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Grossberg S. A Neural Model of Intrinsic and Extrinsic Hippocampal Theta Rhythms: Anatomy, Neurophysiology, and Function. Front Syst Neurosci 2021; 15:665052. [PMID: 33994965 PMCID: PMC8113652 DOI: 10.3389/fnsys.2021.665052] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 03/29/2021] [Indexed: 11/21/2022] Open
Abstract
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.
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Affiliation(s)
- Stephen Grossberg
- Center for Adaptive Systems, Department of Mathematics and Statistics, Department of Psychological and Brain Sciences, and Department of Biomedical Engineering, Boston University, Boston, MA, United States
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2
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Uncertainty-based modulation for lifelong learning. Neural Netw 2019; 120:129-142. [PMID: 31708227 DOI: 10.1016/j.neunet.2019.09.011] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Revised: 08/23/2019] [Accepted: 09/07/2019] [Indexed: 11/21/2022]
Abstract
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.
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John YJ, Zikopoulos B, Bullock D, Barbas H. Visual Attention Deficits in Schizophrenia Can Arise From Inhibitory Dysfunction in Thalamus or Cortex. COMPUTATIONAL PSYCHIATRY (CAMBRIDGE, MASS.) 2018; 2:223-257. [PMID: 30627672 PMCID: PMC6317791 DOI: 10.1162/cpsy_a_00023] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2018] [Accepted: 10/17/2018] [Indexed: 01/13/2023]
Abstract
Schizophrenia is associated with diverse cognitive deficits, including disorders of attention-related oculomotor behavior. At the structural level, schizophrenia is associated with abnormal inhibitory control in the circuit linking cortex and thalamus. We developed a spiking neural network model that demonstrates how dysfunctional inhibition can degrade attentive gaze control. Our model revealed that perturbations of two functionally distinct classes of cortical inhibitory neurons, or of the inhibitory thalamic reticular nucleus, disrupted processing vital for sustained attention to a stimulus, leading to distractibility. Because perturbation at each circuit node led to comparable but qualitatively distinct disruptions in attentive tracking or fixation, our findings support the search for new eye movement metrics that may index distinct underlying neural defects. Moreover, because the cortico-thalamic circuit is a common motif across sensory, association, and motor systems, the model and extensions can be broadly applied to study normal function and the neural bases of other cognitive deficits in schizophrenia.
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Affiliation(s)
- Yohan J. John
- Neural Systems Laboratory, Department of Health Sciences, Boston University, Boston, Massachusetts, USA
| | - Basilis Zikopoulos
- Human Systems Neuroscience Laboratory, Department of Health Sciences, Boston University, Boston, Massachusetts, USA
- Graduate Program for Neuroscience, Boston University, and School of Medicine, Boston, Massachusetts, USA
| | - Daniel Bullock
- Graduate Program for Neuroscience, Boston University, and School of Medicine, Boston, Massachusetts, USA
- Department of Psychological and Brain Sciences, Boston University, Boston, Massachusetts, USA
| | - Helen Barbas
- Neural Systems Laboratory, Department of Health Sciences, Boston University, Boston, Massachusetts, USA
- Graduate Program for Neuroscience, Boston University, and School of Medicine, Boston, Massachusetts, USA
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Grossberg S, Kishnan D. Neural Dynamics of Autistic Repetitive Behaviors and Fragile X Syndrome: Basal Ganglia Movement Gating and mGluR-Modulated Adaptively Timed Learning. Front Psychol 2018; 9:269. [PMID: 29593596 PMCID: PMC5859312 DOI: 10.3389/fpsyg.2018.00269] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2017] [Accepted: 02/19/2018] [Indexed: 11/13/2022] Open
Abstract
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.
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Affiliation(s)
- Stephen Grossberg
- Center for Adaptive Systems, Graduate Program in Cognitive and Neural Systems, Departments of Mathematics & Statistics, Psychological & Brain Sciences, and Biomedical Engineering, Boston University, Boston, MA, United States
| | - Devika Kishnan
- Department of Biomedical Engineering, Boston University, Boston, MA, United States
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Déli E, Tozzi A, Peters JF. Relationships between short and fast brain timescales. Cogn Neurodyn 2017; 11:539-552. [PMID: 29147146 PMCID: PMC5670088 DOI: 10.1007/s11571-017-9450-4] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2017] [Revised: 06/22/2017] [Accepted: 08/16/2017] [Indexed: 01/11/2023] Open
Abstract
Brain electric activity exhibits two important features: oscillations with different timescales, characterized by diverse functional and psychological outcomes, and a temporal power law distribution. In order to further investigate the relationships between low- and high- frequency spikes in the brain, we used a variant of the Borsuk-Ulam theorem which states that, when we assess the nervous activity as embedded in a sphere equipped with a fractal dimension, we achieve two antipodal points with similar features (the slow and fast, scale-free oscillations). We demonstrate that slow and fast nervous oscillations mirror each other over time via a sinusoid relationship and provide, through the Bloch theorem from solid-state physics, the possible equation which links the two timescale activities. We show that, based on topological findings, nervous activities occurring in micro-levels are projected to single activities at meso- and macro-levels. This means that brain functions assessed at the higher scale of the whole brain necessarily display a counterpart in the lower ones, and vice versa. Our topological approach makes it possible to assess brain functions both based on entropy, and in the general terms of particle trajectories taking place on donut-like manifolds. Condensed brain activities might give rise to ideas and concepts by combination of different functional and anatomical levels. Furthermore, cognitive phenomena, as well as social activity can be described by the laws of quantum mechanics; memories and decisions exhibit holographic organization. In physics, the term duality refers to a case where two seemingly different systems turn out to be equivalent. This topological duality holds for all the types of spatio-temporal brain activities, independent of their inter- and intra-level relationships, strength, magnitude and boundaries, allowing us to connect the physiological manifestations of consciousness to the electric activities of the brain.
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Affiliation(s)
- Eva Déli
- Institute for Consciousness Studies (ICS), Benczurter 9, Nyíregyháza, 4400 Hungary
| | - Arturo Tozzi
- Center for Nonlinear Science, University of North Texas, 1155 Union Circle, #311427, Denton, TX 76203-5017 USA
| | - James F. Peters
- Department of Electrical and Computer Engineering, University of Manitoba, 75A Chancellor’s Circle, Winnipeg, MB R3T 5V6 Canada
- Department of Mathematics, Adıyaman University, 02040 Adıyaman, Turkey
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Grossberg S. Acetylcholine Neuromodulation in Normal and Abnormal Learning and Memory: Vigilance Control in Waking, Sleep, Autism, Amnesia and Alzheimer's Disease. Front Neural Circuits 2017; 11:82. [PMID: 29163063 PMCID: PMC5673653 DOI: 10.3389/fncir.2017.00082] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2017] [Accepted: 10/12/2017] [Indexed: 01/30/2023] Open
Abstract
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.
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Affiliation(s)
- Stephen Grossberg
- Center for Adaptive Systems, Graduate Program in Cognitive and Neural Systems, Departments of Mathematics & Statistics, Psychological & Brain Sciences and Biomedical Engineering, Boston University, Boston, MA, United States
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Franklin DJ, Grossberg S. A neural model of normal and abnormal learning and memory consolidation: adaptively timed conditioning, hippocampus, amnesia, neurotrophins, and consciousness. COGNITIVE, AFFECTIVE & BEHAVIORAL NEUROSCIENCE 2017; 17:24-76. [PMID: 27905080 PMCID: PMC5272895 DOI: 10.3758/s13415-016-0463-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
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.
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Affiliation(s)
- Daniel J Franklin
- Center for Adaptive Systems, Graduate Program in Cognitive and Neural Systems, and Departments of Mathematics, Psychological & Brain Sciences, and Biomedical Engineering, Boston University, 677 Beacon Street, Room 213, Boston, MA, 02215, USA
| | - Stephen Grossberg
- Center for Adaptive Systems, Graduate Program in Cognitive and Neural Systems, and Departments of Mathematics, Psychological & Brain Sciences, and Biomedical Engineering, Boston University, 677 Beacon Street, Room 213, Boston, MA, 02215, USA.
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Grossberg S. Towards solving the hard problem of consciousness: The varieties of brain resonances and the conscious experiences that they support. Neural Netw 2016; 87:38-95. [PMID: 28088645 DOI: 10.1016/j.neunet.2016.11.003] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2016] [Revised: 10/21/2016] [Accepted: 11/20/2016] [Indexed: 10/20/2022]
Abstract
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.
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Affiliation(s)
- Stephen Grossberg
- Center for Adaptive Systems, Boston University, 677 Beacon Street, Boston, MA 02215, USA; Graduate Program in Cognitive and Neural Systems, Departments of Mathematics & Statistics, Psychological & Brain Sciences, and Biomedical Engineering Boston University, 677 Beacon Street, Boston, MA 02215, USA.
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John YJ, Zikopoulos B, Bullock D, Barbas H. The Emotional Gatekeeper: A Computational Model of Attentional Selection and Suppression through the Pathway from the Amygdala to the Inhibitory Thalamic Reticular Nucleus. PLoS Comput Biol 2016; 12:e1004722. [PMID: 26828203 PMCID: PMC4734702 DOI: 10.1371/journal.pcbi.1004722] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2015] [Accepted: 12/21/2015] [Indexed: 12/17/2022] Open
Abstract
In a complex environment that contains both opportunities and threats, it is important for an organism to flexibly direct attention based on current events and prior plans. The amygdala, the hub of the brain's emotional system, is involved in forming and signaling affective associations between stimuli and their consequences. The inhibitory thalamic reticular nucleus (TRN) is a hub of the attentional system that gates thalamo-cortical signaling. In the primate brain, a recently discovered pathway from the amygdala sends robust projections to TRN. Here we used computational modeling to demonstrate how the amygdala-TRN pathway, embedded in a wider neural circuit, can mediate selective attention guided by emotions. Our Emotional Gatekeeper model demonstrates how this circuit enables focused top-down, and flexible bottom-up, allocation of attention. The model suggests that the amygdala-TRN projection can serve as a unique mechanism for emotion-guided selection of signals sent to cortex for further processing. This inhibitory selection mechanism can mediate a powerful affective ‘framing’ effect that may lead to biased decision-making in highly charged emotional situations. The model also supports the idea that the amygdala can serve as a relevance detection system. Further, the model demonstrates how abnormal top-down drive and dysregulated local inhibition in the amygdala and in the cortex can contribute to the attentional symptoms that accompany several neuropsychiatric disorders. Emotional experiences grab our attention. Information about the emotional significance of events helps individuals weigh opportunities and dangers to guide goal-directed behavior, but may also lead to irrational decisions when the stakes are perceived to be high. Which neural circuits underlie these contrasting outcomes? A recently discovered pathway links the amygdala—a key center of the emotional system—with the inhibitory thalamic reticular nucleus (TRN) that filters information between the thalamus and cortex. We developed a neural network model—the Emotional Gatekeeper—that demonstrates how the newly discovered pathway from the amygdala to TRN highlights relevant information to help assess threats and opportunities. The model also shows how the amygdala-TRN pathway can lead normal individuals to discount neutral but useful information in highly charged emotional situations, and predicts that disruption of specific nodes in this circuit underlies distinct psychiatric disorders.
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Affiliation(s)
- Yohan J. John
- Neural Systems Laboratory, Department of Health Sciences, Boston University, Boston, Massachusetts, United States of America
| | - Basilis Zikopoulos
- Human Systems Neuroscience Laboratory, Department of Health Sciences, Boston University, Boston, Massachusetts, United States of America
- Graduate Program for Neuroscience, Boston University and School of Medicine, Boston, Massachusetts, United States of America
| | - Daniel Bullock
- Graduate Program for Neuroscience, Boston University and School of Medicine, Boston, Massachusetts, United States of America
- Department of Psychological and Brain Sciences, and the Center for Computational Neuroscience and Neural Technology, Boston University, Boston, Massachusetts, United States of America
| | - Helen Barbas
- Neural Systems Laboratory, Department of Health Sciences, Boston University, Boston, Massachusetts, United States of America
- Graduate Program for Neuroscience, Boston University and School of Medicine, Boston, Massachusetts, United States of America
- * E-mail:
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Grossberg S, Palma J, Versace M. Resonant Cholinergic Dynamics in Cognitive and Motor Decision-Making: Attention, Category Learning, and Choice in Neocortex, Superior Colliculus, and Optic Tectum. Front Neurosci 2016; 9:501. [PMID: 26834535 PMCID: PMC4718999 DOI: 10.3389/fnins.2015.00501] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2015] [Accepted: 12/18/2015] [Indexed: 12/20/2022] Open
Abstract
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.
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Affiliation(s)
- Stephen Grossberg
- Graduate Program in Cognitive and Neural Systems, Boston UniversityBoston, MA, USA
- Center for Adaptive Systems, Boston UniversityBoston, MA, USA
- Departments of Mathematics, Psychology, and Biomedical Engineering, Boston UniversityBoston, MA, USA
- Center for Computational Neuroscience and Neural Technology, Boston UniversityBoston, MA, USA
| | - Jesse Palma
- Center for Computational Neuroscience and Neural Technology, Boston UniversityBoston, MA, USA
| | - Massimiliano Versace
- Graduate Program in Cognitive and Neural Systems, Boston UniversityBoston, MA, USA
- Center for Computational Neuroscience and Neural Technology, Boston UniversityBoston, MA, USA
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Ritter P, Jirsa VK, McIntosh AR, Breakspear M. Editorial: State-dependent brain computation. Front Comput Neurosci 2015; 9:77. [PMID: 26157384 PMCID: PMC4477138 DOI: 10.3389/fncom.2015.00077] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2015] [Accepted: 06/10/2015] [Indexed: 12/16/2022] Open
Affiliation(s)
- Petra Ritter
- Minerva Research Group Brain Modes, Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Germany ; Deparment of Neurology, Charité - University Medicine Berlin, Germany ; Bernstein Focus State Dependencies of Learning and Bernstein Center for Computational Neuroscience Berlin, Germany ; Berlin School of Mind and Brain and Mind and Brain Institute, Humboldt University Berlin, Germany
| | - Viktor K Jirsa
- Institut de Neurosciences des Systèmes UMR INSERM 1106, Aix-Marseille Université Faculté de Médecine Marseille, France
| | - Anthony R McIntosh
- Rotman Research Institute of Baycrest Centre, University of Toronto Toronto, ON, Canada
| | - Michael Breakspear
- Systems Neuroscience Group, QIMR Berghofer Brisbane, QLD, Australia ; The Royal Brisbane and Woman's Hospital Brisbane, QLD, Australia
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From brain synapses to systems for learning and memory: Object recognition, spatial navigation, timed conditioning, and movement control. Brain Res 2014; 1621:270-93. [PMID: 25446436 DOI: 10.1016/j.brainres.2014.11.018] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2014] [Accepted: 11/06/2014] [Indexed: 11/23/2022]
Abstract
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.
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Pilly PK, Grossberg S. How reduction of theta rhythm by medial septum inactivation may covary with disruption of entorhinal grid cell responses due to reduced cholinergic transmission. Front Neural Circuits 2013; 7:173. [PMID: 24198762 PMCID: PMC3814006 DOI: 10.3389/fncir.2013.00173] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2013] [Accepted: 10/07/2013] [Indexed: 11/28/2022] Open
Abstract
Oscillations in the coordinated firing of brain neurons have been proposed to play important roles in perception, cognition, attention, learning, navigation, and sensory-motor control. The network theta rhythm has been associated with properties of spatial navigation, as has the firing of entorhinal grid cells and hippocampal place cells. Two recent studies reduced the theta rhythm by inactivating the medial septum (MS) and demonstrated a correlated reduction in the characteristic hexagonal spatial firing patterns of grid cells. These results, along with properties of intrinsic membrane potential oscillations (MPOs) in slice preparations of medial entorhinal cortex (MEC), have been interpreted to support oscillatory interference models of grid cell firing. The current article shows that an alternative self-organizing map (SOM) model of grid cells can explain these data about intrinsic and network oscillations without invoking oscillatory interference. In particular, the adverse effects of MS inactivation on grid cells can be understood in terms of how the concomitant reduction in cholinergic inputs may increase the conductances of leak potassium (K+) and slow and medium after-hyperpolarization (sAHP and mAHP) channels. This alternative model can also explain data that are problematic for oscillatory interference models, including how knockout of the HCN1 gene in mice, which flattens the dorsoventral gradient in MPO frequency and resonance frequency, does not affect the development of the grid cell dorsoventral gradient of spatial scales, and how hexagonal grid firing fields in bats can occur even in the absence of theta band modulation. These results demonstrate how models of grid cell self-organization can provide new insights into the relationship between brain learning and oscillatory dynamics.
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Affiliation(s)
- Praveen K Pilly
- Center for Neural and Emergent Systems, Information and Systems Sciences Laboratory, HRL Laboratories Malibu, CA, USA
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14
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Adaptive Resonance Theory: How a brain learns to consciously attend, learn, and recognize a changing world. Neural Netw 2013; 37:1-47. [PMID: 23149242 DOI: 10.1016/j.neunet.2012.09.017] [Citation(s) in RCA: 183] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2012] [Revised: 08/24/2012] [Accepted: 09/24/2012] [Indexed: 11/17/2022]
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