After working more than 4 years this is the first image
The project will change the world
This is what Thottagh only to run
Plug legs were made of gold
Like a normal transistor, but he has more legs
Yes, this is the size you believe
Archive for Neuron
Nanowire transistor arrays for mapping neural circuits in acute brain slices
Revealing the functional connectivity in natural neuronal networks is central to understanding circuits in the brain. Here, we show that silicon nanowire field-effect transistor (Si NWFET) arrays fabricated on transparent substrates can be reliably interfaced to acute brain slices. NWFET arrays were readily designed to record across a wide range of length scales, while the transparent device chips enabled imaging of individual cell bodies and identification of areas of healthy neurons at both upper and lower tissue surfaces. Simultaneous NWFET and patch clamp studies enabled unambiguous identification of action potential signals, with additional features detected at earlier times by the nanodevices. NWFET recording at different positions in the absence and presence of synaptic and ion-channel blockers enabled assignment of these features to presynaptic firing and postsynaptic depolarization from regions either close to somata or abundant in dendritic projections. In all cases, the NWFET signal amplitudes were from 0.3–3 mV. In contrast to conventional multielectrode array measurements, the small active surface of the NWFET devices, ∼0.06 μm2, provides highly localized multiplexed measurements of neuronal activities with demonstrated sub-millisecond temporal resolution and, significantly, better than 30 μm spatial resolution. In addition, multiplexed mapping with 2D NWFET arrays revealed spatially heterogeneous functional connectivity in the olfactory cortex with a resolution surpassing substantially previous electrical recording techniques. Our demonstration of simultaneous high temporal and spatial resolution recording, as well as mapping of functional connectivity, suggest that NWFETs can become a powerful platform for studying neural circuits in the brain.
Neurofitter
Neurofitter is software for parameter tuning of electrophysiological neuron models.
It automatically searches for sets of parameters of neuron models that best fit available experimental data, and therefore acts as an interface between neuronsimulators, like Neuron or Genesis, and optimization algorithms, like Particle Swarm Optimization, Evolutionary Strategies, etc.
What is a Neuron Transistor ?
The term “neuron” comes from the name used to describe the conducting nerve cell of the brain, spinal cord, and nerves. Human neurons consist of a cell body containing a nucleus, several nerve processes, and an axon or nerve fiber. The association between the Neuron Chip and the human nerve cell is the similarity of the three parts of a human nerve cell and the Neuron transistor
ip’s three, 8-bit CPUs. One C P U handles protocol for communication to and from the chip, another handles the application progr
am, and a third handles input/output information.
Biomorphic Analog Pulse-Coupled Neural Circuits
These circuits are potentially useful for invariant pattern recognition.
NASA’s Jet Propulsion Laboratory, Pasadena, California
Analog electronic circuits that operate with pulsed input and output signals are undergoing development. The pulsing behavior of these circuits is modeled after a similar behavior, called “spiking,” that occurs in biological neural networks. In these circuits, the pulse times and/or the pulse-repetition rates can convey information. These circuits are intended especially for use in high-speed artificial neural networks, which, like the brains of animals that have vision, would process image data to effect invariant pattern recognition. (As used here, “invariant”signifies that the ability to recognize patterns would not be adversely affected by such effects as translation, rotation, distortion, changes in scale, or changes in brightness.)
Figure 1 depicts an example of input/output behavior according to one mathematical model of a biomorphic spiking neuron. Starting from the beginning of a pulse cycle, a membrane potential rises at rate that decays exponentially until the potential passes a time-varying threshold, at which point the neuron sends a spike along its axon. At the instant of the spike, the membrane potential returns to a resting level from which the cycle starts anew. If the threshold, the resting potential, or the rate of rise of the membrane potential is modulated, then the pulse-repetition rate (also called the “spiking frequency” or the “firing rate”) of the neuron is changed.
By locally connecting neurons like this one into an array in which the axons of neighbors would transmit their spike trains via synapto-dendritic connections that would modulate the thresholds, one could construct a complex processing network. In a computational simulation, such a network has been shown to be capable of invariant mapping of binary patterns.
The invariance of the mapping is a result of encoding images in time rather than space. In particular, if the same image is fed as input to a different set of pixels but the same spatial relationships are maintained among parts of the image, the temporal representation of the image remains the same and the mapping is invariant to translation. Invariance with respect to brightness is achieved partly by recognizing that greater brightness is represented simply by a uniform increase in the average firing rates of all affected neurons.
The upper part of Figure 2 depicts a developmental spiking-neuron circuit. The clock voltage source (Vclk) pumps charge through a subthreshold biased transistor (M1) onto the gate capacitance of transistor M2, the gate potential of which represents the membrane potential. The current source constituted by the clock and M1 is intentionally made fairly poor (i.e., is made to have low resistance) in order to obtain a nonlinear buildup of membrane potential. When the membrane potential becomes high enough to pull M3 out of its linear current-vs.-voltage region, the voltage at the swing node rapidly decreases as M2 pulls the node toward ground. The low voltage on the swing node then triggers the inverter formed with M4 and M5 to go high, and the inverter potential is digitally buffered to the output terminal. The clocked switching transistor M7 latches the voltage output on the noncharging portion of the cycle of the current pump at M1. M8 and M9 are sized to constitute an inverter that triggers at a relatively high dc potential to insure an adequate spike amplitude before the discharge transistor M6 is activated. When M6 is switched on, all charge at the membrane is drained to ground (zero potential) or, alternatively, to a source of nonzero resting potential connected to the source terminal of M6. When the membrane potential falls, M2 shuts down and the swing node is pulled high again as M3 returns to its linear region. This change in the swing node returns the output to low, ending the spike and switching off the discharge transistor at M6.
The lower part of Figure 2 shows a synapto-dendritic input circuit connected to the swing node of a spiking neuron. In a locally connected network, there could be eight input circuits like this one for coupling the outputs from eight nearest-neighbor neurons as inputs to the affected neuron. Transistor Ms1 sets the gain of the coupling, while transistor Ms2 controls the timing. Essentially, the spike from a neighboring neuron injects charge onto the gate of Ms3 through Ms1. This charge then slowly leaks away via Ms2 to produce a decaying exponential current response through Ms3. This current modulates the threshold of the spiking neuron by pulling M3 closer to saturation, thereby enabling a decreased membrane potential to trigger a spike.
Figure 1. The Interval Between Spikes and the Buildup of Membrane Potential can be modulated by modulating the threshold potential.
Figure 2. These Analog Neural Circuits are designed to exhibit spiking behavior that approximates that of figure 1.
This work was done by Tyson Thomas of Caltech for NASA’s Jet Propulsion Laboratory. For further information, access the Technical Support Package (TSP) free on-line at www.nasatech.com under the Electronic Components and Systems category.
NPO-20818
Neuron
Neuron
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Drawing by Santiago Ramón y Cajal of cells in the pigeon cerebellum. (A) Denotes Purkinje cells, an example of a bipolar neuron. (B) Denotes granule cells which are multipolar.
Neurons (also spelled neurones or called nerve cells) are a major class of cells in the nervous system. In vertebrates, they are found in the brain, the spinal cord and in the nerves and ganglia of the peripheral nervous system, and their primary role is to process and transmit neural information. One important characteristic of neurons is that they have excitable membranes which allow them to generate and propagate electrical signals.
The concept of a neuron as the primary computational unit of the nervous system was devised by Spanish anatomist Santiago Ramón y Cajal. Cajal proposed that neurons were discrete cells which communicated with each other via specialized junctions. This became known as the Neuron Doctrine, one of the central tenets of modern neuroscience.
Contents[hide]
1 Anatomy and histology
2 Classes
3 Connectivity
4 Adaptations to carrying action potentials
5 Histology and internal structure
6 Challenges to the neuron doctrine
7 Neurons in the brain
8 See also
9 Sources
10 External links
The Biological Neuron
The brain is a collection of about 10 billion interconnected neurons. Each neuron is a cell [right] that uses biochemical reactions to receive, process and transmit information.
A neuron’s dendritic tree is connected to a thousand neighbouring neurons. When one of those neurons fire, a positive or negative charge is received by one of the dendrites. The strengths of all the received charges are added together through the processes of spatial and temporal summation. Spatial summation occurs when several weak signals are converted into a single large one, while temporal summation converts a rapid series of weak pulses from one source into one large signal. The aggregate input is then passed to the soma (cell body). The soma and the enclosed nucleus don’t play a significant role in the processing of incoming and outgoing data. Their primary function is to perform the continuous maintenance required to keep the neuron functional. The part of the soma that does concern itself with the signal is the axon hillock. If the aggregate input is greater than the axon hillock’s threshold value, then the neuron fires, and an output signal is transmitted down the axon. The strength of the output is constant, regardless of whether the input was just above the threshold, or a hundred times as great. The output strength is unaffected by the many divisions in the axon; it reaches each terminal button with the same intensity it had at the axon hillock. This uniformity is critical in an analogue device such as a brain where small errors can snowball, and where error correction is more difficult than in a digital system.
Each terminal button is connected to other neurons across a small gap called a synapse [left]. The physical and neurochemical characteristics of each synapse determines the strength and polarity of the new input signal. This is where the brain is the most flexible, and the most vulnerable. Changing the constitution of various neuro- transmitter chemicals can increase or decrease the amount of stimulation that 
the firing axon imparts on the neighbouring dendrite. Altering the neurotransmitters can also change whether the stimulation is excitatory or inhibitory. Many drugs such as alcohol and LSD have dramatic effects on the production or destruction of these critical chemicals. The infamous nerve gas sarin can kill because it neutralizes a chemical (acetylcholinesterase) that is normally responsible for the destruction of a neurotransmitter (acetylcholine). This means that once a neuron fires, it keeps on triggering all the neurons in the vicinity. One no longer has control over muscles, and suffocation ensues.
