Biomedical subjects

Henry Markram

Publications and source records attributed to Henry Markram.

7 recordsLinked to original sources

PubMed2003

Deriving physical connectivity from neuronal morphology.

A model is presented that allows prediction of the probability for the formation of appositions between the axons and dendrites of any two neurons based only on their morphological statistics and relative separation. Statistics of axonal and dendritic morphologies of single neurons are obtained from 3D reconstructions of biocytin-filled cells, and a statistical representation of the same cell type is obtained by averaging across neurons according to the model. A simple mathematical formulation is applied to the axonal and dendritic statistical representations to yield the probability for close appositions. The model is validated by a mathematical proof and by comparison of predicted appositions made by layer 5 pyramidal neurons in the rat somatosensory cortex with real anatomical data. The model could be useful for studying microcircuit connectivity and for designing artificial neural networks.

Animals↗

PubMed2002

Stereotypy in neocortical microcircuits.

A central debate regarding neocortical function concerns the degree to which the underlying microcircuitry is stereotypically organized. Stereotypy reflects invariance in structure and function, as a result of common genetic templates and environmental conditions, whereas uniqueness can be caused by genetic variations, differences in environmental conditions as well as random processes. Stereotypy is an appealing concept because it provides strong support for determinism in the formation of neuronal microcircuits and in the relationship between their specific structure and function.

Animals↗

PubMed2002

Synapses as dynamic memory buffers.

This article throws new light on the possible role of synapses in information transmission through theoretical analysis and computer simulations. We show that the internal dynamic state of a synapse may serve as a transient memory buffer that stores information about the most recent segment of the spike train that was previously sent to this synapse. This information is transmitted to the postsynaptic neuron through the amplitudes of the postsynaptic response for the next few spikes. In fact, we show that most of this information about the preceding spike train is already contained in the postsynaptic response for just two additional spikes. It is demonstrated that the postsynaptic neuron receives simultaneously information about the specific type of synapse which has transmitted these pulses. In view of recent findings by Gupta et al. [Science, 287 (2000) 273] that different types of synapses are characteristic for specific types of presynaptic neurons, the postsynaptic neuron receives in this way partial knowledge about the identity of the presynaptic neuron from which it has received information. Our simulations are based on recent data about the dynamics of GABAergic synapses. We show that the relatively large number of synaptic release sites that make up a GABAergic synaptic connection makes these connections suitable for such complex information transmission processes.

Computer Simulation↗

PubMed2002

Anatomical, physiological, molecular and circuit properties of nest basket cells in the developing somatosensory cortex.

Anatomical, electrophysiological and molecular diversity of basket cell-like interneurons in layers II-IV of rat somatosensory cortex were studied using patch-clamp electrodes filled with biocytin. This multiparametric study shows that neocortical basket cells (BCs) are composed of three distinct subclasses: classical large (LBC) and small (SBC) basket cells and a third subclass, the nest basket cell (NBC). Anatomically, NBCs were distinct from LBCs and SBCs in that they formed simpler dendritic arbors and an axonal plexus of inter-mediate density, composed of a few long, smooth axonal branches. Electrophysiologically, NBCs exhibited diverse discharge responses to depolarizing current injections including accommodation, non-accommodation and stuttering. Single-cell multiplex RT-PCR revealed distinct mRNA expression patterns for the calcium binding proteins parvalbumin (PV), calbindin (CB) and calretinin (CR), and the neuropeptides somatostatin (SOM), vasoactive intestinal peptide (VIP), cholecystokinin (CCK) and neuropeptide Y (NPY) for each BC-subclass. SBCs lacked NPY expression but invariably expressed VIP, whereas neither VIP, CR nor SOM expression was detected in LBCs, and VIP and CR expression was absent in NBCs. Electro-physiologically distinct types of NBCs formed GABAergic synapses with specific dynamics onto pyramidal cells (PCs) and received either strongly facilitating or depressing synaptic inputs from PCs. Finally, NBCs were found to be the most common basket cell in layers II/III, while LBCs were the most common in layer IV. These data provide multiparametric distinguishing features of three major subclasses of basket cells and indicate that NBCs are powerful interneurons that provide most of the (peri-)somatic inhibition in the supragranular layers.

Animals↗

PubMed2002

Coding of temporal information by activity-dependent synapses.

Synaptic transmission in the neocortex is dynamic, such that the magnitude of the postsynaptic response changes with the history of the presynaptic activity. Therefore each response carries information about the temporal structure of the preceding presynaptic input spike train. We quantitatively analyze the information about previous interspike intervals, contained in single responses of dynamic synapses, using methods from information theory applied to experimentally based deterministic and probabilistic phenomenological models of depressing and facilitating synapses. We show that for any given dynamic synapse, there exists an optimal frequency of presynaptic spike firing for which the information content is maximal; simple relations between this optimal frequency and the synaptic parameters are derived. Depressing neocortical synapses are optimized for coding temporal information at low firing rates of 0.5-5 Hz, typical to the spontaneous activity of cortical neurons, and carry significant information about the timing of up to four preceding presynaptic spikes. Facilitating synapses, however, are optimized to code information at higher presynaptic rates of 9-70 Hz and can represent the timing of over eight presynaptic spikes.

Action Potentials↗

PubMed2002

Spike frequency adaptation and neocortical rhythms.

Spike-frequency adaptation in neocortical pyramidal neurons was examined using the whole cell patch-clamp technique and a phenomenological model of neuronal activity. Noisy current was injected to reproduce the irregular firing typically observed under in vivo conditions. The response was quantified by computing the poststimulus histogram (PSTH). To simulate the spiking activity of a pyramidal neuron, we considered an integrate-and-fire model to which an adaptation current was added. A simplified model for the mean firing rate of an adapting neuron under noisy conditions is also presented. The mean firing rate model provides a good fit to both experimental and simulation PSTHs and may therefore be used to study the response characteristics of adapting neurons to various input currents. The models enable identification of the relevant parameters of adaptation that determine the shape of the PSTH and allow the computation of the response to any change in injected current. The results suggest that spike frequency adaptation determines a preferred frequency of stimulation for which the phase delay of a neuron's activity relative to an oscillatory input is zero. Simulations show that the preferred frequency of single neurons dictates the frequency of emergent population rhythms in large networks of adapting neurons. Adaptation could therefore be one of the crucial factors in setting the frequency of population rhythms in the neocortex.

Action Potentials↗

PubMed2002

Real-time computing without stable states: a new framework for neural computation based on perturbations.

A key challenge for neural modeling is to explain how a continuous stream of multimodal input from a rapidly changing environment can be processed by stereotypical recurrent circuits of integrate-and-fire neurons in real time. We propose a new computational model for real-time computing on time-varying input that provides an alternative to paradigms based on Turing machines or attractor neural networks. It does not require a task-dependent construction of neural circuits. Instead, it is based on principles of high-dimensional dynamical systems in combination with statistical learning theory and can be implemented on generic evolved or found recurrent circuitry. It is shown that the inherent transient dynamics of the high-dimensional dynamical system formed by a sufficiently large and heterogeneous neural circuit may serve as universal analog fading memory. Readout neurons can learn to extract in real time from the current state of such recurrent neural circuit information about current and past inputs that may be needed for diverse tasks. Stable internal states are not required for giving a stable output, since transient internal states can be transformed by readout neurons into stable target outputs due to the high dimensionality of the dynamical system. Our approach is based on a rigorous computational model, the liquid state machine, that, unlike Turing machines, does not require sequential transitions between well-defined discrete internal states. It is supported, as the Turing machine is, by rigorous mathematical results that predict universal computational power under idealized conditions, but for the biologically more realistic scenario of real-time processing of time-varying inputs. Our approach provides new perspectives for the interpretation of neural coding, the design of experiments and data analysis in neurophysiology, and the solution of problems in robotics and neurotechnology.

Action Potentials↗