Perceptron Learning and Classification in a Modeled Cortical Pyramidal Cell

Moldwin, Toviah; Segev, Idan

doi:10.3389/fncom.2020.00033

Cited by 31 publications

(32 citation statements)

References 68 publications

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“…Mushroom-like spines would standardize local postsynaptic potentials throughout the dendritic tree and reduce the location-dependent variability of excitatory responses ( Gulledge et al, 2012 ). Other modeled distal synapses may not impact the cell’s output ( Moldwin and Segev, 2019 ). Ramified spines have additional functional possibilities by displaying postsynaptic receptors on different parts of the spine heads ( Verzi and Noris, 2009 ) with likely temporal and spatial specificity and signaling microdomains ( Newpher and Ehlers, 2009 ; Chen and Sabatini, 2012 ).…”

Section: Dendrites and Spines In Pyramidal Neuronsmentioning

confidence: 99%

The Subcortical-Allocortical- Neocortical continuum for the Emergence and Morphological Heterogeneity of Pyramidal Neurons in the Human Brain

Rasia‐Filho

Guerra

Vásquez

et al. 2021

Front. Synaptic Neurosci.

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Human cortical and subcortical areas integrate emotion, memory, and cognition when interpreting various environmental stimuli for the elaboration of complex, evolved social behaviors. Pyramidal neurons occur in developed phylogenetic areas advancing along with the allocortex to represent 70–85% of the neocortical gray matter. Here, we illustrate and discuss morphological features of heterogeneous spiny pyramidal neurons emerging from specific amygdaloid nuclei, in CA3 and CA1 hippocampal regions, and in neocortical layers II/III and V of the anterolateral temporal lobe in humans. Three-dimensional images of Golgi-impregnated neurons were obtained using an algorithm for the visualization of the cell body, dendritic length, branching pattern, and pleomorphic dendritic spines, which are specialized plastic postsynaptic units for most excitatory inputs. We demonstrate the emergence and development of human pyramidal neurons in the cortical and basomedial (but not the medial, MeA) nuclei of the amygdala with cells showing a triangular cell body shape, basal branched dendrites, and a short apical shaft with proximal ramifications as “pyramidal-like” neurons. Basomedial neurons also have a long and distally ramified apical dendrite not oriented to the pial surface. These neurons are at the beginning of the allocortex and the limbic lobe. “Pyramidal-like” to “classic” pyramidal neurons with laminar organization advance from the CA3 to the CA1 hippocampal regions. These cells have basal and apical dendrites with specific receptive synaptic domains and several spines. Neocortical pyramidal neurons in layers II/III and V display heterogeneous dendritic branching patterns adapted to the space available and the afferent inputs of each brain area. Dendritic spines vary in their distribution, density, shapes, and sizes (classified as stubby/wide, thin, mushroom-like, ramified, transitional forms, “atypical” or complex forms, such as thorny excrescences in the MeA and CA3 hippocampal region). Spines were found isolated or intermingled, with evident particularities (e.g., an extraordinary density in long, deep CA1 pyramidal neurons), and some showing a spinule. We describe spiny pyramidal neurons considerably improving the connectional and processing complexity of the brain circuits. On the other hand, these cells have some vulnerabilities, as found in neurodegenerative Alzheimer’s disease and in temporal lobe epilepsy.

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Section: Dendrites and Spines In Pyramidal Neuronsmentioning

confidence: 99%

The Subcortical-Allocortical- Neocortical continuum for the Emergence and Morphological Heterogeneity of Pyramidal Neurons in the Human Brain

Rasia‐Filho

Guerra

Vásquez

et al. 2021

Front. Synaptic Neurosci.

View full text Add to dashboard Cite

show abstract

“…2B). In particular, they provide substantially better performance than the perceptron learning rule [Moldwin and Segev, 2020]. The reason is that the latter is guaranteed to work well only if the training examples are linearly separable, in particular if they do not contain contradicting examples that commonly occur in the case of probabilistic predictions.…”

Section: Discussionmentioning

confidence: 99%

“…In order to facilitate visualization, we consider here just two-dimensional firing patterns x, but the same principle applies however for input patterns of arbitrary dimension. These two point clouds are not linearly separable, and hence the perceptron learning rule (as investigated in [Moldwin and Segev, 2020]) cannot do a good job in separating them, as indicated by the red trace in Fig. 2B.…”

Section: A Simple Illustration Of the Capability Of Dlr To Enable Probabilistic Predictionsmentioning

confidence: 99%

Self-supervised learning of probabilistic prediction through synaptic plasticity in apical dendrites: A normative model

Rao

Legenstein

Subramoney

et al. 2021

Preprint

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Predictive coding has been identified as a major driver of computation and learning in corticalmicrocircuits. But it has remained unknown which synaptic plasticity processes install and maintain predictive coding capability. Predictions are inherently uncertain, and learning rules that aim at discriminating linearly separable classes of inputs - such as the perceptron learning rule - are not suitable for learning to predict. We show that experimental data on synaptic plasticity in distal dendrites of pyramidal cells support a functionally more powerful learning rule that produces a spike-based approximation to logistic regression, i.e., to probabilistic prediction. We also show that experimentally found interactions between different dendritic branches support learning of predictions for more complex probability distributions. The resulting learning theory for top-down inputs to pyramidal cells provides a normative framework for evaluating experimental data, and suggests further experiments for tracking the emergence of predictive coding through synaptic plasticity in distal dendrites.

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“…We used the simulated spike trains of the postsynaptic neuron and the population as ground-truth data for deriving connection weights using a perceptron algorithm [51,52]. At each iteration of the algorithm, the derived connection weights were updated using the perceptron learning rule:…”

Section: Deriving Connectivitymentioning

confidence: 99%

Stimulation-mediated reverse engineering of silent neural networks

Ren

Hai

2021

Preprint

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Reconstructing connectivity of neuronal networks from single cell activity is essential to understanding brain function, but the challenge of deciphering connections from populations of silent neurons has been largely unmet. We demonstrate a protocol for deriving connectivity of realistic silent neuronal networks using stimulation combined with a supervised learning algorithm, that enables inferring connection weights with high fidelity and predicting spike trains at the single-spike and single-cell level with high accuracy. These testable predictions about the number and protocol of the required stimulations is expected to enhance future efforts for deriving neuronal connectivity and drive new experiments to better understand brain function.

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Perceptron Learning and Classification in a Modeled Cortical Pyramidal Cell

Cited by 31 publications

References 68 publications

The Subcortical-Allocortical- Neocortical continuum for the Emergence and Morphological Heterogeneity of Pyramidal Neurons in the Human Brain

The Subcortical-Allocortical- Neocortical continuum for the Emergence and Morphological Heterogeneity of Pyramidal Neurons in the Human Brain

Self-supervised learning of probabilistic prediction through synaptic plasticity in apical dendrites: A normative model

Stimulation-mediated reverse engineering of silent neural networks

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