Analog VLSI implementation of resonate-and-fire neuron

We propose an analog integrated circuit that implements a resonate-and-fire neuron (RFN) model based on the Lotka-Volterra (LV) system. The RFN model is a spiking neuron model that has second-order membrane dynamics, and thus exhibits fast damped subthreshold oscillation, resulting in the coincidence detection, frequency preference, and post-inhibitory rebound. The RFN circuit has been derived from the LV system to mimic such dynamical behavior of the RFN model. Through circuit simulations, we demonstrate that the RFN circuit can act as a coincidence detector and a band-pass filter at circuit level even in the presence of additive white noise and background random activity. These results show that our circuit is expected to be useful for very large-scale integration (VLSI) implementation of functional spiking neural networks.     

Hardware implementation of stochastic spiking neural networks

Spiking Neural Networks, the last generation of Artificial Neural Networks, are characterized by its bio-inspired nature and by a higher computational capacity with respect to other neural models. In real biological neurons, stochastic processes represent an important mechanism of neural behavior and are responsible of its special arithmetic capabilities. In this work we present a simple hardware implementation of spiking neurons that considers this probabilistic nature. The advantage of the proposed implementation is that it is fully digital and therefore can be massively implemented in Field Programmable Gate Arrays. The high computational capabilities of the proposed model are demonstrated by the study of both feed-forward and recurrent networks that are able to implement high-speed signal filtering and to solve complex systems of linear equations.     

Construction of a new artificial biomineralization system

Hydroxyapatite (HAP) was mineralized in poly(vinyl alcohol) (PVA)/poly(acrylic acid) (PAA) complex hydrogel immersed in a salt solution containing PAA. The transparent HAP/polymer composite swelled in water depending on the HAP content; high HAP content gave small swelling and vice versa. The HAP content reached about 80 wt % at most. Observation of the cross section of the composite by energy-dispersive analysis of X-ray (EDAX) revealed that the composite consisted of two phases, i.e., a hard HAP-rich phase and a soft polymer-rich phase. In the HAP-rich phase, the space inside the hydrogel was occupied by HAP, while HAP was not mineralized in the polymer-rich phase. The nucleation seemed to take place, at first, at the middle depth of the hydrogel where the HAP-rich phase was formed. The HAP-rich phase grew its size toward the surface of the hydrogel at the cost of the polymer-rich phase. The presence of phosphorus, P, in the polymer-rich phase indicated the adsorption of HPO(4)(2-) on the polymer chain of the hydrogel via hydrogen bonding, accompanied with Ca(2+) because of electrostatic constraints. This adsorption of ions in addition to Donnan distribution of ions leads to the formation of a hypercomplex that can be regarded as a precursor of the HAP-rich phase. The change of the hypercomplex into the HAP-rich phase is discontinuous and hence concluded as a phase transition. By comparison of our mineralization system with the biomineralization system of HAP and CaCO(3), the physicochemical mechanism of the mineralization process in the present system was found to be similar to that in biological systems. In this sense, we termed the present system an artificial biomineralization system.     

Schema design and implementation of the grasp-related mirror neuron system

 Mirror neurons within a monkey's premotor area F5 fire not only when the monkey performs a certain class of actions but also when the monkey observes another monkey (or the experimenter) perform a similar action. It has thus been argued that these neurons are crucial for understanding of actions by others. We offer the hand-state hypothesis as a new explanation of the evolution of this capability: the basic functionality of the F5 mirror system is to elaborate the appropriate feedback – what we call the hand state– for opposition-space based control of manual grasping of an object. Given this functionality, the social role of the F5 mirror system in understanding the actions of others may be seen as an exaptation gained by generalizing from one's own hand to an other's hand. In other words, mirror neurons first evolved to augment the “canonical” F5 neurons (active during self-movement based on observation of an object) by providing visual feedback on “hand state,” relating the shape of the hand to the shape of the object. We then introduce the MNS1 (mirror neuron system 1) model of F5 and related brain regions. The existing Fagg–Arbib–Rizzolatti–Sakata model represents circuitry for visually guided grasping of objects, linking the anterior intraparietal area (AIP) with F5 canonical neurons. The MNS1 model extends the AIP visual pathway by also modeling pathways, directed toward F5 mirror neurons, which match arm–hand trajectories to the affordances and location of a potential target object. We present the basic schemas for the MNS1 model, then aggregate them into three “grand schemas”– visual analysis of hand state, reach and grasp, and the core mirror circuit – for each of which we present a useful implementation (a non-neural visual processing system, a multijoint 3-D kinematics simulator, and a learning neural network, respectively). With this implementation we show how the mirror system may learnto recognize actions already in the repertoire of the F5 canonical neurons. We show that the connectivity pattern of mirror neuron circuitry can be established through training, and that the resultant network can exhibit a range of novel, physiologically interesting behaviors during the process of action recognition. We train the system on the basis of final grasp but then observe the whole time course of mirror neuron activity, yielding predictions for neurophysiological experiments under conditions of spatial perturbation, altered kinematics, and ambiguous grasp execution which highlight the importance of the timingof mirror neuron activity. Received: 6 August 2001 / Accepted in revised form: 5 February 2002     

An artificial hysteresis binary neuron: a model suppressing the oscillatory behaviors of neural dynamics

A hysteresis binary McCulloch-Pitts neuron model is proposed in order to suppress the complicated oscillatory behaviors of neural dynamics. The artificial hysteresis binary neural network is used for scheduling time-multiplex crossbar switches in order to demonstrate the effects of hysteresis. Time-multiplex crossbar switching systems must control traffic on demand such that packet blocking probability and packet waiting time are minimized. The system using n×n processing elements solves an n×n crossbar-control problem with O(1) time, while the best existing parallel algorithm requires O(n) time. The hysteresis binary neural network maximizes the throughput of packets through a crossbar switch. The solution quality of our system does not degrade with the problem size.     

A new route to trihydroxamate-containing artificial siderophores and synthesis of a new fluorescent probe

A fluorescent labelled artificial siderophore 1 was synthesized by coupling a 7-nitrobenz-2-oxa-1,3-diazole (NBD) derivative to the terminal amino group of a new trihydroxamate-containing amine 2, a ferrichrome-type siderophore that was obtained from tris(hydroxymethyl)aminomethane. Compound 1 was shown to be a suitable tool for experiments on siderophore transport and uptake processes in various organisms cells and particularly in Candida albicans cells.     

An artificial maximum neural network: a winner-take-all neuron model forcing the state of the system in a solution domain

A maximum neuron model is proposed in order to force the state of the system to converge to the solution in neural dynamics. The state of the system is always forced in a solution domain. The artificial maximum neural network is used for the module orientation problem and the bipartite subgraph problem. The usefulness of the maximum neural network is empirically demonstrated by simulating randomly generated massive nstances (examples) in both problems. In randomly generated more than one thousand instances our system always converges to the solution within one hundred iteration steps regardless of the problem size. Our simulation results show the effectiveness of our algorithms and support our claim that one class of NP-complete problems may be solvable in a polynomial time.     

Development of a new transcervical artificial insemination method for sheep: effects of a new transcervical artificial insemination catheter and traversing the cervix on semen quality and fertility

The difficulty of traversing the cervix severely limits transcervical artificial insemination (TC AI) in sheep. Cervical trauma and poorly designed instruments can reduce fertility after AI. To overcome problems associated with TC AI, we developed a new TC AI catheter. Three bench experiments were conducted to determine the effects of the new TC AI catheter on semen quality independent of the effects of moving the catheter through the cervix. In each of the three bench experiments, the standard laparoscopic instrument for intrauterine AI in sheep was used as the control for the TC AI catheter. In Experiment 1, the total volume of semen extender expelled and void volumes for both types of AI instrument (TC versus laparoscopic) were determined. In Experiment 2, the effects of each type of AI instrument (TC versus laparoscopic) on semen quality, estimated as percentage motility and percentage forward progressive motility, of frozen-thawed semen was determined. In Experiment 3, the effects of both types of AI instrument (TC versus laparoscopic) on number of spermatozoa expelled was determined. The type of AI instrument affected neither semen quality nor the number of spermatozoa expelled. However, void volume differed (P < 0.01) between the two instruments. After differences in void volume were taken into account, an in vivo experiment was conducted to determine whether using our new TC AI catheter for TC or surgical intrauterine AI affected fertilization and pregnancy rates. For this, ewes were assigned to one of three treatments: (1) TC AI using the new TC AI catheter + sham AI via laparotomy (n = 9); (2) sham TC AI + AI via laparotomy using a laparoscopic AI instrument (n = 8); and (3) sham TC AI + AI via laparotomy using the new TC Al catheter (n = 10). To synchronize estrus, progestogenated pessaries were inserted and left in place for 12 days. On Day 5 after pessary insertion, PGF2alpha (15 mg) was given i.m. At pessary removal, 400 IU of eCG were administered i.m. Ewes were inseminated 48-52 h after pessary removal using fresh diluted semen (200 x 10(6) to 350 x 10(6) spermatozoa per 0.2 ml) pooled from the same four rams each day during the experiment. At 72 h after AI, uteri were collected postmortem and flushed. Oocytes and embryos were recovered and evaluated. Treatments did not affect (P > 0.01) ovum and embryo recovery rate (mean = 87.3%), fertilization rate (59.3%), or Day 3 pregnancy rate (mean = 66.6%). We conclude from these data that the use of our new TC AI catheter for TC AI or intrauterine AI should not impair the success of AI in sheep.     

Inside the brain of a neuron

For many decades, neurons were considered to be the elementary computational units of the brain and were assumed to summate incoming signals and elicit action potentials only in response to suprathreshold stimuli. Although modelling studies predicted that single neurons constitute a much more powerful computational entity, able to perform an array of nonlinear calculations, this possibility was not explored experimentally until the discovery of active mechanisms in the dendrites of most neuron types. Here, we review several modelling studies that have addressed information processing in single neurons, starting with those characterizing the arithmetic of different dendritic components, to those tackling neuronal integration at the cell body and, finally, those analysing the computational abilities of the axon. We present modelling predictions along with supporting experimental data in an effort to highlight the significant contribution of modelling work to enhancing our understanding of single-neuron arithmetic.     

A model of a thalamic neuron

We modify our recent three equilibrium-point model of neuronal bursting by a means of a small deformation of the nullclines in the x-y phase plane to give a model that can have as many as five equilibrium points. In this model the middle stable equilibrium point (e.p.) is separated from the outer stable and unstable e.ps by two saddle points. If the system is started at rest at the middle stable e.p. it has the following complex properties: A short suprathreshold current pulse switches the model from a silent state to a bursting state, or to give a single burst, depending on the choice of parameters. A subthreshold depolarizing current step gives a passive response at rest, but if the model is either constantly hyperpolarized or constantly depolarized, then the same current step gives different active responses. At a hyperpolarized level this consists of a burst response that shows refractoriness. At a depolarized level it consists of tonic firing with a linear frequency--current relationship. Hyperpolarization from rest is followed by post-inhibitory rebound. The model responds in a unique and characteristic way to an applied current ramp. These properties are very similar to those that have been recently recorded intracellularly from neurons in the mammalian thalamus. In the x-y phase plane our models of the repetitively firing neuron, the bursting neuron and the thalamic neuron form a progression of models in which the y nullcline in the subthreshold region is deformed once to give the burst neuron model, and a second time to give the thalamic neuron model. Each deformation can be interpreted as corresponding to the inclusion of a slow inward current in the model. As these currents are included so the associated firing properties increase in complexity.     

Strategies for the implementation of new shift systems

Implementation barriers may be caused by deficiencies in the knowledge, skills, motivation, or support of those involved in the process of implementation of a new shift system. There is no 'one and only' way of planning and implementing a new shift system. However, if the following factors of success are taken into consideration there is a better chance that workers will accept a new shift system: worker participation, information, communication, training, promoter commitment, professional project management, tailor-made solutions and an adequate organizational framework. These factors are particularly relevant in addressing barriers to the implementation of new shift systems. The most important measures to cope with resistance to change of shift systems are: worker participation, information, communication, training, promoter commitment, professional project management, tailor-made solutions and an adequate organizational framework.     

From artificial kidneys to artificial hearts and beyond: new developments offer great hope

Artificial organs have already become an important part of medical care, and with the advent of new devices, materials, and approaches, either in development or already on the market, they will become even more commonplace in the future.     

Stability analysis of a mathematical neuron model

The “second method” of Liapunov is used to perform a stability analysis of a mathematical model of the neuron. This analysis is based on the hypothesis that the firing of the neuron coincides with a temporary state of instability of the system, and that the initiation of all-or-none process depends on the magnitude of membrane depolarization and its first time derivative. It is found that the stability (and hence the possibility of a second firing) is restored approximately when the rate of membrane repolarization is at a maximum. This result predicts that the duration of the period of absolute refractoriness in neurons would be about 75 per cent of the spike duration, and thus shorter than the value usually obtained from experimental measurements.     

On a response characteristic of a mathematical neuron model

A mathematical neuron model in the form of a nonlinear difference equation is proposed and its response characteristic is investigated.If a sequence of pulses with a fixed frequency is applied to the neuron model as an input, and the amplitude of the input pulses is progressively decreased, the firing frequency of the neuron model, regarded as the output, also decreases. The relationship between them is quite complicated, but a mathematical investigation reveals that it takes the form of an extended Cantor's function. This result explains the unusual and unsuspected phenomenon which was found by L. D. Harmon in experimental studies with his transistor neuron models.Besides this, as an analogue of our mathematical neuron model, a very simple circuit composed of a delay line and a negative resistance element is presented and discussed.