedded in genet ically determined brain structures formed by developmental processes that originate through biological evolution (Anderson, 2016). Clearly, instincts and memories are encoded through very dif ferent mechanisms (mutation and neural plasticity, respectively), and it has also been tacitly assumed that they are coded or represented as dif ferent neurobiological substrates (neuroanatomy and synaptic plasticity). But owing to studies of engram ensembles, it now seems likely that long-term memories, like instincts, are embedded in the brain as changes in the connectivity patterns between distributed engram cells (Poo et al. When the same activity- dependent labeling techniques were used to tag subpopulations of olfactory sensory neurons responsive to specific odors innately perceived as attractive or aversive.

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This approach has been successful in a variety of brain areas-most notably treatment interventions buy 100 mcg synthroid amex, in early visual cortex (Hubel and Wiesel 1959), where tuning curves illustrating the orientation and frequency selectivity of V1 neurons laid the groundwork for Gabor wavelet­based models. Relative to the optimization framework described above, the analysis of tuning curves is essentially an attempt to characterize optimal networks A* in non- optimization-based terms. When a small number of mathematically simple stimulus- domain axes can be found in which the tuning curves of A* have a mathematically simple shape, A* can largely be constructed by a simple closed-form procedure without any reference to learning through iterative optimization. This is to some extent feasible for V1 neurons and perhaps in early cortical areas in other domains, such as primary auditory cortex (Chi, Ru, and Shamma 2005). It is possible that this type of simplification is most helpful for understanding neural responses that arise largely from highly constrained stereotyped genetic developmental programs, rather than those that depend heavily on experience- driven learning (Espinosa and Stryker 2012), or where biophysical constraints- such as metabolic cost or noise reduction-might also impose "simplicity priors" on the neural architecture (Olshausen and Field 1996; Sussillo et al. In general, however, it is not guaranteed that closedform expressions describing the response properties of task- optimized models can be found. Evolution and development are under no general constraint to make their products conform to simple mathematical shapes, especially for intermediate and higher cortical areas removed from the sensory or motor periphery. However, even if such analytical simplifications do not exist, the optimization framework nonetheless, provides a method for generating meta-understanding via characterizing the constraints on the system, rather than analyzing the specific outcome network itself. By varying the architectural class, the computational goal, or the learning rule, and identifying which choices lead to networks that best match the observed neural data, it is possible to learn much about the brain system of interest even if its tuning curves are inscrutable. Understanding multiple optima What happens when multiple optimal network solutions exist For many architecture classes, there may be infinitely many qualitatively very similar networks with the same or substantially similar outputs-for example, those created by applying orthonormal rotations to linear transforms present in the network. Sometimes, however, qualitatively very distinct networks might achieve similar perfor mance levels on a task. The optimization framework does not require a unique best solution to the computational goal to make Yamins: An Optimization-Based Approach 391 useful predictions. If several subclasses of highperforming solutions to a given task are identified, this is equivalent to formulating multiple very qualitatively distinct hypotheses for the neural circuits underlying function in a given brain area. Recent work in modeling rodent whisker trigeminal cortex, in which similar task performance on whisker- driven shape recognition can be achieved by several distinct neural architecture classes, illustrates this idea (Zhuang et al. Comparison of the distinct model types to experimental results, either from detailed behavioral or neural experiments, is then likely to point toward one of these hypotheses as explaining the data better than others. Techniques similar to those used to create the models in the first place can be deployed to generate optimal stimuli for separating the predictions of the multiple models as widely as possible, which would in turn directly inform experimental design. In these cases, the optimization framework serves as an efficient generator of strong hypotheses. In contrast, if most high-performing solutions to a computational goal fall into a comparatively narrower band of variability, the set of model solutions may correspond to actual variability in the real subject population. The optimization framework naturally supports at least two potential sources of such variation, including the following: · · Variation of initial conditions, described as a probability distribution over the starting point models A 0 to which the learning rule is applied.

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Alpha oscillations serve to protect working memory maintenance against anticipated distracters treatment for strep throat generic synthroid 200 mcg buy online. Proceedings of the National Academy of Sciences of the United States of America, 108(27), 11262­11267. Proceedings of the National Academy of Sciences of the United States of America, 107(37), 16048­16053. The phase of ongoing oscillations mediates the causal relation between brain excitation and visual perception. Attention- dependent suppression of distracter visual input can be cross-modally cued as indexed by anticipatory parieto- occipital alpha-band oscillations. Somatosensory anticipatory alpha activity increases to suppress distracting input. Alpha- oscillations in the monkey sensorimotor network influence discrimination per for mance by rhythmical inhibition of neuronal spiking. Proceedings of the National Academy of Sciences of the United States of America, 108(48), 19377­19382. Somatosensory working memory per for mance in humans depends on both engagement and disengagement of regions in a distributed network. Alpha oscillations correlate with the successful inhibition of unattended stimuli. Prestimulus oscillations predict visual perception per for mance between and within subjects. Oscillatory power decreases and long-term memory: the information via desynchronization hypothesis. Prestimulus oscillatory phase at 7 Hz gates cortical information flow and visual perception. Directed communication between nucleus accumbens and neocortex in humans is differentially supported by synchronization in the theta and alpha band. Spontaneous neural oscillations bias perception by modulating baseline excitability. Temporal coding organized by coupled alpha and gamma oscillations prioritize visual processing. Oscillations in the alpha band (9­12 Hz) increase with memory load during retention in a short-term memory task. Modulation of gamma and alpha activity during a working memory task engaging the dorsal or ventral stream. Local and long-range functional connectivity is reduced in concert in autism spectrum disorders.

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They examine this question by considering three putative triggers for reorganization: learning symptoms 2 weeks after conception cheap synthroid 200 mcg on-line, loss of cortical inputs from amputation, and loss of cortical substrate following stroke. They conclude that changes in cortical maps from experience or injury are likely not due to reorganization but result from the unmasking of preexisting cortical connections or subcortical reorganization. They also argue that map changes, regardless of their causes, are not the causal factors in behavioral change. The basal ganglia are a set of subcortical nuclei long implicated in motor control and motor learning in health and disease. There is, however, increasing 484 Intention, Action, Control awareness that these nuclei contribute to perception and cognition. Similar to current work on that other prominent subcortical structure, the cerebellum, the holy grail in basal ganglia research seems to be finding a universal computation, with regional differences attributable to this computation being performed on dif ferent variables-an idea that seems to be implied by the multiple parallel cortical-basal ganglionic loops. An important challenge for this endeavor is to reconcile what seem to be distinct learning versus per formance functions of the basal ganglia. David Robbe and Joshua Tate Dudman review human and nonhuman animal data on the role of the striatum and its dopaminergic inputs with regard to action selection, motor control, decision-making, and learning. They favor an emphasis on the role of the basal ganglia in the selection of overlearned actions and their associated degree of vigor. It is less clear, in their view, whether the basal ganglia are needed for either learning or executing a skilled movement. The idea that an action must be planned seems so obvious as to need no re- examination. They argue that movement preparation is a process of setting the state of the motor system once an action goal is identified, priming it to generate a single, task-appropriate movement. Contrary to traditional views, this preparatory process occurs very rapidly and is perhaps completed within approximately 50 ms. However, completing preparation does not directly trigger initiation of the movement; initiation is conceptualized as a separate, independent process. In addition, Haith and Bestmann provide alternative explanations for two prominent ideas in the literature: first, that several movements can be prepared in parallel and second, that the circuitry and mechanisms for decision-making and those for movement representation overlap. The authors argue instead that only one movement- control policy is present at any point in time and that this policy reflects the instantaneous state of decision uncertainty across goals. There is a prevailing assumption, both in the cognitive neuroscience community and in the world at large, that there is something a bit undemanding, intellectually, about having a motor skill-the notion of the "dumb jock. They summarize a series of studies using visuomotor adaptation tasks to show that even simple motor-learning paradigms, like mirror drawing, do in fact comprise implicit learning mechanisms and explicit strategies that combine to accomplish the task. They conclude that, like all other cognitive tasks, motor learning recruits a full taxonomy of memory systems. Their position can be summarized as saying that skilled motor behav iors are far too important to leave to just one part of the brain.

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Falk, 57 years: Significantly, such difficulties are apparent across a range of psychiatric disorders.

Anog, 64 years: Attributebased neural substrates in temporal cortex for perceiving and knowing about objects.