Biology subjects

Maass, W.

Publications and source records attributed to Maass, W..

3 recordsLinked to original sources

bioRxiv2017

Inhibitory networks orchestrate the self-organization of computational function in cortical microcircuit motifs through STDP

Interneurons have diverse morphological and physiological characteristics that potentially contribute to the emergence of powerful computational properties of cortical networks. We investigate the functional role of inhibitory subnetworks in the arguably most common network motif of cortical microcircuits: ensembles of pyramidal cells (PCs) with lateral inhibition, commonly referred to as Winner-Take-All networks. Recent theoretical work has shown that spike-timing-dependent plasticity installs in this network motif an important and ubiquitously useful self-organization process: The emergence of sparse codes and Bayesian inference for repeatedly occurring high-dimensional input patterns. However, this link has so far only been established for strongly simplified models with a symbolic implementation of lateral inhibition, rather than through the interaction of PCs with known types of interneurons. We close this gap in this article, and show that the interaction of PCs with two types of inhibitory networks, that reflect salient properties of somatic-targeting neurons (e.g. basket cells) and dendritic-targeting neurons (e.g. Martinotti cells), provides a good approximation to the theoretically optimal lateral inhibition needed for the self-organization of these network motifs. We provide a step towards unraveling the functional roles of interacting networks of excitatory and inhibitory neurons from the perspective of emergent neural computation.

neuroscience↗

bioRxiv2017

Associations between memory traces emerge in a generic neural circuit model through STDP

Memory traces and associations between them are fundamental for cognitive brain function. Neuron recordings suggest that distributed assemblies of neurons in the brain serve as memory traces for spatial information, real-world items, and concepts. How-ever, there is conflicting evidence regarding neural codes for associated memory traces. Some studies suggest the emergence of overlaps between assemblies during an association, while others suggest that the assemblies themselves remain largely unchanged and new assemblies emerge as neural codes for associated memory items. Here we study the emergence of neural codes for associated memory items in a generic computational model of recurrent networks of spiking neurons with a data-constrained rule for spike-timing-dependent plasticity (STDP). The model depends critically on two parameters, which control the excitability of neurons and the scale of initial synaptic weights. By modifying these two parameters, the model can reproduce both experimental data from the human brain on the fast formation of associations through emergent overlaps between assemblies, and rodent data where new neurons are recruited to encode the associated memories. Hence our findings suggest that the brain can use both of these two neural codes for associations, and dynamically switch between them during consolidation.

neuroscience↗

bioRxiv2016

Searching for Principles of Brain Computation

Highlights O_LIHints for computational principles from experimental data\nC_LIO_LIComputational role of diverse network components\nC_LIO_LIEmergence and computational role of assemblies\nC_LIO_LIProbabilistic inference through stochastic network dynamics\nC_LIO_LIOngoing network rewiring and compensation through synaptic sampling\nC_LI\n\nAbstractExperimental methods in neuroscience, such as calcium-imaging and recordings with multielectrode arrays, are advancing at a rapid pace. They produce insight into the simultaneous activity of large numbers of neurons, and into plasticity processes in the brains of awake and behaving animals. These new data constrain models for neural computation and network plasticity that underlie perception, cognition, behavior, and learning. I will discuss in this short article four such constraints: Inherent recurrent network activity and heterogeneous dynamic properties of neurons and synapses, stereotypical spatio-temporal activity patterns in networks of neurons, high trial-to-trial variability of network responses, and functional stability in spite of permanently ongoing changes in the network. I am proposing that these constraints provide hints to underlying principles of brain computation and learning.

neuroscience↗