A unified approach to linking experimental, statistical and computational analysis of spike train data

A fundamental issue in neuroscience is how to identify the multiple biophysical mechanisms through which neurons generate observed patterns of spiking activity. In previous work, we proposed a method for linking observed patterns of spiking activity to specific biophysical mechanisms based on a state space modeling framework and a sequential Monte Carlo, or particle filter, estimation algorithm. We have shown, in simulation, that this approach is able to identify a space of simple biophysical models that were consistent with observed spiking data (and included the model that generated the data), but have yet to demonstrate the application of the method to identify realistic currents from real spike train data. Here, we apply the particle filter to spiking data recorded from rat layer V cortical neurons, and correctly identify the dynamics of an slow, intrinsic current. The underlying intrinsic current is successfully identified in four distinct neurons, even though the cells exhibit two distinct classes of spiking activity: regular spiking and bursting. This approach – linking statistical, computational, and experimental neuroscience – provides an effective technique to constrain detailed biophysical models to specific mechanisms consistent with observed spike train data.Published versio

Models wagging the dog: are circuits constructed with disparate parameters?

In a recent article, Prinz, Bucher, and Marder (2004) addressed the fundamental question of whether neural systems are built with a fixed blueprint of tightly controlled parameters or in a way in which properties can vary largely from one individual to another, using a database modeling approach. Here, we examine the main conclusion that neural circuits indeed are built with largely varying parameters in the light of our own experimental and modeling observations. We critically discuss the experimental and theoretical evidence, including the general adequacy of database approaches for questions of this kind, and come to the conclusion that the last word for this fundamental question has not yet been spoken

Neuromodulation of Neuromorphic Circuits

We present a novel methodology to enable control of a neuromorphic circuit in close analogy with the physiological neuromodulation of a single neuron. The methodology is general in that it only relies on a parallel interconnection of elementary voltage-controlled current sources. In contrast to controlling a nonlinear circuit through the parameter tuning of a state-space model, our approach is purely input-output. The circuit elements are controlled and interconnected to shape the current-voltage characteristics (I-V curves) of the circuit in prescribed timescales. In turn, shaping those I-V curves determines the excitability properties of the circuit. We show that this methodology enables both robust and accurate control of the circuit behavior and resembles the biophysical mechanisms of neuromodulation. As a proof of concept, we simulate a SPICE model composed of MOSFET transconductance amplifiers operating in the weak inversion regime.The research leading to these results has received funding from the European Research Council under the Advanced ERC Grant Agreement Switchlet n.67064

Object approach computation by a giant neuron and its relation with the speed of escape in the crab Neohelice

Upon detection of an approaching object, the crab Neohelice granulata continuously regulates the direction and speed of escape according to ongoing visual information. These visuomotor transformations are thought to be largely accounted for by a small number of motion-sensitive giant neurons projecting from the lobula (third optic neuropil) towards the supraesophageal ganglion. One of these elements, the monostratified lobula giant neuron of type 2 (MLG2), proved to be highly sensitive to looming stimuli (a 2D representation of an object approach). By performing in vivo intracellular recordings, we assessed the response of the MLG2 neuron to a variety of looming stimuli representing objects of different sizes and velocities of approach. This allowed us to: (1) identify some of the physiological mechanisms involved in the regulation of the MLG2 activity and test a simplified biophysical model of its response to looming stimuli; (2) identify the stimulus optical parameters encoded by the MLG2 and formulate a phenomenological model able to predict the temporal course of the neural firing responses to all looming stimuli; and (3) incorporate the MLG2-encoded information of the stimulus (in terms of firing rate) into a mathematical model able to fit the speed of the escape run of the animal. The agreement between the model predictions and the actual escape speed measured on a treadmill for all tested stimuli strengthens our interpretation of the computations performed by the MLG2 and of the involvement of this neuron in the regulation of the animal's speed of run while escaping from objects approaching with constant speed.Fil: Oliva, Damian Ernesto. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Tomsic, Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Fisiología, Biología Molecular y Neurociencias; Argentin

Behavioral simulation and synthesis of biological neuron systems using synthesizable VHDL

Neurons are complex biological entities which form the basis of nervous systems. Insight can be gained into neuron behavior through the use of computer models and as a result many such models have been developed. However, there exists a trade-off between biological accuracy and simulation time with the most realistic results requiring extensive computation. To address this issue, a novel approach is described in this paper that allows complex models of real biological systems to be simulated at a speed greater than real time and with excellent accuracy. The approach is based on a specially developed neuron model VHDL library which allows complex neuron systems to be implemented on field programmable gate array (FPGA) hardware. The locomotion system of the nematode Caenorhabditis elegans is used as a case study and the measured results show that the real time FPGA based implementation performs 288 times faster than traditional ModelSim simulations for the same accuracy

Behavioural simulation of biological neuron systems using VHDL and VHDL-AMS

The investigation of neuron structures is an incredibly difficult and complex task that yields relatively low rewards in terms of information from biological forms (either animals or tissue). The structures and connectivity of even the simplest invertebrates are almost impossible to establish with standard laboratory techniques, and even when this is possible it is generally time consuming, complex and expensive. Recent work has shown how a simplified behavioural approach to modelling neurons can allow “virtual” experiments to be carried out that map the behaviour of a simulated structure onto a hypothetical biological one, with correlation of behaviour rather than underlying connectivity. The problems with such approaches are numerous. The first is the difficulty of simulating realistic aggregates efficiently, the second is making sense of the results and finally, it would be helpful to have an implementation that could be synthesised to hardware for acceleration. In this paper we present a VHDL implementation of Neuron models that allow large aggregates to be simulated. The models are demonstrated using a system level VHDL and VHDL-AMS model of the C. Elegans locomotory system