Exam V2 Flashcards
Describe the large anatomy of the brain and its functions beginning part:
The major large subdivisions of the brain , on a large scale, is it has a telencephalon, diencephalon, mesencephalon and rhombencephalon.
Describe the large anatomy of the brain and its functions beginning part = telencephalon (3)
The telencephalon consists of a olfactory bulb and subcortical structures (e.g., basal ganglia) .
The function of this division of brain is that the cerebrum is responsible for higher (cortical) function.
The basal ganglia is important for a wide range of functions such as action selection, attention, procedural learning, habit learning, conditional learning and eye movements.
Describe the large anatomy of the brain and its functions beginning part = diencephalon (5)
The diencephalon consists of the thalamus, hypothalamus, epithalamus and subthalamus.
The thalamus is the main relay station for the brain between the telencephalon (cerebral cortex) and the brain stem/spinal cord for sensory information.
The epithalamus helps to regulate circadian rhythms
The subthalamus helps to regulate and coordinate motor function.
The hypothalamus main function is to maintain your body’s internal balance (e.g.. regulating blood pressure, body temperature etc…) , which is known as homeostasis.
Describe the large anatomy of the brain and its functions beginning part = mesencephalon (2)
The mesencephalon is the front portion of the brain stem and contains the tectum and tegmentum.
The mesencephalon is responsible for: 1) controlling auditory processing, 2) pupil dilation, 3) eye movement, 4) hearing and, 5) regulating muscle movement.
Describe the large anatomy of the brain and its functions beginning part = rhombencephalon (2)
The rhombencephalon is the lower part of the brain stem (i.e., hindbrain) and contains the medulla oblongata, pons and cerebellum.
This usually deals with autonomic functions such as breathing, alertness, digestion, sweating heart rate, attention and many more.
Describe the different circuit motifys/artifical neural networks beginning part
There is different types of circuit motifs that is utilised in computational neuroscience models such as: 1) feed-forward neural network, 2) feedback inhibition neural network, 3) recurrent neural networks and 4) lateral inhibition neural networks.
Describe the different circuit motifys/artifical neural networks
feed-foward network (2)
A feed-forward neural network is where there is a group of neurons that project directly (have excitatory network connections) to another group of neurons.
Feed-forward neural network is the simplest artificial neural network that is devised
Describe the different circuit motifys/artifical neural networks
feedback inhbition
. Feedback inhibition neural network, excitatory principal neurons have a synapse with inhibitory interneurons , which then inhibit those neurons by feeding back to them (negative feedback loop; Carl & Jong, 2017).
Describe the different circuit motifys/artifical neural networks
recurrent neural network
In recurrent neural networks, neurons are inside a interconnected circuit that sends feedback signals to one another.
Describe the different circuit motifys/artifical neural networks
lateral inhbition neural network
In lateral inhibition neural network, active neurons suppress neighbouring neurons’ activity through inhibitory synaptic connections (Cao et al., 2018).
What are the different ways to record locomotor modes in salamander and what has research showed? = EMG part (4)
The first technique is through in vivo EMG and EMG records the electrical activity in muscles via electrodes.
The EMG data of salamander shows that the muscle activation is different for the different locomotor modes the salamander has.
From EMG data, the swimming gait of the salamander shows that there is a wave of muscle activity that travels down the body (travelling wave) and that there is alternating muscle contractions on either side of the body as well as constant lag between one muscle and the next.
From the EMG data, the walking gait of the salamander shows that all muscles on one side of the trunk become active at first in unison with two legs and the next cycle these muscles are silent and the other side of the trunk becomes active (standing wave).
What are the different ways to record locomotor modes in salamander and what has research showed? = Fictive Locomotion (8)
The second way to measure locomotor modes in salamander is through fictive locomotion.
The methodology of fictive locomotion involves extracting the whole spinal cord and put it into a solution (which has N-methyl-D-aspartate [NMDA]) that helps to keep tissues in a viable state.
The electrodes are then placed directly on the ganglia.
More specifically, fictive locomotion places the electrodes to measure ventral root recordings (VRs) which are nerve endings goes to the muscles.
Ganglia are nerves that come out of the spinal cord and NMDA is a excitatory neurotransmitter that makes neurons fire.
Fictive locomotion will then measure the electrical activation of spinal cord nerves which will be summed to be collective output of many spinal cord neurons sending action potentials along the nerves.
From fictive locomotion method, researchers found that there are neighbouring spinal cord segments at peak at slightly offset times (i.e., there is phase lag between the spinal cord segments).
This reflects the properties we have seen in spinal cord networks like in muscle activation in salamander as there is a wave of muscle activity that travels down their body (travelling wave) and constant lag between one muscle and the next.
What does it allow us to infer about the spinal cord networks? (Fictive Locomotion Research).
This pattern of collective output of fictive locomotion suggests that the spinal segments (as neural networks) must be coupled to each other to influence each other locally (e.g., one side of the muscle is active while the other side of the muscle is relaxed).
How does the spinal cord network of lampreys work to produce locomotion? (8)
The spinal cord locomotor network of lampreys contains cross-inhibitory neurons (CCINs), excitatory inter-neurons (EINs) and motor neurons (MNs).
The network represents one segment of the spinal cord.
Lamprey’s locomotion (i.e., alternating rhythmic activity) is initiated when the interneurons and motor neurons (MN) receive constant tonic input (i.e, constant flow of action potentials impacting on spinal cord neurons) from the brainstem.
More specifically, the interneurons and MNs receive a descending excitatory drive from reticulospinal (RS) neurons in the brainstem (McCllellan & Grillner, 1984).
There is recurrent connections between the EINs within half-segment of the spinal cord.
These EINs will have an excitatory connection to MNs which will make the muscle contract.
At the same time, EINs will excite the CCINs which will have inhibitory connections to all the neurons of the other side of the spinal cord (contra-lateral half-segment).
This inhibition of contra-lateral half segment means that one side of the spinal cord is active whole the other side is silenced (i.e., prevented from firing action potentials) so both sides of the segment are not active simultaneously.
What are the mechanisms that makes one-half of the spinal cord segment stop firing APs if there is tonic input from the brainstem? beginning part (2)
1) spike-frequency adaptation and
, 2) lateral interneurons (LNs) being active mid-cycle and inhibiting CCINs.
What are the mechanisms that makes one-half of the spinal cord segment stop firing APs if there is tonic input from the brainstem? spike-frequency adapation (5)
The spike-frequency adaptation means the reduction of a neuron’s firing rate to a stimulus of constant intensity.
Spike-frequency adaptation helps to terminate ongoing activity as firstly one side of the spinal cord segment becomes active in which excitatory interneurons (EINs) fire lots of action potentials which inhibits the other side of the spinal cord.
After a while, spike-frequency adaptation takes place so firing rate of EINs reduces.
The intervals of EINs without spike becomes larger with time which makes the other side of the spinal cord not as strongly inhibited (i.e., fewer inhibitory action potentials arrive at the contra-lateral side of the spinal cord segment).
This means the other side of the spinal cord segment has time to be active and starts to fire multiple action potentials quickly and in succession which inhibits the previously active side (this is called escape from inhibition).
What are the mechanisms that makes one-half of the spinal cord segment stop firing APs if there is tonic input from the brainstem? lateral interneurons (4)
LNs also help to terminate ongoing activity so one side of the spinal cord segment is active and other one is not.
LNs are featured in the spinal cord locomotor network of lampreys.
LINs terminate ongoing activity so rhythmic alternating activity can occur in lamprey’s locomotion as
later during ipsilateral bursting activity of EINs and motor neurons (MNs) in the network, the LIN become active and inhibit CCIN so it allows the network neurons on the contralateral side to disinhibit and become active (Wallen et al., 1992).
What are the neural mechanisms for the spinal cord lamprey network? (spike-frequency adaptation) - (5)
Spike-frequency adaptation is due to a phenomenon called spike after hyperpolarisation (sAPH).
Hyperpolarization is when membrane potential becomes more negative than resting membrane potential which makes it difficult for next spikes to be emitted in the neuron.
sAPH is due to calcium ions flowing into cell (due to Ca+ ion channels opening) with each action potential (alongside Na+ ions) and slowly accumulates in neuron.
Ca+ has a hyerpolarisation current through a different ion channel called calcium-dependent potassium channel that brings membrane potential down.
The accumulation of Ca+ is sensed by this calcium-dependent potassium channel. Ca+ accumulates slowly until it reaches a steady state where amount of Ca+ transported away (decay of Ca+ concentration) equals the amount of Ca+ that flows in.
Describe the single cell model of the lamprey spinal cord network (6)
How the neural properties of the lamprey’s spinal cord network determining the function of locomotion can be realised by the single cell multi-compartment Hodgkin Huxley model of the lamprey’s spinal cord.
In normal Hodgkin Huxley model we have an equation for each of the individual ion channels which is then plugged into a rate of change of membrane potential over time equation.
However, in a multi-compartment Hodgkin Huxley model of lamprey’s spinal cord that Ekeberg et al., (1991) created they have multiple compartments that constitute different parts of the neuron.
More specifically, they have a soma compartment and three other compartments for the dendrites of the neuron.
Each compartment is composed of different ion channels, such as: 1) sodium (Na), 2) potassium (K), 3) calcium (Ca) and, 4) calcium-dependent potassium channel (KCa).
In the multi-compartment model, the have an equation of rate of change of membrane potential over time which will be 0 (i.e., neuron will be at rest) if Eleak (resting membrane potential) is equal to E (current membrane potential).
Pros and cons of multi-compartment single cell model of lamprey network (4)
The pros of multi-compartment model is that it is more realistic and closer to biology and allows to stimulate the effects of ion channels.
The cons of the multi-compartment model is: 1) need more data to fix the composition of ion channels as need to measure the elements of the equation in real neurons for model to map loosely to biology which is a very labour intensive task,
2) very expensive computationally to stimulate in computer as need to perform equation of rate of change of membrane potential over time for every compartment and for every ion channels and,
3) hard tot une the parameters of equation as all parameters have not been measured so researchers will need to make a tough decision of what plausible values to use based on a whole range of values available in literature.
Explain the Ca+ dynamics at spinal cord lamprey (5)
From experiments, they found that there is two types of calcium pools: 1) calcium pool is one where calcium ions flow in and enter through Ca+ channels due to each action potential in the soma and, 2) calcium pool is where calcium ions flow in at NMDA synapse (when NMDA receptors are activated).
Ekeberg’s calcium-dependent potassium current’s strength is driven by these two calcium pools.
The two pools of Ca+ we have fast and slow Ca+ dyanmics where intake and decay of Ca+ ions happen at different timescales for these two pools.
It is fast for membrane Ca+ pools and slow for NMDA-synapse Ca+ pool.
For NMDA-synapse pool, the Ca+ goes in due to receptor-docked on NMDA-synapse. After enough Ca+ accumulates (accumulation of Ca+ sensed by calcium-dependent potassium channel) it will trigger a hyperpolarising current which brings membrane potential down.
**What is plateau potentials? **How is plateau potentials related to fictive locomotion/How is fictive locomotion slower than in vivo locomotion? (4)
Plateau potentials are when action potentials are blocked with Tetrodotoxin.
NMDA-plateau potentials are produced when its generation of action potentials blocked with tetrodotoxin (TTX) which suppresses Na+ channels from opening and closing as well as no Ca+ flows into soma does it does not affect membrane potential.
However, membrane potential can still be affected with other ion channels.
There is a strong and constant NMDA input (due to Ca+ flowing into NMDA synapse) which brings membrane potential up, membrane potential then plateaus and then decays when enough Ca+ accumulates in which the calcium-dependent potassium channel brings membrane potential down.
What is plateau potentials? How is plateau potentials related to fictive locomotion/How is fictive locomotion slower than in vivo locomotion? (5)
Fictive locomotion is slower than in-vivo locomotion.
Plateau potentials are related to fictive locomotion.
This is because in fictive locomotion the isolated spinal cord is placed into a solution that contains a large amount of NMDA concentration which docks to all the receptors in the spinal cord neurons.
Thus, it is hypothesised that NMDA Ca+ dynamics produce these slow fictive locomotion signals that are slower than in-vivo locomotion.
In other words, fictive locomotion is slower than actual in-vivo locomotion due to the product of un-naturally large NMDA concentration.
What is a central pattern generator and examples (2)
A central pattern generator is a network that takes simple inputs (e.g., tonic [i.e., constant] signal from brain stem) and produces a more complex pattern of neural activity (e.g., oscillations from rhythmic muscle activation).
Examples of CPG include the lamprey locomotor network, heartbeat and digestion.
What is a central pattern generator and give an example of it? = first paragraph (9)
The heartbeat control system of medicinal leech has been studies for three decades as CPG.
The leech has two tubular hearts running along the length of the body and moves blood through a closed circulatory system.
The beating pattern of leeches (beat period of 4s to 10s) is asymmetric with one heart generating high systolic pressure through front-directed peristaltic wave (peristaltic coordination mode) along its length and another heart generating low systolic pressure through near-synchronous constriction (synchronous coordination mode) along its length.
The peristaltic heart moves blood forward.
As compared to the peristaltic heart, the synchronous heart has been hypothesised to push blood into peripheral blood circulation and supports rearward blood flow.
After about 20 to 40 heart beats (switch period ~ 100 – 400 s) the heart switches roles.
The two heart tubes leech has receives excitatory input from ipsilateral member of a pair of segmental heart motor neurons (HE) which is located in each midbody segmental ganglion.
The firing pattern of HE neurons (i.e., fictive motor pattern) is bilaterally asymmetric with motor neurons on one firing rear-to-front progression while those on other side fire nearly synchronously with appropriate side-to-side coordination of these two firing pattern (i.e., firing pattern switch).
The HE neurons are controlled and coordinated by heartbeat CPG through rhythmic inhibitory drive.
What is a central pattern generator and give an example of it? = second paragraph (7)
There are nine pairs of identified segmental heart interneurons (HN) (plus one identified pair) that compose of CPG.
The core CPG consists of 7 pairs of interneurons located in first seven midbody ganglia of nerve cord and indexed by ganglion number and body side (HN(L,1) – HN(R,7)).
The rhythmic activity in CPG network is paced by highly interconnected timing consisting of coordination (HN(1) and HN(2) interneurons) and osciliatory interneurons (HN(3) and HN(4) interneurons).
The firing pattern of interneurons of core CPG is also bilaterally asymmetric like HE neurons with appropriate side to side coordination.
The asymmetry of firing pattern is not permanent as there are regular side to side switches in CPG network as peristaltic and synchronous pattern in HN underlie changes in both motor pattern and rhythmic constriction pattern in heart tubes.
The switches in coordination is mediated by HN(5) switch interneuron which link the timing of network to middle premotor neurons by bilateral inhibitory connections; only one of the pair of interneurons rhythmically active at a time and other is silent.
The premotor interneurons and motor neurons on one side of the active switch interneurons are coordinated synchronously while those on other side of silent switch interneurons are coordinated peristaltically.
What research implies that WM system is not unitary and have modality specific components (5)
Research has shown that in a task where letters are presented visually, participants show errors that indicate that information is acoustically coded.
For example, participants replace T for G (sound similar) instead of Q for G (appearance of letters look similar).
Similarly, participants found that recalling a wordlist more difficult for similar sounding words and not semantically related words such as recalling ‘rice’ instead of ‘ice’ and not recalling ‘frost’.
Further research also shown that repeating nonsense syllables disrupts the phonological memory.
All these research discussed above indicates that the working memory (WM) system is not unitary but a multi-component system with modality specific components, each can be damaged separately.
What research shown WM components can be damaged separately? (3)
Research has shown that WM components can be damaged separately.
As research shown that damage to Brodmann areas 44 and 40 means individuals can not hold strings or words in their memory or mind and have deficit in the rehearsal process of phonological loop.
Research also shown participants have visuospatial sketchpad WM deficits as damage/lesions to the parieto-occipital causes deficits in visuo-spatial WM for instance that participants with that damage have difficulties memorising and repeating a sequence of blocks the experimenter has touched.
Explain how neural property (ADP) attracts with network property to generate the function of WM maintenance in Lisman Idiart model
First paragraph = explaining ADP (3)
The Lisman-Idiart model proposes that there is a neural mechanism called ADP that helps out in working memory (WM) maintenance.
ADP stands for afterdepolarisation. Depolarisation occurs when the membrane potential increases and more likely to emit a new spike.
The ADP is a positive ‘hump’ in membrane potential that is produced after a spike is emitted in the model.
Explain how neural property (ADP) attracts with network property to generate the function of WM maintenance in Lisman Idiart model
second paragraph = Lisman Idiart network (5)
In Lisman Idiart model they have network in which activity of prefrontal neurons is calculated by their equation of rate of change of membrane potential over time.
In this equation, they have terms such as: 1) VOSC which is a sin function that causes fluctuation in membrane potential in background and, 2) Vinh which is a term added every time an action potential is fired by a presynaptic neuron.
VOSC provides excitatory oscillatory input to the neurons. Vinh has a feedback inhibition circuit.
If VOSC neuron fires a spike it excites all prefrontal neurons and eventually transmitted to Vinh neuron which inhibits all neurons including itself.
The model assumes that the firing of each neuron in network represents one item in WM.
Explain how neural property (ADP) attracts with network property to generate the function of WM maintenance in Lisman Idiart model
third paragraph = Lisman Idiart networ + ADP (7)
How ADP and neuronal network of Lisman and Idiart model work together to implement active rehearsal for content in WM is explained below.
There is background oscillations in the Lisman and Idart model’s network.
If we present a letter G for someone to remember then the neurons in the network can quickly create synaptic connections with neuron in phonological loop which encodes and represents the letter ‘G’.
The part of the phonological loop that represents ‘G’ will make a particular neuron in network fire an action potential.
This neuron will then inhibits itself and all the other neurons in the network via the feedback inhibition circuit.
Then once next peak of osciliation comes around, ADP raises the membrane potential high enough for that neuron that represents letter ‘G’ to fire again.
The ADP and oscillatory inputs maintain the spiking of that neuron.
Why is HH model not used in Lisman-Idiart model? (5)
The Lisman-Idiart model does not use a Hodgkin Huxuley model since they want to see how a neural mechanism of ADP (Afterdepolarisation) helps to carry out working memory (WM) maintenance.
They do not need to think about which ion channel is in charge of ADP as they want to model ADP’s effect on membrane potential over time.
The model does not give an explanation of ADP and use ADP to explain higher-level phenomenon.
The aim of Lisman and Idiart’s model is to demonstrate what ADP can be used for and its effect on membrane potential.
Therefore, a Hodgkin Huxley model is not needed as it will not fulfil the Lisman-Idiart’s aim of adding how ADP functions in terms of a network of neurons.
Advantages and disadvantages of the Lisman-Idiart model of WM (2)
The model demonstrates how neuronal prosperities (ADP) and network structure (feedback inhibition and oscillatory input) work together to implement function
A criticism is that the authors chosen parameters (e.g., oscillation frequency) to make the number 8 for capacity.
Where are head directions cells found in the brain? (2)
Head direction cells are predominantly found in large network of brain areas in Papez circuit (Taube, 2007) such as the: 1) entorhinal cortex, 2) the thalamus (lateral dorsal and anterior dorsal nuclei) and, 3) anterior dorsal thalamus.
Head direction cells are also found in non-Papez circuit brain areas such as: 1) lateral dorsal thalamus, 2) dorsal striatum and, 3) medial precentral cortex.
What are the two types of cells that are important for spatial cognition and their receptive fields (5)
The two types of cells that are important for spatial cognition is: 1) head direction (HD) cells and, 2) place cells.
Receptive fields are areas at which simulation leads to a response of a specific sensory neuron.
Different place cells and HD cells are distinguished by their different receptive fields.
Place fields have a receptive field for spatial location which means a particular place neuron will fire most vigorously at a particular location in the environment.
HD cells have a receptive field for head orientation which means they will have a specific head orientation at which a specific HD fires maximally.
What are the three uses of head-direction cells? (3)
The three uses of head-direction cells is that used for orientation which is very important for navigation.
It is also used for grasping and pointing so if you want to reorient yourself and do some action like pointing somewhere in a specific direction.
Finally, head-direction cells is used to define a point of view (human spatial cognition).
What are the defining properties of head-direction cells and hypotheses from it? (7)
From manipulations of single-cell recordings, experiments found three defining properties of head-direction (HD) cells which is: 1) HD cells depend on vestibular input, 2) cue cards control angular turning and, 3) HD drift in darkness meaning that without any visual input the animal loses its sense of orientation.
Stackman and Taube found HD cells depend on vestibular input (i.e., changes in direction, movement and position of head) as they found that neurotoxic lesions of vestibular labyrinth abolished HD cell signal up to three months post lesions.
Taube also demonstrated cue cards control angular turning by recording HD cells in a cylinder which contains a prominent visual cue (e.g., white cue card) attached to the box.
They rotated this important visual landmark which leads to a corresponding shift in preferred firing direction of HD cells.
Thus, HD cells controlled by landmarks.
Mizumori and Williams found HD cells drift in darkness as when rats are either blindfolded or placed in complete darkness then preferred direction of HD cells become less stable (disrupted) and begins to drift.
The hypotheses from these 3 main defining properties of HD cells is that HD cells used for navigation and when the animal lost it way, HD cells have lost their stable directional tuning which makes them drift.
How can we correct for drift in head-direction cells from visual cells in visual cortex? (3)
We can correct the drift we see in the head-direction (HD) cells when animal lost its way in dark by receiving feedback from visual cue.
Visual cells are somewhere in visual cortex that provide feedback (i.e., providing synaptic inputs at particular orientations to specific HD cells).
When animal sees a cue card ahead in a box, a specific visual cell will be active and give strong synaptic input to the appropriate and correct HD cells which allows the animal to orient itself.
How can you get a tuning curve of a single head-direction (HD) cell? (3)
As animal moves around in a box, our chosen neuron fires at varying rates depending on the heading.
We sum all the activites of the neuron and divide by total time animal spend in box to get the tuning curve.
The tuning curve of a single HD neuron has firing rate of a single HD neuron as a function of heading and data accumulated over time.
How can ring of HD ring CAN (continuous attractor network) sustain activity when the head is still or even in darkness? (4)
Head-direction (HD) cells preferred firing direction still fire maximally if the head is still at a certain head orientation and its firing is maintained briefly in darkness where individual receives no sensory information.
This is done via the short-range excitatory synaptic connections and long-range inhibitory connections the ring of HDs have.
The HD cell that fires and most active at a certain direction sustain their activity , even in darkness, by exciting itself as well as exciting neighbouring HD cells near them due to the short-range excitatory synaptic connections (recurrent connections) as well as having long-range inhibitory synaptic connections to distant HD cells to suppress their activity.
There is close-range excitation and long-range inhibition for each HD neuron in the ring. Thus, symmetric short-range and long-range inhibition gives sustained activity of HD cell.
How can you turn your head in HD ring CAN (continuous attractor network) (7)
To turn your head to another direction, the activity pattern of ring of HD cells will need to be shifted along the line of neurons.
These line of HD neurons active will have offset inhibition in the direction opposite of a turn and offset excitation in direction of a turn.
These connections will be active only when the head is turning (dependent on velocity).
These connections are doubled, one for clockwise and one for counter clockwise.
To turn clockwise, nearby HD cells to the right will be excited along the line of neurons.
To turn anti-clockwise, nearby HD cells to the left will be excited in line of neurons in HD ring.
Thus, velocity dependent asymmetric excitation and inhibition gives capability to turn head and shift pattern of activity across ring of HD neurons.
Two possible behaviours when giving external stimulus to HD ring network? (4)
The two possible behaviours when giving external stimulus to HD ring in Zhang 1996 HD ring continuous attractor network (CAN) is: 1) shift and, 2) reset.
Zhang found that the internal direction maintained by HD cell networks is calibrated by external inputs from a local-view detector
. If the activity of HD cell network is maintained at 180 degrees but heading is actually at 200 degrees then external input from the local-view detector induce a shift in its activity towards 200 degrees.
Reset is when the excitation of HD cells in network is too far away from the actual orientation of heading then external input from local-view detector produce new estimate this resets the heading.
How is HD ring network an example of CAN? (3)
The HD ring network is an example of a continuous attractor network because you can place the activity anywhere you wanted in line of HD neurons.
The activity can be shifted and come to rest at a new position.
The HD ring network can sustain its activity as connectivity pattern is same for each neuron when heading is still at a certain orientation.
What happens if precise connections are perturbed in HD ring network? (3)
All activity of HDCs converge to different locations and at the end only represent a subset of all possible orientations.
The continous attractor becomes a discrete attractor.
Certain HDC attract the activity bump. Discrete basins of attractions (circle)
What is continuous attractor and discrete attractor? (3)
- Continuous attractor is symmetric connections maintain the activity packet in place we can place the ball anywhere
- Discrete attractor: Given a bit of time, the ball settles in one of several valleys (basins).
- Locations between valley are unstable. Is this what happens to HD during aging due to neuron loss?
The advantages of the HD ring CAN (Continuous attractor network) - (4)
The advantages of the HD ring CAN is that it is the best model of HD we have.
It allows us to explain how internal sense of direction is coded and maintained.
It does not make use of spikes, let alone ion channels (assuming all the information about head direction is encoded in firing rate of the network).
It is also good case of rate-coded neurons
The disadvantages of the HD ring CAN (Continuous attractor network)
The disadvantage of the HD CAN is that how the brain learn and maintain such precise connections as neurons die off and affected by biological noise (e.g., temperature) which has not be answered in this model.
Describe the Hopfield (1982) Associative Memory Network: properties and assumptions (6)
The Hopfield (1982) associative memory network uses standard artificial neurons with no dynamics.
The representation of the network is shown below which demonstrates all neurons are connected with each other and that neuron Si is connected to Sj with weight of wij.
The assumption of the Hopfield associative memory network is that it assumes a fully connected network with symmetric connections (wij = wij).
Symmetric connections means that weight going in one direction is the same as weight going in another direction.
The properties of the Hopfield associative memory network is that it contains simple connectionist neurons, no dynamics, we impose the update schelude, a sign function as a transfer function and units can be active (Si = 1) or inactive (Si = -1).
The sign function used as a transfer function means if a value is below 0 then set it to -1 and if a value is above 0 then set it to 1.
What does it mean when Hebbian learning says that: “neurons that fire together wire together” (2)
This means that if the sender and receiver neuron are both active then the sender likely contributed to making that receiver neuron fire!
Thus, it strengthens the connections between the sender and receiver neuron; that is their weight increases.
How do we change the weights of synaptic connections mathematically in Hopfield Associative Memory Network? (3)
In Hebbian learning, we can change the weights of synaptic connections mathematically in Hopfield Associative Network by taking one of the weight (wij) and add to the product of activity of pre and post synaptic neurons (if both active) and multiply by very tiny number which is epsilon.
Epsilon is used in the equation as it is a very tiny number as we don’t want to change the weights in the network too quickly.
Additionally, In most cases, you will want to incrementally learn something new (i.e., have multiple presentations of two stimuli together to associate them together).
How does the Hopfield Network Learn? (Imposing Pattern and Learning Rule) - (10)
The Hopfield Network learns by imposing a pattern we want to learn then letting the learning rule act.
Imposing a pattern means that we clamp an activity of a subset of neurons for one pattern and let the Hebbian learning rule act to change the synaptic weight connections in the network by taking one of the weight between neurons and add to the product of activity of pre and post synaptic neurons (if both active) and multiply by a tiny number (epsilon).
When imposing a pattern, if both neurons are not active (i.e., both -1) then the weight between these neurons increases and their connection is strengthened. If both neurons are active (i.e., both 1) then weight between these neurons are increased and connection between them is strengthened.
However, if activity between two neurons are mixed (i.e., one is inactive [-1] and one is active [1]) then the weight goes down and may lead to pruning of the synaptic connection between these neurons.
These pattern of activation is learned as stable states under the rules for updating activations.
Stable states mean the update rule produces no more changes in the active neurons in network.
Thus, when a pattern of activation does not change anymore, we say a stable state has been reached.
The update rule can be asynchronously where one unit of the network is updated at a time (at random or pre-defined order) or synchronously where all untis are updated at the same time.
In update rule, many patterns can be learned in same network but memory capacity is limited to ~0.14N (N is number of neurons in Hopfield network).
The memory learned in Hopfield network is content addressable, can perform a pattern completion of a partial cue.
What does it mean that memory of Hopfield network is content addressable, performing a pattern completion of a partial cue? (2)
Content addressable simplify means that part of the content of the memory is sufficient to address to find the complete memory.
This means the network can perform a pattern completion of a partial cue of completing pattern of activation of learning from partial input.
Give an example that the Hopfield Network does not work in isolation (4)
Say we have a memory of “I saw a magneta turtle that was squeaking”.
This would tirgger the activity of the neurons in visual cortex that represented a magneta turtle as well as neurons in auditory cortex that represented the squeak sound.
There will be direct connections of neurons in Hopfield associative network to other neurons.
Pattern completion will continue in Hopfield associative memory store and also extend to reactive the neurons in the sensory cortices that was first active when you first memorised the thing (e.g., “the magenta turtle that was squeaking”).
Why is Hopfield Network not working in isolation a toy model? (3)
This is because the hippocampus has an extensive connections to virtually ‘all association areas (polymodal) in neocortex.
But there is no necessarily direct connections to early (unimodal) sensory cortices.
So the sketch is a severe simplification.
What is the learning rule of Sharp’s model of place cells? = first paragraph (describing competitive learn rule model) - (5)
The sharp model of place cells uses a learning rule called competitive learning rule.
There is a diagram of the competitive learning rule below which shows the following: 1) some input patterns, 2) some neurons will be active or not, 3) there will be an output neurons (i.e., receiver neurons), 4) initial random weights between each input and output neuron and, 5) strong inhibition between the output neurons (via lateral inhibition but don’t model them explicitly).
What is the learning rule of Sharp’s model of place cells? = second paragraph (describing competitive learn rule) - (7)
For input pattern X, there will be initial random connections and a particular output neuron (at random) Oi will be the most active neuron by setting it to 1 (maximum activity) and other Ok neurons to 0.
After this, Hebbian learning will be conducted where wij => wij + epilson OiXkj and do this for all other connections where output neuron is active and update weights between neurons.
We do not do this if activity of output neuron is 0 since it will not change the weight.
Epilson is a very tiny number as we want small incremental changes to weights in Hebbian learning.
The equation of Hebbian learning means the activity of given output and input neuron is multipled by each other and output is made very small by epilson and added to pre-existing weight (wij).
The process of Hebbian learning is repeated with presentations of each input pattern.
The result of competitive learning rule is that outputs whose incoming weights are most similar to pattern xn wins and its weight become more similar as we do learning.
What do we do before performing the learning rule in Sharp’s model of place cells and explain it (add to explanation of competitive learning rule) - (6)
Before updating the weights in Hebbian learning equation in competitive learning rule between neurons, we will need to normalise the synaptic connections first.
Normalisation is for a given neuron, the total sum of connection (i.e., input) weights to each output neuron stays constant (e.g., sum [wi] = 1).
The sum(wi) = … can be any random number.
We keep track of normalisation as we divide the weights after each learning step by the sum of total weights (sum[wi]) so sum will always stay 1.
For example, in this diagram sum of all inputs weights going onto output neuron 02 is always 1:
Normalisation process repeats with each presentation of input pattern.
Example of keeping track of normalisation (add to explanation of competitive learning rule) - (6)
We keep track of normalisation as we divide the weights after each learning step by the sum of total weights (sum[wi]) so sum will always stay 1.
For instance in diagram, weights after first learning step is 2,1,8,3 and 2
. Sum(wi) = 2 + 4 +5 +3 +2 = 16 => update weights to be 2/16, 1/16, 8/16, 3/16,2/16 so sum of all weights is still 1.
The strongest weights will be stronger wij wij epilson oixj.
For example, wij = 8/16 grows stronger faster at expensive of wij = 1/16.
The weakest weights get weaker.
Explain Sharp’s actual model of place cells set up and its competitive learning rule - (7)
Sharp’s model of place cells was developed in 1991 and uses standard artificial neurons with no dynamics.
There is a diagram of sharp’s model of place cell firing simulation shown below:
There is a simulation of rat (blue triangle) that will move around the circular box and cues around the box (A,B,C etc…).
At each location, the simulated rat will observe the distance to visual cues and “observes” direction of cues relative to itself
So neural activity is propagated and perform learning updates.
Then the simulated rat will move and explore the box and rotate a small distance.
What happens to the neurons as simulated rat moves around and observes distance to cue and observes direction to cues in Sharp’s model is that there is two stages of competitive learning: 1) conjunction so distance and direction of landmarks is learned (i.e., input pattern is now a representation of distance and direction to all cues) and, 2) conjunctions of there first stage output yields place cells.
Describe BVC model of place cell firing (6)
Hartley et al. (2000) theoretically predicted/hypothesised that boundary vector cells (BVC) may give (geometric) input to place cells (PC) and influence their activity.
In other words, Hartley hypothesised we know how far we are from the wall (BVC) as we know where we are (PC).
They created a BVC model of place cell firing in which it involves ¾ BVC which give input weight to a PC and you can sum BVC input activity * input weight and put through transfer function to produce a place field.
When 3 BVC cells are active, these different BVC cells will be maximally active and respond to barriers at specific directions and distances.
These three different active BVC cells give input to PC and is maximally active at this location.
We can then subtract the background activity and get place fields at particular locations.
Describe simple perceptron model (4)
- One output neuron (O1) and two input neuron (x1 and x2)
- X1 and X2 neuron each have a input weight of w1 = 1 and w2 = 1
- To get output (activity of O1 neuron), you sum the input activity * input weight and put through trasnfer function (which is step function)
- Output neuron is active if both input neurons are active (given they reach a threshold of 1.5) = model performs logical ‘AND’ function
It is only when two input neurons are active that the output neuron is active
Describe from simple perceptron model how they got to producing equation of line and mechanism of learning in perceptron model - (7)
You can produce a graph of all of the possible x1 and x2 combinations from simple perceptron model where dashed line is decision boundary and left of line is where 01 = 0 and right of line is 01 = 1.
We can write equation of line: x1 +x2 = 1.5, x2 = -x1 + 1.5
Then line separating o1 = 1 and o1 = 0 is where net input equals the threshold which is w1X1 + w2X2 = T
We can rearrange threshold equation to make equation of line which is: x2 = -(w1/w2)x1 + T/w2 (format of y = mx + c and implicility assumed W1 = W2 = 1).
Changing the weights will change the equation of line
Learning is changing the weights to classify two input patterns means finding weights so line separates group of 01 = 0 and 01 = 0.
An input pattern is simply a set of activity patterns across input neurons.
Give example of pattern classification on Rosenblatt’s Perceptron on classifying adults and children based on height and weight - (6)
- X1 signals weight
- X2 signals height
- Each individual (child/adult) we test will be one combination of two input neuron (weight/height) = pattern
- We train/learn the network on many example patterns of individuals, each individual will change input neuron’s weights and place a decision boundary to separate two groups (height and weigt)
- Decision boundary can be fuzzy since there may be kids tall or heavy and adults small and light but overall classifer separate children and adult based on height and weight
- After training this network on 100 individuals (50 adults/50 children), if we present 101’s person (that has not been used to train the network) , then we measure performance on how well it generalises (i.e., how well classifies the 101’s individual correctly):
Describe how the perceptron network is trained or learned so that when you present an example pattern it changes weights in model = describe model below - (3)
This is done through the delta rule which is an example of supervised learning rule.
We will use supervised learning rule on a complex model of perceptron which has: 1) 5 input neurons X, 2) 3 output neurons (i.e., 3 possible results of pattern classification) , 3) start with a initial random weights, 4) outputs are n = 3 (o1k, o2k, o3k), 5) input is n = 5 (x1k, x2k, x3k, x4k, x5k).
K means pattern K and output K which is elements of training set (i.e., set of patterns across neurons).
Example of using delta rule numerically in perceptron - (5)
Specifically, looking at connection weight from x1 and output neuron 02
Output activity of 02k , after computation, is o2k= 0.2,
Input activity of x1k was 0.7
We also have target t2k is: 0.3, so output of activity o2 want 0.3 instead of 0.2
The delta learning rule takes into account by changing connection weight between x1 and 02 by calculating:
w12 (connection weight of x1 and 02) + epilson 0.7 (0.3-0.2) so the error is reduced!
The disadvantage of perceptron model (9)
Minsky and Papert (1969) said that the perceptron can perform linear discrimination but can not solve ‘non-linearly separable problems’
A non-linear separable problem , for example, can be shown below where there is a graph of possible combinations of input x1 and x2.
Say you want to learn x1 or x2.
The output neuron would be ON if X2 = 1 and X1=0 OR X2 = 0 and X1 = 1.
Can we place such a line that divides the space such as output is 1 and output is 0 on other side?
We can not do this separation linearly.
Rosenblatt’s Perceptron performs linear discrimination where decision boundary always linear from combinations of input neurons where either side output neuron is active or not (i.e., 1 or 0).
Therefore, perceptron can not solve non-linearly separable problems as it always uses a linear decision boundary.
However, if we include hidden layers in perceptron which gives it more power then able to solve these ‘non-linearly separable problems’.
What is the general setup of model-free reinforcement learning? (7)
agent in the environment that receives reward intermittently.
The rewards are positive or negative but not otherwise specific as in supervised learning.
But the agent does not know the rules of the environment meaning it does not have the model of its environment.
The artificial agent has policy, state and reward.
Agent has a policy which is a set of rules that determine which actions to take for different states of myself in environment such that I (hopefully) maximise my future reward.
Reward R is where the environment provide reward which affects state of agent.
For a given policy, a value V can be associated in given state which is sum of expected future reward for each possible state.
At any moment the agent is at a given state. V is calculated by formula which has a discount factor which means further in future you expect the reward less likely you value it.
How can a robot learn to kick a ball through Q-learning (model-free reinforcement learning)? = first paragraph and second
At any given moment the robot is in a given state (i.e., distance and direction to the ball which characterises its mental repertoire) and can take a set of possible actions (move up/move down/move left/move right/kick ball).
When robot will kick the ball it gets a reward (e.g., a number in memory it wants to maximise) and up until then it gets nothing.
How can a robot learn to kick a ball through Q-learning (model-free reinforcement learning)? = third paragraph = learning algorithm (4)
The learning algorithm guides robot learning which says it wants to maximise reward.
According to learning algorithm, robot has learning episodes where it evaluates its own state, take an action and observe reward (robot may get reward or not) and these episodes last until ball is kicked and reward is received.
if robot receives reward something in environment has been learned.
If it has not then robot takes another action.
How can a robot learn to kick a ball through Q-learning (model-free reinforcement learning)? = fourth and fifth paragraph = robot and first learning (4)
The football kicking robot does not know which actions lead to reward and performs actions initially and only learns at the end of learning episodes.
At its first learning episodes, it performs random actions and kicks when there is no ball in the field.
But at some random point in future the robot manages to kick the ball and receives the ball. It has now learned when it is zero steps from the ball it kicks it.
However, it has not learned when it is one step away from the ball to kick it and only learn in next learning episode. Its only learned the last state-action pair.
How can a robot learn to kick a ball through Q-learning (model-free reinforcement learning)? =sixth and seventh paragraph = Q learning table and second learning episode (4)
In Q-learning, we can keep track of progress of robot learning to kick ball in Q-table which mimics outline of environment and assigns value for each action.
For example, here is kick table for first learning episode. Value is performing action (A) given state (S) and thought of as current prediction of how much reward robot will eventually obtain if given state S it performs action A and subsequent high value actions.
In second learning episode, this time the robot reached the ball above and find ourselves in field with ball and know how to kick because of Q-table for KICK.
Can assign a different value to state-action pair that led us to the field with ball to kick by assigning value of 8 (Smaller than 10 in KICK table as once step away from reward) in B7 in move-down Q-table which was zero prior to learning.
How can a robot learn to kick a ball through Q-learning (model-free reinforcement learning)? =last paragraph (4)
At each learning episodes there is then partial values assigned immediately preceding state-action pair if one steps into field with previous learned value.
The trial and error part of the robot reduces across Q-learning episodes and build more Q-tables which assigns values to all the states of robot of different actions.
Thus, this leads to route of ball to kick.
After robot learned the route to ball to kick, it can look up the actions in Q-table and performed a learned action sequence as does not need to rely on trial and error anymore.
- What is temporal difference learning? (2)
Temporal difference learning is a model-free reinforcement learning (RL) and slightly different from Q-learning.
It links classical conditioning and reinforcement learning together in order to overcome the temporal credit assignment problems that occurs in Rescorla-Wagner model.
- How does the temporal difference learning work?
- How does the temporal difference learning work? - (4)
In temporal difference learning model, we will need the value function V at time t (Vt) to predict the sum of future rewards, not just immediate rewards so we can learn S2 predicts S2 which predicts R (reinforcement = general like food reward/punishment)
In other words, we want the sum of all rewards at times tau greater than now (present at time t).
We want to decompose V(t) to current and estimate of subsequent reward.
To do this, we need a delta rule to ensure this happens.
Delta at time t is current reward + future reward – expected reinforcement (what I expect) so delta becomed the difference between expected reward now and estimate of all future reward.
Diagram of R-W rule
