Untitled

DeFelipe, Javier; Alonso‐Nanclares, Lidia; Arellano, Jon I.

doi:10.1023/a:1024130211265

Cited by 599 publications

(281 citation statements)

References 101 publications

Supporting

Mentioning

265

Contrasting

Order By: Relevance

“…This distinction is based upon the specificity of cortical layers that are the predominant sources and origins of extrinsic connections. Forward connections arise largely in superficial pyramidal cells, in supragranular layers, and terminate on spiny stellate cells of layer 4 in higher cortical areas ( Felleman & Van Essen 1991;DeFelipe et al 2002). Conversely, backward connections arise largely from deep pyramidal cells in infragranular layers and target cells in the infra-and supragranular layers of lower cortical areas.…”

Section: Hierarchical Models In the Brainmentioning

confidence: 99%

Predictive coding under the free-energy principle

Friston

Kiebel

2009

Phil. Trans. R. Soc. B

1,300

1,070

View full text Add to dashboard Cite

This paper considers prediction and perceptual categorization as an inference problem that is solved by the brain. We assume that the brain models the world as a hierarchy or cascade of dynamical systems that encode causal structure in the sensorium. Perception is equated with the optimization or inversion of these internal models, to explain sensory data. Given a model of how sensory data are generated, we can invoke a generic approach to model inversion, based on a free energy bound on the model's evidence. The ensuing free-energy formulation furnishes equations that prescribe the process of recognition, i.e. the dynamics of neuronal activity that represent the causes of sensory input. Here, we focus on a very general model, whose hierarchical and dynamical structure enables simulated brains to recognize and predict trajectories or sequences of sensory states. We first review hierarchical dynamical models and their inversion. We then show that the brain has the necessary infrastructure to implement this inversion and illustrate this point using synthetic birds that can recognize and categorize birdsongs.Keywords: generative models; predictive coding; hierarchical; birdsong INTRODUCTIONThis paper reviews generic models of our sensorium and a Bayesian scheme for their inversion. We then show that the brain has the necessary anatomical and physiological equipment to invert these models, given sensory data. Critically, the scheme lends itself to a relatively simple neural network implementation that shares many features with real cortical hierarchies in the brain. The basic idea that the brain tries to infer the causes of sensations dates back to Helmholtz (e.g. Helmholtz 1860/1962Barlow 1961;Neisser 1967;Ballard et al. 1983;Mumford 1992;Kawato et al. 1993;Dayan et al. 1995;Rao & Ballard 1998), with a recent emphasis on hierarchical inference and empirical Bayes (Friston 2003(Friston , 2005Friston et al. 2006). Here, we generalize this idea to cover dynamics in the world and consider how neural networks could be configured to invert hierarchical dynamical models and deconvolve sensory causes from sensory input. This paper comprises four sections. In §1, we introduce hierarchical dynamical models and their inversion. These models cover most of the models encountered in the statistical literature. An important aspect of these models is their formulation in generalized coordinates of motion, which lends them a hierarchal form in both structure and dynamics. These hierarchies induce empirical priors that provide structural and dynamical constraints, which can be exploited during inversion. In §2, we show how inversion can be formulated as a simple gradient ascent using neuronal networks; in §3, we consider how evoked brain responses might be understood in terms of inference under hierarchical dynamical models of sensory input. 1

show abstract

Section: Hierarchical Models In the Brainmentioning

confidence: 99%

Predictive coding under the free-energy principle

Friston

Kiebel

2009

Phil. Trans. R. Soc. B

1,300

1,070

View full text Add to dashboard Cite

show abstract

“…In rodents, synaptic neuropil contains on average 1.5 to 2.0 synapses per μm 3 (Rusakov et al, 1998), whereas in humans this value is slightly lower, ∼1.1 (DeFelipe et al, 2002). Depending on the brain region, the tissue volume occupied by one (nonoverlapping) astrocyte in rodents varies between 20,000 and 80,000 µm 3 (Bushong et al, 2002; Halassa and Haydon, 2010; Oberheim et al, 2012), and 300 to 600 dendrites of principal neurons normally approach a single astrocyte (Halassa and Haydon, 2010).…”

Section: Astroglial Coverage Of Synapsesmentioning

confidence: 99%

Morphological plasticity of astroglia: Understanding synaptic microenvironment

Heller

Rusakov

2015

Glia

134

137

View full text Add to dashboard Cite

Memory formation in the brain is thought to rely on the remodeling of synaptic connections which eventually results in neural network rewiring. This remodeling is likely to involve ultrathin astroglial protrusions which often occur in the immediate vicinity of excitatory synapses. The phenomenology, cellular mechanisms, and causal relationships of such astroglial restructuring remain, however, poorly understood. This is in large part because monitoring and probing of the underpinning molecular machinery on the scale of nanoscopic astroglial compartments remains a challenge. Here we briefly summarize the current knowledge regarding the cellular organisation of astroglia in the synaptic microenvironment and discuss molecular mechanisms potentially involved in use‐dependent astroglial morphogenesis. We also discuss recent observations concerning morphological astroglial plasticity, the respective monitoring methods, and some of the newly emerging techniques that might help with conceptual advances in the area. GLIA 2015;63:2133–2151

show abstract

“…Interneurons are present in all cortical areas and layers and represent approximately 10%-20% of cortical neurons in rats [2] or 15%-30% of the total population in other species [3]. Interestingly, while in the occipital, parietal and frontal cortex of the rat the same proportion of GABAergic neurons among all neurons was found (15%, in [2]), the numerical density of all neurons in the frontal cortex (34,000 per cubic millimetre) was significantly lower than those in the occipital and parietal regions (52,000 per cubic millimetre and 48,000 per cubic millimetre, respectively) [2].…”

Section: Interneuronsmentioning

confidence: 99%

“…However, interneurons with the same morphology may have different biochemical characteristics and connectivity [3]. Taken into account this consideration certain interneurons can be recognized by their unique morphological characteristics or they can be more generally divided in subgroups on the bases of their pattern of axonal arborization, synaptic connections (both with pyramidal cells or between themselves) and physiological and biochemical characteristics.…”

Section: Interneuronsmentioning

confidence: 99%