The 'prediction imperative' as the basis for self-awareness

Linking cellular-level phenomena to brain architecture: the case of spiking cerebellar controllers

Linking cellular-level phenomena to brain architecture and behavior is a holy grail for theoretical and computational neuroscience. Advances in neuroinformatics have recently allowed scientists to embed spiking neural networks of the cerebellum with realistic neuron models and multiple synaptic plasticity rules into sensorimotor controllers. By minimizing the distance (error) between the desired and the actual sensory state, and exploiting the sensory prediction, the cerebellar network acquires knowledge about the body-environment interaction and generates corrective signals. In doing so, the cerebellum implements a generalized computational algorithm, allowing it "to learn to predict the timing between correlated events" in a rich set of behavioral contexts. Plastic changes evolve trial by trial and are distributed over multiple synapses, regulating the timing of neuronal discharge and fine-tuning high-speed movements on the millisecond timescale. Thus, spiking cerebellar built-in controllers, among various computational approaches to studying cerebellar function, are helping to reveal the cellular-level substrates of network learning and signal coding, opening new frontiers for predictive computing and autonomous learning in robots.

Thalamocortical dysrhythmia and reward deficiency syndrome as uncertainty disorders

A common anatomical core has been described for psychiatric disorders, consisting of the dorsal anterior cingulate cortex (dACC) and anterior insula, processing uncertainty. A common neurophysiological core has been described for other brain related disorders, called thalamocortical dysrhythmia (TCD), consisting of persistent cross-frequency coupling between low and high frequencies. And a common genetic core has been described for yet another set of hypodopaminergic pathologies called reward deficiency syndromes (RDS). Considering that some RDS have the neurophysiological features of TCD, it can be hypothesized that TCD and RDS have a common anatomical core, yet a differentiating associated neurophysiological mechanism. The EEGs of 683 subjects are analysed in source space for both differences and conjunction between TCD and healthy controls, RDS and healthy controls, and between TCD and RDS. A balance between current densities of the pregenual anterior cingulate cortex (pgACC) extending into the ventromedial prefrontal cortex (vmPFC) and dACC is calculated as well. TCD and RDS share a common anatomical and neurophysiological core, consisting of beta activity in the dACC and theta activity in dACC extending into precuneus and dorsolateral prefrontal cortex. TCD and RDS differ in pgACC/vmPFC activity and demonstrate an opposite balance between pgACC/vmPFC and dACC. Based on the Bayesian brain model TCD and RDS can be defined as uncertainty disorders in which the pgACC/vmPFC and dACC have an opposite balance, possibly explained by an inverted-U curve profile of both pgACC/vmPFC and dACC.

Contemporary neurocognitive models of memory: A descriptive comparative analysis

The great complexity involved in the study of memory has given rise to numerous hypotheses and models associated with various phenomena at different levels of analysis. This has allowed us to delve deeper in our knowledge about memory but has also made it difficult to synthesize and integrate data from different lines of research. In this context, this work presents a descriptive comparative analysis of contemporary models that address the structure and function of multiple memory systems. The main goal is to outline a panoramic view of the key elements that constitute these models in order to visualize both the current state of research and possible future directions. The elements that stand out from different levels of analysis are distributed neural networks, hierarchical organization, predictive coding, homeostasis, and evolutionary perspective.

The self and conscious experience

The primary determinant of the self (S) is the conscious experience (CE) we have of it. Therefore, it does not come as a surprise that empirical research on S mainly resorts to the CE (or lack of CE) that subjects have of their S. What comes as a surprise is that empirical research on S does not tackle the problem of how CE contributes to building S. Empirical research investigates how S either biases the cognitive processing of stimuli or is altered through a wide range of means (meditation, hypnosis, etc.). In either case, even for different reasons, considerations of how CE contributes to building S are left unspecified in empirical research. This article analyzes these reasons and proposes a theoretical model of how CE contributes to building S. According to the proposed model, the phenomenal aspect of consciousness is produced by the modulation-engendered by attentional activity-of the energy level of the neural substrate (that is, the organ of attention) that underpins attentional activity. The phenomenal aspect of consciousness supplies the agent with a sense of S and informs the agent on how its S is affected by the agent's own operations. The phenomenal aspect of consciousness performs its functions through its five main dimensions: qualitative, quantitative, hedonic, temporal, and spatial. Each dimension of the phenomenal aspect of consciousness can be explained by a specific aspect of the modulation of the energy level of the organ of attention. Among other advantages, the model explains the various forms of S as outcomes resulting from the operations of a single mechanism and provides a unifying framework for empirical research on the neural underpinnings of S.

Establishing a New Link between Fuzzy Logic, Neuroscience, and Quantum Mechanics through Bayesian Probability: Perspectives in Artificial Intelligence and Unconventional Computing

Human interaction with the world is dominated by uncertainty. Probability theory is a valuable tool to face such uncertainty. According to the Bayesian definition, probabilities are personal beliefs. Experimental evidence supports the notion that human behavior is highly consistent with Bayesian probabilistic inference in both the sensory and motor and cognitive domain. All the higher-level psychophysical functions of our brain are believed to take the activities of interconnected and distributed networks of neurons in the neocortex as their physiological substrate. Neurons in the neocortex are organized in cortical columns that behave as fuzzy sets. Fuzzy sets theory has embraced uncertainty modeling when membership functions have been reinterpreted as possibility distributions. The terms of Bayes' formula are conceivable as fuzzy sets and Bayes' inference becomes a fuzzy inference. According to the QBism, quantum probabilities are also Bayesian. They are logical constructs rather than physical realities. It derives that the Born rule is nothing but a kind of Quantum Law of Total Probability. Wavefunctions and measurement operators are viewed epistemically. Both of them are similar to fuzzy sets. The new link that is established between fuzzy logic, neuroscience, and quantum mechanics through Bayesian probability could spark new ideas for the development of artificial intelligence and unconventional computing.

Mechanisms for the Approach/Avoidance Decision Applied to Autism

As a neurodevelopmental disorder with serious lifelong consequences, autism has received considerable attention from neuroscientists and geneticists. We present a hypothesis of mechanisms plausibly affected during brain development in autism, based on neural pathways that are associated with social behavior and connect the prefrontal cortex (PFC) to the basal ganglia (BG). We consider failure of social approach in autism as a special case of imbalance in the fundamental dichotomy between behavioral approach and avoidance. Differential combinations of genes mutated, differences in the timing of their impact during development, and graded degrees of hormonal influences may help explain the heterogeneity in symptomatology in autism and predominance in boys.

Emotional Theory of Rationality

In recent decades, the existence of a close relationship between emotional phenomena and rational processes has certainly been established, yet there is still no unified definition or effective model to describe them. To advance our understanding of the mechanisms governing the behavior of living beings, we must integrate multiple theories, experiments, and models from both fields. In this article we propose a new theoretical framework that allows integrating and understanding the emotion-cognition duality, from a functional point of view. Based on evolutionary principles, our reasoning adds to the definition and understanding of emotion, justifying its origin, explaining its mission and dynamics, and linking it to higher cognitive processes, mainly with attention, cognition, decision-making, and consciousness. According to our theory, emotions are the mechanism for brain function optimization, aside from the contingency and stimuli prioritization system. As a result of this approach, we have developed a dynamic systems-level model capable of providing plausible explanations for certain psychological and behavioral phenomena and establishing a new framework for the scientific definition of some fundamental psychological terms.

Depression disorder in patients with cerebellar damage: Awareness of the mood state

BACKGROUND

Although depressive symptoms are often reported to be comorbid with degenerative cerebellar diseases, the role of the cerebellum in depressive disorder needs to be elucidated. To address this aim, we investigated self-perception of the negative mood state in patients with cerebellar pathology and depressive symptoms.

METHODS

Thirty-eight patients with cerebellar damage (10 with depressive symptoms - CB-DP and 28 with no depressive symptoms - CB-nDP), 11 subjects with depressive disorders without cerebellar damage (DP) and 29 healthy controls (CTs) were enrolled. A device for self-monitoring of the mood state (MoMo) and validated scales such as the Profile of Mood States questionnaire (POMS), the Self-Report Symptom Inventory-Revised (SCL-90-R) and the Hamilton Depression Rating Scale (HDRS) were used to evaluate depressive symptoms.

RESULTS

Both CB-DP and DP patients showed higher scores than CTs on the POMS and SCL-90-R for depressive factors and on the HDRS. DP patients showed a lower frequency of 'good' mood and a higher frequency of 'bad' mood than CTs when using the MoMo device. However, although the two depressed populations showed comparable scores on these validated scales, CB-DP patients showed impaired self-awareness of the mood experience in 'the here and now', as evidenced by the absence of significant differences, compared with CTs, in the subjective mood evaluation performed with the MoMo device.

LIMITATIONS

The number of CB patients and inhomogeneity across MRI scans were study limitations.

CONCLUSION

Cerebellar dysfunction might slow the data integration necessary for mood state awareness, resulting in difficulty of depressed CB patients in explicitly recognizing their mood "in the here and now".

The 'Self' Aspects: the Sense of the Existence, Identification, and Location

The present article is limited to research studies focused on understanding the phenomenon and construction of the concept of 'Self.' When we look at one's experience of the Self, as a whole, it involves various components associated with different aspects like self-identification, self-location and the sense of the existence of oneself or the sense of Self. While exploring the Self phenomenon, many scientific studies consider only partial aspects of the experience, and hence any understanding resulting from such an evaluation makes it difficult to comment on the nature of the Self. We emphasize that while studying the Self, to understand it totally, one would need to include all the components of the Self. In this connection, we raise the following two theses: a) Ontologically, the Self is conceived as a sentient entity, the bearer of the "what it is like to be" type of feeling, and b) Phenomenologically, we do not have a direct apprehension of the Self, but experience various aspects of the Self through the Senses of Existence, Identification, and Location.

Physiology of the cerebellum

The cerebellum is a central brain structure deeply integrated into major loops with the cerebral cortex, brainstem, and spinal cord. The cerebellum shows a complex regional organization consisting of modules with sagittal orientation. The cerebellum takes part in motor control and its lesions cause a movement incoordination syndrome called ataxia. Recent observations also imply involvement of the cerebellum in cognition and executive control, with an impact on pathologies like dyslexia and autism. The cerebellum operates as a forward controller learning to predict the precise timing of correlated events. The physiologic mechanisms of cerebellar functioning are still the object of intense research. The signals entering the cerebellum through the mossy fibers are processed in the granular layer and transmitted to Purkinje cells, while a collateral pathway activates the deep cerebellar nuclei (DCN). Purkinje cells in turn inhibit DCN, so that the cerebellar cortex operates as a side loop controlling the DCN. Learning is now known to occur through synaptic plasticity at multiple synapses in the granular layer, molecular layer, and DCN, extending the original concept of the Motor Learning Theory that predicted a single form of plasticity at the synapse between parallel fibers and Purkinje cells under the supervision of climbing fibers deriving from the inferior olive. Coordination derives from the precise regulation of timing and gain in the different cerebellar modules. The investigation of cerebellar dynamics using advanced physiologic recordings and computational models is now providing new clues on how the cerebellar network performs its internal computations.

Resting state electrical brain activity and connectivity in fibromyalgia

The exact mechanism underlying fibromyalgia is unknown, but increased facilitatory modulation and/or dysfunctional descending inhibitory pathway activity are posited as possible mechanisms contributing to sensitization of the central nervous system. The primary goal of this study is to identify a fibromyalgia neural circuit that can account for these abnormalities in central pain. The second goal is to gain a better understanding of the functional connectivity between the default and the executive attention network (salience network plus dorsal lateral prefrontal cortex) in fibromyalgia. We examine neural activity associated with fibromyalgia (N = 44) and compare these with healthy controls (N = 44) using resting state source localized EEG. Our data support an important role of the pregenual anterior cingulate cortex but also suggest that the degree of activation and the degree of integration between different brain areas is important. The inhibition of the connectivity between the dorsal lateral prefrontal cortex and the posterior cingulate cortex on the pain inhibitory pathway seems to be limited by decreased functional connectivity with the pregenual anterior cingulate cortex. Our data highlight the functional dynamics of brain regions integrated in brain networks in fibromyalgia patients.

A Neurocognitive Framework for Human Creative Thought

We are an intensely creative species. Creativity is the fountainhead of our civilizations and a defining characteristic of what makes us human. But for all its prominence at the apex of human mental faculties, we know next to nothing about how brains generate creative ideas. With all previous attempts to tighten the screws on this vexed problem unsuccessful – right brains, divergent thinking, defocused attention, default mode network, alpha enhancement, prefrontal activation, etc. (Dietrich and Kanso, 2010) – the neuroscientific study of creativity finds itself in a theoretical arid zone that has perhaps no equal in psychology. We propose here a general framework for a fresh attack on the problem and set it out under 10 foundational concepts. Most of the ideas we favor are part and parcel of the standard conceptual toolbox of cognitive neuroscience but their combination and significance to creativity are original. By outlining, even in such broad strokes, the theoretical landscape of cognitive neuroscience as it relates to creative insights, we hope to bring into clear focus the key enabling factors that are likely to have a hand in computing ideational combinations in the brain.

Regular Cycles of Forward and Backward Signal Propagation in Prefrontal Cortex and in Consciousness

This paper addresses two fundamental questions: (1) Is it possible to develop mathematical neural network models which can explain and replicate the way in which higher-order capabilities like intelligence, consciousness, optimization, and prediction emerge from the process of learning (Werbos, 1994, 2016a; National Science Foundation, 2008)? and (2) How can we use and test such models in a practical way, to track, to analyze and to model high-frequency (≥ 500 hz) many-channel data from recording the brain, just as econometrics sometimes uses models grounded in the theory of efficient markets to track real-world time-series data (Werbos, 1990)? This paper first reviews some of the prior work addressing question (1), and then reports new work performed in MATLAB analyzing spike-sorted and burst-sorted data on the prefrontal cortex from the Buzsaki lab (Fujisawa et al., 2008, 2015) which is consistent with a regular clock cycle of about 153.4 ms and with regular alternation between a forward pass of network calculations and a backwards pass, as in the general form of the backpropagation algorithm which one of us first developed in the period 1968-1974 (Werbos, 1994, 2006; Anderson and Rosenfeld, 1998). In business and finance, it is well known that adjustments for cycles of the year are essential to accurate prediction of time-series data (Box and Jenkins, 1970); in a similar way, methods for identifying and using regular clock cycles offer large new opportunities in neural time-series analysis. This paper demonstrates a few initial footprints on the large "continent" of this type of neural time-series analysis, and discusses a few of the many further possibilities opened up by this new approach to "decoding" the neural code (Heller et al., 1995).

Human creativity, evolutionary algorithms, and predictive representations: The mechanics of thought trials

Creative thinking is arguably the pinnacle of cerebral functionality. Like no other mental faculty, it has been omnipotent in transforming human civilizations. Probing the neural basis of this most extraordinary capacity, however, has been doggedly frustrated. Despite a flurry of activity in cognitive neuroscience, recent reviews have shown that there is no coherent picture emerging from the neuroimaging work. Based on this, we take a different route and apply two well established paradigms to the problem. First is the evolutionary framework that, despite being part and parcel of creativity research, has no informed experimental work in cognitive neuroscience. Second is the emerging prediction framework that recognizes predictive representations as an integrating principle of all cognition. We show here how the prediction imperative revealingly synthesizes a host of new insights into the way brains process variation-selection thought trials and present a new neural mechanism for the partial sightedness in human creativity. Our ability to run offline simulations of expected future environments and action outcomes can account for some of the characteristic properties of cultural evolutionary algorithms running in brains, such as degrees of sightedness, the formation of scaffolds to jump over unviable intermediate forms, or how fitness criteria are set for a selection process that is necessarily hypothetical. Prospective processing in the brain also sheds light on how human creating and designing - as opposed to biological creativity - can be accompanied by intentions and foresight. This paper raises questions about the nature of creative thought that, as far as we know, have never been asked before.

Thalamocortical Dysrhythmia: A Theoretical Update in Tinnitus

Tinnitus is the perception of a sound in the absence of a corresponding external sound source. Pathophysiologically it has been attributed to bottom-up deafferentation and/or top-down noise-cancelling deficit. Both mechanisms are proposed to alter auditory ­thalamocortical signal transmission, resulting in thalamocortical dysrhythmia (TCD). In deafferentation, TCD is characterized by a slowing down of resting state alpha to theta activity associated with an increase in surrounding gamma activity, resulting in persisting cross-frequency coupling between theta and gamma activity. Theta burst-firing increases network synchrony and recruitment, a mechanism, which might enable long-range synchrony, which in turn could represent a means for finding the missing thalamocortical information and for gaining access to consciousness. Theta oscillations could function as a carrier wave to integrate the tinnitus-related focal auditory gamma activity in a consciousness enabling network, as envisioned by the global workspace model. This model suggests that focal activity in the brain does not reach consciousness, except if the focal activity becomes functionally coupled to a consciousness enabling network, aka the global workspace. In limited deafferentation, the missing information can be retrieved from the auditory cortical neighborhood, decreasing surround inhibition, resulting in TCD. When the deafferentation is too wide in bandwidth, it is hypothesized that the missing information is retrieved from theta-mediated parahippocampal auditory memory. This suggests that based on the amount of deafferentation TCD might change to parahippocampocortical persisting and thus pathological theta–gamma rhythm. From a Bayesian point of view, in which the brain is conceived as a prediction machine that updates its memory-based predictions through sensory updating, tinnitus is the result of a prediction error between the predicted and sensed auditory input. The decrease in sensory updating is reflected by decreased alpha activity and the prediction error results in theta–gamma and beta–gamma coupling. Thus, TCD can be considered as an adaptive mechanism to retrieve missing auditory input in tinnitus.

Integrative hippocampal and decision-making neurocircuitry during goal-relevant predictions and encoding

It has become clear that the hippocampus plays a critical role in the identification of new contexts and for the detection of changes in familiar contexts. The hippocampus accomplishes these goals through a continual process of comparing predicted features of a context or situation to those actually experienced. A mismatch between expected and experienced context expectations is thought to lead to the generation of a context prediction error (Mizumori, 2013) that functionally alerts connected brain areas to alter subsequent decision making and response selection. Little is understood about how hippocampal context analyses impact downstream decision processes. This issue is evaluated here first by comparing the nature of the information represented in hippocampus and decision-related midbrain-striatal structures, while rats perform a hippocampal-dependent spatial memory task in which rewards of different value are found at different locations. In contrast to place-specific and egocentric neural representations, neural representations of goal information are broadly distributed in hippocampal and decision neural circuitry, but they appear in different forms for different brain structures. It is suggested that further researching on how goal information processing occurs in hippocampus and decision neural circuitry may reveal insights into the nature of the interaction between memory and decision systems. The second part of this review describes neural pathways by which hippocampal context information might arrive within the decision circuit. The third section presents a hypothesis that the nature of the interactions between hippocampal and midbrain-striatal circuitry is regulated by the prefrontal cortex.

A New Perspective on Delusional States - Evidence for Claustrum Involvement

Delusions are a hallmark positive symptom of schizophrenia, although they are also associated with a wide variety of other psychiatric and neurological disorders. The heterogeneity of clinical presentation and underlying disease, along with a lack of experimental animal models, make delusions exceptionally difficult to study in isolation, either in schizophrenia or other diseases. To date, no detailed studies have focused specifically on the neural mechanisms of delusion, although some studies have reported characteristic activation of specific brain areas or networks associated with them. Here, we present a novel hypothesis and extant supporting evidence implicating the claustrum, a relatively poorly understood forebrain nucleus, as a potential common center for delusional states.

Homeostatic regulation of memory systems and adaptive decisions

While it is clear that many brain areas process mnemonic information, understanding how their interactions result in continuously adaptive behaviors has been a challenge. A homeostatic-regulated prediction model of memory is presented that considers the existence of a single memory system that is based on a multilevel coordinated and integrated network (from cells to neural systems) that determines the extent to which events and outcomes occur as predicted. The “multiple memory systems of the brain” have in common output that signals errors in the prediction of events and/or their outcomes, although these signals differ in terms of what the error signal represents (e.g., hippocampus: context prediction errors vs. midbrain/striatum: reward prediction errors). The prefrontal cortex likely plays a pivotal role in the coordination of prediction analysis within and across prediction brain areas. By virtue of its widespread control and influence, and intrinsic working memory mechanisms. Thus, the prefrontal cortex supports the flexible processing needed to generate adaptive behaviors and predict future outcomes. It is proposed that prefrontal cortex continually and automatically produces adaptive responses according to homeostatic regulatory principles: prefrontal cortex may serve as a controller that is intrinsically driven to maintain in prediction areas an experience-dependent firing rate set point that ensures adaptive temporally and spatially resolved neural responses to future prediction errors. This same drive by prefrontal cortex may also restore set point firing rates after deviations (i.e. prediction errors) are detected. In this way, prefrontal cortex contributes to reducing uncertainty in prediction systems. An emergent outcome of this homeostatic view may be the flexible and adaptive control that prefrontal cortex is known to implement (i.e. working memory) in the most challenging of situations. Compromise to any of the prediction circuits should result in rigid and suboptimal decision making and memory as seen in addiction and neurological disease. © 2013 The Authors. Hippocampus Published by Wiley Periodicals, Inc.

A role for the lateral dorsal tegmentum in memory and decision neural circuitry

A role for the hippocampus in memory is clear, although the mechanism for its contribution remains a matter of debate. Converging evidence suggests that hippocampus evaluates the extent to which context-defining features of events occur as expected. The consequence of mismatches, or prediction error, signals from hippocampus is discussed in terms of its impact on neural circuitry that evaluates the significance of prediction errors: Ventral tegmental area (VTA) dopamine cells burst fire to rewards or cues that predict rewards (Schultz, Dayan, & Montague, 1997). Although the lateral dorsal tegmentum (LDTg) importantly controls dopamine cell burst firing (Lodge & Grace, 2006) the behavioral significance of the LDTg control is not known. Therefore, we evaluated LDTg functional activity as rats performed a spatial memory task that generates task-dependent reward codes in VTA (Jo, Lee, & Mizumori, 2013; Puryear, Kim, & Mizumori, 2010) and another VTA afferent, the pedunculopontine nucleus (PPTg, Norton, Jo, Clark, Taylor, & Mizumori, 2011). Reversible inactivation of the LDTg significantly impaired choice accuracy. LDTg neurons coded primarily egocentric information in the form of movement velocity, turning behaviors, and behaviors leading up to expected reward locations. A subset of the velocity-tuned LDTg cells also showed high frequency bursts shortly before or after reward encounters, after which they showed tonic elevated firing during consumption of small, but not large, rewards. Cells that fired before reward encounters showed stronger correlations with velocity as rats moved toward, rather than away from, rewarded sites. LDTg neural activity was more strongly regulated by egocentric behaviors than that observed for PPTg or VTA cells that were recorded by Puryear et al. and Norton et al. While PPTg activity was uniquely sensitive to ongoing sensory input, all three regions encoded reward magnitude (although in different ways), reward expectation, and reward encounters. Only VTA encoded reward prediction errors. LDTg may inform VTA about learned goal-directed movement that reflects the current motivational state, and this in turn may guide VTA determination of expected subjective goal values. When combined it is clear the LDTg and PPTg provide only a portion of the information that dopamine cells need to assess the value of prediction errors, a process that is essential to future adaptive decisions and switches of cognitive (i.e. memorial) strategies and behavioral responses.

Realistic modeling of neurons and networks: towards brain simulation

Realistic modeling is a new advanced methodology for investigating brain functions. Realistic modeling is based on a detailed biophysical description of neurons and synapses, which can be integrated into microcircuits. The latter can, in turn, be further integrated to form large-scale brain networks and eventually to reconstruct complex brain systems. Here we provide a review of the realistic simulation strategy and use the cerebellar network as an example. This network has been carefully investigated at molecular and cellular level and has been the object of intense theoretical investigation. The cerebellum is thought to lie at the core of the forward controller operations of the brain and to implement timing and sensory prediction functions. The cerebellum is well described and provides a challenging field in which one of the most advanced realistic microcircuit models has been generated. We illustrate how these models can be elaborated and embedded into robotic control systems to gain insight into how the cellular properties of cerebellar neurons emerge in integrated behaviors. Realistic network modeling opens up new perspectives for the investigation of brain pathologies and for the neurorobotic field.

Can modular strategies simplify neural control of multidirectional human locomotion?

Each human lower limb contains over 50 muscles that are coordinated during locomotion. It has been hypothesized that the nervous system simplifies muscle control through modularity, using neural patterns to activate muscles in groups called synergies. Here we investigate how simple modular controllers based on invariant neural primitives (synergies or patterns) might generate muscle activity observed during multidirectional locomotion. We extracted neural primitives from unilateral electromyographic recordings of 25 lower limb muscles during five locomotor tasks: walking forward, backward, leftward and rightward, and stepping in place. A subset of subjects also performed five variations of forward (unidirectional) walking: self-selected cadence, fast cadence, slow cadence, tiptoe, and uphill (20% incline). We assessed the results in the context of dimensionality reduction, defined here as the number of neural signals needing to be controlled. For an individual task, we found that modular architectures could theoretically reduce dimensionality compared with independent muscle control, but we also found that modular strategies relying on neural primitives shared across different tasks were limited in their ability to account for muscle activations during multi- and unidirectional locomotion. The utility of shared primitives may thus depend on whether they can be adapted for specific task demands, for instance, by means of sensory feedback or by being embedded within a more complex sensorimotor controller. Our findings indicate the need for more sophisticated formulations of modular control or alternative motor control hypotheses in order to understand muscle coordination during locomotion.

Neural network architecture for cognitive navigation in dynamic environments

Navigation in time-evolving environments with moving targets and obstacles requires cognitive abilities widely demonstrated by even simplest animals. However, it is a long-standing challenging problem for artificial agents. Cognitive autonomous robots coping with this problem must solve two essential tasks: 1) understand the environment in terms of what may happen and how I can deal with this and 2) learn successful experiences for their further use in an automatic subconscious way. The recently introduced concept of compact internal representation (CIR) provides the ground for both the tasks. CIR is a specific cognitive map that compacts time-evolving situations into static structures containing information necessary for navigation. It belongs to the class of global approaches, i.e., it finds trajectories to a target when they exist but also detects situations when no solution can be found. Here we extend the concept of situations with mobile targets. Then using CIR as a core, we propose a closed-loop neural network architecture consisting of conscious and subconscious pathways for efficient decision-making. The conscious pathway provides solutions to novel situations if the default subconscious pathway fails to guide the agent to a target. Employing experiments with roving robots and numerical simulations, we show that the proposed architecture provides the robot with cognitive abilities and enables reliable and flexible navigation in realistic time-evolving environments. We prove that the subconscious pathway is robust against uncertainty in the sensory information. Thus if a novel situation is similar but not identical to the previous experience (because of, e.g., noisy perception) then the subconscious pathway is able to provide an effective solution.

Context prediction analysis and episodic memory

Events that happen at a particular place and time come to define our episodic memories. Extensive experimental and clinical research illustrate that the hippocampus is central to the processing of episodic memories, and this is in large part due to its analysis of context information according to spatial and temporal references. In this way, hippocampus defines ones expectations for a given context as well as detects errors in predicted contextual features. The detection of context prediction errors is hypothesized to distinguished events into meaningful epochs that come to be recalled as separate episodic memories. The nature of the spatial and temporal context information processed by hippocampus is described, as is a hypothesis that the apparently self-regulatory nature of hippocampal context processing may ultimately be mediated by natural homeostatic operations and plasticity. Context prediction errors by hippocampus are suggested to be valued by the midbrain dopamine system, the output of which is ultimately fed back to hippocampus to update memory-driven context expectations for future events. Thus, multiple network functions (both within and outside hippocampus) combine to result in adaptive episodic memories.

Realistic modeling of neurons and networks: towards brain simulation

Realistic modeling is a new advanced methodology for investigating brain functions. Realistic modeling is based on a detailed biophysical description of neurons and synapses, which can be integrated into microcircuits. The latter can, in turn, be further integrated to form large-scale brain networks and eventually to reconstruct complex brain systems. Here we provide a review of the realistic simulation strategy and use the cerebellar network as an example. This network has been carefully investigated at molecular and cellular level and has been the object of intense theoretical investigation. The cerebellum is thought to lie at the core of the forward controller operations of the brain and to implement timing and sensory prediction functions. The cerebellum is well described and provides a challenging field in which one of the most advanced realistic microcircuit models has been generated. We illustrate how these models can be elaborated and embedded into robotic control systems to gain insight into how the cellular properties of cerebellar neurons emerge in integrated behaviors. Realistic network modeling opens up new perspectives for the investigation of brain pathologies and for the neurorobotic field.

The cerebellar Golgi cell and spatiotemporal organization of granular layer activity

The cerebellar granular layer has been suggested to perform a complex spatiotemporal reconfiguration of incoming mossy fiber signals. Central to this role is the inhibitory action exerted by Golgi cells over granule cells: Golgi cells inhibit granule cells through both feedforward and feedback inhibitory loops and generate a broad lateral inhibition that extends beyond the afferent synaptic field. This characteristic connectivity has recently been investigated in great detail and been correlated with specific functional properties of these neurons. These include theta-frequency pacemaking, network entrainment into coherent oscillations and phase resetting. Important advances have also been made in terms of determining the membrane and synaptic properties of the neuron, and clarifying the mechanisms of activation by input bursts. Moreover, voltage sensitive dye imaging and multi-electrode array (MEA) recordings, combined with mathematical simulations based on realistic computational models, have improved our understanding of the impact of Golgi cell activity on granular layer circuit computations. These investigations have highlighted the critical role of Golgi cells in: generating dense clusters of granule cell activity organized in center-surround structures, implementing combinatorial operations on multiple mossy fiber inputs, regulating transmission gain, and cut-off frequency, controlling spike timing and burst transmission, and determining the sign, intensity and duration of long-term synaptic plasticity at the mossy fiber-granule cell relay. This review considers recent advances in the field, highlighting the functional implications of Golgi cells for granular layer network computation and indicating new challenges for cerebellar research.

Seeking a unified framework for cerebellar function and dysfunction: from circuit operations to cognition

Following the fundamental recognition of its involvement in sensory-motor coordination and learning, the cerebellum is now also believed to take part in the processing of cognition and emotion. This hypothesis is recurrent in numerous papers reporting anatomical and functional observations, and it requires an explanation. We argue that a similar circuit structure in all cerebellar areas may carry out various operations using a common computational scheme. On the basis of a broad review of anatomical data, it is conceivable that the different roles of the cerebellum lie in the specific connectivity of the cerebellar modules, with motor, cognitive, and emotional functions (at least partially) segregated into different cerebro-cerebellar loops. We here develop a conceptual and operational framework based on multiple interconnected levels (a meta-levels hypothesis): from cellular/molecular to network mechanisms leading to generation of computational primitives, thence to high-level cognitive/emotional processing, and finally to the sphere of mental function and dysfunction. The main concept explored is that of intimate interplay between timing and learning (reminiscent of the “timing and learning machine” capabilities long attributed to the cerebellum), which reverberates from cellular to circuit mechanisms. Subsequently, integration within large-scale brain loops could generate the disparate cognitive/emotional and mental functions in which the cerebellum has been implicated. We propose, therefore, that the cerebellum operates as a general-purpose co-processor, whose effects depend on the specific brain centers to which individual modules are connected. Abnormal functioning in these loops could eventually contribute to the pathogenesis of major brain pathologies including not just ataxia but also dyslexia, autism, schizophrenia, and depression.

Emulation as an integrating principle for cognition

Emulations, defined as ongoing internal representations of potential actions and the futures those actions are expected to produce, play a critical role in directing human bodily activities. Studies of gross motor behavior, perception, allocation of attention, response to errors, interoception, and homeostatic activities, and higher cognitive reasoning suggest that the proper execution of all these functions relies on emulations. Further evidence supports the notion that reinforcement learning in humans is aimed at updating emulations, and that action selection occurs via the advancement of preferred emulations toward realization of their action and environmental prediction. Emulations are hypothesized to exist as distributed active networks of neurons in cortical and sub-cortical structures. This manuscript ties together previously unrelated theories of the role of prediction in different aspects of human information processing to create an integrated framework for cognition.

Toward a neurobiology of delusions

Delusions are the false and often incorrigible beliefs that can cause severe suffering in mental illness. We cannot yet explain them in terms of underlying neurobiological abnormalities. However, by drawing on recent advances in the biological, computational and psychological processes of reinforcement learning, memory, and perception it may be feasible to account for delusions in terms of cognition and brain function. The account focuses on a particular parameter, prediction error--the mismatch between expectation and experience--that provides a computational mechanism common to cortical hierarchies, fronto-striatal circuits and the amygdala as well as parietal cortices. We suggest that delusions result from aberrations in how brain circuits specify hierarchical predictions, and how they compute and respond to prediction errors. Defects in these fundamental brain mechanisms can vitiate perception, memory, bodily agency and social learning such that individuals with delusions experience an internal and external world that healthy individuals would find difficult to comprehend. The present model attempts to provide a framework through which we can build a mechanistic and translational understanding of these puzzling symptoms.