scholarly journals Indexing sensory plasticity: Evidence for distinct Predictive Coding and Hebbian Learning mechanisms in the cerebral cortex

2017 ◽  
Author(s):  
Meg J Spriggs ◽  
Rachael L Sumner ◽  
Rebecca L McMillan ◽  
Rosalyn J Moran ◽  
Ian J Kirk ◽  
...  
Keyword(s):  
Hebbian Learning ◽  
Predictive Coding ◽  
Task Demands ◽  
Learning Mechanisms ◽  
Sensory Plasticity ◽  
Hebbian Plasticity ◽  
Underlying Network ◽  
Generative Mechanisms ◽  
The Brain ◽  
Hebbian Mechanisms

The Roving Mismatch Negativity (MMN), and Visual LTP paradigms are widely used as independent measures of sensory plasticity. However, the paradigms are built upon fundamentally different (and seemingly opposing) models of perceptual learning; namely, Predictive Coding (MMN) and Hebbian plasticity (LTP). The aims of the current study were to 1) compare the generative mechanisms of the MMN and visual LTP, therefore assessing whether Predictive Coding and Hebbian mechanisms co-occur in the brain, and 2) assess whether the paradigms identify similar group differences in plasticity. Forty participants were split into two groups based on the BDNF Val66Met polymorphism and were presented with both paradigms. Consistent with Predictive Coding and Hebbian predictions, Dynamic Causal Modelling revealed that the generation of the MMN modulates forward and backward connections in the underlying network, while visual LTP only modulates forward connections. Genetic differences were identified in the ERPs for both paradigms, but were only apparent in backward connections of the MMN network. These results suggest that both Predictive Coding and Hebbian mechanisms are utilized by the brain under different task demands. Additionally, both tasks provide unique insight into plasticity mechanisms, which has important implications for future studies of aberrant plasticity in clinical populations.

scholarly journals Indexing sensory plasticity: Evidence for distinct Predictive Coding and Hebbian learning mechanisms in the cerebral cortex

NeuroImage ◽  
2018 ◽  
Vol 176 ◽  
pp. 290-300 ◽  
Author(s):  
M.J. Spriggs ◽  
R.L. Sumner ◽  
R.L. McMillan ◽  
R.J. Moran ◽  
I.J. Kirk ◽  
...  
Keyword(s):  
Cerebral Cortex ◽  
Hebbian Learning ◽  
Predictive Coding ◽  
Learning Mechanisms ◽  
Sensory Plasticity

The Web in the Spider: Associative Learning Theory

2018 ◽  
pp. 11-36
Author(s):  
Vsevolod Kapatsinski
Keyword(s):  
Learning Theory ◽  
Hebbian Learning ◽  
Ideal Observer ◽  
Learning Mechanisms ◽  
Prior Beliefs ◽  
Belief Updating ◽  
Learning Mechanism ◽  
Systems Perspective ◽  
Biological Learning ◽  
The Brain

This chapter provides an overview of basic learning mechanisms proposed within associationist learning theory: error-driven learning, Hebbian learning, and chunking. It takes the complementary learning systems perspective, which is contrasted with a Bayesian perspective in which the learner is an ‘ideal observer’. The discussion focuses on two issues. First, what is a learning mechanism? It is argued that two brain areas implement two different learning mechanisms if they would learn different things from the same input. The available data from neuroscience suggests that the brain contains multiple learning mechanisms in this sense but each learning mechanism is domain-general in applying to many different types of input. Second, what are the sources of bias that influence what a learner acquires from a certain experience? Bayesian theorists have distinguished between inductive bias implemented in prior beliefs and channel bias implemented in the translation from input to intake and output to behaviour. Given the intake and prior beliefs, belief updating in Bayesian models is unbiased, following Bayes Theorem. However, biased belief updating may be another source of bias in biological learning mechanisms.

scholarly journals The temporal paradox of Hebbian learning and homeostatic plasticity

2017 ◽  
Author(s):  
Friedemann Zenke ◽  
Wulfram Gerstner ◽  
Surya Ganguli
Keyword(s):  
Learning And Memory ◽  
Neuronal Activity ◽  
Neural Circuits ◽  
Hebbian Learning ◽  
Homeostatic Plasticity ◽  
Hebbian Plasticity ◽  
Slow Evolution ◽  
The Brain

AbstractHebbian plasticity, a synaptic mechanism which detects and amplifies co-activity between neurons, is considered a key ingredient underlying learning and memory in the brain. However, Hebbian plasticity alone is unstable, leading to runaway neuronal activity, and therefore requires stabilization by additional compensatory processes. Traditionally, a diversity of homeostatic plasticity phenomena found in neural circuits are thought to play this role. However, recent modelling work suggests that the slow evolution of homeostatic plasticity, as observed in experiments, is insufficient to prevent instabilities originating from Hebbian plasticity. To remedy this situation, we suggest that homeostatic plasticity is complemented by additional rapid compensatory processes, which rapidly stabilize neuronal activity on short timescales.

How to grow a self: development of the self in a Bayesian brain

2019 ◽  
Author(s):  
Mateusz Wozniak
Keyword(s):  
Conceptual Development ◽  
Human Life ◽  
Predictive Coding ◽  
Body Representation ◽  
Developmental Trajectory ◽  
The Self ◽  
Learning Mechanisms ◽  
Bayesian Brain ◽  
Third Person ◽  
Complex Forms

The last two decades have brought several attempts to explain the self as a part of the Bayesian brain, typically within the framework of predictive coding. However, none of these attempts have looked comprehensively at the developmental aspect of self-representation. The goal of this paper is to argue that looking at the developmental trajectory is crucial for understanding the structure of an adult self-representation. The paper argues that the emergence of the self should be understood as an instance of conceptual development, which in the context of a Bayesian brain can be understood as a process of acquisition of new internal models of hidden causes of sensory input. The paper proposes how such models might emerge and develop over the course of human life by looking at different stages of development of bodily and extra-bodily self-representations. It argues that the self arises gradually in a series of discrete steps: from first-person multisensory representations of one’s body to third-person multisensory body representation, and from basic forms of the extended and social selves to progressively more complex forms of abstract self-representation. It discusses how each of them might emerge based on domain-general learning mechanisms, while also taking into account the potential role of innate representations. Finally it suggests how the conceptual structure of self-representation might inform the debate about the structure of self-consciousness.

Frontal lobes and attention: Processes and networks, fractionation and integration

2006 ◽  
Vol 12 (2) ◽  
pp. 261-271 ◽  
Author(s):  
DONALD T. STUSS
Keyword(s):  
Frontal Lobes ◽  
Task Demands ◽  
Activation Process ◽  
Top Down ◽  
Supervisory System ◽  
Current Task ◽  
Frontal Processes ◽  
General Activation ◽  
Lesion Studies ◽  
The Brain

The frontal lobes (FL), are they a general adaptive global capacity processor, or a series of fractionated processes? Our lesion studies focusing on attention have demonstrated impairments in distinct processes due to pathology in different frontal regions, implying fractionation of the “supervisory system.” However, when task demands are manipulated, it becomes evident that the frontal lobes are not just a series of independent processes. Increased complexity of task demands elicits greater involvement of frontal regions along a fixed network related to a general activation process. For some task demands, one or more anatomically distinct frontal processes may be recruited. In other conditions, there is a bottom-up nonfrontal/frontal network, with impairment noted maximally for the lesser task demands in the nonfrontal automatic processing regions, and then as task demands change, increased involvement of different frontal (more “strategic”) regions, until it appears all frontal regions are involved. With other measures, the network is top-down, with impairment in the measure first noted in the frontal region and then, with changing task demands, involving a posterior region. Adaptability is not just a property of FL, it is the fluid recruitment of different processes anywhere in the brain as required by the current task. (JINS, 2006,12, 261–271.)

Is Our Brain an Open or Closed System? Prediction Model of Brain and World–Brain Relation

2018 ◽  
pp. 55-76
Author(s):  
Georg Northoff
Keyword(s):  
Prediction Model ◽  
Closed System ◽  
Neural Activity ◽  
Resting State ◽  
Clinical Symptoms ◽  
Predictive Coding ◽  
Human Cognition ◽  
The World ◽  
World Brain ◽  
The Brain

Some recent philosophical discussions consider whether the brain is best understood as an open or closed system. This issue has major epistemic consequences akin to the scepticism engendered by the famous Cartesian demon. Specifically, one and the same empirical theory of brain function, predictive coding, entailing a prediction model of brain, have been associated with contradictory views of the brain as either open (Clark, 2012, 2013) or closed (Hohwy, 2013, 2014). Based on recent empirical evidence, the present paper argues that contrary to appearances, these views of the brain are compatible with one another. I suggest that there are two main forms of neural activity in the brain, one of which can be characterized as open, and the other as closed. Stimulus-induced activity, because it relies on predictive coding is indeed closed to the world, which entails that in certain respects, the brain is an inferentially secluded and self-evidencing system. In contrast, the brain’s resting state or spontaneous activity is best taken as open because it is a world-evidencing system that allows for the brain’s neural activity to align with the statistically-based spatiotemporal structure of objects and events in the world. This model requires an important caveat, however. Due to its statistically-based nature, the resting state’s alignment to the world comes in degrees. In extreme cases, the degree of alignment can be extremely low, resulting in a resting state that is barely if at all aligned to the world. This is for instance the case in schizophrenia. Clinical symptoms such as delusions and hallucinations in schizophrenics are indicative of the fundamental delicateness of the alignment between the brain’s resting-state and the world’s phenomena. Nevertheless, I argue that so long as we are dealing with a well-functioning brain, the more dire epistemic implications of predictive coding can be forestalled. That the brain is in part a self-evidencing system does not yield any generalizable reason to worry that human cognition is out of step with the real world. Instead, the brain is aligned to the world accounting for “world-brain relation” that mitigates sceptistic worries.

The development of MMN

2019 ◽  
pp. 53-66
Author(s):  
Risto Näätänen ◽  
Teija Kujala ◽  
Gregory Light
Keyword(s):  
Neural Development ◽  
Event Related Potential ◽  
Peak Latency ◽  
School Age ◽  
Learning Mechanisms ◽  
Stimulation Parameters ◽  
Memory Traces ◽  
Sound Changes ◽  
Infant Brain ◽  
The Brain

The brain can detect sound changes very early on, even prenatally. Both positively and negatively displaced responses to deviant stimuli have been found in infancy, with the majority of studies reporting, however, positive mismatch responses (MMR) in infants within the first few months of life. Besides neural development, stimulation parameters may influence polarity. The positively displaced MMR develops towards the adult-like MMN between the ages of 3 and 9 months, there being a wide inter-individual variation in this development. From school age onwards, sound changes elicit MMNs with negative polarities fairly systematically. The MMN peak latency becomes shorter with development, similar to other event-related potential components, which is consistent with the development of myelination. MMN/MMR studies have illuminated auditory abilities and learning mechanisms in infants, suggesting, for example, that the infant brain can extract information on the regularities of sound input and foetuses can form long-lasting memory traces.

Difficulties with synaptic theory of learning and memory and possible remedies

2000 ◽  
Vol 23 (4) ◽  
pp. 550-551
Author(s):  
Mikhail N. Zhadin
Keyword(s):  
Cerebral Cortex ◽  
Associative Learning ◽  
Learning And Memory ◽  
Hebbian Learning ◽  
Behavioral Responses ◽  
Learning Rules ◽  
The Brain

The absence of a clear influence of an animal's behavioral responses to Hebbian associative learning in the cerebral cortex requires some changes in the Hebbian learning rules. The participation of the brain monoaminergic systems in Hebbian associative learning is considered.

TOLERANCE OF ON-CHIP LEARNING TO VARIOUS CIRCUIT INACCURACIES

1998 ◽  
Vol 08 (02) ◽  
pp. 315-327
Author(s):  
HOWARD C. CARD ◽  
DEAN K. McNEILL ◽  
CHRISTIAN R. SCHNEIDER ◽  
ROLAND S. SCHNEIDER ◽  
BRION K. DOLENKO
Keyword(s):  
Analog Circuit ◽  
Hebbian Learning ◽  
Minimum Weight ◽  
Learning Mechanisms ◽  
Weight Decay ◽  
Analog Cmos ◽  
Statistical Variation ◽  
On Chip ◽  
Learning Circuits ◽  
Computational Element

An investigation is made of the tolerance of various in-circuit learning algorithms to component imprecision and other circuit limitations in artificial neural networks. In contrast with most previous work, the various circuit limitations are treated separately for their effects on learning. Supervised learning mechanisms including backpropagation and contrastive Hebbian learning, and unsupervised soft competitive learning were found to be sufficiently tolerant of those levels of arithmetic inaccuracy, noise, nonlinearity, weight decay, and statistical variation from fabrication that we have experienced in 1.2 μm analog CMOS circuits employing Gilbert multipliers as the primary computational element. These learning circuits also function properly in the presence of offset errors in analog multipliers and adders, provided that the computed weight updates are constrained by the circuitry to be made only when they exceed certain minimum or threshold values. These results may also be relevant for other analog circuit approaches and for compact (low bit rate) digital implementations, although in this case, the minimum weight increment defined by the bit precision could necessitate stochastic updating.

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