frontal thalamus first draft complete.

Author: rcoreilly

citedrefs.json

@@ -21,11 +21,13 @@ There are a number of limitations of the PBWM framework that are addressed in th
 
 * Limited learning capabilities. The reliance on phasic dopamine to train BG gating signals via something like standard [[reinforcement learning]] mechanisms is very inefficient, especially as the dimensionality of the problem space increases (i.e., the [[curse of dimensionality]]). It essentially amounts to serial trial-and-error [[search]] over when to update vs. maintain, whereas only a _dedicated-parallel, gradient-based_ learning mechanism scales well as dimensionality increases.
 
-* Fine-grained gating signals are implausible and impractical. An LSTM model typically has separate input and output gates for each individual memory unit, providing a very fine-grained level of control. PBWM hypothesized larger pools or stripes of PFC units all controlled by a common set of gates, which is more consistent with the biological parameters. However, the relevant thalamic connectivity into the PFC appears to be relatively broad and diffuse, which is inconsistent with a fine-grained level of control. Furthermore, finer-grained control signals exacerbate the curse of dimensionality learning problems.
+* Fine-grained gating signals are implausible and impractical. An LSTM model typically has separate input and output gates for each individual memory unit, providing a very fine-grained level of control. PBWM hypothesized larger pools or stripes of PFC units all controlled by a common set of gates, which is more consistent with the biological parameters. However, the relevant thalamic connectivity into the PFC appears to be relatively broad and diffuse, which is inconsistent with a fine-grained level of control. Furthermore, finer-grained control signals exacerbate the curse of dimensionality learning problems, by creating many more independent degrees of freedom.
 
 These limitations are addressed in the current [[Axon]] and [[Rubicon]] models as follows:
 
-* Goal-driven areas of PFC (i.e., most of rodent PFC) are all updated by a common goal-engagement gating signal driven by ventral and medial BG areas, which modulate the MD (mediodorsal) thalamus that provides broad connectivity across all of the distributed goal areas in PFC. This low-dimensional signal is learned in the context of a careful goal-selection process that leverages prior learning across multiple relevant dimensions, and bidirectional [[constraint satisfaction]] among all the goal PFC areas, to make optimized choices.
+* Goal-driven areas of PFC (i.e., most of rodent PFC) are all updated by a common goal-engagement gating signal driven by ventral and medial BG areas, which modulate the MD (mediodorsal) thalamus that provides broad connectivity across all of the distributed goal areas in PFC. This low-dimensional signal is learned in the context of a careful goal-selection process that leverages prior learning across multiple relevant dimensions, and bidirectional [[constraint satisfaction]] among all the goal PFC areas, to make optimized choices. There is extensive neuromodulatory support via [[acetylcholine]] and [[dopamine]] for driving the phasic gating signals.
 
 * Motor areas of PFC and frontal neocortex that are involved in executing motor actions under influence of an engaged goal can use BG modulated thalamic signals as parallel, graded learning signals, similar to how the pulvinar nucleus of the thalamus operates in [[predictive learning]]. This builds on the powerful graded, parallel BG learning mechanisms, rather than the serial gating-like functionality envisioned in PBWM.
 
+TODO: PTp rename to IT neurons that have BG gating from AM, VA/VM and do predictive learning, provide lots of input to thalamus and BG for gating! [[@ShepherdYamawaki21]].
+

content/pbwm.md

@@ -100,9 +100,12 @@ The LSTM model demonstrates the computational power of a system with dynamic mul
 Recent experimental data has provided strong support for this gating-like influence of the thalamus over PFC active maintenance. In terms of anatomy, [[@^GuoYamawakiSvobodaEtAl18]] used multiple advanced neuroscience tools to determine the precise nature of the thalamocortical loops between layer 5b PT (pyramidal tract) neurons in area ALM (rodent dlPFC) and the VM (ventromedial) thalamic nucleus, as shown in [[#figure_alm-thal-loop]]. VM also projects to primary motor cortex (M1), but the specific neurons that receive inputs from the PFC PT neurons also send back up to ALM, not M1, forming a closed excitatory loop that is likely important for sustaining active neural firing.
 
 {id="figure_economo-18" style="height:30em"}
-![Evidence for two different types of layer 5 pyramidal-track (PT) neurons, from Economo et al., 2018. The PT-upper neurons participate in the thalamic loops as shown in the prior figure, while the PT-lower neurons project to the motor control areas in the medulla oblongata. Panel d shows this distribution in terms of retrogradely labeled neurons projecting to the thalamus vs medulla.](media/fig_pfc_economo_etal_18_pt_types.png)
+![Evidence for two different types of layer 5b pyramidal-track (PT) neurons, from Economo et al., 2018. The PT-upper neurons participate in the thalamic loops as shown in the prior figure, while the PT-lower neurons project to the motor control areas in the medulla oblongata. Panel d shows this distribution in terms of retrogradely labeled neurons projecting to the thalamus vs medulla.](media/fig_pfc_economo_etal_18_pt_types.png)
+
+Further refinement of the organization of the deep layer 5 PT neurons is provided by [[@^EconomoViswanathanTasicEtAl18]], who found evidence for two distinct types of these neurons as shown in [[#figure_economo-18]]. The PT-upper neurons (in layer 5b upper) are the ones involved in the thalamocortical loops shown in [[#figure_alm-thal-loop]], while the PT-lower neurons (5b lower) project down to brainstem [[motor]] areas such as the medulla oblongata.
+
+TODO: figure this out: L5a IT -> thal, str (?), L5bU PT -> thal, str (PTp?), L5bL PT -> subcort
 
-Further refinement of the organization of the deep layer 5 PT neurons is provided by [[@^EconomoViswanathanTasicEtAl18]], who found evidence for two distinct types of these neurons as shown in [[#figure_economo-18]]. The PT-upper neurons are the ones involved in the thalamocortical loops shown in [[#figure_alm-thal-loop]], while the PT-lower neurons project down to brainstem motor areas such as the medulla oblongata.
 
 The presence of these two neuron types provides a mechanism for distinguishing between preparatory motor planning versus actual motor execution, to prevent premature execution during the planning stage ([[@ChurchlandShenoy24]]). However, [[@^EconomoViswanathanTasicEtAl18]] found that there was not a simple clean dissociation between these two neural populations, with both types of neurons exhibiting activity during delay and response phases. Nevertheless, there was an increased likelihood of PT-lower neurons firing immediately prior and during the response, while PT-upper neurons were more likely to exhibit sustained firing during the preparatory delay period. Thus, as discussed in [[distributed representations]] and shown in [[#figure_hunt-rsa]], population-level patterns shaped by learning are always the most relevant for driving behavior.
 
@@ -159,8 +162,6 @@ TODO:
 
 Several neuroimaging studies in humans have investigated potential gating relationships between basal ganglia and PFC. For example, [[@^vanSchouwenburgdenOudenCools10]] showed using [[dynamic causal modeling]] under [[fMRI]] that ventral striatum activity led to task switching as reflected in dlPFC areas (specifically the IFG, which has often been implicated in human task representation maintenance), which then drove activity in stimulus-specific posterior cortical areas (for faces and scenes). While it is difficult to precisely exclude the involvement of the DMS which presumably is what drives the VAmc, the VS-specific activity implies that these areas, which project to MD thalamus, may be driving more of a larger-scale goal-state updating in the task switching case, versus a more focal, purely cognitive level update. A subsequent study also found ventral striatum as the locus of attentional switching ([[@vanSchouwenburgdenOudenCools15]]), and [[@vandenBoschLambregtsMaattaEtAl22]] show differences in dopamine levels in ventral putamen are associated with individual differences and phamacological manipulations in reversal learning.
 
-* 
-
 ## Computational implementation of PFC
 
-* PT, PTp
+* PT, PTp rename to IT [[@ShepherdYamawaki21]].

content/prefrontal-cortex.md

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