scholarly journals Can the Brain Use Waves to Solve Planning Problems?

2021 ◽  
Author(s):  
Henry Powell ◽  
Mathias Winkel ◽  
Alexander V. Hopp ◽  
Helmut Linde
Keyword(s):  
Hebbian Learning ◽  
Cognitive Maps ◽  
Recurrent Network ◽  
Synaptic Connections ◽  
Activation Patterns ◽  
Starting Position ◽  
Graph Traversal ◽  
As Graph ◽  
Planning Problems ◽  
The Brain

Abstract A variety of behaviors like spatial navigation or bodily motion can be formulated as graph traversal problems through cognitive maps. We present a neural network model which can solve such tasks and is compatible with a broad range of empirical findings about the mammalian neocortex and hippocampus. The neurons and synaptic connections in the model represent structures that can result from self-organization into a cognitive map via Hebbian learning, i.e. into a graph in which each neuron represents a point of some abstract task-relevant manifold and the recurrent connections encode a distance metric on the manifold. Graph traversal problems are solved by wave-like activation patterns which travel through the recurrent network and guide a localized peak of activity onto a path from some starting position to a target state.

Biologically Plausible Error-Driven Learning Using Local Activation Differences: The Generalized Recirculation Algorithm

1996 ◽  
Vol 8 (5) ◽  
pp. 895-938 ◽  
Author(s):  
Randall C. O'Reilly
Keyword(s):  
Hebbian Learning ◽  
Learning Algorithm ◽  
Mean Field ◽  
Recurrent Network ◽  
Local Activation ◽  
Runge Kutta Method ◽  
Synaptic Modification ◽  
Learning Speed ◽  
Stochastic Framework ◽  
The Brain

The error backpropagation learning algorithm (BP) is generally considered biologically implausible because it does not use locally available, activation-based variables. A version of BP that can be computed locally using bidirectional activation recirculation (Hinton and McClelland 1988) instead of backpropagated error derivatives is more biologically plausible. This paper presents a generalized version of the recirculation algorithm (GeneRec), which overcomes several limitations of the earlier algorithm by using a generic recurrent network with sigmoidal units that can learn arbitrary input/output mappings. However, the contrastive Hebbian learning algorithm (CHL, also known as DBM or mean field learning) also uses local variables to perform error-driven learning in a sigmoidal recurrent network. CHL was derived in a stochastic framework (the Boltzmann machine), but has been extended to the deterministic case in various ways, all of which rely on problematic approximations and assumptions, leading some to conclude that it is fundamentally flawed. This paper shows that CHL can be derived instead from within the BP framework via the GeneRec algorithm. CHL is a symmetry-preserving version of GeneRec that uses a simple approximation to the midpoint or second-order accurate Runge-Kutta method of numerical integration, which explains the generally faster learning speed of CHL compared to BI. Thus, all known fully general error-driven learning algorithms that use local activation-based variables in deterministic networks can be considered variations of the GeneRec algorithm (and indirectly, of the backpropagation algorithm). GeneRec therefore provides a promising framework for thinking about how the brain might perform error-driven learning. To further this goal, an explicit biological mechanism is proposed that would be capable of implementing GeneRec-style learning. This mechanism is consistent with available evidence regarding synaptic modification in neurons in the neocortex and hippocampus, and makes further predictions.

Sequential Bayesian Decoding with a Population of Neurons

2003 ◽  
Vol 15 (5) ◽  
pp. 993-1012 ◽  
Author(s):  
Si Wu ◽  
Danmei Chen ◽  
Mahesan Niranjan ◽  
Shun-ichi Amari
Keyword(s):  
Prior Knowledge ◽  
Hebbian Learning ◽  
Learning Rule ◽  
Recurrent Network ◽  
Population Coding ◽  
Distributed Information ◽  
Hebbian Learning Rule ◽  
Map Decoding ◽  
Short Term Adaptation ◽  
The Brain

Population coding is a simplified model of distributed information processing in the brain. This study investigates the performance and implementation of a sequential Bayesian decoding (SBD) paradigm in the framework of population coding. In the first step of decoding, when no prior knowledge is available, maximum likelihood inference is used; the result forms the prior knowledge of stimulus for the second step of decoding. Estimates are propagated sequentially to apply maximum a posteriori (MAP) decoding in which prior knowledge for any step is taken from estimates from the previous step. Not only do we analyze the performance of SBD, obtaining the optimal form of prior knowledge that achieves the best estimation result, but we also investigate its possible biological realization, in the sense that all operations are performed by the dynamics of a recurrent network. In order to achieve MAP, a crucial point is to identify a mechanism that propagates prior knowledge. We find that this could be achieved by short-term adaptation of network weights according to the Hebbian learning rule. Simulation results on both constant and time-varying stimulus support the analysis.

scholarly journals Learning spatiotemporal signals using a recurrent spiking network that discretizes time

2019 ◽  
Author(s):  
Amadeus Maes ◽  
Mauricio Barahona ◽  
Claudia Clopath
Keyword(s):  
Computational Models ◽  
Hebbian Learning ◽  
Network Models ◽  
Recurrent Network ◽  
Time Component ◽  
Common Task ◽  
Wide Range ◽  
Spiking Network ◽  
Neural Substrate ◽  
The Brain

AbstractLearning to produce spatiotemporal sequences is a common task the brain has to solve. The same neural substrate may be used by the brain to produce different sequential behaviours. The way the brain learns and encodes such tasks remains unknown as current computational models do not typically use realistic biologically-plausible learning. Here, we propose a model where a spiking recurrent network of excitatory and inhibitory biophysical neurons drives a read-out layer: the dynamics of the recurrent network is constrained to encode time while the read-out neurons encode space. Space is then linked with time through plastic synapses that follow common Hebbian learning rules. We demonstrate that the model is able to learn spatiotemporal dynamics on a timescale that is behaviourally relevant. Learned sequences are robustly replayed during a regime of spontaneous activity.Author summaryThe brain has the ability to learn flexible behaviours on a wide range of time scales. Previous studies have successfully build spiking network models that learn a variety of computational tasks. However, often the learning involved is not local. Here, we investigate a model using biological-plausible plasticity rules for a specific computational task: spatiotemporal sequence learning. The architecture separates time and space into two different parts and this allows learning to bind space to time. Importantly, the time component is encoded into a recurrent network which exhibits sequential dynamics on a behavioural time scale. This network is then used as an engine to drive spatial read-out neurons. We demonstrate that the model can learn complicated spatiotemporal spiking dynamics, such as the song of a bird, and replay the song robustly.

scholarly journals Active Hebbian learning algorithm to train fuzzy cognitive maps

2004 ◽  
Vol 37 (3) ◽  
pp. 219-249 ◽  
Author(s):  
E.I. Papageorgiou ◽  
C.D. Stylios ◽  
P.P. Groumpos
Keyword(s):  
Hebbian Learning ◽  
Learning Algorithm ◽  
Cognitive Maps ◽  
Fuzzy Cognitive Maps

scholarly journals Mapping the common and distinct neural correlates of visual, rule and motor conflict

2020 ◽  
Author(s):  
Bryony Goulding Mew ◽  
Darije Custovic ◽  
Eyal Soreq ◽  
Romy Lorenz ◽  
Ines Violante ◽  
...  
Keyword(s):  
Region Of Interest ◽  
Neural Correlates ◽  
Brain Regions ◽  
Control Mechanisms ◽  
Activation Patterns ◽  
The Common ◽  
Full Factorial ◽  
The Brain ◽  
Categorical Scale ◽  
Conflict Types

AbstractFlexible behaviour requires cognitive-control mechanisms to efficiently resolve conflict between competing information and alternative actions. Whether a global neural resource mediates all forms of conflict or this is achieved within domainspecific systems remains debated. We use a novel fMRI paradigm to orthogonally manipulate rule, response and stimulus-based conflict within a full-factorial design. Whole-brain voxelwise analyses show that activation patterns associated with these conflict types are distinct but partially overlapping within Multiple Demand Cortex (MDC), the brain regions that are most commonly active during cognitive tasks. Region of interest analysis shows that most MDC sub-regions are activated for all conflict types, but to significantly varying levels. We propose that conflict resolution is an emergent property of distributed brain networks, the functional-anatomical components of which place on a continuous, not categorical, scale from domain-specialised to domain general. MDC brain regions place towards one end of that scale but display considerable functional heterogeneity.

Inter-Hemispherical Investigations on the Functional Connectivity in Controls and Autism Spectrum Using Resting State fMRI

2020 ◽  
pp. 169-186
Author(s):  
S. Vidhusha ◽  
A. Kavitha
Keyword(s):  
Functional Connectivity ◽  
Granger Causality ◽  
Resting State ◽  
Cognitive Task ◽  
Resting State Fmri ◽  
Autism Spectrum ◽  
Control Group ◽  
Neural Connectivity ◽  
Activation Patterns ◽  
The Brain

Autism spectrum disorders are connected with disturbances of neural connectivity. Functional connectivity is typically examined during a cognitive task, but also exists in the absence of a task. While a number of studies have performed functional connectivity analysis to differentiate controls and autism individuals, this work focuses on analyzing the brain activation patterns not only between controls and autistic subjects, but also analyses the brain behaviour present within autism spectrum. This can bring out more intuitive ways to understand that autism individuals differ individually. This has been performed between autism group relative to the control group using inter-hemispherical analysis. Indications of under connectivity were exhibited by the Granger Causality (GC) and Conditional Granger Causality (CGC) in autistic group. Results show that as connectivity decreases, the GC and CGC values also get decreased. Further, to demark the differences present within the spectrum of autistic individuals, GC and CGC values have been calculated.

An action-oriented perspective on space and affordances

2021 ◽  
pp. 73-140
Author(s):  
Michael A. Arbib
Keyword(s):  
Motor Control ◽  
Body Schema ◽  
Cognitive Maps ◽  
The Body ◽  
Perceptual Cues ◽  
Motor Patterns ◽  
General Rules ◽  
Hand Eye Coordination ◽  
Ongoing Behavior ◽  
The Brain

Architects design spaces that offer perceptual cues, affordances, for our various effectivities. Lina Bo Bardi’s São Paulo Museum demonstrates how praxic and contemplative actions are interleaved—space is effective and affective. Navigation often extends beyond wayfinding to support ongoing behavior. Scripts set out the general rules for a particular kind of behavior, and may suggest places that a building must provide. Cognitive maps support wayfinding. Other maps in the brain represent sensory or motor patterns of activity. Juhani Pallasmaa’s reflections on The Thinking Hand lead into a view of how the brain mediates that thinking, modeling hand–eye coordination at two levels. The first coordinates perceptual and motor schemas. The body schema is an adaptable collage of perceptual and motor skills. The second coordinates the ventral “what” pathway that can support planning of actions, and the dorsal “how” pathway that links affordance-related details to motor control. A complementary challenge is understanding how schemas in the head relate to social schemas. Finally, the chapter compares the cognitive challenges in designing a building and in developing a computational brain model of cognitive processes.

The Stories Stones Tell of Language and Its Evolution

2019 ◽  
pp. 304-318
Author(s):  
Shelby S. Putt
Keyword(s):  
Language Evolution ◽  
Evolutionary Relationship ◽  
Stone Tools ◽  
Stone Tool ◽  
Motor Tasks ◽  
Activation Patterns ◽  
Cognitive Operations ◽  
Pars Opercularis ◽  
The Brain ◽  
Neuroimaging Techniques

Language origins remain shrouded in mystery. With little remaining from our earliest ancestors, language evolution researchers have turned to stone tools to learn about ancestral language capacities, as discussed in this chapter. Because inferior frontal areas of the brain, once thought specific to language, are now known to participate during manual motor tasks as well, technological-origin hypotheses propose that tool-making was a potential cause or contributor to the evolution of language. Cutting-edge neuroimaging techniques to monitor regional brain activation patterns associated with tool-making processes are helping to investigate the potential evolutionary relationship between language and tool-making. These experiments have identified one area in the left dorsal pars opercularis portion of Broca’s area where language and stone tool-making functions rely on similar cognitive operations. A more general motor origin for language seems likely in other inferior frontal areas of the brain. Clearly, stone tools have stories to tell if we know how to listen.

Introduction

2017 ◽  
pp. 3-12
Author(s):  
Riitta Hari ◽  
Aina Puce
Keyword(s):  
Magnetic Fields ◽  
Human Brain ◽  
Brain Dynamics ◽  
Electric Currents ◽  
Electrical Currents ◽  
Activation Patterns ◽  
Neuronal Communication ◽  
Electrical Potentials ◽  
Electric And Magnetic Fields ◽  
The Brain

Neuronal communication in the brain is associated with minute electrical currents that give rise to both electrical potentials on the scalp (measurable by means of electroencephalography [EEG]) and magnetic fields outside the head (measurable by magnetoencephalography [MEG]). Both MEG and EEG are noninvasive neurophysiological methods used to study brain dynamics, that is temporal changes in the activation patterns, and sequences in signal progression. Differences between MEG and EEG mainly reflect differences in the spread of electric and magnetic fields generated by the same electric currents in the human brain. This chapter provides an overall description of the main principles of MEG and EEG and provides background for the following chapters in this and subsequent sections.

Future Trends in Functional MRI

2018 ◽  
pp. 230-240
Keyword(s):  
Imaging Technique ◽  
Diffusion Imaging ◽  
Tumor Resection ◽  
Clot Lysis ◽  
Activation Patterns ◽  
Imaging Tool ◽  
Fmri Study ◽  
Activation Response ◽  
Brain Fmri ◽  
The Brain

While MRI became a standard workhorse in neurology/neurosurgery within a few years of installation of the first MRI unit, fMRI, in spite of being a powerful imaging tool, remains primarily a research tool, even though the first fMRI study was published 25 years ago. Scientifically, fMRI has made a major impact, judging by the number of PubMed citations and publications in high-impact journals. In cognitive neuroscience, fMRI is the most commonly used imaging technique in published peer-reviewed articles. fMRI is used clinically for preoperative brain mapping in neurosurgery to delineate the proximity of the lesion (tumor) to eloquent areas of the brain, with the aim of achieving adequate tumor resection with minimal functional damage to the brain. fMRI connectivity and activation maps have identified altered activation patterns and resting-state networks in psychiatric disorders like schizophrenia, bipolar disorder, autism, and Alzheimer’s disease, but fMRI is still not a standard diagnostic procedure in psychiatry. Diffusion imaging technique is being used for triaging stroke patients who are likely to respond to stroke therapy (embolectomy and/or clot lysis). Meanwhile, major collaborative fMRI studies are in progress in many institutions to collect normative data on connectivity, activation response, and behavioral response as well as correlation among them. Studies focused on specific neuropsychiatric disorders also have been initiated by the National Institutes of Health. All this is a reflection of the huge potential application of fMRI in clinical practice envisioned by the scientific community.

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