Experimentally-constrained biophysical models of tonic and burst firing modes in thalamocortical neurons

Somatosensory thalamocortical (TC) neurons from the ventrobasal (VB) thalamus are central components in the flow of sensory information between the periphery and the cerebral cortex, and participate in the dynamic regulation of thalamocortical states including wakefulness and sleep. This property is reflected at the cellular level by the ability to generate action potentials in two distinct firing modes, called tonic firing and low-threshold bursting. Although the general properties of TC neurons are known, we still lack a detailed characterization of their morphological and electrical properties in the VB thalamus. The aim of this study was to build biophysically-detailed models of VB TC neurons explicitly constrained with experimental data from rats. We recorded the electrical activity of VB neurons (N = 49) and reconstructed morphologies in 3D (N = 50) by applying standardized protocols. After identifying distinct electrical types, we used a multi-objective optimization to fit single neuron electrical models (e-models), which yielded multiple solutions consistent with the experimental data. The models were tested for generalization using electrical stimuli and neuron morphologies not used during fitting. A local sensitivity analysis revealed that the e-models are robust to small parameter changes and that all the parameters were constrained by one or more features. The e-models, when tested in combination with different morphologies, showed that the electrical behavior is substantially preserved when changing dendritic structure and that the e-models were not overfit to a specific morphology. The models and their analysis show that automatic parameter search can be applied to capture complex firing behavior, such as co-existence of tonic firing and low-threshold bursting over a wide range of parameter sets and in combination with different neuron morphologies.

Minimal Hodgkin-Huxley type models for different classes of cortical and thalamic neurons

Alain Destexhe, Henry Markram

We review here the development of Hodgkin-Huxley (HH) type models of cerebral cortex and thalamic neurons for network simulations. The intrinsic electrophysiological properties of cortical neurons were analyzed from several preparations, and we selected the four most prominent electrophysiological classes of neurons. These four classes are "fast spiking", "regular spiking", "intrinsically bursting" and "low-threshold spike" cells. For each class, we fit "minimal" HH type models to experimental data. The models contain the minimal set of voltage-dependent currents to account for the data. To obtain models as generic as possible, we used data from different preparations in vivo and in vitro, such as rat somatosensory cortex and thalamus, guinea-pig visual and frontal cortex, ferret visual cortex, cat visual cortex and cat association cortex. For two cell classes, we used automatic fitting procedures applied to several cells, which revealed substantial cell-to-cell variability within each class. The selection of such cellular models constitutes a necessary step towards building network simulations of the thalamocortical system with realistic cellular dynamical properties.

2008

Emergent Properties of in silico Synaptic Transmission in a Model of the Rat Neocortical Column

Srikanth Ramaswamy

The cerebral cortex occupies nearly 80% of the entire volume of the mammalian brain and is thought to subserve higher cognitive functions like memory, attention and sensory perception. The neocortex is the newest part in the evolution of the cerebral cortex and is perhaps the most intricate brain region ever studied. The neocortical microcircuit is the smallest Œecosystem‚ of the neocortex that consists of a rich assortment of neurons, which are diverse in both their morphological and electrical properties. In the neocortical microcircuit, neurons are horizontally arranged in 6 distinct sheets called layers. The fundamental operating unit of the neocortical microcircuit is believed to be the Neocortical Column (NCC). Functionally, a single NCC is an arrangement of thousands of neurons in a vertical fashion spanning across all the 6 layers. The structure of the entire neocortex arises from a repeated and stereotypical arrangement of several thousands of such columns, where neurons transmit information to each other through specialized points of information transfer called synapses. The dynamics of synaptic transmission can be as diverse as the neurons defining a connection and are crucial to foster the functional properties of the neocortical microcircuit. The Blue Brain Project (BBP) is the first comprehensive endeavour to build a unifying model of the NCC by systematic data integration and biologically detailed simulations. Through the past 5 years, the BBP has built a facility for a data-constraint driven approach towards modelling and integrating biological information across multiple levels of complexity. Guided by fundamental principles derived from biological experiments, the BBP simulation toolchain has undergone a process of continuous refinement to facilitate the frequent construction of detailed in silico models of the NCC. The focus of this thesis lies in characterizing the functional properties of in silico synaptic transmission by incorporating principles of synaptic communication derived through biological experiments. In order to study in silico synaptic transmission it is crucial to gain an understanding of the key players influencing the manner in which synaptic signals are processed in the neocortical microcircuit - ion channel kinetics and distribution profiles, single neuron models and dynamics of synaptic pathways. First, by means of exhaustive literature survey, I identified ion channel kinetics and their distribution profiles on neocortical neurons to build in silico ion channel models. Thereafter, I developed a prototype framework to analyze the somatic and dendritic features of single neuron models constrained by ion channel kinetics. Finally, within a simulation framework integrating the ion channels, single neuron models and dynamics of synaptic transmission, I replicated in vitro experimental protocols in silico, to characterize the transmission properties of monosynaptic connections. These synaptic connections, arising from the axo-dendritric apposition of neuronal arbours were sampled across many instances of in silico NCC models constructed a priori through the BBP simulation toolchain. In this thesis, I show that when principles of synaptic transmission derived from in vitro experiments are incorporated to model in silico synaptic connections, the resulting anatomy and physiology of synaptic connections modelled from elementary biological rules closely match in vitro data. This thesis work demonstrates that the average synaptic response properties in silico are robust to perturbations in the anatomical and physiological properties of modelled connections in the local neocortical microcircuit. A fundamental discovery through this thesis is an insight into the function of the local neocortical microcircuit by examining the effect of morphological diversity on in silico synaptic transmission. I demonstrate here that intrinsic morphological diversity confers an invariance to the average synaptic response properties in silico in the local neocortical microcircuit, termed "microcircuit level robustness and invariance".

EPFL2011

Emergent Properties of in silico Synaptic Transmission in a Model of the Rat Neocortical Column

Srikanth Ramaswamy

EPFL2011