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Network Supervision of Adult Experience and Learning Dependent Sensory Cortical Plasticity

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ABSTRACT

The brain is capable of remodeling throughout life. The sensory cortices provide a useful preparation for studying neuroplasticity both during development and thereafter. In adulthood, sensory cortices change in the cortical area activated by behaviorally relevant stimuli, by the strength of response within that activated area, and by the temporal profiles of those responses. Evidence supports forms of unsupervised, reinforcement, and fully supervised network learning rules. Studies on experience‐dependent plasticity have mostly not controlled for learning, and they find support for unsupervised learning mechanisms. Changes occur with greatest ease in neurons containing α‐CamKII, which are pyramidal neurons in layers II/III and layers V/VI. These changes use synaptic mechanisms including long term depression. Synaptic strengthening at NMDA‐containing synapses does occur, but its weak association with activity suggests other factors also initiate changes. Studies that control learning find support of reinforcement learning rules and limited evidence of other forms of supervised learning. Behaviorally associating a stimulus with reinforcement leads to a strengthening of cortical response strength and enlarging of response area with poor selectivity. Associating a stimulus with omission of reinforcement leads to a selective weakening of responses. In some preparations in which these associations are not as clearly made, neurons with the most informative discharges are relatively stronger after training. Studies analyzing the temporal profile of responses associated with omission of reward, or of plasticity in studies with different discriminanda but statistically matched stimuli, support the existence of limited supervised network learning. © 2017 American Physiological Society. Compr Physiol 7:977‐1008, 2017.

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Figure 1. Figure 1. Image, with permission, from Kaas et al. (). The “map” shows the organization of the body on the surface of the brain. The areas labelled “D1,” for example, indicate regions on the surface of the brain where a microelectrode penetration into the middle cortical layers would sample from neurons that responded to the first digit, or thumb. The lines indicate borders between regions in which sharp transitions in representation from one body part to the next are found. Of particular interest is the hand map in area 3b, shown as the left half of the map above, primary somatosensory cortex, as it contains adjacent and largely nonoverlapping representations of the digits. This portion of the map has been extensively studied in experience and learning induced plasticity experiments. Anterior is left, medial is up, and this map is taken from a left hemisphere.
Figure 2. Figure 2. Image, with permission, from Allard et al. (). The third and fourth digits of monkeys were sewn together to induce digital syndactyly for three months. The top inset of the image shows the hand representational map in area 3b. The label “area 3a” is placed above the hand map (anterior in the anatomical coordinates) to indicate the relative position of the area 3b hand map to area 3a. The numbers 2, 3, 4, and 5 indicate regions responsive to the corresponding digits. The numbers on the hand map above indicate the cortical locations with receptive fields shown on the numbers on the images below. The shaded zone encompassing digits 3 and 4 in the top map image show the area in which neurons respond to both of the digits involved in the syndactyly.
Figure 3. Figure 3. This image shows the concept of unsupervised learning in sensory cortex plasticity. The inputs from the thalamus stay the same, and the patterns of activity in primary sensory cortex interact with learning rules to lead to the new map in cortex. External inputs from association cortex and from neuromodulators are omitted in this conceptual figure, as these inputs play no role in unsupervised learning.
Figure 4. Figure 4. Image taken, with permission, from Zarzecki et al. (). Intracellular studies show response latencies for stimuli delivered to the primary digit and an adjacent digit. (A) Normal digits (open bars) have a range of latencies from 10 to 25 ms, and adjacent digits (filled bars) have a latency range displaced 5 ms later. (B) Amputation responses to the previously adjacent digit representation have the same latency as normal primary digit responses. (C) Syndactyly has latency ranges matching those in the normal animal.
Figure 5. Figure 5. Image, with permission, from Jenkins et al. (). Owl monkeys were trained at a tactile task that involved touching the tips of the second and third digits to a spinning disk. The cortical maps before and after several months of training are shown. The animals in this study were double mapped, to ensure that any changes in representational size were caused by training.
Figure 6. Figure 6. Image, with permission, from Recanzone et al. (). The behavioral threshold is plotted against the area of cortex activated by the sensory stimulus, as estimated from the map reconstruction.
Figure 7. Figure 7. Image, with permission, from Recanzone et al. (). The reconstruction of the population response to the trained stimulus is compared to the analogous response from another digit in the same animal. The increased sharpness of the onset portion of the response, and its decreased variability, was predictive of behavioral threshold. This increasingly sharp temporal onset is a common observation in animals trained to process short phasic stimuli in somatosensation and audition.
Figure 8. Figure 8. Image, with permission, from Wang et al. (). (A) A lateral view of the owl monkey brain showing the position of the hand representation. (B) The behavioral task. Monkeys received alternating taps on the distal segments of digits 2, 3, and 4, or on the proximal segments of the same digits. The positions of the stimulation bars are shown in (C). In (D), the hand map is shown in an animal that trained on this task for 8 months. Conventions are as in the hand map in (A), except that the dark red shows brain regions responsive to all three distal digit tips used in the task. The light red shows two distal digit receptive fields. The dark and light blue show proximal multidigit receptive fields.
Figure 9. Figure 9. Conceptual view of primary sensory cortical plasticity caused by learning as influenced by map plasticity studies. Primary sensory cortex contains self‐organizing rules that operate based on timing of sensory stimuli to cause inputs that are activated together to be represented at the same cortical location. These rules operate only while stimuli are attended, perhaps through a permissive role by neuromodulators to explain why plasticity occurs in response to task performance but not during presentation of task irrelevant stimuli.
Figure 10. Figure 10. Image, with permission, from Diamond et al. (). The response of cortical neurons in the D2 barrel to whisker deflections is plotted as a function of the whisker identity. Whiskers D2 and D3 were left untrimmed. The left column shows control observations, and the right column shows trimmed animals after three days. A shows supragranular responses, B shows granular layer responses, and C shows infragranular layer responses. The major finding is the difference in D3 responses in the right column of supra‐ and infragranular layer neurons, but not in granular layer neurons.
Figure 11. Figure 11. Image, with permission, from Zhang et al. () EPSPs were recorded from a Xenopus tectal neuron. Then, action potentials in the tectal neuron were paired with synaptic input from the retina at different timing latencies. If the input occurred prior to tectal activation, the synaptic connection grew stronger. If the input occurred after the tectal activation, the connection weakened. This process is called Spike Timing Dependent Plasticity.
Figure 12. Figure 12. Image, with permission, from Blake et al (). In experiments designed to test whether Spike Timing Dependent Plasticity could cause cortical plasticity, monkeys were trained to discriminate time intervals of pairs of taps delivered to their index and middle fingers sequentially. The target, 100 ms, interval responses are shown on different experimental days. A tap is delivered to the index finger at time 0, and to the middle finger at time 100 ms. In A, an electrode that initially sampled only middle finger tap responses develops an index finger response. The converse order is shown in B.
Figure 13. Figure 13. Image, with permission, from Dahmen et al. () Pairs of acoustic stimuli were presented with short temporal intervals to bias the responses of single neurons in auditory cortex. The shift in best frequency (A) illustrates the experimentally measured plasticity variable. It lasts up to 12 min after the pairing procedure ends (C).
Figure 14. Figure 14. Image, with permission, from Ghose et al. (). Monkeys were trained on an orientation discrimination task with orientations close to 45°. Neurons were sampled in V1 and V2, and neurons that matched the selectivity of an orientation selective cell were included in the histogram analysis as part of the sample that preferred the corresponding orientation.
Figure 15. Figure 15. Image, with permission, from Blake et al. 2015 (). Receptive fields measured over more than a 200‐day span are superimposed. The color map on the right shows the conserved core of the receptive field does not change, although the weaker inputs to the receptive field do change. The same results were found on all implanted electrodes that provided clear sampling over any length of time.
Figure 16. Figure 16. Image, with permission, from Bakin and Weinberger (). Guinea pigs were harness restrained, and presentations of the CS (conditioning stimulus), 6 kHz in this case, were paired with foot shock. Neural responses to the CS increased relative to neural responses to the prebehavioral best responding frequency. Although animals conditioned to the CS, the authors are cautious to interpret this plasticity as integral in creating the conditioned response.
Figure 17. Figure 17. Image, with permission, from Blake et al. 2002 (). Population spiking response summed from all implanted electrodes is shown. Recordings were made in response to short tonal stimuli in a ten minute period prior to each behavioral session. The animal began to respond to the task target to receive reward on Day 0 after recordings were made. Responses from the same neurons are plotted as a function of the sensory stimuli used to elicit them.
Figure 18. Figure 18. Image, with permission, from Blake et al. 2006 (). The animal on the right learned to perform the operant task in (B). The animal on the left listened to the behavioral trials and received rewards that matched those of the operant animal in timing and magnitude. (B) Task. The animal breaks a head beam to initiate a trial. A series of tonal stimuli are presented. The animal is rewarded for breaking the head beam immediately after the frequency of the stimulus changes.
Figure 19. Figure 19. Image, with permission, from Blake et al. 2006. Response fields were measured from the animals in the yoke and classic conditioning phases of training. Excitability is the total number of spikes caused by a range of frequency stimuli spanning the frequency range of the neuron at semitonal intervals. Selectivity is the proportion of the excitability in response to the frequency range of stimuli used as conditioning stimuli.
Figure 20. Figure 20. Adapted, with permission, from Spingath et al. 2011 (). Animals initially were trained to pull a lever with their unrestrained hand, and hold for one second. The restrained hand was positioned with tactile motors indenting one point on each of the index and middle fingers. In the detection condition, a tap was delivered to the index finger either 800 or 1200 ms after lever hold initiation, and the animal was rewarded for ending the lever hold after the tap was delivered. The discrimination task is identical except that on half the trials, the middle finger is presented with a task distractor at 800 ms, and the target index finger tap is delivered at 1200 ms. The animal is always rewarded for ending the lever hold after the target tap.
Figure 21. Figure 21. Image, with permission, from Spingath et al. 2011. Mean population responses before (dashed line) and after learning to detect a tap at the target location. Recordings on the left were an average of locations that had a significant response to the task target, and are an average of the responses taken prior to each day's behavioral session. Recordings on the right are taken from the hotspot location in the receptive field of each nontarget‐responding location.
Figure 22. Figure 22. Adapted, with permission, from Spingath et al. 2011. Data are taken from the same animals and paired in the same way as Figure 20. Included are locations that contain the distractor skin location in their receptive field.
Figure 23. Figure 23. Image, with permission, from Carpenter‐Hyland et al. 2010 (). The rat is inside an operant chamber containing a nose poke and pellet reward well.
Figure 24. Figure 24. Image, with permission, based on data in Carpenter‐Hyland et al. 2010. (A) The top box shows the waveform from a short tonal stimulus. The bottom box shows the spike filtered voltage trace from an electrode in auditory cortex. (B) The action potentials occurring 5 to 35 ms after stimulus onset are counted and plotted as a function of sound intensity and frequency. (C) All recordings were classified either as anterior or posterior auditory cortex based the intrinsic imaging border between cortical responses to 5 and 13 kHz tone stimuli. Average recording plots were calculated for caged controls and trained animals. The per pixel percent change between caged controls and 2 week trained animals is shown for anterior, and posterior, auditory cortex. The target was a 5 kHz, 50 dB SPL, sound which activated only anterior auditory cortical neurons in control mapped animals.
Figure 25. Figure 25. Image, with permission, from Guo, Fei, (2013) (). Individual animal layer II/III current source density responses to the 5‐kHz target sound are plotted against the behavioral performance of that animal in detection the previous day.
Figure 26. Figure 26. A subset of Figure 3 from Law and Gold (). Neural responses to a random dot display are shown. All dots were moving, and a subset, indicated as the percent motion coherence, moved in the preferred direction of each neuron (solid lines), or in the opposite direction (dashed lines). Each line is the average response of all single units recorded in the indicated sessions.
Figure 27. Figure 27. The results of neuromodulatory studies suggest a reinforcement learning rule in which patterns of activity that precede the neuromodulatory activity are strengthened. Inputs from the thalamus remain unchanged, and self‐organizing rules in primary sensory cortex operate in this model, but the changes in sensory cortex are modulated, scaled, or gated on by the neuromodulators.
Figure 28. Figure 28. An integrative view of studies on sensory learning and plasticity suggest minimal changes in the thalamic inputs to cortex. Stimuli associated with reinforcement are strengthened, with poor selectivity, by neuromodulator release beginning 100 m after the stimulus. Stimuli associated with omission of reward are suppressed with an instructional cue from association cortex.


Figure 1. Image, with permission, from Kaas et al. (). The “map” shows the organization of the body on the surface of the brain. The areas labelled “D1,” for example, indicate regions on the surface of the brain where a microelectrode penetration into the middle cortical layers would sample from neurons that responded to the first digit, or thumb. The lines indicate borders between regions in which sharp transitions in representation from one body part to the next are found. Of particular interest is the hand map in area 3b, shown as the left half of the map above, primary somatosensory cortex, as it contains adjacent and largely nonoverlapping representations of the digits. This portion of the map has been extensively studied in experience and learning induced plasticity experiments. Anterior is left, medial is up, and this map is taken from a left hemisphere.


Figure 2. Image, with permission, from Allard et al. (). The third and fourth digits of monkeys were sewn together to induce digital syndactyly for three months. The top inset of the image shows the hand representational map in area 3b. The label “area 3a” is placed above the hand map (anterior in the anatomical coordinates) to indicate the relative position of the area 3b hand map to area 3a. The numbers 2, 3, 4, and 5 indicate regions responsive to the corresponding digits. The numbers on the hand map above indicate the cortical locations with receptive fields shown on the numbers on the images below. The shaded zone encompassing digits 3 and 4 in the top map image show the area in which neurons respond to both of the digits involved in the syndactyly.


Figure 3. This image shows the concept of unsupervised learning in sensory cortex plasticity. The inputs from the thalamus stay the same, and the patterns of activity in primary sensory cortex interact with learning rules to lead to the new map in cortex. External inputs from association cortex and from neuromodulators are omitted in this conceptual figure, as these inputs play no role in unsupervised learning.


Figure 4. Image taken, with permission, from Zarzecki et al. (). Intracellular studies show response latencies for stimuli delivered to the primary digit and an adjacent digit. (A) Normal digits (open bars) have a range of latencies from 10 to 25 ms, and adjacent digits (filled bars) have a latency range displaced 5 ms later. (B) Amputation responses to the previously adjacent digit representation have the same latency as normal primary digit responses. (C) Syndactyly has latency ranges matching those in the normal animal.


Figure 5. Image, with permission, from Jenkins et al. (). Owl monkeys were trained at a tactile task that involved touching the tips of the second and third digits to a spinning disk. The cortical maps before and after several months of training are shown. The animals in this study were double mapped, to ensure that any changes in representational size were caused by training.


Figure 6. Image, with permission, from Recanzone et al. (). The behavioral threshold is plotted against the area of cortex activated by the sensory stimulus, as estimated from the map reconstruction.


Figure 7. Image, with permission, from Recanzone et al. (). The reconstruction of the population response to the trained stimulus is compared to the analogous response from another digit in the same animal. The increased sharpness of the onset portion of the response, and its decreased variability, was predictive of behavioral threshold. This increasingly sharp temporal onset is a common observation in animals trained to process short phasic stimuli in somatosensation and audition.


Figure 8. Image, with permission, from Wang et al. (). (A) A lateral view of the owl monkey brain showing the position of the hand representation. (B) The behavioral task. Monkeys received alternating taps on the distal segments of digits 2, 3, and 4, or on the proximal segments of the same digits. The positions of the stimulation bars are shown in (C). In (D), the hand map is shown in an animal that trained on this task for 8 months. Conventions are as in the hand map in (A), except that the dark red shows brain regions responsive to all three distal digit tips used in the task. The light red shows two distal digit receptive fields. The dark and light blue show proximal multidigit receptive fields.


Figure 9. Conceptual view of primary sensory cortical plasticity caused by learning as influenced by map plasticity studies. Primary sensory cortex contains self‐organizing rules that operate based on timing of sensory stimuli to cause inputs that are activated together to be represented at the same cortical location. These rules operate only while stimuli are attended, perhaps through a permissive role by neuromodulators to explain why plasticity occurs in response to task performance but not during presentation of task irrelevant stimuli.


Figure 10. Image, with permission, from Diamond et al. (). The response of cortical neurons in the D2 barrel to whisker deflections is plotted as a function of the whisker identity. Whiskers D2 and D3 were left untrimmed. The left column shows control observations, and the right column shows trimmed animals after three days. A shows supragranular responses, B shows granular layer responses, and C shows infragranular layer responses. The major finding is the difference in D3 responses in the right column of supra‐ and infragranular layer neurons, but not in granular layer neurons.


Figure 11. Image, with permission, from Zhang et al. () EPSPs were recorded from a Xenopus tectal neuron. Then, action potentials in the tectal neuron were paired with synaptic input from the retina at different timing latencies. If the input occurred prior to tectal activation, the synaptic connection grew stronger. If the input occurred after the tectal activation, the connection weakened. This process is called Spike Timing Dependent Plasticity.


Figure 12. Image, with permission, from Blake et al (). In experiments designed to test whether Spike Timing Dependent Plasticity could cause cortical plasticity, monkeys were trained to discriminate time intervals of pairs of taps delivered to their index and middle fingers sequentially. The target, 100 ms, interval responses are shown on different experimental days. A tap is delivered to the index finger at time 0, and to the middle finger at time 100 ms. In A, an electrode that initially sampled only middle finger tap responses develops an index finger response. The converse order is shown in B.


Figure 13. Image, with permission, from Dahmen et al. () Pairs of acoustic stimuli were presented with short temporal intervals to bias the responses of single neurons in auditory cortex. The shift in best frequency (A) illustrates the experimentally measured plasticity variable. It lasts up to 12 min after the pairing procedure ends (C).


Figure 14. Image, with permission, from Ghose et al. (). Monkeys were trained on an orientation discrimination task with orientations close to 45°. Neurons were sampled in V1 and V2, and neurons that matched the selectivity of an orientation selective cell were included in the histogram analysis as part of the sample that preferred the corresponding orientation.


Figure 15. Image, with permission, from Blake et al. 2015 (). Receptive fields measured over more than a 200‐day span are superimposed. The color map on the right shows the conserved core of the receptive field does not change, although the weaker inputs to the receptive field do change. The same results were found on all implanted electrodes that provided clear sampling over any length of time.


Figure 16. Image, with permission, from Bakin and Weinberger (). Guinea pigs were harness restrained, and presentations of the CS (conditioning stimulus), 6 kHz in this case, were paired with foot shock. Neural responses to the CS increased relative to neural responses to the prebehavioral best responding frequency. Although animals conditioned to the CS, the authors are cautious to interpret this plasticity as integral in creating the conditioned response.


Figure 17. Image, with permission, from Blake et al. 2002 (). Population spiking response summed from all implanted electrodes is shown. Recordings were made in response to short tonal stimuli in a ten minute period prior to each behavioral session. The animal began to respond to the task target to receive reward on Day 0 after recordings were made. Responses from the same neurons are plotted as a function of the sensory stimuli used to elicit them.


Figure 18. Image, with permission, from Blake et al. 2006 (). The animal on the right learned to perform the operant task in (B). The animal on the left listened to the behavioral trials and received rewards that matched those of the operant animal in timing and magnitude. (B) Task. The animal breaks a head beam to initiate a trial. A series of tonal stimuli are presented. The animal is rewarded for breaking the head beam immediately after the frequency of the stimulus changes.


Figure 19. Image, with permission, from Blake et al. 2006. Response fields were measured from the animals in the yoke and classic conditioning phases of training. Excitability is the total number of spikes caused by a range of frequency stimuli spanning the frequency range of the neuron at semitonal intervals. Selectivity is the proportion of the excitability in response to the frequency range of stimuli used as conditioning stimuli.


Figure 20. Adapted, with permission, from Spingath et al. 2011 (). Animals initially were trained to pull a lever with their unrestrained hand, and hold for one second. The restrained hand was positioned with tactile motors indenting one point on each of the index and middle fingers. In the detection condition, a tap was delivered to the index finger either 800 or 1200 ms after lever hold initiation, and the animal was rewarded for ending the lever hold after the tap was delivered. The discrimination task is identical except that on half the trials, the middle finger is presented with a task distractor at 800 ms, and the target index finger tap is delivered at 1200 ms. The animal is always rewarded for ending the lever hold after the target tap.


Figure 21. Image, with permission, from Spingath et al. 2011. Mean population responses before (dashed line) and after learning to detect a tap at the target location. Recordings on the left were an average of locations that had a significant response to the task target, and are an average of the responses taken prior to each day's behavioral session. Recordings on the right are taken from the hotspot location in the receptive field of each nontarget‐responding location.


Figure 22. Adapted, with permission, from Spingath et al. 2011. Data are taken from the same animals and paired in the same way as Figure 20. Included are locations that contain the distractor skin location in their receptive field.


Figure 23. Image, with permission, from Carpenter‐Hyland et al. 2010 (). The rat is inside an operant chamber containing a nose poke and pellet reward well.


Figure 24. Image, with permission, based on data in Carpenter‐Hyland et al. 2010. (A) The top box shows the waveform from a short tonal stimulus. The bottom box shows the spike filtered voltage trace from an electrode in auditory cortex. (B) The action potentials occurring 5 to 35 ms after stimulus onset are counted and plotted as a function of sound intensity and frequency. (C) All recordings were classified either as anterior or posterior auditory cortex based the intrinsic imaging border between cortical responses to 5 and 13 kHz tone stimuli. Average recording plots were calculated for caged controls and trained animals. The per pixel percent change between caged controls and 2 week trained animals is shown for anterior, and posterior, auditory cortex. The target was a 5 kHz, 50 dB SPL, sound which activated only anterior auditory cortical neurons in control mapped animals.


Figure 25. Image, with permission, from Guo, Fei, (2013) (). Individual animal layer II/III current source density responses to the 5‐kHz target sound are plotted against the behavioral performance of that animal in detection the previous day.


Figure 26. A subset of Figure 3 from Law and Gold (). Neural responses to a random dot display are shown. All dots were moving, and a subset, indicated as the percent motion coherence, moved in the preferred direction of each neuron (solid lines), or in the opposite direction (dashed lines). Each line is the average response of all single units recorded in the indicated sessions.


Figure 27. The results of neuromodulatory studies suggest a reinforcement learning rule in which patterns of activity that precede the neuromodulatory activity are strengthened. Inputs from the thalamus remain unchanged, and self‐organizing rules in primary sensory cortex operate in this model, but the changes in sensory cortex are modulated, scaled, or gated on by the neuromodulators.


Figure 28. An integrative view of studies on sensory learning and plasticity suggest minimal changes in the thalamic inputs to cortex. Stimuli associated with reinforcement are strengthened, with poor selectivity, by neuromodulator release beginning 100 m after the stimulus. Stimuli associated with omission of reward are suppressed with an instructional cue from association cortex.
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Teaching Material

 

D. T. Blake. Network Supervision of Adult Experience and Learning Dependent Sensory Cortical Plasticity. Compr Physiol 7 2017, 977-1008.

 

 

Didactic Synopsis

 

 

 

 

Major Teaching Points:

 

 

 

     

  • Primary sensory cortex is plastic, or can change, throughout life.
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  • The change is strongly influenced by behaviors in which the animal associates a stimulus with reinforcement or omission of reinforcement.
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  • Association with reinforcement leads to a poorly selective increased responsiveness.
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  • Association with omission of reward leads to a selective response suppression.
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  • The neural mechanisms underlying increased responsiveness are poorly described and are not well explained solely by known synaptic plasticity mechanisms.
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  • The neural mechanisms underlying response suppression appear to be well explained by synaptic long-term depression.
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  • Changes appear greatest in principal neurons in layers II/III and V/VI.
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  • The changes are influenced by the neuromodulator acetylcholine.
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  • Evidence supports that more central sensory cortices guide the response suppression seen when a stimulus is associated with omission of reward.
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Didactic Legends

 

The figures—in a freely downloadable PowerPoint format—can be found on the Images tab along with the formal legends published in the article. The following legends to the same figures are written to be useful for teaching.

 

 

 

Figure 1. This figure illustrates a cortical map. The map groups regions of the brain by the dominant inputs that activate them. D1 refers to the first digit, commonly called the thumb. Area 3b is the primary somatosensory cortex, and areas 1 and 2 are secondary areas. Area 3a is thought to be a hybrid area contributing to motor feedback and sensory function. This figure was defined by making microelectrode penetrations into the brain into the central cortical layers about 1 mm deep. Action potentials are recorded, and the skin regions where stimulation will evoke action potentials are observed.

Figure 2. This figure illustrates changes in the cortical map caused by suturing two fingers together. In the normal hand map, seen in Figure 1, regions of cortex that respond to distal fingers tend to respond to only one distal finger. In the animal with fingers sutured together for several months, the hand map contains regions that respond to the two fingertips of the fingers sewn together. This experiment provides a clear demonstration of plasticity in adulthood, and suggested that providing common stimulation to the digits sewn together contributed to the plasticity.

Figure 3. This figure illustrates the types of learning rules hypothesized to be operating in primary sensory cortex by the studies discussed in this section of the review. The sensory thalamus inputs project to primary sensory cortex, and the local patterns of activity lead to plasticity through self-organizing rules.

Figure 4. This figure illustrates the latencies, or time to activation, from positions on the raccoon paw to primary sensory cortex. In the case of amputation of the fourth digit (B), the forepaw reorganizes, but the new latencies are the same as in the normal raccoon. In the case of syndactyly, the latencies for the emergent (plastic) responses are longer. Combined with other data discussed in the text, this figure supports a different, cortically based, mechanism for plasticity in syndactyly from the mechanism for plasticity in amputation.

Figure 5. This figure comes from a breakthrough study that cleanly and clearly demonstrates that primary sensory cortex plasticity can be caused by voluntary behavior. Owl monkeys maintained touch with a spinning disk to receive a reward. The tips of the fingers they used developed larger cortical areas in the primary sensory cortex map (lower) compared to before training (upper). Several months of training were used.

Figure 6. This figure suggests a relationship between cortical area and behavior. After training in discriminating the frequency of a flutter stimulus on the skin, the threshold frequency change that could be detected by the monkey was dependent on the position on the skin where the probe was placed. This dependency was well correlated with the cortical area that responded to the skin area where the probe was placed. The suggestion is that recruiting a larger cortical area improves temporal precision.

Figure 7. This figure illustrates a peristimulus time histogram from an untrained digit and a trained digit. The monkey performed a flutter frequency discrimination for months. The temporal precision in the population response to the trained stimulus was increased relative to the population response to a stimulus on a different digit. This improved precision predicted the improvement in discrimination.

Figure 8. This figure illustrates the outcomes of a study designed to test whether neurons that are coactivated will lead to map reorganization. (A) Illustration of a side view of an owl monkey brain and the location of the hand map. (B) The behavioral task. This task delivers alternating taps to the proximal and distal fingers of the second, third, and fourth fingers. The hypothesis is that the coactivation of these skin surfaces will cause their representations in the cortical map to merge. (C) This subfigure illustrates the positions the bars contacted on the fingers, and some receptive fields observed in the map. (D). The dark red and dark blue areas indicate regions where the cortical map has reorganized to include all three stimulated fingers.

Figure 9. This figure illustrates the types of learning rules hypothesized to be operating in primary sensory cortex by the studies discussed in this section of the review. The sensory thalamus inputs project to primary sensory cortex, and the local patterns of activity lead to plasticity through self-organizing rules. In addition, neuromodulators, which are acting during periods of attention to that sensory system, are permitting the plasticity to occur.

Figure 10. This figure illustrates the laminar dependency of plasticity, which appears generalizable. Each plot shows the number of action potentials elicited by activation of the same whisker, D2. In the experiment, one whisker next to it, D-paired, was left intact, while all other whiskers were trimmed short. In A and C, the D-paired responses are enhanced, while at the same time point the D-paired response in B is not enhanced. B is the response in the middle, input, cortical layers, while A and C illustrate the upper and lower layers of cortex, respectively.

Figure 11. This figure is one of many of the time period 1995 to 2000 that illustrate spike timing dependent plasticity, which rapidly became an influential concept in the field. The investigators patched onto a neuron in the retina of Xenopus, and also onto a connected neuron in the tectum. The investigators made both neurons fire action potentials with control over the timing, and measured the change in the excitatory potential in the tectal neuron caused by an action potential in the retinal neuron. If the retinal neuron action potential consistently occurs earlier than the tectal neuron action potential, the excitatory potential is enhanced (left upper quadrant). The converse occurs if the timing is reversed (lower right quadrant).

Figure 12. This figure illustrates plasticity caused by nearly coincident sensory stimuli. Monkeys were trained to detect a pair of taps delivered over a 100 ms interval. In the histograms, the tap to the index finger was delivered at time 0, while the tap to the middle finger was delivered at 100 ms. The prediction from spike timing dependent plasticity was that multi-digit responses would not emerge until the interval was shortened to 40 ms or less, and that the response to the index finger would appear in middle finger responsive locations as in A, but the converse would not happen, as in B. However, multidigit responses did occur with a 100 ms interval, and were not order dependent, which cast doubt on the initial hypothesis.

Figure 13. This figure illustrates that short, but not long, term changes in auditory cortical neuron selectivity can be elicited by pairing stimuli with short temporal intervals. The shift in selectivity is shown in A, and it builds over more than 1000 conditioning pairs. However, upon halting conditioning, and waiting 12 min, all plasticity is gone. Again, this finding challenges spike timing dependent plasticity as a dominant mechanism underlying adult learning or experience dependent plasticity.

 

 

Figure 14. This figure illustrates a challenge to the map plasticity hypothesis that attended sensory stimuli develop larger cortical response fields and stronger responses. Monkeys were trained on an orientation discrimination task with orientations close to 45°. Neurons were sampled in V1 and V2, and neurons that matched the selectivity of an orientation selective cell were included in the histogram analysis as part of the sample that preferred the corresponding orientation. Fewer, not greater, neurons with selectivity for the trained stimuli were found in both V1 and V2.

 

 

Figure 15. This figure illustrates both the stability of implanted electrodes in somatosensory cortex, and the stability of receptive fields. Receptive fields were measured over more than 200 days, and overlaid. A small zone on the distal segment of the index finger was the most sensitive region for all samples. Based on the size of the cortical column, and known relations for how receptive fields change with electrode movement, this electrode tip moved less than 100 microns relative to the neutrophil over 200 days, and the most sensitive inputs to a cortical column are largely spatially immutable in this time period.

Figure 16. This figure illustrates the major finding of the receptive field model of associative plasticity. A neuron in auditory cortex is found, and the selectivity of its action potential responses to sound is measured. Then, a 6 kHz sound is paired with a footshock, and after conditioning the responses to 6 kHz sounds are increased, while responses to the previous best responses are decreased.

 

 

Figure 17. This figure illustrates how responses in auditory cortex are altered in the days after learning an auditory task. The animal began to respond to the task target to receive reward on Day 0 after recordings were made. Responses from the same neurons are plotted as a function of the sensory stimuli used to elicit them. Within 2 days of learning to respond to acoustic stimuli, the responses to the task target nearly tripled.

 

 

Figure 18. This figure illustrates a control experiment. The yoked animal listened to successful behavioral trials and received appropriately timed rewards, but did not learn the sounds were associated with the rewards, and no significant plasticity occurred.

Figure 19. This figure illustrates that learning, and not stimulus-reward pairing without learning, causes cortical plasticity. The same stimuli were used in the Y condition without learning, as in the CC condition with learning. The excitability increased under the CC condition in both tested animals, and the selectivity was altered in one of the two.

Figure 20. This figure illustrates a series of tasks designed to isolate associations learned in sensory discrimination. In Detection, a lever pull is ended after a target is delivered. In Discrimination, the task design is the same, but on some trials, a distractor is delivered first. Animals learn the detection first, which experimentally isolates the impact of the association between the target and reward. The addition of the distractor isolates the impact of the association between the distractor and omission of reward.

Figure 21. This figure illustrates the impact of detection learning on neurons that are selective for the detection target, and on control neurons that do not respond to the detection target. In each figure, the stimulus is introduced at time zero, and the action potential rate is measured thereafter. Both groups of neurons increase their responses comparably after learning. This finding suggested that the formation of an association between a sensory stimulus and reinforcement leads to a poorly or non-selective increase in responsiveness.

Figure 22. This figure illustrates the impact of adding a distractor while an animal performs a detection task. The addition of the distractor creates an association between the distractor and omission of reward. Only neurons selective for the distractor become less responsive after learning.

Figure 23. This figure illustrates the rat version of a simple detection behavior. The rat is inside a chamber containing a nose poke and pellet reward well. It enters the nose poke, and exits only after hearing a brief sound.

Figure 24. This figure illustrates an experiment testing the selectivity of plasticity after detection learning. Animals performed the detection task from Figure 23 for 2 weeks. Then, responses were collected from auditory cortex. In A, the response (bottom) to a single sound (top) is shown. The response is a voltage with frequencies less than 300 Hz filtered out to show the action potentials more clearly. Subpanel B shows the number of action potentials occurring shortly after the sound onset as a function of sound intensity and frequency. C and D show the average change in the posterior (C) and anterior (D) halves of auditory cortex. The target was 50 dB SPL, and 5 kHz. The major point of this figure is that the responses to the target are not boosted more than the responses to other sounds, even after detecting the target over 2000 times. Arguably, they are boosted less in the low-frequency region (C).

Figure 25. This figure illustrates changes in response strength that correlate with detection behavioral acuity. Animals performed the task from Figure 23 for 2 weeks. The responses that relate to behavioral performance are found in layers II and III. This finding is similar to the finding from Figure 10.

Figure 26. This figure illustrates enhanced responsiveness after learning in the monkey visual system. During sessions 1 to 50, responses at high dot coherences are increased relative to pretraining responses. This effect goes away by sessions 101 to 160.

Figure 27. This figure illustrates the concepts introduced by studies that includes the learning rules caused by association with reward and omission of reward. Neuromodulators instantiate a neural network reinforcement supervisor role, and otherwise unsupervised learning rules are contributing to learning and experience-dependent plasticity.

Figure 28. This figure adds the teaching signal from association cortex to primary sensory cortex, which at least has a hypothesized role to guide suppression of neural responses associated with omission of reward.

 

 

 

 

 

 

 


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David T. Blake. Network Supervision of Adult Experience and Learning Dependent Sensory Cortical Plasticity. Compr Physiol 2017, 7: 977-1008. doi: 10.1002/cphy.c160036