Spikes, synchrony, and attentive learning by laminar thalamocortical circuits

Electromagnetic field enhanced flow state: Insights from electrophysiological measures, self-reported experiences, and gameplay

The intersection of neuroscience and technology hinges on the development of wearable devices and electrodes that can augment brain networks to improve cognitive capabilities such as learning and concentration. The capacity to enhance networks associated with these functions above baseline capabilities, holds the potential to benefit numerous individuals. The purpose of this study was to determine if electromagnetic field exposure modeled from physiological data would increase instances of flow in participants playing a computer game. The flow state refers to a subjective state of optimal performance experienced by individuals during a variety of tasks. For this study, participants (n = 39, 18-65 years, nfemale = 20) played the arcade game Snake for two ten-minute periods (each with a ten-minute rest period immediately following). For one of the trials, an electromagnetic field was applied bilaterally to the temporal lobes, with the other serving as the control. Brain activity was measured using quantitative electroencephalography, flow experience was measured using the Flow Short Scale and game play scores were also recorded. Results showed deceased beta 1 (12-16 Hz) activity in the left cuneus [t = 4.650, p < 0.01] and left precuneus [t = 4.603, p < 0.01], left posterior cingulate [t = 4.521, p < 0.05], insula [t = 4.234, p < 0.05], and parahippocampal gyrus [t = 4.113, p < 0.05] for trials when the field was active, compared to controls during rest periods. Results from the Flow Short Scale showed a statistically significant difference in mean "concentration ease" scores across electromagnetic field conditions, irrespective of difficulty [t = 2.131, p < 0.05]. In the EMF exposure trials, there was no discernible experience effect; participants with prior experience in the game Snake did not exhibit significantly better performance compared to those without prior experience. This anticipated effect was observed in control conditions. The comparable performance observed between novices and experienced players in the EMF condition indicate a noteworthy learning curve for novices. In all, these results provide evidence supporting the ability of EMF patterned from amygdaloid firing (6-20 Hz) to elicit neurological correlates of flow in brain regions previously reported in the literature, facilitate concentration, and subtly improve game scores. The possibility for wearable devices to support learning, concentration, and focus are discussed.

Analyzing Biomedical Datasets with Symbolic Tree Adaptive Resonance Theory

Biomedical datasets distill many mechanisms of human diseases, linking diseases to genes and phenotypes (signs and symptoms of disease), genetic mutations to altered protein structures, and altered proteins to changes in molecular functions and biological processes. It is desirable to gain new insights from these data, especially with regard to the uncovering of hierarchical structures relating disease variants. However, analysis to this end has proven difficult due to the complexity of the connections between multi-categorical symbolic data. This article proposes symbolic tree adaptive resonance theory (START), with additional supervised, dual-vigilance (DV-START), and distributed dual-vigilance (DDV-START) formulations, for the clustering of multi-categorical symbolic data from biomedical datasets by demonstrating its utility in clustering variants of Charcot–Marie–Tooth disease using genomic, phenotypic, and proteomic data.

Synapses, predictions, and prediction errors: A neocortical computational study of MDD using the temporal memory algorithm of HTM

Introduction

Synapses and spines play a significant role in major depressive disorder (MDD) pathophysiology, recently highlighted by the rapid antidepressant effect of ketamine and psilocybin. According to the Bayesian brain and interoception perspectives, MDD is formalized as being stuck in affective states constantly predicting negative energy balance. To understand how spines and synapses relate to the predictive function of the neocortex and thus to symptoms, we used the temporal memory (TM), an unsupervised machine-learning algorithm. TM models a single neocortical layer, learns in real-time, and extracts and predicts temporal sequences. TM exhibits neocortical biological features such as sparse firing and continuous online learning using local Hebbian-learning rules.

Methods

We trained a TM model on random sequences of upper-case alphabetical letters, representing sequences of affective states. To model depression, we progressively destroyed synapses in the TM model and examined how that affected the predictive capacity of the network. We found that the number of predictions decreased non-linearly.

Results

Destroying 50% of the synapses slightly reduced the number of predictions, followed by a marked drop with further destruction. However, reducing the synapses by 25% distinctly dropped the confidence in the predictions. Therefore, even though the network was making accurate predictions, the network was no longer confident about these predictions.

Discussion

These findings explain how interoceptive cortices could be stuck in limited affective states with high prediction error. Connecting ketamine and psilocybin's proposed mechanism of action to depression pathophysiology, the growth of new synapses would allow representing more futuristic predictions with higher confidence. To our knowledge, this is the first study to use the TM model to connect changes happening at synaptic levels to the Bayesian formulation of psychiatric symptomatology. Linking neurobiological abnormalities to symptoms will allow us to understand the mechanisms of treatments and possibly, develop new ones.

Acute effects of Δ9-tetrahydrocannabinol and cannabidiol on auditory mismatch negativity

RATIONALE

Mismatch negativity (MMN) is a candidate endophenotype for schizophrenia subserved by N-methyl-D-aspartate receptor (NMDAR) function and there is increasing evidence that prolonged cannabis use adversely affects MMN generation. Few human studies have investigated the acute effects of cannabinoids on brain-based biomarkers of NMDAR function and synaptic plasticity.

OBJECTIVES

The current study investigated the acute effects of Δ⁹-tetrahydrocannabinol (THC) and cannabidiol (CBD) alone and in combination on the mismatch negativity (MMN).

METHODS

In a randomised, double-blind, crossover placebo-controlled study, 18 frequent and 18 less-frequent cannabis users underwent 5 randomised drug sessions administered via vaporiser: (1) placebo; (2) THC 8 mg; (3) CBD 400 mg; (4) THC 8 mg + CBD 4 mg [THC + CBD_low]; (5) THC 12 mg + CBD 400 mg [THC + CBD_high]. Participants completed a multifeature MMN auditory oddball paradigm with duration, frequency and intensity deviants (6% each).

RESULTS

Relative to placebo, both THC and CBD were observed to increase duration and intensity MMN amplitude in less-frequent users, and THC also increased frequency MMN in this group. The addition of low-dose CBD added to THC attenuated the effect of THC on duration and intensity MMN amplitude in less-frequent users. The same pattern of effects was observed following high-dose CBD added to THC on duration and frequency MMN in frequent users.

CONCLUSIONS

The pattern of effects following CBD combined with THC on MMN may be subserved by different underlying neurobiological interactions within the endocannabinoid system that vary as a function of prior cannabis exposure. These results highlight the complex interplay between the acute effects of exogenous cannabinoids and NMDAR function. Further research is needed to determine how this process normalises after the acute effects dissipate and following repeated acute exposure.

Five Breakthroughs: A First Approximation of Brain Evolution From Early Bilaterians to Humans

Retracing the evolutionary steps by which human brains evolved can offer insights into the underlying mechanisms of human brain function as well as the phylogenetic origin of various features of human behavior. To this end, this article presents a model for interpreting the physical and behavioral modifications throughout major milestones in human brain evolution. This model introduces the concept of a "breakthrough" as a useful tool for interpreting suites of brain modifications and the various adaptive behaviors these modifications enabled. This offers a unique view into the ordered steps by which human brains evolved and suggests several unique hypotheses on the mechanisms of human brain function.

A Neural Model of Intrinsic and Extrinsic Hippocampal Theta Rhythms: Anatomy, Neurophysiology, and Function

This article describes a neural model of the anatomy, neurophysiology, and functions of intrinsic and extrinsic theta rhythms in the brains of multiple species. Topics include how theta rhythms were discovered; how theta rhythms organize brain information processing into temporal series of spatial patterns; how distinct theta rhythms occur within area CA1 of the hippocampus and between the septum and area CA3 of the hippocampus; what functions theta rhythms carry out in different brain regions, notably CA1-supported functions like learning, recognition, and memory that involve visual, cognitive, and emotional processes; how spatial navigation, adaptively timed learning, and category learning interact with hippocampal theta rhythms; how parallel cortical streams through the lateral entorhinal cortex (LEC) and the medial entorhinal cortex (MEC) represent the end-points of the What cortical stream for perception and cognition and the Where cortical stream for spatial representation and action; how the neuromodulator acetylcholine interacts with the septo-hippocampal theta rhythm and modulates category learning; what functions are carried out by other brain rhythms, such as gamma and beta oscillations; and how gamma and beta oscillations interact with theta rhythms. Multiple experimental facts about theta rhythms are unified and functionally explained by this theoretical synthesis.

A Canonical Laminar Neocortical Circuit Whose Bottom-Up, Horizontal, and Top-Down Pathways Control Attention, Learning, and Prediction

All perceptual and cognitive circuits in the human cerebral cortex are organized into layers. Specializations of a canonical laminar network of bottom-up, horizontal, and top-down pathways carry out multiple kinds of biological intelligence across different neocortical areas. This article describes what this canonical network is and notes that it can support processes as different as 3D vision and figure-ground perception; attentive category learning and decision-making; speech perception; and cognitive working memory (WM), planning, and prediction. These processes take place within and between multiple parallel cortical streams that obey computationally complementary laws. The interstream interactions that are needed to overcome these complementary deficiencies mix cell properties so thoroughly that some authors have noted the difficulty of determining what exactly constitutes a cortical stream and the differences between streams. The models summarized herein explain how these complementary properties arise, and how their interstream interactions overcome their computational deficiencies to support effective goal-oriented behaviors.

The Thalamus as a Blackboard for Perception and Planning

It has been suggested that the thalamus acts as a blackboard, on which the computations of different cortical modules are composed, coordinated, and integrated. This article asks what blackboard role the thalamus might play, and whether that role is consistent with the neuroanatomy of the thalamus. It does so in a context of Bayesian belief updating, expressed as a Free Energy Principle. We suggest that the thalamus-as-a-blackboard offers important questions for research in spatial cognition. Several prominent features of the thalamus-including its lack of olfactory relay function, its lack of internal excitatory connections, its regular and conserved shape, its inhibitory interneurons, triadic synapses, and diffuse cortical connectivity-are consistent with a blackboard role.Different thalamic nuclei may play different blackboard roles: (1) the Pulvinar, through its reciprocal connections to posterior cortical regions, coordinates perceptual inference about "what is where" from multi-sense-data. (2) The Mediodorsal (MD) nucleus, through its connections to the prefrontal cortex, and the other thalamic nuclei linked to the motor cortex, uses the same generative model for planning and learning novel spatial movements. (3) The paraventricular nucleus may compute risk-reward trade-offs. We also propose that as any new movement is practiced a few times, cortico-thalamocortical (CTC) links entrain the corresponding cortico-cortical links, through a process akin to supervised learning. Subsequently, the movement becomes a fast unconscious habit, not requiring the MD nucleus or other thalamic nuclei, and bypassing the thalamic bottleneck.

A Unified Neural Theory of Conscious Seeing, Hearing, Feeling, and Knowing

Adaptive Resonance Theory does more than satisfy 'hard criteria' for ToCs.

An Attempt at a Unified Theory of the Neocortical Microcircuit in Sensory Cortex

The neocortex performs a wide range of functions, including working memory, sensory perception, and motor planning. Despite this diversity in function, evidence suggests that the neocortex is made up of repeating subunits ("macrocolumns"), each of which is largely identical in circuitry. As such, the specific computations performed by these macrocolumns are of great interest to neuroscientists and AI researchers. Leading theories of this microcircuit include models of predictive coding, hierarchical temporal memory (HTM), and Adaptive Resonance Theory (ART). However, these models have not yet explained: (1) how microcircuits learn sequences input with delay (i.e., working memory); (2) how networks of columns coordinate processing on precise timescales; or (3) how top-down attention modulates sensory processing. I provide a theory of the neocortical microcircuit that extends prior models in all three ways. Additionally, this theory provides a novel working memory circuit that extends prior models to support simultaneous multi-item storage without disrupting ongoing sensory processing. I then use this theory to explain the functional origin of a diverse set of experimental findings, such as cortical oscillations.

A Path Toward Explainable AI and Autonomous Adaptive Intelligence: Deep Learning, Adaptive Resonance, and Models of Perception, Emotion, and Action

Biological neural network models whereby brains make minds help to understand autonomous adaptive intelligence. This article summarizes why the dynamics and emergent properties of such models for perception, cognition, emotion, and action are explainable, and thus amenable to being confidently implemented in large-scale applications. Key to their explainability is how these models combine fast activations, or short-term memory (STM) traces, and learned weights, or long-term memory (LTM) traces. Visual and auditory perceptual models have explainable conscious STM representations of visual surfaces and auditory streams in surface-shroud resonances and stream-shroud resonances, respectively. Deep Learning is often used to classify data. However, Deep Learning can experience catastrophic forgetting: At any stage of learning, an unpredictable part of its memory can collapse. Even if it makes some accurate classifications, they are not explainable and thus cannot be used with confidence. Deep Learning shares these problems with the back propagation algorithm, whose computational problems due to non-local weight transport during mismatch learning were described in the 1980s. Deep Learning became popular after very fast computers and huge online databases became available that enabled new applications despite these problems. Adaptive Resonance Theory, or ART, algorithms overcome the computational problems of back propagation and Deep Learning. ART is a self-organizing production system that incrementally learns, using arbitrary combinations of unsupervised and supervised learning and only locally computable quantities, to rapidly classify large non-stationary databases without experiencing catastrophic forgetting. ART classifications and predictions are explainable using the attended critical feature patterns in STM on which they build. The LTM adaptive weights of the fuzzy ARTMAP algorithm induce fuzzy IF-THEN rules that explain what feature combinations predict successful outcomes. ART has been successfully used in multiple large-scale real world applications, including remote sensing, medical database prediction, and social media data clustering. Also explainable are the MOTIVATOR model of reinforcement learning and cognitive-emotional interactions, and the VITE, DIRECT, DIVA, and SOVEREIGN models for reaching, speech production, spatial navigation, and autonomous adaptive intelligence. These biological models exemplify complementary computing, and use local laws for match learning and mismatch learning that avoid the problems of Deep Learning.

Developmental Designs and Adult Functions of Cortical Maps in Multiple Modalities: Perception, Attention, Navigation, Numbers, Streaming, Speech, and Cognition

This article unifies neural modeling results that illustrate several basic design principles and mechanisms that are used by advanced brains to develop cortical maps with multiple psychological functions. One principle concerns how brains use a strip map that simultaneously enables one feature to be represented throughout its extent, as well as an ordered array of another feature at different positions of the strip. Strip maps include circuits to represent ocular dominance and orientation columns, place-value numbers, auditory streams, speaker-normalized speech, and cognitive working memories that can code repeated items. A second principle concerns how feature detectors for multiple functions develop in topographic maps, including maps for optic flow navigation, reinforcement learning, motion perception, and category learning at multiple organizational levels. A third principle concerns how brains exploit a spatial gradient of cells that respond at an ordered sequence of different rates. Such a rate gradient is found along the dorsoventral axis of the entorhinal cortex, whose lateral branch controls the development of time cells, and whose medial branch controls the development of grid cells. Populations of time cells can be used to learn how to adaptively time behaviors for which a time interval of hundreds of milliseconds, or several seconds, must be bridged, as occurs during trace conditioning. Populations of grid cells can be used to learn hippocampal place cells that represent the large spaces in which animals navigate. A fourth principle concerns how and why all neocortical circuits are organized into layers, and how functionally distinct columns develop in these circuits to enable map development. A final principle concerns the role of Adaptive Resonance Theory top-down matching and attentional circuits in the dynamic stabilization of early development and adult learning. Cortical maps are modeled in visual, auditory, temporal, parietal, prefrontal, entorhinal, and hippocampal cortices.

Experimental Study of Reinforcement Learning in Mobile Robots Through Spiking Architecture of Thalamo-Cortico-Thalamic Circuitry of Mammalian Brain

In this paper, the behavioral learning of robots through spiking neural networks is studied in which the architecture of the network is based on the thalamo-cortico-thalamic circuitry of the mammalian brain. According to a variety of neurons, the Izhikevich model of single neuron is used for the representation of neuronal behaviors. One thousand and ninety spiking neurons are considered in the network. The spiking model of the proposed architecture is derived and prepared for the learning problem of robots. The reinforcement learning algorithm is based on spike-timing-dependent plasticity and dopamine release as a reward. It results in strengthening the synaptic weights of the neurons that are involved in the robot’s proper performance. Sensory and motor neurons are placed in the thalamus and cortical module, respectively. The inputs of thalamo-cortico-thalamic circuitry are the signals related to distance of the target from robot, and the outputs are the velocities of actuators. The target attraction task is used as an example to validate the proposed method in which dopamine is released when the robot catches the target. Some simulation studies, as well as experimental implementation, are done on a mobile robot named Tabrizbot. Experimental studies illustrate that after successful learning, the meantime of catching target is decreased by about 36%. These prove that through the proposed method, thalamo-cortical structure could be trained successfully to learn to perform various robotic tasks.

Uncertainty-based modulation for lifelong learning

The creation of machine learning algorithms for intelligent agents capable of continuous, lifelong learning is a critical objective for algorithms being deployed on real-life systems in dynamic environments. Here we present an algorithm inspired by neuromodulatory mechanisms in the human brain that integrates and expands upon Stephen Grossberg's ground-breaking Adaptive Resonance Theory proposals. Specifically, it builds on the concept of uncertainty, and employs a series of "neuromodulatory" mechanisms to enable continuous learning, including self-supervised and one-shot learning. Algorithm components were evaluated in a series of benchmark experiments that demonstrate stable learning without catastrophic forgetting. We also demonstrate the critical role of developing these systems in a closed-loop manner where the environment and the agent's behaviors constrain and guide the learning process. To this end, we integrated the algorithm into an embodied simulated drone agent. The experiments show that the algorithm is capable of continuous learning of new tasks and under changed conditions with high classification accuracy (>94%) in a virtual environment, without catastrophic forgetting. The algorithm accepts high dimensional inputs from any state-of-the-art detection and feature extraction algorithms, making it a flexible addition to existing systems. We also describe future development efforts focused on imbuing the algorithm with mechanisms to seek out new knowledge as well as employ a broader range of neuromodulatory processes.

The resonant brain: How attentive conscious seeing regulates action sequences that interact with attentive cognitive learning, recognition, and prediction

This article describes mechanistic links that exist in advanced brains between processes that regulate conscious attention, seeing, and knowing, and those that regulate looking and reaching. These mechanistic links arise from basic properties of brain design principles such as complementary computing, hierarchical resolution of uncertainty, and adaptive resonance. These principles require conscious states to mark perceptual and cognitive representations that are complete, context sensitive, and stable enough to control effective actions. Surface-shroud resonances support conscious seeing and action, whereas feature-category resonances support learning, recognition, and prediction of invariant object categories. Feedback interactions between cortical areas such as peristriate visual cortical areas V2, V3A, and V4, and the lateral intraparietal area (LIP) and inferior parietal sulcus (IPS) of the posterior parietal cortex (PPC) control sequences of saccadic eye movements that foveate salient features of attended objects and thereby drive invariant object category learning. Learned categories can, in turn, prime the objects and features that are attended and searched. These interactions coordinate processes of spatial and object attention, figure-ground separation, predictive remapping, invariant object category learning, and visual search. They create a foundation for learning to control motor-equivalent arm movement sequences, and for storing these sequences in cognitive working memories that can trigger the learning of cognitive plans with which to read out skilled movement sequences. Cognitive-emotional interactions that are regulated by reinforcement learning can then help to select the plans that control actions most likely to acquire valued goal objects in different situations. Many interdisciplinary psychological and neurobiological data about conscious and unconscious behaviors in normal individuals and clinical patients have been explained in terms of these concepts and mechanisms.

Admiring the Great Mountain: A Celebration Special Issue in Honor of Stephen Grossberg's 80th Birthday

This editorial summarizes selected key contributions of Prof. Stephen Grossberg and describes the papers in this 80th birthday special issue in his honor. His productivity, creativity, and vision would each be enough to mark a scientist of the first caliber. In combination, they have resulted in contributions that have changed the entire discipline of neural networks. Grossberg has been tremendously influential in engineering, dynamical systems, and artificial intelligence as well. Indeed, he has been one of the most important mentors and role models in my career, and has done so with extraordinary generosity and encouragement. All authors in this special issue have taken great pleasure in hereby commemorating his extraordinary career and contributions.

The Embodied Brain of SOVEREIGN2: From Space-Variant Conscious Percepts During Visual Search and Navigation to Learning Invariant Object Categories and Cognitive-Emotional Plans for Acquiring Valued Goals

This article develops a model of how reactive and planned behaviors interact in real time. Controllers for both animals and animats need reactive mechanisms for exploration, and learned plans to efficiently reach goal objects once an environment becomes familiar. The SOVEREIGN model embodied these capabilities, and was tested in a 3D virtual reality environment. Neural models have characterized important adaptive and intelligent processes that were not included in SOVEREIGN. A major research program is summarized herein by which to consistently incorporate them into an enhanced model called SOVEREIGN2. Key new perceptual, cognitive, cognitive-emotional, and navigational processes require feedback networks which regulate resonant brain states that support conscious experiences of seeing, feeling, and knowing. Also included are computationally complementary processes of the mammalian neocortical What and Where processing streams, and homologous mechanisms for spatial navigation and arm movement control. These include: Unpredictably moving targets are tracked using coordinated smooth pursuit and saccadic movements. Estimates of target and present position are computed in the Where stream, and can activate approach movements. Motion cues can elicit orienting movements to bring new targets into view. Cumulative movement estimates are derived from visual and vestibular cues. Arbitrary navigational routes are incrementally learned as a labeled graph of angles turned and distances traveled between turns. Noisy and incomplete visual sensor data are transformed into representations of visual form and motion. Invariant recognition categories are learned in the What stream. Sequences of invariant object categories are stored in a cognitive working memory, whereas sequences of movement positions and directions are stored in a spatial working memory. Stored sequences trigger learning of cognitive and spatial/motor sequence categories or plans, also called list chunks, which control planned decisions and movements toward valued goal objects. Predictively successful list chunk combinations are selectively enhanced or suppressed via reinforcement learning and incentive motivational learning. Expected vs. unexpected event disconfirmations regulate these enhancement and suppressive processes. Adaptively timed learning enables attention and action to match task constraints. Social cognitive joint attention enables imitation learning of skills by learners who observe teachers from different spatial vantage points.

Lateral Inhibition Organizes Beta Attentional Modulation in the Primary Visual Cortex

We have previously shown that during top-down attentional modulation (stimulus expectation) correlations of the beta signals across the primary visual cortex were uniform, while during bottom-up attentional processing (visual stimulation) their values were heterogeneous. These different patterns of attentional beta modulation may be caused by feed-forward lateral inhibitory interactions in the visual cortex, activated solely during stimulus processing. To test this hypothesis, we developed a large-scale computational model of the cortical network. We first identified the parameter range needed to support beta rhythm generation, and next, simulated the different activity states corresponding to experimental paradigms. The model matched our experimental data in terms of spatial organization of beta correlations during different attentional states and provided a computational confirmation of the hypothesis that the paradigm-specific beta activation spatial maps depend on the lateral inhibitory mechanism. The model also generated testable predictions that cross-correlation values depend on the distance between the activated columns and on their spatial position with respect to the location of the sensory inputs from the thalamus.

Neural Dynamics of Autistic Repetitive Behaviors and Fragile X Syndrome: Basal Ganglia Movement Gating and mGluR-Modulated Adaptively Timed Learning

This article develops the iSTART neural model that proposes how specific imbalances in cognitive, emotional, timing, and motor processes that involve brain regions like prefrontal cortex, temporal cortex, amygdala, hypothalamus, hippocampus, and cerebellum may interact together to cause behavioral symptoms of autism. These imbalances include underaroused emotional depression in the amygdala/hypothalamus, learning of hyperspecific recognition categories that help to cause narrowly focused attention in temporal and prefrontal cortices, and breakdowns of adaptively timed motivated attention and motor circuits in the hippocampus and cerebellum. The article expands the model's explanatory range by, first, explaining recent data about Fragile X syndrome (FXS), mGluR, and trace conditioning; and, second, by explaining distinct causes of stereotyped behaviors in individuals with autism. Some of these stereotyped behaviors, such as an insistence on sameness and circumscribed interests, may result from imbalances in the cognitive and emotional circuits that iSTART models. These behaviors may be ameliorated by operant conditioning methods. Other stereotyped behaviors, such as repetitive motor behaviors, may result from imbalances in how the direct and indirect pathways of the basal ganglia open or close movement gates, respectively. These repetitive behaviors may be ameliorated by drugs that augment D2 dopamine receptor responses or reduce D1 dopamine receptor responses. The article also notes the ubiquitous role of gating by basal ganglia loops in regulating all the functions that iSTART models.

Desirability, availability, credit assignment, category learning, and attention: Cognitive-emotional and working memory dynamics of orbitofrontal, ventrolateral, and dorsolateral prefrontal cortices

BACKGROUND

The prefrontal cortices play an essential role in cognitive-emotional and working memory processes through interactions with multiple brain regions.

METHODS

This article further develops a unified neural architecture that explains many recent and classical data about prefrontal function and makes testable predictions.

RESULTS

Prefrontal properties of desirability, availability, credit assignment, category learning, and feature-based attention are explained. These properties arise through interactions of orbitofrontal, ventrolateral prefrontal, and dorsolateral prefrontal cortices with the inferotemporal cortex, perirhinal cortex, parahippocampal cortices; ventral bank of the principal sulcus, ventral prearcuate gyrus, frontal eye fields, hippocampus, amygdala, basal ganglia, hypothalamus, and visual cortical areas V1, V2, V3A, V4, middle temporal cortex, medial superior temporal area, lateral intraparietal cortex, and posterior parietal cortex. Model explanations also include how the value of visual objects and events is computed, which objects and events cause desired consequences and which may be ignored as predictively irrelevant, and how to plan and act to realise these consequences, including how to selectively filter expected versus unexpected events, leading to movements towards, and conscious perception of, expected events. Modelled processes include reinforcement learning and incentive motivational learning; object and spatial working memory dynamics; and category learning, including the learning of object categories, value categories, object-value categories, and sequence categories, or list chunks.

CONCLUSION

This article hereby proposes a unified neural theory of prefrontal cortex and its functions.

Acetylcholine Neuromodulation in Normal and Abnormal Learning and Memory: Vigilance Control in Waking, Sleep, Autism, Amnesia and Alzheimer's Disease

Adaptive Resonance Theory, or ART, is a neural model that explains how normal and abnormal brains may learn to categorize and recognize objects and events in a changing world, and how these learned categories may be remembered for a long time. This article uses ART to propose and unify the explanation of diverse data about normal and abnormal modulation of learning and memory by acetylcholine (ACh). In ART, vigilance control determines whether learned categories will be general and abstract, or specific and concrete. ART models how vigilance may be regulated by ACh release in layer 5 neocortical cells by influencing after-hyperpolarization (AHP) currents. This phasic ACh release is mediated by cells in the nucleus basalis (NB) of Meynert that are activated by unexpected events. The article additionally discusses data about ACh-mediated tonic control of vigilance. ART proposes that there are often dynamic breakdowns of tonic control in mental disorders such as autism, where vigilance remains high, and medial temporal amnesia, where vigilance remains low. Tonic control also occurs during sleep-wake cycles. Properties of Up and Down states during slow wave sleep arise in ACh-modulated laminar cortical ART circuits that carry out processes in awake individuals of contrast normalization, attentional modulation, decision-making, activity-dependent habituation, and mismatch-mediated reset. These slow wave sleep circuits interact with circuits that control circadian rhythms and memory consolidation. Tonic control properties also clarify how Alzheimer's disease symptoms follow from a massive structural degeneration that includes undermining vigilance control by ACh in cortical layers 3 and 5. Sleep disruptions before and during Alzheimer's disease, and how they contribute to a vicious cycle of plaque formation in layers 3 and 5, are also clarified from this perspective.

Neural implementation of operations used in quantum cognition

Quantum probability theory has been successfully applied outside of physics to account for numerous findings from psychology regarding human judgement and decision making behavior. However, the researchers who have made these applications do not rely on the hypothesis that the brain is some type of quantum computer. This raises the question of how could the brain implement quantum algorithms other than quantum physical operations. This article outlines one way that a neural based system could perform the computations required by applications of quantum probability to human behavior.

A neural model of normal and abnormal learning and memory consolidation: adaptively timed conditioning, hippocampus, amnesia, neurotrophins, and consciousness

How do the hippocampus and amygdala interact with thalamocortical systems to regulate cognitive and cognitive-emotional learning? Why do lesions of thalamus, amygdala, hippocampus, and cortex have differential effects depending on the phase of learning when they occur? In particular, why is the hippocampus typically needed for trace conditioning, but not delay conditioning, and what do the exceptions reveal? Why do amygdala lesions made before or immediately after training decelerate conditioning while those made later do not? Why do thalamic or sensory cortical lesions degrade trace conditioning more than delay conditioning? Why do hippocampal lesions during trace conditioning experiments degrade recent but not temporally remote learning? Why do orbitofrontal cortical lesions degrade temporally remote but not recent or post-lesion learning? How is temporally graded amnesia caused by ablation of prefrontal cortex after memory consolidation? How are attention and consciousness linked during conditioning? How do neurotrophins, notably brain-derived neurotrophic factor (BDNF), influence memory formation and consolidation? Is there a common output path for learned performance? A neural model proposes a unified answer to these questions that overcome problems of alternative memory models.

Towards solving the hard problem of consciousness: The varieties of brain resonances and the conscious experiences that they support

The hard problem of consciousness is the problem of explaining how we experience qualia or phenomenal experiences, such as seeing, hearing, and feeling, and knowing what they are. To solve this problem, a theory of consciousness needs to link brain to mind by modeling how emergent properties of several brain mechanisms interacting together embody detailed properties of individual conscious psychological experiences. This article summarizes evidence that Adaptive Resonance Theory, or ART, accomplishes this goal. ART is a cognitive and neural theory of how advanced brains autonomously learn to attend, recognize, and predict objects and events in a changing world. ART has predicted that "all conscious states are resonant states" as part of its specification of mechanistic links between processes of consciousness, learning, expectation, attention, resonance, and synchrony. It hereby provides functional and mechanistic explanations of data ranging from individual spikes and their synchronization to the dynamics of conscious perceptual, cognitive, and cognitive-emotional experiences. ART has reached sufficient maturity to begin classifying the brain resonances that support conscious experiences of seeing, hearing, feeling, and knowing. Psychological and neurobiological data in both normal individuals and clinical patients are clarified by this classification. This analysis also explains why not all resonances become conscious, and why not all brain dynamics are resonant. The global organization of the brain into computationally complementary cortical processing streams (complementary computing), and the organization of the cerebral cortex into characteristic layers of cells (laminar computing), figure prominently in these explanations of conscious and unconscious processes. Alternative models of consciousness are also discussed.

Phoneme restoration and empirical coverage of Interactive Activation and Adaptive Resonance models of human speech processing

Magnuson [J. Acoust. Soc. Am. 137, 1481-1492 (2015)] makes claims for Interactive Activation (IA) models and against Adaptive Resonance Theory (ART) models of speech perception. Magnuson also presents simulations that claim to show that the TRACE model can simulate phonemic restoration, which was an explanatory target of the cARTWORD ART model. The theoretical analysis and review herein show that these claims are incorrect. More generally, the TRACE and cARTWORD models illustrate two diametrically opposed types of neural models of speech and language. The TRACE model embodies core assumptions with no analog in known brain processes. The cARTWORD model defines a hierarchy of cortical processing regions whose networks embody cells in laminar cortical circuits as part of the paradigm of laminar computing. cARTWORD further develops ART speech and language models that were introduced in the 1970s. It builds upon Item-Order-Rank working memories, which activate learned list chunks that unitize sequences to represent phonemes, syllables, and words. Psychophysical and neurophysiological data support Item-Order-Rank mechanisms and contradict TRACE representations of time, temporal order, silence, and top-down processing that exhibit many anomalous properties, including hallucinations of non-occurring future phonemes. Computer simulations of the TRACE model are presented that demonstrate these failures.

Resonant Cholinergic Dynamics in Cognitive and Motor Decision-Making: Attention, Category Learning, and Choice in Neocortex, Superior Colliculus, and Optic Tectum

Freely behaving organisms need to rapidly calibrate their perceptual, cognitive, and motor decisions based on continuously changing environmental conditions. These plastic changes include sharpening or broadening of cognitive and motor attention and learning to match the behavioral demands that are imposed by changing environmental statistics. This article proposes that a shared circuit design for such flexible decision-making is used in specific cognitive and motor circuits, and that both types of circuits use acetylcholine to modulate choice selectivity. Such task-sensitive control is proposed to control thalamocortical choice of the critical features that are cognitively attended and that are incorporated through learning into prototypes of visual recognition categories. A cholinergically-modulated process of vigilance control determines if a recognition category and its attended features are abstract (low vigilance) or concrete (high vigilance). Homologous neural mechanisms of cholinergic modulation are proposed to focus attention and learn a multimodal map within the deeper layers of superior colliculus. This map enables visual, auditory, and planned movement commands to compete for attention, leading to selection of a winning position that controls where the next saccadic eye movement will go. Such map learning may be viewed as a kind of attentive motor category learning. The article hereby explicates a link between attention, learning, and cholinergic modulation during decision making within both cognitive and motor systems. Homologs between the mammalian superior colliculus and the avian optic tectum lead to predictions about how multimodal map learning may occur in the mammalian and avian brain and how such learning may be modulated by acetycholine.

How a (sub)Cellular Coincidence Detection Mechanism Featuring Layer-5 Pyramidal Cells May Help Produce Various Visual Phenomena

Perceptual phenomena such as spatio-temporal illusions and masking are typically explained by psychological (cognitive) processing theories or large-scale neural theories involving inter-areal connectivity and neural circuits comprising of hundreds or more interconnected single cells. Subcellular mechanisms are hardly used for such purpose. Here, a mechanistic theoretical view is presented on how a subcellular brain mechanism of integration of presynaptic signals that arrive at different compartments of layer-5 pyramidal neurons could explain a couple of spatiotemporal visual-phenomenal effects unfolding along very brief time intervals within the range of the sub-second temporal scale.

Effects of Spike Anticipation on the Spiking Dynamics of Neural Networks

Synchronization is one of the central phenomena involved in information processing in living systems. It is known that the nervous system requires the coordinated activity of both local and distant neural populations. Such an interplay allows to merge different information modalities in a whole processing supporting high-level mental skills as understanding, memory, abstraction, etc. Though, the biological processes underlying synchronization in the brain are not fully understood there have been reported a variety of mechanisms supporting different types of synchronization both at theoretical and experimental level. One of the more intriguing of these phenomena is the anticipating synchronization, which has been recently reported in a pair of unidirectionally coupled artificial neurons under simple conditions (Pyragiene and Pyragas, 2013), where the slave neuron is able to anticipate in time the behavior of the master one. In this paper, we explore the effect of spike anticipation over the information processing performed by a neural network at functional and structural level. We show that the introduction of intermediary neurons in the network enhances spike anticipation and analyse how these variations in spike anticipation can significantly change the firing regime of the neural network according to its functional and structural properties. In addition we show that the interspike interval (ISI), one of the main features of the neural response associated with the information coding, can be closely related to spike anticipation by each spike, and how synaptic plasticity can be modulated through that relationship. This study has been performed through numerical simulation of a coupled system of Hindmarsh–Rose neurons.

Laminar Neural Field Model of Laterally Propagating Waves of Orientation Selectivity

We construct a laminar neural-field model of primary visual cortex (V1) consisting of a superficial layer of neurons that encode the spatial location and orientation of a local visual stimulus coupled to a deep layer of neurons that only encode spatial location. The spatially-structured connections in the deep layer support the propagation of a traveling front, which then drives propagating orientation-dependent activity in the superficial layer. Using a combination of mathematical analysis and numerical simulations, we establish that the existence of a coherent orientation-selective wave relies on the presence of weak, long-range connections in the superficial layer that couple cells of similar orientation preference. Moreover, the wave persists in the presence of feedback from the superficial layer to the deep layer. Our results are consistent with recent experimental studies that indicate that deep and superficial layers work in tandem to determine the patterns of cortical activity observed in vivo.

A mechanistic cortical microcircuit of attention for amplification, normalization and suppression

Computational models of visual attention have replicated a large number of data from visual attention experiments. However, typically each computational model has been shown to account for only a few data sets. We developed a novel model of attention, particularly focused on explaining single cell recordings in multiple brain areas, to better understand the underlying computational circuits of attention involved in spatial- and feature-based biased competition, modulation of the contrast response function, modulation of the neuronal tuning curve, and modulation of surround suppression. In contrast to previous models, we use a two layer structure inspired by the layered cortical architecture which implements amplification, divisive normalization and suppression as well as spatial pooling.

From brain synapses to systems for learning and memory: Object recognition, spatial navigation, timed conditioning, and movement control

This article provides an overview of neural models of synaptic learning and memory whose expression in adaptive behavior depends critically on the circuits and systems in which the synapses are embedded. It reviews Adaptive Resonance Theory, or ART, models that use excitatory matching and match-based learning to achieve fast category learning and whose learned memories are dynamically stabilized by top-down expectations, attentional focusing, and memory search. ART clarifies mechanistic relationships between consciousness, learning, expectation, attention, resonance, and synchrony. ART models are embedded in ARTSCAN architectures that unify processes of invariant object category learning, recognition, spatial and object attention, predictive remapping, and eye movement search, and that clarify how conscious object vision and recognition may fail during perceptual crowding and parietal neglect. The generality of learned categories depends upon a vigilance process that is regulated by acetylcholine via the nucleus basalis. Vigilance can get stuck at too high or too low values, thereby causing learning problems in autism and medial temporal amnesia. Similar synaptic learning laws support qualitatively different behaviors: Invariant object category learning in the inferotemporal cortex; learning of grid cells and place cells in the entorhinal and hippocampal cortices during spatial navigation; and learning of time cells in the entorhinal-hippocampal system during adaptively timed conditioning, including trace conditioning. Spatial and temporal processes through the medial and lateral entorhinal-hippocampal system seem to be carried out with homologous circuit designs. Variations of a shared laminar neocortical circuit design have modeled 3D vision, speech perception, and cognitive working memory and learning. A complementary kind of inhibitory matching and mismatch learning controls movement. This article is part of a Special Issue entitled SI: Brain and Memory.

Functional brain networks: linking thalamic atrophy to clinical disability in multiple sclerosis, a multimodal fMRI and MEG study

Thalamic atrophy is known to be one of the most important predictors for clinical dysfunction in multiple sclerosis (MS). As the thalamus is highly connected to many cortical areas, this suggests that thalamic atrophy is associated with disruption of cortical functional networks. We investigated this thalamo-cortical system to explain the presence of physical and cognitive problems in MS. Functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) were performed in 86 MS patients and 21 healthy subjects. We computed cortical functional networks for fMRI and MEG by respectively the Pearson's correlation coefficient and the phase lag index using the same automated anatomical labeling atlas for both modalities. Thalamo-cortical functional connectivity was only estimated using fMRI. We computed conventional network metrics such as clustering coefficient and path length and analyzed the minimum spanning tree (MST), a subnetwork and backbone of the original network. MS patients showed reduced thalamic volumes and increased thalamo-cortical connectivity. MEG cortical functional networks showed a lower level of integration in MS in terms of the MST, whereas fMRI cortical networks did not differ between groups. Lower integration of MEG cortical functional networks was both related to thalamic atrophy as well as to increased thalamo-cortical functional connectivity in fMRI and to worse cognitive and clinical status. This study demonstrated for the first time that thalamic atrophy is associated with global disruption of cortical functional networks in MS and this global disruption of network activity was related to worse cognitive and clinical function in MS. Hum Brain Mapp 36:603-618, 2015. © 2014 Wiley Periodicals, Inc.

Preferential effect of isoflurane on top-down vs. bottom-up pathways in sensory cortex

The mechanism of loss of consciousness (LOC) under anesthesia is unknown. Because consciousness depends on activity in the cortico-thalamic network, anesthetic actions on this network are likely critical for LOC. Competing theories stress the importance of anesthetic actions on bottom-up “core” thalamo-cortical (TC) vs. top-down cortico-cortical (CC) and matrix TC connections. We tested these models using laminar recordings in rat auditory cortex in vivo and murine brain slices. We selectively activated bottom-up vs. top-down afferent pathways using sensory stimuli in vivo and electrical stimulation in brain slices, and compared effects of isoflurane on responses evoked via the two pathways. Auditory stimuli in vivo and core TC afferent stimulation in brain slices evoked short latency current sinks in middle layers, consistent with activation of core TC afferents. By contrast, visual stimuli in vivo and stimulation of CC and matrix TC afferents in brain slices evoked responses mainly in superficial and deep layers, consistent with projection patterns of top-down afferents that carry visual information to auditory cortex. Responses to auditory stimuli in vivo and core TC afferents in brain slices were significantly less affected by isoflurane compared to responses triggered by visual stimuli in vivo and CC/matrix TC afferents in slices. At a just-hypnotic dose in vivo, auditory responses were enhanced by isoflurane, whereas visual responses were dramatically reduced. At a comparable concentration in slices, isoflurane suppressed both core TC and CC/matrix TC responses, but the effect on the latter responses was far greater than on core TC responses, indicating that at least part of the differential effects observed in vivo were due to local actions of isoflurane in auditory cortex. These data support a model in which disruption of top-down connectivity contributes to anesthesia-induced LOC, and have implications for understanding the neural basis of consciousness.

Real-time learning of predictive recognition categories that chunk sequences of items stored in working memory

How are sequences of events that are temporarily stored in a cognitive working memory unitized, or chunked, through learning? Such sequential learning is needed by the brain in order to enable language, spatial understanding, and motor skills to develop. In particular, how does the brain learn categories, or list chunks, that become selectively tuned to different temporal sequences of items in lists of variable length as they are stored in working memory, and how does this learning process occur in real time? The present article introduces a neural model that simulates learning of such list chunks. In this model, sequences of items are temporarily stored in an Item-and-Order, or competitive queuing, working memory before learning categorizes them using a categorization network, called a Masking Field, which is a self-similar, multiple-scale, recurrent on-center off-surround network that can weigh the evidence for variable-length sequences of items as they are stored in the working memory through time. A Masking Field hereby activates the learned list chunks that represent the most predictive item groupings at any time, while suppressing less predictive chunks. In a network with a given number of input items, all possible ordered sets of these item sequences, up to a fixed length, can be learned with unsupervised or supervised learning. The self-similar multiple-scale properties of Masking Fields interacting with an Item-and-Order working memory provide a natural explanation of George Miller's Magical Number Seven and Nelson Cowan's Magical Number Four. The article explains why linguistic, spatial, and action event sequences may all be stored by Item-and-Order working memories that obey similar design principles, and thus how the current results may apply across modalities. Item-and-Order properties may readily be extended to Item-Order-Rank working memories in which the same item can be stored in multiple list positions, or ranks, as in the list ABADBD. Comparisons with other models, including TRACE, MERGE, and TISK, are made.

Chronic effects of cannabis use on the auditory mismatch negativity

BACKGROUND

Cannabis use is associated with the development of psychotic symptoms and increased risk for schizophrenia. The mismatch negativity (MMN) is a brain event-related potential marker of change detection thought to index glutamatergic N-methyl-D-aspartate receptor-mediated neurotransmission, which is known to be deficient in schizophrenia. This study examined auditory MMN in otherwise healthy chronic cannabis users compared with nonuser control subjects.

METHODS

Forty-two chronic cannabis users and 44 nonuser healthy control subjects completed a multi-feature MMN paradigm, which included duration, frequency, and intensity deviants (deviants 6%; standards 82%). The MMN was compared between users and control subjects as well as between long- and short-term users and age- and gender-matched control subjects. Associations between MMN, cannabis use measures, and symptoms were examined.

RESULTS

The MMN amplitude was significantly reduced to frequency but not duration or intensity deviants in overall cannabis users relative to control subjects. Frequency MMN was similarly attenuated in short- and long-term users relative to control subjects. Long-term users also exhibited reduced duration MMN relative to control subjects and short-term users and this was correlated with increased duration of exposure to cannabis and increased psychotic-like experiences during intoxication. In short-term users, a younger age of onset of regular cannabis use and greater frequency of use were associated with greater psychotic-like experiences and symptomatic distress.

CONCLUSIONS

These results suggest impaired sensory memory that might reflect N-methyl-D-aspartate receptor dysfunction in chronic cannabis users. The pattern of MMN alterations in cannabis users differed from that typically observed in patients with schizophrenia, indicating overlapping but distinct underlying pathology.

Learning to ignore: a modeling study of a decremental cholinergic pathway and its influence on attention and learning

Learning to ignore irrelevant stimuli is essential to achieving efficient and fluid attention, and serves as the complement to increasing attention to relevant stimuli. The different cholinergic (ACh) subsystems within the basal forebrain regulate attention in distinct but complementary ways. ACh projections from the substantia innominata/nucleus basalis region (SI/nBM) to the neocortex are necessary to increase attention to relevant stimuli and have been well studied. Lesser known are ACh projections from the medial septum/vertical limb of the diagonal band (MS/VDB) to the hippocampus and the cingulate that are necessary to reduce attention to irrelevant stimuli. We developed a neural simulation to provide insight into how ACh can decrement attention using this distinct pathway from the MS/VDB. We tested the model in behavioral paradigms that require decremental attention. The model exhibits behavioral effects such as associative learning, latent inhibition, and persisting behavior. Lesioning the MS/VDB disrupts latent inhibition, and drastically increases perseverative behavior. Taken together, the model demonstrates that the ACh decremental pathway is necessary for appropriate learning and attention under dynamic circumstances and suggests a canonical neural architecture for decrementing attention.

Coordinated learning of grid cell and place cell spatial and temporal properties: multiple scales, attention and oscillations

A neural model proposes how entorhinal grid cells and hippocampal place cells may develop as spatial categories in a hierarchy of self-organizing maps (SOMs). The model responds to realistic rat navigational trajectories by learning both grid cells with hexagonal grid firing fields of multiple spatial scales, and place cells with one or more firing fields, that match neurophysiological data about their development in juvenile rats. Both grid and place cells can develop by detecting, learning and remembering the most frequent and energetic co-occurrences of their inputs. The model's parsimonious properties include: similar ring attractor mechanisms process linear and angular path integration inputs that drive map learning; the same SOM mechanisms can learn grid cell and place cell receptive fields; and the learning of the dorsoventral organization of multiple spatial scale modules through medial entorhinal cortex to hippocampus (HC) may use mechanisms homologous to those for temporal learning through lateral entorhinal cortex to HC ('neural relativity'). The model clarifies how top-down HC-to-entorhinal attentional mechanisms may stabilize map learning, simulates how hippocampal inactivation may disrupt grid cells, and explains data about theta, beta and gamma oscillations. The article also compares the three main types of grid cell models in the light of recent data.

New innovations: therapeutic opportunities for intellectual disabilities

Intellectual disability is common and is associated with significant morbidity. Until the latter half of the 20th century, there were no efficacious treatments. Following initial breakthroughs associated with newborn screening and metabolic corrections, little progress was made until recently. With improved understanding of genetic and cellular mechanisms, novel treatment options are beginning to appear for a number of specific conditions. Fragile X and tuberous sclerosis offer paradigms for the development of targeted therapeutics, but advances in understanding of other disorders such as Down syndrome and Rett syndrome, for example, are also resulting in promising treatment directions. In addition, better understanding of the underlying neurobiology is leading to novel developments in enzyme replacement for storage disorders and adjunctive therapies for metabolic disorders, as well as potentially more generalizable approaches that target dysfunctional cell regulation via RNA and chromatin. Physiologic therapies, including deep brain stimulation and transcranial magnetic stimulation, offer yet another direction to enhance cognitive functioning. Current options and evolving opportunities for the intellectually disabled are reviewed and exemplified.

Mechanisms underlying the basal forebrain enhancement of top-down and bottom-up attention

Both attentional signals from frontal cortex and neuromodulatory signals from basal forebrain (BF) have been shown to influence information processing in the primary visual cortex (V1). These two systems exert complementary effects on their targets, including increasing firing rates and decreasing interneuronal correlations. Interestingly, experimental research suggests that the cholinergic system is important for increasing V1's sensitivity to both sensory and attentional information. To see how the BF and top-down attention act together to modulate sensory input, we developed a spiking neural network model of V1 and thalamus that incorporated cholinergic neuromodulation and top-down attention. In our model, activation of the BF had a broad effect that decreases the efficacy of top-down projections and increased the reliance of bottom-up sensory input. In contrast, we demonstrated how local release of acetylcholine in the visual cortex, which was triggered through top-down gluatmatergic projections, could enhance top-down attention with high spatial specificity. Our model matched experimental data showing that the BF and top-down attention decrease interneuronal correlations and increase between-trial reliability. We found that decreases in correlations were primarily between excitatory-inhibitory pairs rather than excitatory-excitatory pairs and suggest that excitatory-inhibitory decorrelation is necessary for maintaining low levels of excitatory-excitatory correlations. Increased inhibitory drive via release of acetylcholine in V1 may then act as a buffer, absorbing increases in excitatory-excitatory correlations that occur with attention and BF stimulation. These findings will lead to a better understanding of the mechanisms underyling the BF's interactions with attention signals and influences on correlations.

Set and setting: how behavioral state regulates sensory function and plasticity

Recently developed neuroimaging and electrophysiological techniques are allowing us to answer fundamental questions about how behavioral states regulate our perception of the external environment. Studies using these techniques have yielded surprising insights into how sensory processing is affected at the earliest stages by attention and motivation, and how new sensory information received during wakefulness (e.g., during learning) continues to affect sensory brain circuits (leading to plastic changes) during subsequent sleep. This review aims to describe how brain states affect sensory response properties among neurons in primary and secondary sensory cortices, and how this relates to psychophysical detection thresholds and performance on sensory discrimination tasks. This is not intended to serve as a comprehensive overview of all brain states, or all sensory systems, but instead as an illustrative description of how three specific state variables (attention, motivation, and vigilance [i.e., sleep vs. wakefulness]) affect sensory systems in which they have been best studied.

A realistic neural mass model of the cortex with laminar-specific connections and synaptic plasticity - evaluation with auditory habituation

In this work we propose a biologically realistic local cortical circuit model (LCCM), based on neural masses, that incorporates important aspects of the functional organization of the brain that have not been covered by previous models: (1) activity dependent plasticity of excitatory synaptic couplings via depleting and recycling of neurotransmitters and (2) realistic inter-laminar dynamics via laminar-specific distribution of and connections between neural populations. The potential of the LCCM was demonstrated by accounting for the process of auditory habituation. The model parameters were specified using Bayesian inference. It was found that: (1) besides the major serial excitatory information pathway (layer 4 to layer 2/3 to layer 5/6), there exists a parallel "short-cut" pathway (layer 4 to layer 5/6), (2) the excitatory signal flow from the pyramidal cells to the inhibitory interneurons seems to be more intra-laminar while, in contrast, the inhibitory signal flow from inhibitory interneurons to the pyramidal cells seems to be both intra- and inter-laminar, and (3) the habituation rates of the connections are unsymmetrical: forward connections (from layer 4 to layer 2/3) are more strongly habituated than backward connections (from Layer 5/6 to layer 4). Our evaluation demonstrates that the novel features of the LCCM are of crucial importance for mechanistic explanations of brain function. The incorporation of these features into a mass model makes them applicable to modeling based on macroscopic data (like EEG or MEG), which are usually available in human experiments. Our LCCM is therefore a valuable building block for future realistic models of human cognitive function.

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.

Neuronal functional connection graphs among multiple areas of the rat somatosensory system during spontaneous and evoked activities

Small-World Networks (SWNs) represent a fundamental model for the comprehension of many complex man-made and biological networks. In the central nervous system, SWN models have been shown to fit well both anatomical and functional maps at the macroscopic level. However, the functional microscopic level, where the nodes of a network are represented by single neurons, is still poorly understood. At this level, although recent evidences suggest that functional connection graphs exhibit small-world organization, it is not known whether and how these maps, potentially distributed in multiple brain regions, change across different conditions, such as spontaneous and stimulus-evoked activities. We addressed these questions by analyzing the data from simultaneous multi-array extracellular recordings in three brain regions of rats, diversely involved in somatosensory information processing: the ventropostero-lateral thalamic nuclei, the primary somatosensory cortex and the centro-median thalamic nuclei. From both spike and Local Field Potential (LFP) recordings, we estimated the functional connection graphs by using the Normalized Compression Similarity for spikes and the Phase Synchrony for LFPs. Then, by using graph-theoretical statistics, we characterized the functional topology both during spontaneous activity and sensory stimulation. Our main results show that: (i) spikes and LFPs show SWN organization during spontaneous activity; (ii) after stimulation onset, while substantial functional graph reconfigurations occur both in spike and LFPs, small-worldness is nonetheless preserved; (iii) the stimulus triggers a significant increase of inter-area LFP connections without modifying the topology of intra-area functional connections. Finally, investigating computationally the functional substrate that supports the observed phenomena, we found that (iv) the fundamental concept of cell assemblies, transient groups of activating neurons, can be described by small-world networks. Our results suggest that activity of neurons from multiple areas of the rat somatosensory system contributes to the integration of local computations arisen in distributed functional cell assemblies according to the principles of SWNs.

Cell assemblies (sequences of neuronal activations), seem to represent a functional unit of information processing. However, it remains unclear how groups of neurons may organize their activity during information processing, working as a sole functional unit. One prominent principle in complex network theory is covered by small-world networks, in which each node is easily reachable by each other and organized in highly dense clusters. Small-world networks have been already observed on large scales in human and primate brain areas while their presence at the neuronal level remains unclear. The aim of this work was to investigate the possibility that functional, related neural populations, encompassing multiple brain regions, could be organized in small-world networks. We investigated the coherent neuronal activity among multiple rat brain regions involved in somatosensory information processing. We found that the recorded neuronal populations represented small-world networks and that these topologies were maintained during stimulations. Furthermore, by using simulations to explore the hidden substrates supporting the observed topological features, we inferred that small-world networks represent a plausible topology for cell assemblies. This work suggests that the coherent activity of neurons from multiple brain areas promotes the integration of local computations, the functional principle of small-world networks.

Gamma band plasticity in sensory cortex is a signature of the strongest memory rather than memory of the training stimulus

Gamma oscillations (∼30-120Hz) are considered to be a reflection of coordinated neuronal activity, linked to processes underlying synaptic integration and plasticity. Increases in gamma power within the cerebral cortex have been found during many cognitive processes such as attention, learning, memory and problem solving in both humans and animals. However, the specificity of gamma to the detailed contents of memory remains largely unknown. We investigated the relationship between learning-induced increased gamma power in the primary auditory cortex (A1) and the strength of memory for acoustic frequency. Adult male rats (n=16) received three days (200 trials each) of pairing a tone (3.66 kHz) with stimulation of the nucleus basalis, which implanted a memory for acoustic frequency as assessed by associatively-induced disruption of ongoing behavior, viz., respiration. Post-training frequency generalization gradients (FGGs) revealed peaks at non-CS frequencies in 11/16 cases, likely reflecting normal variation in pre-training acoustic experiences. A stronger relationship was found between increased gamma power and the frequency with the strongest memory (peak of the difference between individual post- and pre-training FGGs) vs. behavioral responses to the CS training frequency. No such relationship was found for the theta/alpha band (4-15 Hz). These findings indicate that the strength of specific increased neuronal synchronization within primary sensory cortical fields can determine the specific contents of memory.