A theory of cortical responses

This article concerns the nature of evoked brain responses and the principles underlying their generation. We start with the premise that the sensory brain has evolved to represent or infer the causes of changes in its sensory inputs. The problem of inference is well formulated in statistical terms. The statistical fundaments of inference may therefore afford important constraints on neuronal implementation. By formulating the original ideas of Helmholtz on perception, in terms of modern-day statistical theories, one arrives at a model of perceptual inference and learning that can explain a remarkable range of neurobiological facts.It turns out that the problems of inferring the causes of sensory input (perceptual inference) and learning the relationship between input and cause (perceptual learning) can be resolved using exactly the same principle. Specifically, both inference and learning rest on minimizing the brain's free energy, as defined in statistical physics. Furthermore, inference and learning can proceed in a biologically plausible fashion. Cortical responses can be seen as the brain's attempt to minimize the free energy induced by a stimulus and thereby encode the most likely cause of that stimulus. Similarly, learning emerges from changes in synaptic efficacy that minimize the free energy, averaged over all stimuli encountered. The underlying scheme rests on empirical Bayes and hierarchical models of how sensory input is caused. The use of hierarchical models enables the brain to construct prior expectations in a dynamic and context-sensitive fashion. This scheme provides a principled way to understand many aspects of cortical organization and responses. The aim of this article is to encompass many apparently unrelated anatomical, physiological and psychophysical attributes of the brain within a single theoretical perspective.In terms of cortical architectures, the theoretical treatment predicts that sensory cortex should be arranged hierarchically, that connections should be reciprocal and that forward and backward connections should show a functional asymmetry (forward connections are driving, whereas backward connections are both driving and modulatory). In terms of synaptic physiology, it predicts associative plasticity and, for dynamic models, spike-timing-dependent plasticity. In terms of electrophysiology, it accounts for classical and extra classical receptive field effects and long-latency or endogenous components of evoked cortical responses. It predicts the attenuation of responses encoding prediction error with perceptual learning and explains many phenomena such as repetition suppression, mismatch negativity (MMN) and the P300 in electroencephalography. In psychophysical terms, it accounts for the behavioural correlates of these physiological phenomena, for example, priming and global precedence. The final focus of this article is on perceptual learning as measured with the MMN and the implications for empirical studies of coupling among cortical areas using evoked sensory responses.     

A Burst-Based “Hebbian” Learning Rule at Retinogeniculate Synapses Links Retinal Waves to Activity-Dependent Refinement

Patterned spontaneous activity in the developing retina is necessary to drive synaptic refinement in the lateral geniculate nucleus (LGN). Using perforated patch recordings from neurons in LGN slices during the period of eye segregation, we examine how such burst-based activity can instruct this refinement. Retinogeniculate synapses have a novel learning rule that depends on the latencies between pre- and postsynaptic bursts on the order of one second: coincident bursts produce long-lasting synaptic enhancement, whereas non-overlapping bursts produce mild synaptic weakening. It is consistent with “Hebbian” development thought to exist at this synapse, and we demonstrate computationally that such a rule can robustly use retinal waves to drive eye segregation and retinotopic refinement. Thus, by measuring plasticity induced by natural activity patterns, synaptic learning rules can be linked directly to their larger role in instructing the patterning of neural connectivity.     

Learning Multisensory Integration and Coordinate Transformation via Density Estimation

Sensory processing in the brain includes three key operations: multisensory integration—the task of combining cues into a single estimate of a common underlying stimulus; coordinate transformations—the change of reference frame for a stimulus (e.g., retinotopic to body-centered) effected through knowledge about an intervening variable (e.g., gaze position); and the incorporation of prior information. Statistically optimal sensory processing requires that each of these operations maintains the correct posterior distribution over the stimulus. Elements of this optimality have been demonstrated in many behavioral contexts in humans and other animals, suggesting that the neural computations are indeed optimal. That the relationships between sensory modalities are complex and plastic further suggests that these computations are learned—but how? We provide a principled answer, by treating the acquisition of these mappings as a case of density estimation, a well-studied problem in machine learning and statistics, in which the distribution of observed data is modeled in terms of a set of fixed parameters and a set of latent variables. In our case, the observed data are unisensory-population activities, the fixed parameters are synaptic connections, and the latent variables are multisensory-population activities. In particular, we train a restricted Boltzmann machine with the biologically plausible contrastive-divergence rule to learn a range of neural computations not previously demonstrated under a single approach: optimal integration; encoding of priors; hierarchical integration of cues; learning when not to integrate; and coordinate transformation. The model makes testable predictions about the nature of multisensory representations.     

Energy Efficient Sparse Connectivity from Imbalanced Synaptic Plasticity Rules

It is believed that energy efficiency is an important constraint in brain evolution. As synaptic transmission dominates energy consumption, energy can be saved by ensuring that only a few synapses are active. It is therefore likely that the formation of sparse codes and sparse connectivity are fundamental objectives of synaptic plasticity. In this work we study how sparse connectivity can result from a synaptic learning rule of excitatory synapses. Information is maximised when potentiation and depression are balanced according to the mean presynaptic activity level and the resulting fraction of zero-weight synapses is around 50%. However, an imbalance towards depression increases the fraction of zero-weight synapses without significantly affecting performance. We show that imbalanced plasticity corresponds to imposing a regularising constraint on the L ₁-norm of the synaptic weight vector, a procedure that is well-known to induce sparseness. Imbalanced plasticity is biophysically plausible and leads to more efficient synaptic configurations than a previously suggested approach that prunes synapses after learning. Our framework gives a novel interpretation to the high fraction of silent synapses found in brain regions like the cerebellum.     

Efficiency and cost of economical brain functional networks

Brain anatomical networks are sparse, complex, and have economical small-world properties. We investigated the efficiency and cost of human brain functional networks measured using functional magnetic resonance imaging (fMRI) in a factorial design: two groups of healthy old (N = 11; mean age = 66.5 years) and healthy young (N = 15; mean age = 24.7 years) volunteers were each scanned twice in a no-task or “resting” state following placebo or a single dose of a dopamine receptor antagonist (sulpiride 400 mg). Functional connectivity between 90 cortical and subcortical regions was estimated by wavelet correlation analysis, in the frequency interval 0.06–0.11 Hz, and thresholded to construct undirected graphs. These brain functional networks were small-world and economical in the sense of providing high global and local efficiency of parallel information processing for low connection cost. Efficiency was reduced disproportionately to cost in older people, and the detrimental effects of age on efficiency were localised to frontal and temporal cortical and subcortical regions. Dopamine antagonism also impaired global and local efficiency of the network, but this effect was differentially localised and did not interact with the effect of age. Brain functional networks have economical small-world properties—supporting efficient parallel information transfer at relatively low cost—which are differently impaired by normal aging and pharmacological blockade of dopamine transmission.     

Green Tea Polyphenols Rescue of Brain Defects Induced by Overexpression of DYRK1A

Individuals with partial HSA21 trisomies and mice with partial MMU16 trisomies containing an extra copy of the DYRK1A gene present various alterations in brain morphogenesis. They present also learning impairments modeling those encountered in Down syndrome. Previous MRI and histological analyses of a transgenic mice generated using a human YAC construct that contains five genes including DYRK1A reveal that DYRK1A is involved, during development, in the control of brain volume and cell density of specific brain regions. Gene dosage correction induces a rescue of the brain volume alterations. DYRK1A is also involved in the control of synaptic plasticity and memory consolidation. Increased gene dosage results in brain morphogenesis defects, low BDNF levels and mnemonic deficits in these mice. Epigallocatechin gallate (EGCG) — a member of a natural polyphenols family, found in great amount in green tea leaves — is a specific and safe DYRK1A inhibitor. We maintained control and transgenic mice overexpressing DYRK1A on two different polyphenol-based diets, from gestation to adulthood. The major features of the transgenic phenotype were rescued in these mice.     

Morphological and electrophysiological examination of olfactory sensory neurons during the early developmental prolarval stage of the sea lamprey Petromyzon marinus L

This study examined olfactory sensory neuron morphology and physiological responsiveness in newly hatched sea lamprey, Petromyzon marinus L. These prolarvae hatch shortly after neural tube formation, and stay within nests for approximately 18 days, before moving downstream to silty areas where they burrow, feed and pass to the larval stage. To explore the possibility that the olfactory system is functioning during this prolarval stage, morphological and physiological development of olfactory sensory neurons was examined. The nasal cavity contained an olfactory epithelium with ciliated olfactory sensory neurons. Axons formed aggregates in the basal portion of the olfactory epithelium and spanned the narrow distance between the olfactory epithelium and the brain. The presence of asymmetric synapses with agranular vesicles within fibers in the brain, adjacent to the olfactory epithelium suggests that there was synaptic connectivity between olfactory sensory axons and the brain. Neural recordings from the surface of the olfactory epithelium showed responses following the application of L-arginine, taurocholic acid, petromyzonol sulfate (a lamprey migratory pheromone), and water conditioned by conspecifics. These results suggest that lampreys may respond to olfactory sensory input during the prolarval stage.     

Cortical Hierarchies Perform Bayesian Causal Inference in Multisensory Perception

To form a veridical percept of the environment, the brain needs to integrate sensory signals from a common source but segregate those from independent sources. Thus, perception inherently relies on solving the “causal inference problem.” Behaviorally, humans solve this problem optimally as predicted by Bayesian Causal Inference; yet, the underlying neural mechanisms are unexplored. Combining psychophysics, Bayesian modeling, functional magnetic resonance imaging (fMRI), and multivariate decoding in an audiovisual spatial localization task, we demonstrate that Bayesian Causal Inference is performed by a hierarchy of multisensory processes in the human brain. At the bottom of the hierarchy, in auditory and visual areas, location is represented on the basis that the two signals are generated by independent sources (= segregation). At the next stage, in posterior intraparietal sulcus, location is estimated under the assumption that the two signals are from a common source (= forced fusion). Only at the top of the hierarchy, in anterior intraparietal sulcus, the uncertainty about the causal structure of the world is taken into account and sensory signals are combined as predicted by Bayesian Causal Inference. Characterizing the computational operations of signal interactions reveals the hierarchical nature of multisensory perception in human neocortex. It unravels how the brain accomplishes Bayesian Causal Inference, a statistical computation fundamental for perception and cognition. Our results demonstrate how the brain combines information in the face of uncertainty about the underlying causal structure of the world.     

A Neurocomputational Model of the Mismatch Negativity

The mismatch negativity (MMN) is an event related potential evoked by violations of regularity. Here, we present a model of the underlying neuronal dynamics based upon the idea that auditory cortex continuously updates a generative model to predict its sensory inputs. The MMN is then modelled as the superposition of the electric fields evoked by neuronal activity reporting prediction errors. The process by which auditory cortex generates predictions and resolves prediction errors was simulated using generalised (Bayesian) filtering – a biologically plausible scheme for probabilistic inference on the hidden states of hierarchical dynamical models. The resulting scheme generates realistic MMN waveforms, explains the qualitative effects of deviant probability and magnitude on the MMN – in terms of latency and amplitude – and makes quantitative predictions about the interactions between deviant probability and magnitude. This work advances a formal understanding of the MMN and – more generally – illustrates the potential for developing computationally informed dynamic causal models of empirical electromagnetic responses.     

Mirrored STDP Implements Autoencoder Learning in a Network of Spiking Neurons

The autoencoder algorithm is a simple but powerful unsupervised method for training neural networks. Autoencoder networks can learn sparse distributed codes similar to those seen in cortical sensory areas such as visual area V1, but they can also be stacked to learn increasingly abstract representations. Several computational neuroscience models of sensory areas, including Olshausen & Field’s Sparse Coding algorithm, can be seen as autoencoder variants, and autoencoders have seen extensive use in the machine learning community. Despite their power and versatility, autoencoders have been difficult to implement in a biologically realistic fashion. The challenges include their need to calculate differences between two neuronal activities and their requirement for learning rules which lead to identical changes at feedforward and feedback connections. Here, we study a biologically realistic network of integrate-and-fire neurons with anatomical connectivity and synaptic plasticity that closely matches that observed in cortical sensory areas. Our choice of synaptic plasticity rules is inspired by recent experimental and theoretical results suggesting that learning at feedback connections may have a different form from learning at feedforward connections, and our results depend critically on this novel choice of plasticity rules. Specifically, we propose that plasticity rules at feedforward versus feedback connections are temporally opposed versions of spike-timing dependent plasticity (STDP), leading to a symmetric combined rule we call Mirrored STDP (mSTDP). We show that with mSTDP, our network follows a learning rule that approximately minimizes an autoencoder loss function. When trained with whitened natural image patches, the learned synaptic weights resemble the receptive fields seen in V1. Our results use realistic synaptic plasticity rules to show that the powerful autoencoder learning algorithm could be within the reach of real biological networks.     

Task-induced neural covariability as a signature of approximate Bayesian learning and inference

Perception is often characterized computationally as an inference process in which uncertain or ambiguous sensory inputs are combined with prior expectations. Although behavioral studies have shown that observers can change their prior expectations in the context of a task, robust neural signatures of task-specific priors have been elusive. Here, we analytically derive such signatures under the general assumption that the responses of sensory neurons encode posterior beliefs that combine sensory inputs with task-specific expectations. Specifically, we derive predictions for the task-dependence of correlated neural variability and decision-related signals in sensory neurons. The qualitative aspects of our results are parameter-free and specific to the statistics of each task. The predictions for correlated variability also differ from predictions of classic feedforward models of sensory processing and are therefore a strong test of theories of hierarchical Bayesian inference in the brain. Importantly, we find that Bayesian learning predicts an increase in so-called “differential correlations” as the observer’s internal model learns the stimulus distribution, and the observer’s behavioral performance improves. This stands in contrast to classic feedforward encoding/decoding models of sensory processing, since such correlations are fundamentally information-limiting. We find support for our predictions in data from existing neurophysiological studies across a variety of tasks and brain areas. Finally, we show in simulation how measurements of sensory neural responses can reveal information about a subject’s internal beliefs about the task. Taken together, our results reinterpret task-dependent sources of neural covariability as signatures of Bayesian inference and provide new insights into their cause and their function.     

Directional Connectivity between Frontal and Posterior Brain Regions Is Altered with Increasing Concentrations of Propofol

Recent studies using electroencephalography (EEG) suggest that alteration of coherent activity between the anterior and posterior brain regions might be used as a neurophysiologic correlate of anesthetic-induced unconsciousness. One way to assess causal relationships between brain regions is given by renormalized partial directed coherence (rPDC). Importantly, directional connectivity is evaluated in the frequency domain by taking into account the whole multichannel EEG, as opposed to time domain or two channel approaches. rPDC was applied here in order to investigate propofol induced changes in causal connectivity between four states of consciousness: awake (AWA), deep sedation (SED), loss (LOC) and return of consciousness (ROC) by gathering full 10/20 system human EEG data in ten healthy male subjects. The target-controlled drug infusion was started at low rate with subsequent gradual stepwise increases at 10 min intervals in order to carefully approach LOC (defined as loss of motor responsiveness to a verbal stimulus). The direction of the causal EEG-network connections clearly changed from AWA to SED and LOC. Propofol induced a decrease (p = 0.002–0.004) in occipital-to-frontal rPDC of 8-16 Hz EEG activity and an increase (p = 0.001–0.040) in frontal-to-occipital rPDC of 10–20 Hz activity on both sides of the brain during SED and LOC. In addition, frontal-to-parietal rPDC within 1–12 Hz increased in the left hemisphere at LOC compared to AWA (p = 0.003). However, no significant changes were detected between the SED and the LOC states. The observed decrease in back-to-front EEG connectivity appears compatible with impaired information flow from the posterior sensory and association cortices to the executive prefrontal areas, possibly related to decreased ability to perceive the surrounding world during sedation. The observed increase in the opposite (front-to-back) connectivity suggests a propofol concentration dependent association and is not directly related to the level of consciousness per se.     

Diverse processing underlying frequency integration in midbrain neurons of barn owls

Emergent response properties of sensory neurons depend on circuit connectivity and somatodendritic processing. Neurons of the barn owl’s external nucleus of the inferior colliculus (ICx) display emergence of spatial selectivity. These neurons use interaural time difference (ITD) as a cue for the horizontal direction of sound sources. ITD is detected by upstream brainstem neurons with narrow frequency tuning, resulting in spatially ambiguous responses. This spatial ambiguity is resolved by ICx neurons integrating inputs over frequency, a relevant processing in sound localization across species. Previous models have predicted that ICx neurons function as point neurons that linearly integrate inputs across frequency. However, the complex dendritic trees and spines of ICx neurons raises the question of whether this prediction is accurate. Data from in vivo intracellular recordings of ICx neurons were used to address this question. Results revealed diverse frequency integration properties, where some ICx neurons showed responses consistent with the point neuron hypothesis and others with nonlinear dendritic integration. Modeling showed that varied connectivity patterns and forms of dendritic processing may underlie observed ICx neurons’ frequency integration processing. These results corroborate the ability of neurons with complex dendritic trees to implement diverse linear and nonlinear integration of synaptic inputs, of relevance for adaptive coding and learning, and supporting a fundamental mechanism in sound localization.     

Subjective visual perception: from local processing to emergent phenomena of brain activity

The combination of electrophysiological recordings with ambiguous visual stimulation made possible the detection of neurons that represent the content of subjective visual perception and perceptual suppression in multiple cortical and subcortical brain regions. These neuronal populations, commonly referred to as the neural correlates of consciousness, are more likely to be found in the temporal and prefrontal cortices as well as the pulvinar, indicating that the content of perceptual awareness is represented with higher fidelity in higher-order association areas of the cortical and thalamic hierarchy, reflecting the outcome of competitive interactions between conflicting sensory information resolved in earlier stages. However, despite the significant insights into conscious perception gained through monitoring the activities of single neurons and small, local populations, the immense functional complexity of the brain arising from correlations in the activity of its constituent parts suggests that local, microscopic activity could only partially reveal the mechanisms involved in perceptual awareness. Rather, the dynamics of functional connectivity patterns on a mesoscopic and macroscopic level could be critical for conscious perception. Understanding these emergent spatio-temporal patterns could be informative not only for the stability of subjective perception but also for spontaneous perceptual transitions suggested to depend either on the dynamics of antagonistic ensembles or on global intrinsic activity fluctuations that may act upon explicit neural representations of sensory stimuli and induce perceptual reorganization. Here, we review the most recent results from local activity recordings and discuss the potential role of effective, correlated interactions during perceptual awareness.     

Chronic murine toxoplasmosis is defined by subtle changes in neuronal connectivity

Recent studies correlate chronic Toxoplasma gondii (T. gondii) infection with behavioral changes in rodents; additionally, seropositivity in humans is reported to be associated with behavioral and neuropsychiatric diseases. In this study we investigated whether the described behavioral changes in a murine model of chronic toxoplasmosis are associated with changes in synaptic plasticity and brain neuronal circuitry. In mice chronically infected with T. gondii, magnetic resonance imaging (MRI) data analysis displayed the presence of heterogeneous lesions scattered throughout all brain areas. However, a higher density of lesions was observed within specific regions such as the somatosensory cortex (SSC). Further histopathological examination of these brain areas indicated the presence of activated resident glia and recruited immune cells accompanied by limited alterations of neuronal viability. In vivo diffusion-tensor MRI analysis of neuronal fiber density within the infected regions revealed connectivity abnormalities in the SSC. Altered fiber density was confirmed by morphological analysis of individual, pyramidal and granule neurons, showing a reduction in dendritic arbor and spine density within the SSC, as well as in the hippocampus. Evaluation of synapse efficacy revealed diminished levels of two key synaptic proteins, PSD95 and synaptophysin, within the same brain areas, indicating deficits in functionality of the synaptic neurotransmission in infected mice. Our results demonstrate that persistent T. gondii infection in a murine model results in synaptic deficits within brain structures leading to disturbances in the morphology of noninfected neurons and modified brain connectivity, suggesting a potential explanation for the behavioral and neuropsychiatric alterations.KEY WORDS: Parasites, Behavioral manipulation, Neuronal connectivity     

Evidence for training-induced plasticity in multisensory brain structures: an MEG study

Multisensory learning and resulting neural brain plasticity have recently become a topic of renewed interest in human cognitive neuroscience. Music notation reading is an ideal stimulus to study multisensory learning, as it allows studying the integration of visual, auditory and sensorimotor information processing. The present study aimed at answering whether multisensory learning alters uni-sensory structures, interconnections of uni-sensory structures or specific multisensory areas. In a short-term piano training procedure musically naive subjects were trained to play tone sequences from visually presented patterns in a music notation-like system [Auditory-Visual-Somatosensory group (AVS)], while another group received audio-visual training only that involved viewing the patterns and attentively listening to the recordings of the AVS training sessions [Auditory-Visual group (AV)]. Training-related changes in cortical networks were assessed by pre- and post-training magnetoencephalographic (MEG) recordings of an auditory, a visual and an integrated audio-visual mismatch negativity (MMN). The two groups (AVS and AV) were differently affected by the training. The results suggest that multisensory training alters the function of multisensory structures, and not the uni-sensory ones along with their interconnections, and thus provide an answer to an important question presented by cognitive models of multisensory training.     

Information and Efficiency in the Nervous System—A Synthesis

In systems biology, questions concerning the molecular and cellular makeup of an organism are of utmost importance, especially when trying to understand how unreliable components—like genetic circuits, biochemical cascades, and ion channels, among others—enable reliable and adaptive behaviour. The repertoire and speed of biological computations are limited by thermodynamic or metabolic constraints: an example can be found in neurons, where fluctuations in biophysical states limit the information they can encode—with almost 20–60% of the total energy allocated for the brain used for signalling purposes, either via action potentials or by synaptic transmission. Here, we consider the imperatives for neurons to optimise computational and metabolic efficiency, wherein benefits and costs trade-off against each other in the context of self-organised and adaptive behaviour. In particular, we try to link information theoretic (variational) and thermodynamic (Helmholtz) free-energy formulations of neuronal processing and show how they are related in a fundamental way through a complexity minimisation lemma.     

Aging in Sensory and Motor Neurons Results in Learning Failure in Aplysia californica

The physiological and molecular mechanisms of age-related memory loss are complicated by the complexity of vertebrate nervous systems. This study takes advantage of a simple neural model to investigate nervous system aging, focusing on changes in learning and memory in the form of behavioral sensitization in vivo and synaptic facilitation in vitro. The effect of aging on the tail withdrawal reflex (TWR) was studied in Aplysia californica at maturity and late in the annual lifecycle. We found that short-term sensitization in TWR was absent in aged Aplysia. This implied that the neuronal machinery governing nonassociative learning was compromised during aging. Synaptic plasticity in the form of short-term facilitation between tail sensory and motor neurons decreased during aging whether the sensitizing stimulus was tail shock or the heterosynaptic modulator serotonin (5-HT). Together, these results suggest that the cellular mechanisms governing behavioral sensitization are compromised during aging, thereby nearly eliminating sensitization in aged Aplysia.     

Hippocampal Extracellular Matrix Levels and Stochasticity in Synaptic Protein Expression Increase with Age and Are Associated with Age-dependent Cognitive Decline

Age-related cognitive decline is a serious health concern in our aging society. Decreased cognitive function observed during healthy brain aging is most likely caused by changes in brain connectivity and synaptic dysfunction in particular brain regions. Here we show that aged C57BL/6J wild-type mice have hippocampus-dependent spatial memory impairments. To identify the molecular mechanisms that are relevant to these memory deficits, we investigated the temporal profile of mouse hippocampal synaptic proteome changes at 20, 40, 50, 60, 70, 80, 90, and 100 weeks of age. Extracellular matrix proteins were the only group of proteins that showed robust and progressive up-regulation over time. This was confirmed by immunoblotting and histochemical analysis, which indicated that the increased levels of hippocampal extracellular matrix might limit synaptic plasticity as a potential cause of age-related cognitive decline. In addition, we observed that stochasticity in synaptic protein expression increased with age, in particular for proteins that were previously linked with various neurodegenerative diseases, whereas low variance in expression was observed for proteins that play a basal role in neuronal function and synaptic neurotransmission. Together, our findings show that both specific changes and increased variance in synaptic protein expression are associated with aging and may underlie reduced synaptic plasticity and impaired cognitive performance in old age.As the proportion of aged individuals in our population continues to grow, we are faced with an increase in age-related health problems. Brain aging invariably leads to functional decline and impairments in cognitive function and motor skills, which can seriously affect quality of life. A better understanding of the neurobiological mechanisms underlying age-related cognitive decline is crucial to facilitate maintenance of cognitive health in the elderly and to reveal potential causes of highly prevalent age-related forms of dementia, in particular Alzheimer disease, in which cognitive decline is severely impaired by yet unknown mechanisms.Several studies showed that normal brain aging is associated with subtle morphological and functional alterations in specific neuronal circuits (1, 2) and that reduced cognitive function with increasing age is likely due to synaptic dysfunction (3). Increasing evidence supports the idea that alterations in hippocampal activity are correlated with deficits in learning and memory in healthy aging humans (4, 5). In addition, rodent models of healthy aging demonstrate strong correlations between impaired performance in learning and memory tests and disturbed hippocampal network activity (6, 7). Electrophysiological studies provide additional evidence that age-related disturbances in the hippocampus involve changes in the principal cellular features of learning and memory, synaptic long-term potentiation and long-term depression (8, 9). Together, these observations suggest that a decline in hippocampal synaptic efficacy and plasticity plays a critical role in age-dependent cognitive impairment.Aging is also the primary risk factor for Alzheimer disease, which clinically manifests as severe and accelerated age-dependent cognitive decline (10). Genetic causes of familial early-onset Alzheimer disease all point to a key role in disease etiology for increased brain levels of the protein amyloid-β (11). Familial Alzheimer disease, however, is rare, and it is likely that increased amyloid-β levels in sporadic Alzheimer disease result from age-dependent and/or genetically determined alterations in the expression of other genes or proteins (12, 13). Thus, the identification of molecular mechanisms of normal brain aging might also contribute to our understanding of cognitive decline under pathological conditions, in particular in Alzheimer disease.Although the exact mechanisms underlying brain aging remain to be fully determined, they likely include changes at the molecular, cellular, and neuronal-network levels. In particular, characterization of alterations in the molecular composition and dynamics of hippocampal synapses could potentially reveal important aspects of the underlying mechanisms of brain aging. Age-related changes in global hippocampal gene and protein expression have been investigated previously (14, 15), but these studies were not geared to identify the specific synaptic molecular substrates of brain aging. Here, we made use of iTRAQ¹ technology and high-coverage mass spectrometry to study the effects of aging on the proteomic composition of mouse hippocampal synaptosomes. We investigated the synaptic proteomes of individual mice at 20, 40, 50, 60, 70, 80, 90, and 100 weeks of age. Our findings show that both specific changes and increased variance in synaptic protein expression are associated with age-related cognitive decline.     

Intracellular Neural Recording with Pure Carbon Nanotube Probes

The computational complexity of the brain depends in part on a neuron’s capacity to integrate electrochemical information from vast numbers of synaptic inputs. The measurements of synaptic activity that are crucial for mechanistic understanding of brain function are also challenging, because they require intracellular recording methods to detect and resolve millivolt- scale synaptic potentials. Although glass electrodes are widely used for intracellular recordings, novel electrodes with superior mechanical and electrical properties are desirable, because they could extend intracellular recording methods to challenging environments, including long term recordings in freely behaving animals. Carbon nanotubes (CNTs) can theoretically deliver this advance, but the difficulty of assembling CNTs has limited their application to a coating layer or assembly on a planar substrate, resulting in electrodes that are more suitable for in vivo extracellular recording or extracellular recording from isolated cells. Here we show that a novel, yet remarkably simple, millimeter-long electrode with a sub-micron tip, fabricated from self-entangled pure CNTs can be used to obtain intracellular and extracellular recordings from vertebrate neurons in vitro and in vivo. This fabrication technology provides a new method for assembling intracellular electrodes from CNTs, affording a promising opportunity to harness nanotechnology for neuroscience applications.