Theory of Cortical Plasticity

Cooper, Leon N.; Intrator, Nathan; Blais, Brian S.; Shouval, Harel Z.

doi:10.1142/9789812562555

Cited by 77 publications

(115 citation statements)

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“…Going significantly beyond earlier studies, we explicitly address the question of whether functional connectivity patterns of cortical pyramidal neurons measured in recent electrophysiological studies 3--4 could be the result of plasticity during continued stimulation of neuronal model networks, in particular bidirectional connections 3 which are incompatible with standard STDP models 21,23 . The mathematical simplicity of our model enables us to identify conditions under which it becomes equivalent to the well--known Bienenstock--Cooper--Munro (BCM) model 26 used in classical rate--based descriptions of developmental learning; and similar to some earlier models of STDP 28--29 ----and why our model is fundamentally different from classical STDP models 14,16,21 , widely used for temporal coding.…”

Section: Introductionmentioning

confidence: 93%

Section: Development Of Localized Receptive Fields and Relation To Ramentioning

confidence: 99%

“…Both our spiking rule and the rate--based BCM model 26 require presynaptic activity in order to induce a change. Furthermore, for our rule as well as for the simplest BCM rule (see 26 ), the depression terms are linear and the potentiation terms are quadratic in the postsynaptic variables (i.e., the postsynaptic potential or the postsynaptic firing rate). More quantitatively, for Poisson input the total weight change Δw in our model is proportional to ν pre ν post (ν post -where ν pre and ν post denote the firing rates of pre--and postsynaptic neurons, respectively, and is a sliding threshold related to the ratio between the LTP and LTD inducing processes (see Methods, Eq.…”

Section: Development Of Localized Receptive Fields and Relation To Ramentioning

confidence: 99%

“…The sliding threshold arises in our plasticity model because the amount of LTD A LTD depends on the long--term average of the voltage on the slow time scale of homeostatic processes. Due to its similarities to BCM, it is not surprising that our spike--based learning rule with sliding threshold is able to support the development of localized receptive fields, a feature related to independent component analysis (ICA) and sparse coding 26,36 . In our experiments, the input consists of small patches of natural images using standard preprocessing 37 .…”

Section: Development Of Localized Receptive Fields and Relation To Ramentioning

confidence: 99%

“…3) and BCM theory 26 under the assumption of a linear Poisson (LP) neuron model with spikes. In a LP neuron, input spike trains are low--pass filtered and weighted to give a subthreshold potential where is the time course of an EPSP and is measured with respect to the resting potential θ --.…”

Section: Analysis Of Plasticity Modelmentioning

confidence: 99%

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Connectivity reflects coding: a model of voltage-based STDP with homeostasis

et al. 2010

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Electrophysiological connectivity patterns in cortex often show a few strong connections, sometimes bidirectional, in a sea of weak connections. In order to explain these connectivity patterns, we use a model of Spike--Timing--Dependent Plasticity where synaptic changes depend on presynaptic spike arrival and the postsynaptic membrane potential, filtered with two different time constants. The model describes several nonlinear effects in STDP experiments, as well as the voltage dependence of plasticity. We show that in a simulated recurrent network of spiking neurons our plasticity rule leads not only to development of localized receptive fields, but also to connectivity patterns that reflect the neural code: for temporal coding paradigms with spatio--temporal input correlations, strong connections are predominantly unidirectional, whereas they are bidirectional under rate coded input with spatial correlations only. Thus variable connectivity patterns in the brain could reflect different coding principles across brain areas; moreover simulations suggest that plasticity is surprisingly fast.

show abstract

Section: Introductionmentioning

confidence: 93%

Section: Development Of Localized Receptive Fields and Relation To Ramentioning

confidence: 99%

Section: Development Of Localized Receptive Fields and Relation To Ramentioning

confidence: 99%

Section: Development Of Localized Receptive Fields and Relation To Ramentioning

confidence: 99%

Section: Analysis Of Plasticity Modelmentioning

confidence: 99%

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Connectivity reflects coding: a model of voltage-based STDP with homeostasis

et al. 2010

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Second‐Order Memristor Based on All‐Oxide Multiferroic Tunnel Junction for Biorealistic Emulation of Synapses

Khanas

Hébert

Becerra

et al. 2022

Adv Elect Materials

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The brain has the ability to learn and evaluate as it receives and registers information. Signals between neurons are transmitted via synapses whose plasticity is modulated by usage. A bio-realistic electrical analog is a secondorder memristor, where the short-term internal dynamics influence the longterm state. It is achieved here with an all-oxide multiferroic tunnel junction: La 0.7 Sr 0.3 MnO 3 / BaTiO 3 / La 0.7 Sr 0.3 MnO 3 . Similar to the modulation of synaptic weight by stimuli, multi-levels of resistance may be encoded by mean of voltage pulses, on long time scale and in correlation with short-term effects. Neuromimetic learning functions are demonstrated: short and long-term potentiation/depression, paired-pulse facilitation/depression, spike-rate-and experience-dependent plasticity. The threshold frequency of pulse trains at which depression changes into potentiation depends on the previous activity, with a sliding effect as in neurobiology. The voltage pulses induce reversible changes of both dielectric polarization and oxygen vacancies distribution, generating transient trapped/detrapped charges at the two interfaces that govern the dynamic response. The resistance levels are determined by the final cationic ordering (the redox Mn 3+ /Mn 4+ ratio) at the two interfacial layers. The stimulation/relaxation dynamics are close to that in biological counterparts. Such memristors can be used in hardware artificial networks for advanced processing/storage of the information.

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Degeneracy in hippocampal physiology and plasticity

2019

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Degeneracy, defined as the ability of structurally disparate elements to perform analogous function, has largely been assessed from the perspective of maintaining robustness of physiology or plasticity. How does the framework of degeneracy assimilate into an encoding system where the ability to change is an essential ingredient for storing new incoming information? Could degeneracy maintain the balance between the apparently contradictory goals of the need to change for encoding and the need to resist change towards maintaining homeostasis? In this review, we explore these fundamental questions with the mammalian hippocampus as an example encoding system. We systematically catalog lines of evidence, spanning multiple scales of analysis that point to the expression of degeneracy in hippocampal physiology and plasticity. We assess the potential of degeneracy as a framework to achieve the conjoint goals of encoding and homeostasis without cross‐interferences. We postulate that biological complexity, involving interactions among the numerous parameters spanning different scales of analysis, could establish disparate routes towards accomplishing these conjoint goals. These disparate routes then provide several degrees of freedom to the encoding‐homeostasis system in accomplishing its tasks in an input‐ and state‐dependent manner. Finally, the expression of degeneracy spanning multiple scales offers an ideal reconciliation to several outstanding controversies, through the recognition that the seemingly contradictory disparate observations are merely alternate routes that the system might recruit towards accomplishment of its goals.

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Theory of Cortical Plasticity

Cited by 77 publications

References 0 publications

Connectivity reflects coding: a model of voltage-based STDP with homeostasis

Connectivity reflects coding: a model of voltage-based STDP with homeostasis

Second‐Order Memristor Based on All‐Oxide Multiferroic Tunnel Junction for Biorealistic Emulation of Synapses

Degeneracy in hippocampal physiology and plasticity

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