minor syn plast updates

Author: rcoreilly

content/oreilly-2026-cortlearn.md

@@ -97,7 +97,7 @@ In BTSP, elevated _plateau potentials_ in distal dendrites provide the critical
 
 In both of these BTSP cases, there is rapid learning driven by the distal dendritic inputs, with an inhibitory negative feedback loop that prevents overtraining. There is evidence in the CA1 neurons that this plasticity is generally transient, consistent with a _fast mapping_ type of learning that can rapidly adapt to read out behaviorally-relevant signals from the more slowly-adapting internal representations of the relevant systems (hippocampus or neocortex) ([[@VaidyaLiChitwoodEtAl25]]).
 
-The driving target signal for plasticity in the layer 5 neocortical neurons remains unclear ([[@Magee26]]), but we do know that these neurons receive extensive thalamic input targeting the distal dendritic tuft in layer 1. The thalamic projections that target layer 1 are generally of the _matrix_ type, which means they typically have broad axonal arbors targeting many different neurons across multiple cortical areas. A prominent and widespread source of such projections comes from the ventral anterior (VA) nucleus, which receives inputs from layer 5 neurons in motor cortical areas, and is also under disinhibitory control from the basal ganglia ([[@PhillipsKambiRedinbaughEtAl21]]; [[@XiaoZikopoulosBarbas09]]; [[@KuramotoOhnoFurutaEtAl15]]; [[@EconomoViswanathanTasicEtAl18]]). This would provide a way for direct motor-relevant target signals to drive the rapid tuning of neocortical output neurons across most of neocortex, even all the way down in area V1 ([[@YaegerSoto-AlborsLiuEtAl25]]; [[@KuramotoOhnoFurutaEtAl15]])
+The driving target signal for plasticity in the layer 5 neocortical neurons remains unclear ([[@Magee26]]), but we do know that these neurons receive extensive thalamic input targeting the distal dendritic tuft in layer 1. The thalamic projections that target layer 1 are generally of the _matrix_ type, which means they typically have broad axonal arbors targeting many different neurons across multiple cortical areas. A prominent and widespread source of such projections comes from the ventral anterior (VA) nucleus, which receives inputs from layer 5 neurons in motor cortical areas, and is also under disinhibitory control from the basal ganglia ([[@PhillipsKambiRedinbaughEtAl21]]; [[@XiaoZikopoulosBarbas09]]; [[@KuramotoOhnoFurutaEtAl15]]; [[@EconomoViswanathanTasicEtAl18]]). This would provide a way for direct motor-relevant target signals to drive the rapid tuning of neocortical output neurons across most of neocortex, even all the way down in area V1 ([[@YaegerSoto-AlborsLiuEtAl25]]; [[@KuramotoOhnoFurutaEtAl15]]).
 
 The temporally-extended nature of the eligibility-trace mechanism allows later outcome or motor action signals to drive learning from earlier state representations, providing a solution to the _temporal_ version of the credit assignment process (this is what the behavioral timescale connotes). In other brain areas such as the basal ganglia, and recently in the neocortex, neuromodulatory signals including dopamine have been found to drive eligibility-trace learning mechanisms, bridging the temporal gap until reinforcement signals become available ([[@HeHuertasHongEtAl15]]; [[@ShouvalKirkwood25]]).
 

content/synaptic-plasticity.md

@@ -192,7 +192,7 @@ In addition to rapid temporal dynamics on the timescale of a few 100 millisecond
 
 The initial synaptic activity associated with the performance of the action is thought to initiate an _eligibility trace_ for the relevant synapses that allows them to be modified by the activity that occurs during the subsequent outcome, thereby establishing a critical cause-and-effect relationship across these two points in time.
 
-Recent data from neurons in area CA1 of the [[hipppocampus]] shows evidence of learning contingencies that extend across the shorter end of these _behavioral_ timescales, which has been termed _behavioral timescale synaptic plasticity_ (BTSP; [[@BittnerGrienbergerVaidyaEtAl15]]; [[@BittnerMilsteinGrienbergerEtAl17]]; [[@MilsteinLiBittnerEtAl21]]; [[@FanKimJenningsEtAl23]]; [[@Magee26]]). Specifically, these neurons can exhibit rapid changes in their spatial tuning as a result of elevated dendritic _plateau potentials_, which can be separated from the relevant presynaptic activity by several seconds.
+Recent data from neurons in area CA1 of the [[hippocampus]] shows evidence of learning contingencies that extend across the shorter end of these _behavioral_ timescales, which has been termed _behavioral timescale synaptic plasticity_ (BTSP; [[@BittnerGrienbergerVaidyaEtAl15]]; [[@BittnerMilsteinGrienbergerEtAl17]]; [[@MilsteinLiBittnerEtAl21]]; [[@FanKimJenningsEtAl23]]; [[@Magee26]]). Specifically, these neurons can exhibit rapid changes in their spatial tuning as a result of elevated dendritic _plateau potentials_, which can be separated from the relevant presynaptic activity by several seconds.
 
 These plateau potentials in BTSP occur as a result of synaptic inputs from the entorhinal cortex, and are associated with goal outcomes. Thus, they are thought to function like a "third factor" training signal that indicates the consequences of actions ([[@GerstnerLehmannLiakoniEtAl18]]). Other such third factors include neuromodulators such as [[dopamine]], [[acetylcholine]], [[norepinepherine]], and [[serotonin]], all of which have been shown in various ways to modulate learning, and are associated with reward and punishment outcomes in [[reinforcement learning]] paradigms. Two of these neuromodulators, [[norepinepherine]] and [[serotonin]], where found to have a delayed BTSP-like effect on plasticity in the cortex, for example ([[@HeHuertasHongEtAl15]]; [[@ShouvalKirkwood25]]).
 
@@ -202,7 +202,9 @@ One interesting property of the CA1-based BTSP mechanism is that there is a nega
 
 A similar BTSP-like mechanism has recently been described in layer 5 pyramidal neurons in [[neocortex]] ([[@YaegerSoto-AlborsLiuEtAl25]]; [[@XiaoLiSullivanEtAl25]]). These neurons provide the primary cortical output signals, and are thus appropriate targets for a rapid but transient output mapping process, similar to that used in [[reservoir computing]]. Like the CA1 pyramidal neurons, these layer 5 neurons also have a prominent distal dendritic tuft, in layer 1, where the plateau potentials were found. Also, some of these studies specifically looked for and did not find evidence of these plateau potentials in other neocortical pyramidal neurons ([[@YaegerSoto-AlborsLiuEtAl25]]), suggesting that these are unique to these deep output layer neurons.
 
-The driving target signal for plasticity in these layer 5 neurons remains unclear ([[@Magee26]]), but we do know that these neurons receive extensive [[thalamus|thalamic]] input targeting the distal dendritic tuft in layer 1. The thalamic projections that target layer 1 are generally of the _matrix_ type, which means they typically have broad axonal arbors targeting many different neurons across multiple cortical areas. Thus, they appear ideally situated to provide the same kind of very broad signal as the neuromodulators like dopamine.  One class of such projections arises specifically from motor-associated thalamic areas (see [[thalamus#frontal thalamus]]), which could then provide a way for widespread cortical output learning to optimize the alignment of cortical representations for the current behavioral outputs.
+The driving target signal for plasticity in these layer 5 neurons remains unclear ([[@Magee26]]), but we do know that these neurons receive extensive [[thalamus|thalamic]] input targeting the distal dendritic tuft in layer 1. The thalamic projections that target layer 1 are generally of the _matrix_ type, which means they typically have broad axonal arbors targeting many different neurons across multiple cortical areas. Thus, they appear ideally situated to provide the same kind of very broad signal as the neuromodulators like dopamine.  
+
+One class of such projections arises specifically from motor-associated thalamic areas (see [[thalamus#frontal thalamus]]), which could then provide a way for widespread cortical output learning to optimize the alignment of cortical representations for the current behavioral outputs. Specifically the ventral anterior (VA) nucleus projects to essentially the entire neocortex, and receives inputs from layer 5 neurons in motor cortical areas, and is also under disinhibitory control from the basal ganglia ([[@PhillipsKambiRedinbaughEtAl21]]; [[@XiaoZikopoulosBarbas09]]; [[@KuramotoOhnoFurutaEtAl15]]; [[@EconomoViswanathanTasicEtAl18]]).
 
 Another earlier literature on these temporal bridging mechanisms focused on _synaptic tags_ that could persist for an hour or more, based on the distinct mechanisms involved in _late maintenance_ of plastic changes as mentioned above ([[@MorrisFrey97]]; [[@ReymannFrey07]]; [[@RedondoMorris11]]). Given the need to connect the induction events with these later protein-synthesis dependent mechanisms, it would seem that this kind of temporal bridging is already a necessary feature of the post-induction chemical cascades.