This document outlines the key concepts in neural networking including how excitatory and inhibitory signals interact in nerve cells, how neurons are organized to relay signals through convergence and divergence, how reflexes like the stretch reflex work through reverberatory circuits, and provides an overview of major neural pathways and structures in the brain like the spinothalamic tract, pyramidal tract, auditory pathway, cerebellum, basal ganglia, white matter tracts, and visual pathway.
Introduction to the histology and pathology of the nervous system. Brief overview of most common brain cancers and histological changes of neurons and glial cells
Introduction to the histology and pathology of the nervous system. Brief overview of most common brain cancers and histological changes of neurons and glial cells
Cells of the Nervous System- Glial cells I Macroglia and Microglia I Nervous ...HM Learnings
Cells of the Nervous System- Glial cells I Macroglia and Microglia I Nervous System Physiology I
This video will be about
1. Types of cells in nervous system
2. Glial cells
3. Types of glial cells- macroglia and microglia
4. Oligodendrocytes
5. Schwann cells
6. Astrocytes
7. Ependyma cells
8. Microglia
You can also watch the same topic on HM Learnings Youtube channel.
You can also follow HM Learnings on facebook, instagram and twitter for daily updates
Objective of the study:- Structure of a typical Neuron, Classification of Neuron based on Polarity, on conduction direction, on neurotransmitters released, on their shape, Glial cells, major type of Glial cells present in CNS and PNS and their functions.
Cells of the Nervous System- Glial cells I Macroglia and Microglia I Nervous ...HM Learnings
Cells of the Nervous System- Glial cells I Macroglia and Microglia I Nervous System Physiology I
This video will be about
1. Types of cells in nervous system
2. Glial cells
3. Types of glial cells- macroglia and microglia
4. Oligodendrocytes
5. Schwann cells
6. Astrocytes
7. Ependyma cells
8. Microglia
You can also watch the same topic on HM Learnings Youtube channel.
You can also follow HM Learnings on facebook, instagram and twitter for daily updates
Objective of the study:- Structure of a typical Neuron, Classification of Neuron based on Polarity, on conduction direction, on neurotransmitters released, on their shape, Glial cells, major type of Glial cells present in CNS and PNS and their functions.
This power point presentation was made for a second year lecture class of neuroanatomy. It was based on answers of different questions regarding neuroanatomy. The class was taken by Dr. Zobayer Mahmud Khan, Lecturer, Department of Anatomy, Sir Salimullah Medical College, Dhaka.
Functions the Nervous System
Sensory Functions- Helps the body respond to stimuli.
Integrative Functions- Interprets responses to stimuli in the body.
Motor Functions- Coordinates responses to stimuli in the body.
Neurons
The sophisticated signal processing techniques developed during last years for structural and functional imaging methods allow us to detect abnormalities of brain connectivity in brain disorders with unprecedented detail. Interestingly, recent works shed light on both functional and structural underpinnings of musical anhedonia (i.e., the individual's incapacity to enjoy listening to music). On the other hand, computational models based on brain simulation tools are being used more and more for mapping the functional consequences of structural abnormalities. The latter could help to better understand the mechanism that is impaired in people unable to derive pleasure from music, and formulate hypotheses on how music acquired reward value. The presentation gives an overview of today's studies and proposes a possible simulation pipeline to reproduce such scenario.
Chronic progressive external ophthalmoplegiaPS Deb
Chronic progressive external ophthalmoplegia (CPEO) is a descriptive term for a heterogeneous group of disorders characterized by chronic, progressive, bilateral, and usually symmetric ocular motility deficit and ptosis, without pain, proptosis and pupil involvement. Commonly a syndrome of Mitochondrial Cytopathy.
This is a short presentation at Down Town Hospital clinical meeting for DNB Medicine students. It dose not cover the all aspects of stroke care especially Thrombolysis, since it is difficult to practice for Medical specialist, and ischemic stroke is not common in North East India
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
GridMate - End to end testing is a critical piece to ensure quality and avoid...ThomasParaiso2
End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
3. Excitatory and Inhibitory signals Excitatory and inhibitory currents have competitive effects in a single nerve cell. (Adapted from Eckert et al., 1988.)
Figure 2-2 Structure of a neuron. Most neurons in the vertebrate nervous system have several main features in common. The cell body contains the nucleus, the storehouse of genetic information, and gives rise to two types of cell processes, axons and dendrites. Axons, the transmitting element of neurons, can vary greatly in length; some can extend more than 3 m within the body. Most axons in the central nervous system are very thin (between 0.2 and 20 μm in diameter) compared with the diameter of the cell body (50 μm or more). Many axons are insulated by a fatty sheath of myelin that is interrupted at regular intervals by the nodes of Ranvier. The action potential, the cell's conducting signal, is initiated either at the axon hillock, the initial segment of the axon, or in some cases slightly farther down the axon at the first node of Ranvier. Branches of the axon of one neuron (the presynaptic neuron) transmit signals to another neuron (the postsynaptic cell) at a site called the synapse. The branches of a single axon may form synapses with as many as 1000 other neurons. Whereas the axon is the output element of the neuron, the dendrites (apical and basal) are input elements of the neuron. Together with the cell body, they receive synaptic contacts from other neurons. Figure 2-4 Neurons can be classified as unipolar, bipolar, or multipolar according to the number of processes that originate from the cell body. A. Unipolar cells have a single process, with different segments serving as receptive surfaces or releasing terminals. Unipolar cells are characteristic of the invertebrate nervous system. B. Bipolar cells have two processes that are functionally specialized: the dendrite carries information to the cell, and the axon transmits information to other cells. C. Certain neurons that carry sensory information, such as information about touch or stretch, to the spinal cord belong to a subclass of bipolar cells designated as pseudo-unipolar. As such cells develop, the two processes of the embryonic bipolar cell become fused and emerge from the cell body as a single process. This outgrowth then splits into two processes, both of which function as axons, one going to peripheral skin or muscle, the other going to the central spinal cord. D. Multipolar cells have an axon and many dendrites. They are the most common type of neuron in the mammalian nervous system. Three examples illustrate the large diversity of these cells. Spinal motor neurons (left) innervate skeletal muscle fibers. Pyramidal cells (middle) have a roughly triangular cell body; dendrites emerge from both the apex (the apical dendrite) and the base (the basal dendrites). Pyramidal cells are found in the hippocampus and throughout the cerebral cortex. Purkinje cells of the cerebellum (right) are characterized by the rich and extensive dendritic tree in one plane. Such a structure permits enormous synaptic input. (Adapted from Ramón y Cajal 1933.)
A. An excitatory input at the base of a dendrite causes inward current to flow through cation-selective channels (Na+ and K+). This current flows outward across the membrane capacitance at the initial segment where it produces a large depolarizing synaptic potential. B. An inhibitory input causes an outward (Cl-) current at the synapse on the cell body and an inward current across the membrane capacitance at other regions of the cell, producing a large hyperpolarization at the initial segment. C. The shunting action of inhibition. When the cell receives both excitatory and inhibitory synaptic current, the channels opened by the inhibitory pathway shunt the excitatory current, thereby reducing the excitatory synaptic potential.
Figure 2-7 Inhibitory interneurons can produce either feed forward or feedback inhibition. A. Feed-forward inhibition is common in monosynaptic reflex systems, such as the knee-jerk reflex (see Figure 2-5). Afferent neurons from extensor muscles excite not only the extensor motor neurons, but also inhibitory neurons that prevent the firing of the motor cells in the opposingflexor muscles. Feedforward inhibition enhances the effect of the active pathway by suppressing the activity of other, opposing, pathways. B. Negative feedback inhibition is a self-regulating mechanism. The effect is to dampen activity within the stimulated pathway and prevent it from exceeding a certain critical maximum. Here the extensor motor neurons act on inhibitory interneurons, which feed back to the extensor motor neurons themselves and thus reduce the probability of firing by these cells. A stretch reflex such as the knee jerk is a simple behavior produced by two classes of neurons connecting at excitatory synapses. But not all important signals in the brain are excitatory. In fact, half of all neurons produce inhibitory signals. Inhibitory neurons release a transmitter that reduces the likelihood of firing. As we have seen, even in the knee-jerk reflex, the sensory neurons make both excitatory connections and connections through inhibitory interneurons. Excitatory connections with the leg's extensor muscles cause these muscles to contract, while connections with certain inhibitory interneurons prevent the antagonist flexor muscles from being called to action. This feature of the circuit is an example of feed-forward inhibition (Figure 2-7A). Feedforward inhibition in the knee-jerk reflex is reciprocal , ensuring that the flexor and extensor pathways always inhibit each other, so only muscles appropriate for the movement, and not those that oppose it, are recruited. Neurons can also have connections that provide feedback inhibition. For example, an active neuron may have excitatory connections withboth a target cell and an inhibitory interneuron that has its own feedbackconnection with the active neuron. In this way signals from the active neuron simultaneously excite the target neuron and the inhibitory interneuron, which thus is able to limit the ability of the active neuron to excite its target (Figure 2-7B). We will encounter many examples of feed-forward and feedback inhibition when we examine more complex behaviors in later chapters.
Organization of Neurons for Relaying Signals. Figure 46–9 is a schematic diagram of several neurons in a neuronal pool, showing “input” fibers to the left and “output” fibers to the right. Each input fiber divides hundreds to thousands of times, providing a thousand or more terminal fibrils that spread into a large area in the pool to synapse with dendrites or cell bodies of the neurons in the pool. The dendrites usually also arborize and spread hundreds to thousands of micrometers in the pool. The neuronal area stimulated by each incoming nerve fiber is called its stimulatory field. Note in Figure 46–9 that large numbers of the terminals from each input fiber lie on the nearest neuron in its “field,” but progressively fewer terminals lie on the neurons farther away. Threshold and Subthreshold Stimuli—Excitation or Facilitation. From the discussion of synaptic function in Chapter 45, it will be recalled that discharge of a single excitatory presynaptic terminal almost never causes an action potential in a postsynaptic neuron. Instead, large numbers of input terminals must discharge on the same neuron either simultaneously or in rapid succession to cause excitation. For instance, in Figure 46–9, let us assume that six terminals must discharge almost simultaneously to excite any one of the neurons. If the student counts the number of terminals on each one of the neurons from each input fiber, he or she will see that input fiber 1 has more than enough terminals to cause neuron a to discharge.The stimulus from input fiber 1 to this neuron is said to be an excitatory stimulus; it is also called a suprathreshold stimulus because it is above the threshold required for excitation. Input fiber 1 also contributes terminals to neurons b and c, but not enough to cause excitation. Nevertheless, discharge of these terminals makes both these neurons more likely to be excited by signals arriving through other incoming nerve fibers. Therefore, the stimuli to these neurons are said to be subthreshold, and the neurons are said to be facilitated. Similarly, for input fiber 2, the stimulus to neuron d is a suprathreshold stimulus, and the stimuli to neurons b and c are subthreshold, but facilitating, stimuli. Figure 46–9 represents a highly condensed version of a neuronal pool because each input nerve fiber usually provides massive numbers of branching terminals to hundreds or thousands of neurons in its distribution “field,” as shown in Figure 46–10. In the central portion of the field in this figure, designated by the circled area, all the neurons are stimulated by the incoming fiber. Therefore, this is said to be the discharge zone of the incoming fiber, also called the excited zone or liminal zone.To each side, the neurons are facilitated but not excited, and these areas are called the facilitated zone, also called the subthreshold zone or subliminal zone.
Divergence of Signals Passing Through Neuronal Pools Often it is important for weak signals entering a neuronal pool to excite far greater numbers of nerve fibers leaving the pool. This phenomenon is called divergence. Two major types of divergence occur and have entirely different purposes. An amplifying type of divergence is shown in Figure 46–11 A.This means simply that an input signal spreads to an increasing number of neurons as it passes through successive orders of neurons in its path. This type of divergence is characteristic of the corticospinal pathway in its control of skeletal muscles, with a single large pyramidal cell in the motor cortex capable, under highly facilitated conditions, of exciting as many as 10,000 muscle fibers. The second type of divergence, shown in Figure 46–11 B, is divergence into multiple tracts. In this case, the signal is transmitted in two directions from the pool. For instance, information transmitted up the dorsal columns of the spinal cord takes two courses in the lower part of the brain: (1) into the cerebellum and (2) on through the lower regions of the brain to the thalamus and cerebral cortex. Likewise, in the thalamus, almost all sensory information is relayed both into still deeper structures of the thalamus and at the same time to discrete regions of the cerebral cortex. Convergence of Signals Convergence means signals from multiple inputs uniting to excite a single neuron. Figure 46–12 A shows convergence from a single source.That is, multiple terminals from a single incoming fiber tract terminate on the same neuron. The importance of this is that neurons are almost never excited by an action potential from a single input terminal. But action potentials converging on the neuron from multiple terminals provide enough spatial summation to bring the neuron to the threshold required for discharge. Convergence can also result from input signals (excitatory or inhibitory) from multiple sources, as shown in Figure 46–12 B. For instance, the interneurons of the spinal cord receive converging signals from (1) peripheral nerve fibers entering the cord, (2) propriospinal fibers passing from one segment of the cord to another, (3) corticospinal fibers from the cerebral cortex, and (4) several other long pathways descending from the brain into the spinal cord.Then the signals from the interneurons converge on the anterior motor neurons to control muscle function. Such convergence allows summation of information from different sources, and the resulting response is a summated effect of all the different types of information. Convergence is one of the important means by which the central nervous system correlates, summates, and sorts different types of information.
Figure 2-6 Diverging and convergingneuronal connections are a key organizational feature of the brain. A. In the sensory systems receptor neurons at the input stage usually branch out and make multiple, divergent connections with neurons that represent the second stage of processing. Subsequent connections diverge even more. B. By contrast, motor neurons are the targets of progressively converging connections. With convergence, the target cell receives the sum of information from many presynaptic cells. The stretching of just one muscle, the quadriceps, activates several hundred sensory neurons, each of which makes direct contact with 100–150 motor neurons (Figure 2-6A). This pattern of connection, in which one neuron activates many target cells, is called neuronal divergence; it is especially common in the input stages of the nervous system. By distributing its signals to many target cells, a single neuron can exert wide and diverse influence. For example, sensory neurons involved in a stretch reflex also contact projection interneurons that transmit information about the local neural activity to higher brain regions concerned with coordinating movements. In contrast, because there are usually five to 10 times more sensory neurons than motor neurons, a single motor cell typically receives input from many sensory cells (Figure 2-6B). This pattern of connection, called convergence , is common at the output stages of the nervous system. By receiving signals from numerous neurons, the target motor cell is able to integrate diverse information from many sources.
Reverberatory (Oscillatory) Circuit as a Cause of Signal Prolongation. One of the most important of all circuits in the entire nervous system is the reverberatory , or oscillatory , circuit. Such circuits are caused by positive feedback within the neuronal circuit that feeds back to re-excite the input of the same circuit. Consequently, once stimulated, the circuit may discharge repetitively for a long time. Several possible varieties of reverberatory circuits are shown in Figure 46–14. The simplest, shown in Figure 46–14 A, involves only a single neuron. In this case, the output neuron simply sends a collateral nerve fiber back to its own dendrites or soma to restimulate itself. Although this type of circuit probably is not an important one, theoretically, once the neuron discharges, the feedback stimuli could keep the neuron discharging for a protracted time thereafter. Figure 46–14 B shows a few additional neurons in the feedback circuit, which causes a longer delay between initial discharge and the feedback signal. Figure 46–14 C shows a still more complex system in which both facilitatory and inhibitory fibers impinge on the reverberating circuit.A facilitatory signal enhances the intensity and frequency of reverberation, whereas an inhibitory signal depresses or stops the reverberation. Figure 46–14 D shows that most reverberating pathways are constituted of many parallel fibers. At each cell station, the terminal fibrils spread widely. In such a system, the total reverberating signal can be eitherweak or strong, depending on how many parallel nerve fibers are momentarily involved in the reverberation. Characteristics of Signal Prolongation from a Reverberatory Circuit. Figure 46–15 shows output signals from a typical reverberatory circuit. The input stimulus may last only 1 millisecond or so, and yet the output can last for many milliseconds or even minutes. The figure demonstrates that the intensity of the output signal usually increases to a high value early in reverberation and then decreases to a critical point, at which it suddenly ceases entirely. The cause of this sudden cessation of reverberation is fatigue of synaptic junctions in the circuit. Fatigue beyond a certain critical level lowers the stimulation of the next neuron in the circuit below threshold level so that the circuit feedback is suddenly broken.
Figure 36-11 Sensory signals produce reflex responses through spinal reflex pathways and long-loop reflex pathways that involve supraspinal regions. (Adapted from Matthews 1991.) A. In normal individuals a brief stretch of a thumb muscle produces a short-latency M1 response in the stretched muscle followed by a long-latency M2 response. The M2 response is the result of transmission of the sensory signal via the motor cortex. B. In individuals with Klippel-Feil syndrome M2 response is also evoked in the corresponding thumb muscle of the opposite hand because neurons in the motor cortex activate motor neurons bilaterally. EMG = electromyogram. Reflexes Involving Limb Muscles Are Mediated Through Spinal and Supraspinal Pathways Reflexes involving the limbs are mediated by multiple pathways acting in parallel via spinal and supraspinal pathways (Figure 36-11A). Consider the response evoked by a sudden stretch of a flexor muscle of the thumb. This response has two discrete components. The first, the M1 response, is generated via the monosynaptic connection of muscle spindle afferents to the spinal motor neurons. The second response, the M2 response, is also a reflex response since its latency is shorter than the voluntary reaction time. The M2 response has been observed in virtually all limb muscles. In the distal muscles M2 responses are evoked via pathways that include the motor cortex, as shown in studies of patients with Klippel-Feil syndrome (Figure 36-11B). In this unusual condition neurons descending from the motor cortex bifurcate and make connections to homologous motor neurons on both sides of the body. One consequence is that when the individual voluntarily moves the fingers of one hand, these movements are mirrored by movements of the fingers of the other hand. Similarly, when the M2 component is evoked by stretching muscles of one hand, a response with the same latency is evoked in the corresponding muscle of the other hand even though there is no M1 response in the other hand. Thus, the reflex pathway responsible for the M2 response must have traversed the motor cortex. Reflex responses mediated via the motor cortex and other supraspinal structures are termed long-loop reflexes. Long-loop reflexes have been investigated in numerous muscles in humans and other animals. The general conclusion is that the cortical route for long-loop reflexes may be of primary importance in regulating contractions in distal muscles, while subcortical reflex pathways may be largely responsible for the afferent regulation of proximal muscles. This type of organization is related to functional demands. Many tasks involving distal muscles require precise regulation by voluntary commands. Presumably, the transmission of afferent signals to regions of the cortex most involved in controlling voluntary movements allows the commands to be quickly adapted to the evolving needs of the task. On the other hand, more automatic motor functions, such as maintaining balance and producing gross bodily movements, can be efficiently executed largely via subcortical and spinal pathways.
Figure 19.8. Neurons and circuits of the cerebellum. (A) Neuronal types in the cerebellar cortex. Note that the various neuron classes are found in distinct layers. (B) Diagram showing convergent inputs onto the Purkinje cell from parallel fibers and local circuit neurons [boxed region shown at higher magnification in (C)]. The output of the Purkinje cells is to the deep cerebellar nuclei. (C) Electron micrograph showing Purkinje cell dendritic shaft with three spines contacted by synapses from a trio of parallel fibers. (C courtesy of A.-S. La Mantia and P. Rakic.) Circuits within the Cerebellum The ultimate destination of the afferent pathways to the cerebellar cortex is a distinctive cell type called the Purkinje cell ( Figure 19.8 ). However, the input from the cerebral cortex to the Purkinje cells is quite indirect. Neurons in the pontine nuclei receive a projection from the cerebral cortex and then relay the information to the contralateral cerebellar cortex. The axons from the pontine nuclei and other sources are called mossy fibers because of the appearance of their synaptic terminals . Mossy fibers synapse on granule cells in the granule cell layer of the cerebellar cortex (see Figures 19.8 and 19.9 ). The cerebellar granule cells are widely held to be the most abundant class of neurons in the human brain. They give rise to specialized axons called parallel fibers that ascend to the molecular layer of the cerebellar cortex. The parallel fibers bifurcate in the molecular layer to form T-shaped branches that relay information via excitatory synapses onto the dendritic spines of the Purkinje cells. The Purkinje cells present the most striking histological feature of the cerebellum. Elaborate dendrites extend into the molecular layer from a single subjacent layer of these giant nerve cell bodies (called the Purkinje layer). Once in the molecular layer, the Purkinje cell dendrites branch extensively in a plane at right angles to the trajectory of the parallel fibers ( Figure 19.8A ). In this way, each Purkinje cell is in a position to receive input from a large number of parallel fibers, and each parallel fiber can contact a very large number of Purkinje cells (on the order of tens of thousands). The Purkinje cells receive a direct modulatory input on their dendritic shafts from the climbing fibers , all of which arise in the inferior olive ( Figure 19.8B ). Each Purkinje cell receives numerous synaptic contacts from a single climbing fiber. In most models of cerebellum function, the climbing fibers regulate movement by modulating the effectiveness of the mossy—parallel fiber connection with the Purkinje cells. The Purkinje cells project in turn to the deep cerebellar nuclei. They are the only output cells of the cerebellar cortex. Since the Purkinje cells are GABAergic, the output of the cerebellar cortex is wholly inhibitory. However, the deep cerebellar nuclei receive excitatory input from the collaterals of the mossy and climbing fibers. The Purkinje cell inhibition of the deep nuclei serves to modulate the level of this excitation ( Figure 19.9 ). Inputs from local circuit neurons modulate the inhibitory activity of Purkinje cells and occur on both dendritic shafts and the cell body. The most powerful of these local inputs are inhibitory complexes of synapses made around the Purkinje cell bodies by basket cells (see Figure 19.8A , B ). Another type of local circuit neuron, the stellate cell , receives input from the parallel fibers and provides an inhibitory input to the Purkinje cell dendrites. Finally, the molecular layer contains the apical dendrites of a cell type called Golgi cells ; these neurons have their cell bodies in the granular cell layer. The Golgi cells receive input from the parallel fibers and provide an inhibitory feedback to the cells of origin of the parallel fibers (the granule cells). This basic circuit is repeated over and over throughout every subdivision of the cerebellum in all mammals and is the fundamental functional module of thecerebellum. Modulation of signal flow through these modules provides the basis for both real-time regulation of movement and the long-term changes in regulation that underlie motor learning. The flow of signals through this admittedly complex intrinsic circuitry is best described in reference to the Purkinje cells (see Figure 19.9 ). The Purkinje cells receive two types of excitatory input from outside of the cerebellum, one directly from the climbing fibers and the other indirectly via the parallel fibers of the granule cells. The Golgi, stellate, and basket cells control the flow of information through the cerebellar cortex. For example, the Golgi cells form an inhibitory feedback that may limit the duration of the granule cell input to the Purkinje cells, whereas the basket cells provide lateral inhibition that may focus the spatial distribution of Purkinje cell activity. The Purkinje cells modulate the activity of the deep cerebellar nuclei, which are driven by the direct excitatory input they receive from the collaterals of the mossy and climbing fibers. The modulation of cerebellar output also occurs at the level of the Purkinje cells (see Figure 19.9 ). This latter modulation may be responsible for the motor learning aspect of cerebellar function. According to a model proposed by Masao Ito and his colleagues at Tokyo University, the climbing fibers relay the message of a motor error to the Purkinje cells. This message produces long-term reductions in the Purkinje cell responses to mossy-parallel fiber inputs. This inhibitory effect on the Purkinje cell responses disinhibits the deep cerebellar nuclei (for an account of the probable cellular mechanism for this long-term reduction in the efficacy of the parallel fiber synapse on Purkinje cells, see Chapter 25 ). As a result, the output of the cerebellum to the various sources of upper motor neurons is enhanced, in much the way that this process occurs in the basal ganglia (see Chapter 18 ).
Figure 38-5 The motor cortex receives inputs from the cerebellum via the thalamus. VLo and VLc = oral (rostral) and caudal portions of the ventrolateral nucleus; VPLo = oral portion of the ventral posterolateral nucleus; X = nucleus X. The premotor areas and primary motor cortex also receive input from the basal ganglia and cerebellum via different sets of nuclei in the ventrolateral thalamus (Figure 38-5). The basal ganglia and cerebellum do not project directly to the spinal cord. An important feature of the relationship between cortical areas and subcortical structures is the reciprocal nature of their connections. Each cortical motor area appears to have a unique pattern of cortical and subcortical input. Thus there are many cortico-subcortical loops, each one making a different contribution to a motor behavior (Chapter 43).
Figure 18.6. A chain of nerve cells arranged in a disinhibitory circuit. Top: Diagram of the connections between two inhibitory neurons, A and B, and an excitatory neuron, C. Bottom: Pattern of the action potential activity of cells A, B, and C when A is at rest, and when neuron A fires transiently as a result of its excitatory inputs. Such circuits are central to the gating operations of the basal ganglia.
Figure 19-4 Unimodal sensory inputs converge on multimodal association areas in the prefrontal, the parietotemporal, and limbic cortices. (The limbic cortices form an unbroken stretch along the medial edge of the hemisphere, surrounding the corpus callosum and the diencephalon.) Orange = somatosensory association cortex; purple = visual association cortex; yellow = auditory association cortex. Sensory Information From Unimodal Areas of Cortex Converges in Multimodal Areas Sensory pathways dedicated solely to visual, auditory, or somatic information converge in multimodal association areas in the prefrontal, parietotemporal, and limbic cortices (Figure 19-4). Neurons in these areas respond to combinations of signals representing different sensory modalities by constructing an internal representation of the sensory stimulus concerned with a specific aspect of behavior. For example, the multimodal sensory association cortex in the inferior parietal lobule is concerned with directing visual attention to objects in the contralateral visual field. Neurons in this area receive information about the position of a stimulus in the world as well as its spatial relationship to the individual's personal space. In monkeys, neurons in this area may respond to sight of a reward if the reward is within arm's reach (personal space) but not if it is across the room (extra-personal space). These neurons also receive highly specific information from the cingulate cortex (the limbic association area), such that emotional state is a factor in their firing. For example, if a monkey is presented with a syringe filled with juice, neurons in the inferior parietal lobule may respond more vigorously if the monkey is thirsty than if it is sated. .
Connections between the hippocampus and possible memory storage sites. The rhesus monkey brain is shown because these connections are much better documented in subhuman primates than in humans. Projections to this region are shown in (A); the efferent projections from the hippocampus are shown in (B). Projections from numerous cortical areas converge on the hippocampus and the related structures known to be involved in human memory; most of these sites also send projections to the same cortical areas. Medial and lateral views are shown, the latter rotated 180° for clarity. (After Van Hoesen, 1982.) The Long-Term Storage of Information Revealing though they have been, clinical studies of amnesic patients have provided relatively little insight into the long-term storage of information in the brain (other than to indicate quite clearly that such information is not stored in the midline diencephalic structures that are affected in anterograde amnesia). Nonetheless, a good deal of circumstantial evidence implies that the cerebral cortex is the major long-term repository for many aspects of memory. One line of evidence comes from observations of patients undergoing electroconvulsive therapy (ECT). Individuals with severe depression are often treated by the passage of enough electrical current through the brain to cause the equivalent of a full-blown seizure (this procedure being done under anesthesia in well-controlled circumstances). This remarkably useful treatment was discovered because depression in epileptics was perceived to be alleviated after a spontaneous seizure. However, ECT often causes both anterograde and retrograde amnesia. The patients typically do not remember the treatment itself or the events of the preceding days, and their recall of events of the previous 1–3 years can also be affected. Animal studies (rats tested for maze learning, for example) have confirmed the amnesic consequences of ECT. The memory loss usually clears over a period of weeks to months. However, to mitigate this side effect (which may be the result of excitotoxicity; see Box B in Chapter 6 ), ECT is often delivered to only one hemisphere at a time. The nature of amnesia following ECT supports the conclusion that long-term memories are widely stored in the cerebral cortex, since this is the part of the brain predominantly affected by this therapy. Since different cortical regions have different cognitive functions (see Chapters 26 and 27 ), it is not surprising that these sites store information that reflects the cognitive function of the relevant part of the brain. For example, the lexicon that links speech sounds and their symbolic significance is located in the association cortex of the superior temporal lobe, since damage to this area typically results in an inability to link words and meanings (Wernicke's aphasia; see Chapter 27 ). Presumably, the widespread connections of the hippocampus to the language areas serve to consolidate declarative information in these and other language-related cortical sites ( Figure 31.7 ). By the same token, the inability of patients with temporal lobe lesions to recognize objects and/or faces suggests that such memories are stored in that location. Similarly, frontal lobe syndromes imply that memories about appropriate behaviors in a given social context and future plans reside in the frontal cortex (see Chapter 26 ). With respect to procedural learning, the motor skills gradually acquired through practice are evidently stored in the basal ganglia, cerebellum, and premotor cortex (see Chapters 17 – 19 ). Thus lesions of these sites cause a loss of the ability to make complex coordinated movements that can be considered a sort of “motor amnesia.” This scheme for long-term information storage is diagrammed in Figure 31.8 , although the generality of the diagram only emphasizes the rudimentary state of present thinking about exactly how and where long-term memories are stored. A reasonable guess is that each complex memory is instantiated in the activity of an extensive network of neurons whose triggering depends on synaptic weightings that have been molded and modified by experience
Figure 19-12 Common output targets of parietal and prefrontal association areas in cortical and subcortical areas. The connections of the posterior parietal (intraparietal sulcus) and caudal principal sulcus are based on double-label studies in which one anterograde tracer was injected into the prefrontal cortex and another into the parietal cortex of the same animal. Superimposition of adjacent sections shows these areas projecting to common target areas including (1) limbic areas on the medial surface, (2) opercular and superior temporal cortices on the lateral surface, and (3) a range of subcortical sites. (Adapted from Goldman-Rakic 1987.) Interaction Among Association Areas Leads to Comprehension, Cognition, and Consciousness The dorsolateral prefrontal association cortex and parietal association cortex are among the most densely interconnected regions of association cortex, and both project to numerous common cortical and subcortical structures (Figure 19-12). The interactions between the posterior and anterior association areas are critical in guiding behaviors. Neurons in the posterior association areas often also continue firing after the stimulus has ceased. They may also respond to a particular stimulus only when the stimulus is involved in a behavior, and not when the stimulus is not involved. For example, they might fire in response to a light if it is a cue to explore the nearby space (to obtain a reward). These neurons fire regardless of the type of behavioral response required, such as an eye or hand movement, and may even fire when the animal is prevented from making any exploratory movement but merely required to attend to a part of space from the periphery of its vision to obtain a reward. Hence, neurons in the posterior association area are most tightly linked to the sensory rather than motor aspects of a complex behavior. Neurons in the premotor cortex may have similarly selective responses to sensory stimuli, but they fire only if action (motor output) is required. The interactions between the posterior and anterior association areas determine whether an action will occur and what the temporal pattern of motor responses will be. More than a century ago John Hughlings Jackson expressed the view that the conscious sense of a coherent self is not the outcome of a distinct system in the brain. Rather, he argued, consciousness emerges from the operation of the association cortices. Patients with focal lesions of association areas have selective and quite restricted loss of self-awareness for certain classes of stimuli while maintaining awareness for others. For example, a patient with a large lesion in the right (nondominant) parietal lobe may be unaware of the contralateral world. Lacking the concept of “left,” the patient will eat only the food on the right side of the tray and, if still hungry, will learn to rotate to the right in order to position the remaining food on the right side. Similarly, a patient with language disturbance resulting from damage to Wernicke's area will be unaware of the symbolic content of language. The patient will prattle on in response to a question, without understanding the question. Because the patient's “speech” is inflected normally with emotional tone, it appears from the patient's behavior as if words are merely an adornment to gestural communication. Similar dissociation is found in the so-called split-brain patient, in whom the cerebral hemispheres have been separated (by surgically sectioning the corpus callosum and anterior commissure) in order to control chronic epileptic seizures. Split-brain patients seem to have two independent conscious selves. Because the nondominant (usually right) hemisphere is “mute,” some might assume that only the dominant (left) hemisphere, which “talks,” is conscious. However, as we shall see next, by forcing behavioral choices that rely upon information available only to the right hemisphere, it is possible to identify a broad range of cognitive functions that are mediated by the right hemisphere alone.
Neural Function of the Retina Neural Circuitry of the Retina Figure 50–1 shows the tremendous complexity of neural organization in the retina. To simplify this, Figure 50–11 presents the essentials of the retina’s neural connections, showing at the left the circuit in the peripheral retina and at the right the circuit in the foveal retina. The different neuronal cell types are as follows: 1. The photoreceptors themselves—the rods and cones —which transmit signals to the outer plexiform layer, where they synapse with bipolar cells and horizontal cells 2. The horizontal cells, which transmit signals horizontally in the outer plexiform layer from the rods and cones to bipolar cells 3. The bipolar cells, which transmit signals vertically from the rods, cones, and horizontal cells to the inner plexiform layer, where they synapse with ganglion cells and amacrine cells 4. The amacrine cells, which transmit signals in two directions, either directly from bipolar cells to ganglion cells or horizontally within the inner plexiform layer from axons of the bipolar cells to dendrites of the ganglion cells or to other amacrine cells 5. The ganglion cells, which transmit output signals from the retina through the optic nerve into the brain A sixth type of neuronal cell in the retina, not very prominent and not shown in the figure, is the interplexiform cell. This cell transmits signals in the retrograde direction from the inner plexiform layer to the outer plexiform layer. These signals are inhibitory and are believed to control lateral spread of visual signals by the horizontal cells in the outer plexiform layer. Their role may be to help control the degree of contrast in the visual image. The Visual Pathway from the Cones to the Ganglion Cells Functions Differently from the Rod Pathway. As is true for many of our other sensory systems, the retina has both an old type of vision based on rod vision and a new type of vision based on cone vision. The neurons and nerve fibers that conduct the visual signals for cone vision are considerably larger than those that conduct the visual signals for rod vision, and the signals are conducted to the brain two to five times as rapidly. Also, the circuitry for the two systems is slightly different, as follows. To the right in Figure 50–11 is the visual pathway from the foveal portion of the retina, representing the new, fast cone system. This shows three neurons in the direct pathway: (1) cones, (2) bipolar cells, and (3) ganglion cells. In addition, horizontal cells transmit inhibitory signals laterally in the outer plexiform layer, and amacrine cells transmit signals laterally in the inner plexiform layer. To the left in Figure 50–11 are the neural connections for the peripheral retina, where both rods and cones are present. Three bipolar cells are shown; the middle of these connects only to rods, representing the type of visual system present in many lower animals. The output from the bipolar cell passes only to amacrine cells, which relay the signals to the ganglion cells. Thus, for pure rod vision, there are four neurons in the direct visual pathway: (1) rods, (2) bipolar cells, (3) amacrine cells, and (4) ganglion cells. Also, horizontal and amacrine cells provide lateral connectivity. The other two bipolar cells shown in the peripheral retinal circuitry of Figure 50–11 connect with both rods and cones; the outputs of these bipolar cells pass both directly to ganglion cells and by way of amacrine cells. Neurotransmitters Released by Retinal Neurons. Not all the neurotransmitter chemical substances used for synaptic transmission in the retina have been entirely delineated. However, both the rods and the cones release glutamate at their synapses with the bipolar cells. Histological and pharmacological studies have shown there to be many types of amacrine cells secreting at least eight types of transmitter substances, including gamma-aminobutyric acid, glycine, dopamine, acetylcholine, and indolamine, all of which normally function as inhibitory transmitters. The transmitters of the bipolar, horizontal, and interplexiform cells are unclear, but at least some of the horizontal cells release inhibitory transmitters. Transmission of Most Signals Occurs in the Retinal Neurons by Electrotonic Conduction, Not by Action Potentials. The only retinal neurons that always transmit visual signals by means of action potentials are the ganglion cells, and they send their signals all the way to the brain through the optic nerve. Occasionally, action potentials have also been recorded in amacrine cells, although the importance of these action potentials is questionable. Otherwise, all the retinal neurons conduct their visual signals by electrotonic conduction, which can be explained as follows. Electrotonic conduction means direct flow of electric current, not action potentials, in the neuronal cytoplasm and nerve axons from the point of excitation all the way to the output synapses. Even in the rods and cones, conduction from their outer segments, where the visual signals are generated, to the synaptic bodies is by electrotonic conduction. That is, when hyperpolarization occurs in response to light in the outer segment of a rod or a cone, almost the same degree of hyperpolarization is conducted by direct electric current flow in the cytoplasm all the way to the synaptic body, and no action potential is required. Then, when the transmitter from a rod or cone stimulates a bipolar cell or horizontal cell, once again the signal is transmitted from the input to the output by direct electric current flow, not by action potentials. The importance of electrotonic conduction is that it allows graded conduction of signal strength. Thus, for the rods and cones, the strength of the hyperpolarizing output signal is directly related to the intensity of illumination; the signal is not all or none, as would be the case for each action potential. Lateral Inhibition to Enhance Visual Contrast— Function of the Horizontal Cells The horizontal cells, shown in Figure 50–11, connect laterally between the synaptic bodies of the rods and cones, as well as connecting with the dendrites of the bipolar cells. The outputs of the horizontal cells are always inhibitory. Therefore, this lateral connection provides the same phenomenon of lateral inhibition that is important in all other sensory systems—that is, helping to ensure transmission of visual patterns with proper visual contrast. This phenomenon is demonstrated in Figure 50–12, which shows a minute spot of light focused on the retina. The visual pathway from the centralmost area where the light strikes is excited, whereas an area to the side is inhibited. In other words, instead of the excitatory signal spreading widely in the retina because of spreading dendritic and axonal trees in the plexiform layers, transmission through the horizontal cells puts a stop to this by providing lateral inhibition in the surrounding areas. This is essential to allow high visual accuracy in transmitting contrast borders in the visual image. Some of the amacrine cells probably provide additional lateral inhibition and further enhancement of visual contrast in the inner plexiform layer of the retina as well.