Derivation and analysis of basic computational operations of thalamocortical circuits

On the contrast dependence of crowding

Visual clutter affects our ability to see. Objects that would be identifiable on their own may become unrecognizable when presented close together ("crowding"), but the psychophysical characteristics of crowding have resisted simplification. Image properties initially thought to produce crowding have paradoxically yielded unexpected results; for example, adding flanking objects can ameliorate crowding (Manassi, Sayim, & Herzog, 2012; Herzog, Sayim, Chcherov, & Manassi, 2015; Pachai, Doerig, & Herzog, 2016). The resulting theory revisions have been sufficiently complex and specialized as to make it difficult to discern what principles may underlie the observed phenomena. Here, a generalized formulation of simple visual contrast energy is presented, arising from straightforward analyses of center and surround neurons in the early visual stream. Extant contrast measures, such as root mean square contrast, are easily shown to fall out as reduced special cases. The new generalized contrast energy metric surprisingly predicts the principal findings of a broad range of crowding studies. These early crowding phenomena may thus be said to arise predominantly from contrast or are, at least, severely confounded by contrast effects. Note that these findings may be distinct from accounts of other, likely downstream, "configural" or "semantic" instances of crowding, suggesting at least two separate forms of crowding that may resist unification. The new fundamental contrast energy formulation provides a candidate explanatory framework that addresses multiple psychophysical phenomena beyond crowding.

Revealing the Computational Meaning of Neocortical Interarea Signals

To understand the function of the neocortex, which is a hierarchical distributed network, it is useful giving meaning to the signals transmitted between these areas from the computational viewpoint. The overall anatomical structure or organs related to this network, including the neocortex, thalamus, and basal ganglia, has been roughly revealed, and much physiological knowledge, though often fragmentary, is being accumulated. The computational theories involving the neocortex have also been developed considerably. By introducing the assumption “The signals transmitted by interarea axonal projections of pyramidal cells in the neocortex carry different meanings for each cell type, common to all areas,” derived from its nature as a distributed network in the neocortex, allows us to specify the computational meanings of interarea signals. In this paper, first, the types of signals exchanged between neocortical areas are investigated, taking into account biological constraints, and employing theories such as predictive coding, reinforcement learning, representation emulation theory, and BDI logic as theoretical starting points, two types of feedforward signals (observation and deviation) and three types of feedback signals (prediction, plan, and intention) are identified. Next, based on the anatomical knowledge of the neocortex and thalamus, the pathways connecting the areas are organized and summarized as three corticocortical pathways and two thalamocortical pathways. Using this summation as preparation, this paper proposes a hypothesis that gives meaning to each type of signals transmitted in the different pathways in the neocortex, from the viewpoint of their functions. This hypothesis reckons that the feedforward corticocortical pathway transmits observation signals, the feedback corticocortical pathway transmits prediction signals, and the corticothalamic pathway mediated by core relay cells transmits deviation signals. The thalamocortical pathway, which is mediated by matrix relay cells, would be responsible for transmitting the signals that activate a part of prediction signals as intentions, due to the reason that the nature of the other available feedback pathways are not sufficient for conveying plans and intentions as signals. The corticocortical pathway, which is projected from various IT cells to the first layer, would be responsible for transmitting signals that activate a part of prediction signals as plans.

Acetylcholine-mediated top-down attention improves the response to bottom-up inputs by deformation of the attractor landscape

To understand the effect of attention on neuronal dynamics, we propose a multi-module network, with each module consisting of fully interconnected groups of excitatory and inhibitory neurons. This network shows transitive dynamics among quasi-attractors as its typical dynamics. When the release of acetylcholine onto the network is simulated by attention, the transitive dynamics change into stable dynamics in which the system converges to an attractor. We found that this network can reproduce three experimentally observed properties of attention-dependent response modulation, namely an increase in the firing rate, a decrease in the Fano factor of the firing rate, and a decrease in the correlation coefficients between the firing rates of pairs of neurons. Moreover, we also showed theoretically that the release of acetylcholine increases the sensitivity to bottom-up inputs by changing the response function.

Axonal Anatomy Optimizes Spatial Encoding in the Rat Entorhinal-Dentate System: A Computational Study

OBJECTIVE

The network architecture connecting neural regions is defined by the organization and anatomical properties of the projecting axons, but its contributions to neural encoding and system function are difficult to study experimentally.

METHODS

Using a large-scale, spiking neuronal network model of rat dentate gyrus, the role of the anatomy of the entorhinal-dentate axonal projection was evaluated in the context of spatial encoding by incorporating grid cell activity to provide physiological, spatially-correlated input. The dorso-ventral extents of the entorhinal axon terminal fields were varied to generate different feedforward architectures, and the resulting spatial representations and spatial information scores of the network were evaluated. Position was decoded from the population activity using a point process filter to investigate the contributions of network architecture on spatial encoding.

RESULTS

The model predicted the emergence of anatomical gradients within the dentate gyrus for place field size and spatial information along its dorso-ventral axis, which were dependent on the extents of the entorhinal axon terminal fields. The decoding results revealed an optimal performance at an axon terminal field extent of 2 mm that lies within the biological range.

CONCLUSION

The axonal anatomy mediates a tradeoff between encoding multiple place field sizes or achieving a high spatial information score, and the combination of both properties is necessary to maximize spatial encoding by a network.

SIGNIFICANCE

In total, this paper establishes a mechanistic neuronal network model that, in concert with information-theoretic and statistical methods, can be used to investigate how lower level properties contribute to higher level function.

Lexical category acquisition is facilitated by uncertainty in distributional co-occurrences

This paper analyzes distributional properties that facilitate the categorization of words into lexical categories. First, word-context co-occurrence counts were collected using corpora of transcribed English child-directed speech. Then, an unsupervised k-nearest neighbor algorithm was used to categorize words into lexical categories. The categorization outcome was regressed over three main distributional predictors computed for each word, including frequency, contextual diversity, and average conditional probability given all the co-occurring contexts. Results show that both contextual diversity and frequency have a positive effect while the average conditional probability has a negative effect. This indicates that words are easier to categorize in the face of uncertainty: categorization works best for words which are frequent, diverse, and hard to predict given the co-occurring contexts. This shows how, in order for the learner to see an opportunity to form a category, there needs to be a certain degree of uncertainty in the co-occurrence pattern.

Reduced White Matter Integrity With Cognitive Impairments in End Stage Renal Disease

Background: End-stage renal disease (ESRD) is a serious public health problem, which can often lead to multiorgan dysfunction, such as cerebrovascular disease and cognitive damage. It is essential to understand cognitive impairment in patients with ESRD to develop better ESRD treatment and prevent further cognitive impairment. Cognitive impairment is believed to be related to structural abnormalities in the brain. Purpose: To investigate white matter microstructural abnormalities in patients with ESRD using TBSS analysis of DTI and to explore the possible mechanisms underlying the impaired cognitive function. Materials and Methods: A TBSS analysis of DTI data was to investigate the microstructural changes in their WM over the whole brain. We chose the white matter tracts or regions with significantly reduced FA as the regions of interest (ROIs), Pearson's correlations were performed between clinical indicators (Mini-Mental State Examination (MMSE), digit span task scores, serum creatinine, blood urea nitrogen and hemodialysis duration) and the mean FA value of the ROIs in the ESRD patients. Results: Lower FA and higher MD, AD and RD values were observed in widespread and symmetrical WM in ESRD patients than healthy controls (HCs), Pearson correlation analysis revealed a significantly positive correlation between the Mini-Mental State Examination (MMSE) scores and FA values in the right corona radiata and left anterior thalamic radiation (ATR) and demonstrated a significantly negative correlation between FA values and the serum creatinine and blood urea nitrogen in the ATR (P < 0.01) in addition, digit span task scores positively correlate with the FA value in the left anterior rather than in the corona radiata. No cluster survived when we adopted the False Discovery Rate (FDR) correction to multiple comparisons. Conclusion: Our study indicate widespread impairment of the white matter in ESRD patients. Damage to the thalamic radiation and corona radiata may affect cognitive function in ESRD patients, the reduced integrity of ATR may tend to affect the working memory while the damage to the corona radiata may involve the executive function impaired in ESRD patients. The accumulation of serum creatinine and blood urea nitrogen may contribute to the WM impairment.

Toward an Integration of Deep Learning and Neuroscience

Neuroscience has focused on the detailed implementation of computation, studying neural codes, dynamics and circuits. In machine learning, however, artificial neural networks tend to eschew precisely designed codes, dynamics or circuits in favor of brute force optimization of a cost function, often using simple and relatively uniform initial architectures. Two recent developments have emerged within machine learning that create an opportunity to connect these seemingly divergent perspectives. First, structured architectures are used, including dedicated systems for attention, recursion and various forms of short- and long-term memory storage. Second, cost functions and training procedures have become more complex and are varied across layers and over time. Here we think about the brain in terms of these ideas. We hypothesize that (1) the brain optimizes cost functions, (2) the cost functions are diverse and differ across brain locations and over development, and (3) optimization operates within a pre-structured architecture matched to the computational problems posed by behavior. In support of these hypotheses, we argue that a range of implementations of credit assignment through multiple layers of neurons are compatible with our current knowledge of neural circuitry, and that the brain's specialized systems can be interpreted as enabling efficient optimization for specific problem classes. Such a heterogeneously optimized system, enabled by a series of interacting cost functions, serves to make learning data-efficient and precisely targeted to the needs of the organism. We suggest directions by which neuroscience could seek to refine and test these hypotheses.

Risk variant of oligodendrocyte lineage transcription factor 2 is associated with reduced white matter integrity

The oligodendrocyte lineage transcription factor 2 (OLIG2) regulates the genesis of oligodendrocytes, the brain cells responsible for axonal myelination. Although it has been associated with psychiatric and neurological disorders, the impact of this gene on white matter integrity has never been investigated in humans. Using diffusion tensor imaging, we examined the effect of a single nucleotide polymorphism (rs1059004) in OLIG2 previously associated with reduced gene expression, and with psychiatric disorders on fractional anisotropy in 78 healthy subjects. We found that the risk allele (A) was associated with reduced white matter integrity in the corona radiata bilaterally. This is consistent with evidence that it is a schizophrenia susceptibility gene, and suggests that it may confer increased risk through an effect on neuroanatomical connectivity.

Top-down beta rhythms support selective attention via interlaminar interaction: a model

Cortical rhythms have been thought to play crucial roles in our cognitive abilities. Rhythmic activity in the beta frequency band, around 20 Hz, has been reported in recent studies that focused on neural correlates of attention, indicating that top-down beta rhythms, generated in higher cognitive areas and delivered to earlier sensory areas, can support attentional gain modulation. To elucidate functional roles of beta rhythms and underlying mechanisms, we built a computational model of sensory cortical areas. Our simulation results show that top-down beta rhythms can activate ascending synaptic projections from L5 to L4 and L2/3, responsible for biased competition in superficial layers. In the simulation, slow-inhibitory interneurons are shown to resonate to the 20 Hz input and modulate the activity in superficial layers in an attention-related manner. The predicted critical roles of these cells in attentional gain provide a potential mechanism by which cholinergic drive can support selective attention.

Top-down signals originate from higher cognitive areas such as parietal and prefrontal cortex and propagate to earlier stages of the brain. They have been thought to be associated with selective attention, and recent physiological studies suggest that top-down signals in the beta frequency band can support selective attention. In this study, we employ a computational model to investigate potential mechanisms by which top-down beta rhythms can influence neural responses induced by presentation of stimuli. The model includes several cell types, reportedly crucial for generating cortical rhythmic activity in the gamma and beta frequency bands, and the simulation results show that top-down beta rhythms are capable of reproducing experimentally observed attentional effects on neural responses to visual stimuli. These modulatory effects of top-down beta rhythms are mainly induced via activation of ascending inhibition originating from deep layer slow inhibitory interneurons. Since the excitability of slow interneurons can be increased by cholinergic neuromodulators, these interneurons may mediate the effects of cholinergic tone on attention.

Deformation of attractor landscape via cholinergic presynaptic modulations: a computational study using a phase neuron model

Corticopetal acetylcholine (ACh) is released transiently from the nucleus basalis of Meynert (NBM) into the cortical layers and is associated with top-down attention. Recent experimental data suggest that this release of ACh disinhibits layer 2/3 pyramidal neurons (PYRs) via muscarinic presynaptic effects on inhibitory synapses. Together with other possible presynaptic cholinergic effects on excitatory synapses, this may result in dynamic and temporal modifications of synapses associated with top-down attention. However, the system-level consequences and cognitive relevance of such disinhibitions are poorly understood. Herein, we propose a theoretical possibility that such transient modifications of connectivity associated with ACh release, in addition to top-down glutamatergic input, may provide a neural mechanism for the temporal reactivation of attractors as neural correlates of memories. With baseline levels of ACh, the brain returns to quasi-attractor states, exhibiting transitive dynamics between several intrinsic internal states. This suggests that top-down attention may cause the attention-induced deformations between two types of attractor landscapes: the quasi-attractor landscape (Q-landscape, present under low-ACh, non-attentional conditions) and the attractor landscape (A-landscape, present under high-ACh, top-down attentional conditions). We present a conceptual computational model based on experimental knowledge of the structure of PYRs and interneurons (INs) in cortical layers 1 and 2/3 and discuss the possible physiological implications of our results.

Neural population densities shape network correlations

The way sensory microcircuits manage cellular response correlations is a crucial question in understanding how such systems integrate external stimuli and encode information. Most sensory systems exhibit heterogeneities in terms of population sizes and features, which all impact their dynamics. This work addresses how correlations between the dynamics of neural ensembles depend on the relative size or density of excitatory and inhibitory populations. To do so, we study an apparently symmetric system of coupled stochastic differential equations that model the evolution of the populations' activities. Excitatory and inhibitory populations are connected by reciprocal recurrent connections, and both receive different stimuli exhibiting a certain level of correlation with each other. A stability analysis is performed, which reveals an intrinsic asymmetry in the distribution of the fixed points with respect to the threshold of the nonlinearities. Based on this, we show how the cross correlation between the population responses depends on the density of the inhibitory population, and that a specific ratio between both population sizes leads to a state of zero correlation. We show that this so-called asynchronous state subsists, despite the presence of stimulus correlation, and most importantly, that it occurs only in asymmetrical systems where one population outnumbers the other. Using linear approximations, we derive analytical expressions for the root of the cross-correlation function and study how the asynchronous state is impacted by the model's parameters. This work suggests a possible explanation for why inhibitory cells outnumber excitatory cells in the visual system.

The feeling of action tendencies: on the emotional regulation of goal-directed behavior

In this article, we review the nature of the functional and causal relationship between neurophysiologically/psychologically generated states of emotional feeling and action tendencies and extrapolate a novel perspective. Emotion theory, over the past century and beyond, has tended to regard feeling and action tendency as independent phenomena: attempts to outline the functional and causal relationship that exists between them have been framed therein. Classically, such relationships have been viewed as unidirectional, but an argument for bidirectionality rooted in a dynamic systems perspective has gained strength in recent years whereby the feeling–action tendency relationship is viewed as a composite whole. On the basis of our review of somatic–visceral theories of feelings, we argue that feelings are grounded upon neural-dynamic representations (elevated and stable activation patterns) of action tendency. Such representations amount to predictions updated by cognitive and bodily feedback. Specifically, we view emotional feelings as minimalist predictions of the action tendency (what the agent is physiologically and cognitively primed to do) in a given situation. The essence of this point is captured by our exposition of action tendency prediction–feedback loops which we consider, above all, in the context of emotion regulation, and in particular, of emotional regulation of goal-directed behavior. The perspective outlined may be of use to emotion theorists, computational modelers, and roboticists.

Neurotransmitters in disorders of consciousness and brain damage

Restorations from disorders of consciousness such as the minimally conscious state and the vegetative state have been achieved spontaneously or by pharmacological agents such as zolpidem, baclofen, dopaminergic agents and tricyclic antidepressants in some patients. Other restoration methods have included electric and magnetic nerve stimulation, oxygen, Kreb's cycle constituent substitution and axonal re-growth. Although apparently unrelated, these methods all influence neurotransmitter availability or production within the brain. This review proposes depleted neurotransmitter function as a cause for long term brain suppression and disorders of consciousness. It unifies fundamentally different treatment approaches and explores the restoration of neurotransmitter function as a common theme to improve brain function after brain damage.

A biologically based model for the integration of sensory-motor contingencies in rules and plans: a prefrontal cortex based extension of the Distributed Adaptive Control architecture

Intelligence depends on the ability of the brain to acquire and apply rules and representations. At the neuronal level these properties have been shown to critically depend on the prefrontal cortex. Here we present, in the context of the Distributed Adaptive Control architecture (DAC), a biologically based model for flexible control and planning based on key physiological properties of the prefrontal cortex, i.e. reward modulated sustained activity and plasticity of lateral connectivity. We test the model in a series of pertinent tasks, including multiple T-mazes and the Tower of London that are standard experimental tasks to assess flexible control and planning. We show that the model is both able to acquire and express rules that capture the properties of the task and to quickly adapt to changes. Further, we demonstrate that this biomimetic self-contained cognitive architecture generalizes to planning. In addition, we analyze the extended DAC architecture, called DAC 6, as a model that can be applied for the creation of intelligent and psychologically believable synthetic agents.

Sequence memory for prediction, inference and behaviour

In this paper, we propose a mechanism which the neocortex may use to store sequences of patterns. Storing and recalling sequences are necessary for making predictions, recognizing time-based patterns and generating behaviour. Since these tasks are major functions of the neocortex, the ability to store and recall time-based sequences is probably a key attribute of many, if not all, cortical areas. Previously, we have proposed that the neocortex can be modelled as a hierarchy of memory regions, each of which learns and recalls sequences. This paper proposes how each region of neocortex might learn the sequences necessary for this theory. The basis of the proposal is that all the cells in a cortical column share bottom-up receptive field properties, but individual cells in a column learn to represent unique incidences of the bottom-up receptive field property within different sequences. We discuss the proposal, the biological constraints that led to it and some results modelling it.

The hypergeometric connectivity hypothesis: divergent performance of brain circuits with different synaptic connectivity distributions

The development of connectivity among brain networks (e.g., thalamocortical, cortico-thalamic, cortico-cortical) proceeds via a combination of axon and dendrite growth followed by a later process of synaptic pruning [Purves, D., Lichtman, J.W., 1980. Elimination of synapses in the developing nervous system. Science, 210, 153-157; Oppenheim, R.W., 1991. Cell death during development of the nervous system. Annual Review of Neuroscience, 14(1), 453-501.; Oppenheim, R., Qin-Wei Y., Prevette D., Yan Q., 1992. Brain-derived neurotrophic factor rescues developing avian motoneurons from cell death. Nature, 360, 755-757]. Sparse synaptic distribution (i.e., the low probability (<0.1) of contact among neurons; [Braitenberg, V., Schüz, A., 1998. Cortex: Statistics and geometry of neuronal connectivity: Springer Berlin.] can conform to any of a range of connectivity patterns with different distributional characteristics; and different distribution patterns can yield networks with very different functional properties. We rigorously investigate a range of different connectivity characteristics, and show that different synaptic distributions can substantially affect the functional capabilities of the resulting networks. In particular, we provide formal measures of information loss in transmission from one set of neurons to another as a function of synaptic distribution, and show a set of empirical cases with different information-theoretic utility. We characterize the trade-offs among utility and costs, and their dependence on different classes of developmental strategies by which axons from one cell group are "assigned" to synapses on dendrites from a target cell group. It is shown that hypergeometric distributions minimize a range of measured costs, compared to competing synaptic distributions. It is also found that the divergent performance among differently organized brain circuits expands with brain size, rendering the effects increasingly consequential for big brains. In summary, we propose that the characteristics of hypergeometric connectivity provide a coherent explanatory hypothesis of a range of developmental and anatomical data.

Ampakines and the threefold path to cognitive enhancement

Ampakines are the first peripherally administered drugs that increase excitatory monosynaptic responses in the brain. Because of this effect, the compounds improve communication in complex networks, potently facilitate long-term potentiation (LTP) and induce the expression of neurotrophic factors. Ampakines are effective in animal models of neuropsychiatric disorders and have produced positive results in a small number of human studies. Neurobiological considerations and modeling studies suggest that the drugs, in addition to their effects on disturbed behavior, will alter the encoding and organization of information in normal brains. Results from physiological and behavioral studies accord with this prediction. Building on these findings, this article considers how the threefold effects of ampakines will modify, and enhance, cognition.

Magnetic resonance findings in bipolar disorder

The MR findings reviewed in this article suggest structural, chemical, and functional abnormalities in specific brain regions participating in mood and cognitive regulation, such as the DLPFC, anterior cingulate, amygdala,STG, and corpus callosum in subjects with bipolar disorder. These abnormalities would represent an altered anterior-limbic network disrupting inter- and intrahemispheric communication and underlying the expression of bipolar disorder. Available studies are limited by several confounding variables, such as small and heterogeneous patient samples, differences in clinical and medication status, and cross-sectional design. It is still unclear whether abnormalities in neurodevelopment or neurodegeneration play a major role in the pathophysiology of bipolar disorder. These processes could act together in a unitary model of the disease, with excessive neuronal pruning/apoptosis during childhood and adolescence being responsible for the onset of the disorder and subsequent neurotoxic mechanisms and impaired neuroplasticity and cellular resilience being responsible for further disease progression. Future MR studies should investigate larger samples of first-episode drug-free patients, pediatric patients, subjects at high risk for bipolar disorder, and unaffected family members longitudinally. Such a study population is crucial to examine systematically whether brain changes are present before the appearance of symptoms (eg, maldevelopment) or whether they develop afterwards, as a result of illness course (eg, neurodegeneration). These studies will also be instrumental in minimizing potentially confounding factors commonly found in adult samples, such as the effects of long-term medication, chronicity, and hospitalizations. Juvenile bipolar patients often have a strong family history of bipolar disorder. Future studies could help elucidate the relevance of brain abnormalities as reflections of genetic susceptibility to the disorder. MR studies associated with genetic, post-mortem, and neuropsychologic studies will be valuable in separating state from trait brain abnormalities and in further characterizing the genetic determinants, the neuropathologic underpinnings, and the cognitive disturbances of bipolar disorder.