To study the basic functioning of Hexcore circuit to understand neuron network
Human brain is a complex structure containing millions of neuronal cells of different types that are interconnected by billons of synapses. Microcircuits become a new and prominent area in brain research .Brain microcircuit refers to a “collection of interconnected neurons within the brain regions”. To know more about brain computation, it is essential to understand the coding of nerve impulses within the brain. Microcircuits are formed within individual layers of the cerebral cortex or it is composed of interconnected neurons in different layers. The microcircuits in specific layers of brain are made of specific types of neuronal cells. These circuits in the cortex are arranged in a stereotypic manner so that they can receive specific stimulus or inputs from a similar source and send the outputs to the similar targets. The functions of brain microcircuit reveal more about the properties of cells and synapses, junctions between neurons, changes in the pattern of neuronal activity that generate behavior. The main function of brain microcircuit is to carry out specific operations with in a region of the nervous system. Cortical microcircuit study can be done by determining the input –output connectivity and physiology of individual cell types such as basket cells, axo-axonic cells and the double bouqetcells.Thus brain microcircuit is used as a base for developing various computational modeling.
CEREBRAL CORTEX OF HUMAN BRAIN
The cerebral cortex of human brain consists of about 1011 interacting neural cells which makes it as a complex system. Cerebral cortex plays a key role in memory, attention, perceptual awareness, thought, and language band consciousness. The thick of cerebral cortex is about 2-4 millimeters. The neurons present in the cerebral cortex are organized to form microcircuits. In cerebral cortex, it occurs as a columns and the function of each column varies on its location in the brain. To get a clear picture of cerebral circuit is essential to understand the different areas of cerebral cortex and the types of neurons present in it.
Areas of Cerebral Cortex
Cerebral cortexes are mainly divided into two main areas. They are
- Neocortex: It is the large area of cerebellum which consists of 6 layers.
- Allocortex: It consists of 4 layers which is the smaller are of the cortex.
- Paralimbic Cortex : It is a transional area between neocortex and allocortex known as paralimbic cortex in which 2-4 layers are merged.
Neocortical Neuronal Cells
The neocortex area of cerebellum consists of different types of neuronal cells that are interconnected to form a microcircuit. The neuronal cell types in neocortex are grouped into three major classes. They are
- Pyramidal cells
- Spiny Non-Pyramidal cells
- Aspiny Non-Pyramidal cells
These neuronal cells are located in all layers of neocortex area expect in the layer I. It is most abundant cortical neurons present which comprises of about 70-80% of total cell population. Majority of projection neurons are represented by these cells in which their axon as many intra cortical collaterals. The pyramidal cells are glutamatergic neurons where these collaterals are the main source of excitatory synapses in the cortex.
Spiny Non-pyramidal Cells:
These neurons are the short –axon cells (spiny interneurons) that are located in the middle layer of the neocortex. On the basis of dendrite morphology, axonal arbors and laminar connections, various types of spiny non-pyramidal cells are recognized. These types of cells are star shaped and are glutamatergic neurons.
Aspiny Non-pyramidal cells:
These are the smooth interneurons which contain only few or no dendrite spines. These are short axon cells. It adopts extremely varied morphology on the basis of their distributions of their axons. Aspiny non pyramidal cells are found in all layers and form a vast variety of short axon cells. These neurons are present in a smaller numbers comprises of about 15-30% of total neuronal cell population. The percentage of these smooth interneurons varies in species. These types of interneurons are GABAergic and are the main source of inhibitory synapses in the cortex.
The six layered neocortical microcuits are more complex compared to hippocampus circuits. It constitutes about 80-90% of neuronal cells (i.e. pyramidal cells) in the neocortex area. It has a feed forward structure where afferent information are processed by the granular layer and transmitted by the parallel fibers to the output neuron, the purkinjie cell.
DO IT YOURSELF
COMPUTATIONAL NEURAL CIRCUITS
Neuronal microcircuit is a network of biological neurons. Analog neurons in networks mimic the biological neural network. In other experiments, we have seen analog neurons and its properties. For knowing more about neuronal circuits go through the link http://vlab.amrita.edu/?sub=3&brch=257&sim=1321&cnt=1
Here we are describing about different states of quad core network using simple electronic components. BEAM walking robots contain Nv networks, which consist of NV neurons each of which is a very simple oscillator setup.
MECHANISM BEHIND THE WORKING OF A SIX CORE BRAIN CIRCUIT
In previous experiment, Mechanism behind the movement of a Walker robot with 4 neurons (http://vlab.amrita.edu/?sub=3&brch=257&sim=1669&cnt=1), we have gone through the mechanism of working of Quadcore circuit.
The Quadcore consists of 4-Nv net. It’s a micro core topology. Here we use 74C14 Schmitt trigger which acts as oscillator and offers a Schmitt's hysteresis ensures a clean transition. This is achieved by connecting a single RC integrating circuit between the output and the input of an inverting Schmitt trigger. The output will be a continuous square wave whose frequency depends on the values of R and C (Figure 1).
Figure 1: 74C14 Schmitt trigger
In Figure 2 each circle represents each Nv net. Green color represents active Nv net. i.e. one bit is present in that particular Nv net.
Figure 2: Nv net
In the circuits each neuron is represented by LEDs. Initially when we give power to the circuit all neurons will fire at maximum rate. This state is known as saturate state. Saturation is the natural power-on state. At this state there is maximum of 3 processes or three bits in the network at any one time as shown in the Figure 2. This is indicated by LED light. So there will never be two adjacent LEDs on at the same time.
Next we will remove one process.ie one bit from the data. For this we use a Process Neutralization circuit (PNC). PNC circuit make includes a switch to short out one of the input bias resistors as shown in Figure 3.
Figure 3: Process Neutralization circuit
Closing this switch destroys any passing process. If we hold it long enough and all processes are destroyed.
By holding the switch closed for approximately for 2 seconds we get a two process state as shown in Figure 4.
Figure 4: Nv net two process state
Figure 5 indicates the null state. This state is a Nv net at rest.
Figure 5 : Nv net at null state
The other state you will learn in the next experiment.
The 74ALS245 is an octal bus transceiver designed for data transmission (Figure 6). Well, we're going to use it to drive motors. If you've chosen you motor / gear combination properly then this won't be a problem.
Figure 6: 74ALS245 bus transceiver
For convenience of conducting th experiment, we are showing only single state .