178 lines
11 KiB
Markdown
178 lines
11 KiB
Markdown
# BEH-BD.md
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Qui comprendiamo:
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- BEH-BD: Dendritic Branch
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## BEH-BD: Container
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**Discursive description**:
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The dendrite is the receiving arm of a neuron — a long, branching extension of the cell body whose job is to collect the electrical signals generated by postsynaptic spines, integrate them in space and time, and route their combined effect toward the soma, where the decision to fire an action potential is made. A single dendritic branch can be thought of as a shared electrical highway: dozens to hundreds of postsynaptic spines line its length, each one a private compartment where synaptic signals are first detected, and the dendrite shaft is the common conductor that carries all of their contributions forward.
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Each spine sits along the branch and generates a small electrical signal — an excitatory postsynaptic potential, or EPSP — whenever its AMPA and NDMA receptors are activated by neurotransmitters from the presynapse. This EPSP spreads from the spine head through the narrow spine neck and into the dendrite shaft, where it joins a shared pool of electrical activity. The spine neck is not a neutral conduit — it has electrical resistance that attenuates and slows the signal as it passes through, and its geometry can change with synaptic activity. A wider, shorter neck passes the EPSP more faithfully; a narrower, longer neck attenuates it more severely. This geometry is one of the mechanisms through which plasticity expresses itself physically: LTP widens the neck, making a strengthened synapse electrically closer to the dendrite.
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Once in the shaft, EPSPs from different spines summate. If two spines fire close together in time, their EPSPs overlap and their combined depolarisation is larger than either alone — this is temporal summation. If two spines fire simultaneously but are located close together along the branch, their EPSPs also overlap in space before they decay — this is spatial summation. The dendrite is therefore performing a continuous integration across both time and space, weighting each spine's contribution by how recently it fired and how well its signal survived the journey through the neck and along the shaft.
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The shaft itself is passive in this model — it conducts electrical signals without amplifying them. The key property of a passive cable is the membrane time constant: how long a voltage change persists before leaking back to rest through the membrane. A long time constant means EPSPs linger and are more likely to overlap with subsequent arrivals, broadening the temporal window for summation. A short time constant means only very precisely timed inputs summate, sharpening the temporal selectivity of the branch. The length constant — how far a signal travels along the shaft before decaying to a fraction of its original amplitude — sets the spatial window: spines farther from the soma contribute a smaller fraction of their EPSP to the somatic potential than nearby spines.
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In the full biological model, the dendrite is far from passive. Voltage-gated sodium, potassium, and calcium channels are distributed throughout the dendritic shaft and can generate local regenerative events called dendritic spikes — brief, locally amplified depolarisations that boost the signal and ensure it reaches the soma with sufficient strength. Dendritic spikes give individual branches a degree of computational independence: a branch can, under some conditions, generate a strong enough local event to drive somatic firing even when other branches are quiet. This makes the dendrite not just a wire but a computational unit in its own right. However, in the simplified passive model we adopt here, these active conductances are not included — the shaft sums and attenuates, and nothing more.
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The soma sits at the convergence point of all dendritic branches. It continuously integrates the summed depolarisation arriving from the dendrites and compares it against a threshold at the axon hillock — the narrow junction where the soma meets the axon, and the site with the highest density of voltage-gated sodium channels in the neuron. When the summed input crosses this threshold, an action potential is triggered. This AP propagates forward down the axon to the next neuron, and simultaneously backward up all dendritic branches as a back-propagating action potential, or bAP.
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The bAP is one of the most important signals in the postsynaptic system. It travels from the soma back toward every spine on every branch, carrying the information that the neuron has just fired. At each spine it arrives as a brief, strong depolarisation — in the full biological system its amplitude decays with distance from the soma, so distal spines receive a weaker bAP than proximal ones. This attenuation is not merely a physical limitation: it is a functional gradient that makes the synapse's location on the dendrite matter for plasticity. A distal spine must generate a stronger local AMPA signal to achieve the coincidence needed for LTP, because the bAP it receives is weaker. A proximal spine achieves coincidence more easily because it receives a stronger bAP. Dendritic location is therefore a form of synaptic weighting that is built into the geometry of the cell rather than into the receptor count.
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The bAP is the bridge that closes the loop between the postsynapse and the dendrite. Without it, the NMDA coincidence gate at each spine can only be opened by local AMPA depolarisation — which is rarely sufficient alone to fully clear the magnesium block. With the bAP, any spine that has NT in its cleft at the moment the neuron fires receives the full coincidence signal: NT from the presynapse and depolarisation from the soma simultaneously, opening the NMDA gate and allowing the calcium surge that drives plasticity. The bAP is how the neuron reports its own firing back to the very synapses that contributed to it, enabling each synapse to assess whether its own contribution was relevant to the outcome.
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The dendrite therefore runs three interlocking processes across its timescales. On the millisecond scale, it continuously integrates arriving EPSPs and distributes the bAP to all spines. On the seconds scale, it does not itself perform any active computation — the integration is purely electrical and instantaneous relative to the slower processes happening in the spines and at the soma. On the minutes to hours scale, structural changes driven by plasticity — spine neck widening under LTP, spine retraction under LTD — alter the dendritic geometry and therefore the weighting of individual spines in the summation. The dendrite learns not by changing its own proteins but by changing its shape.
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---
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**Simplified comprehension**:
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In this model we decide to simplify:
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- We model a single dendritic branch, not a full dendritic tree
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- We do not model the spine neck geometry or its resistance — EPSPs pass from spine to dendrite without attenuation
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- We do not model active dendritic conductances — the shaft is a passive cable with no dendritic spikes
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- We do not model bAP distance attenuation — all spines receive the bAP at full amplitude regardless of their position
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- We do not model structural plasticity — spine neck widening and retraction are not implemented
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The simplifications imply that:
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- Removing spine neck resistance means all spines contribute equally to V_dend regardless of their geometry or location. The physical basis of synaptic weighting by dendritic position is lost. All EPSPs are treated as equivalent inputs to the shared pool.
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- Removing active conductances means the dendrite cannot generate dendritic spikes. Summation is strictly linear — two spines together produce exactly twice the V_dend of one spine alone. There is no threshold event within the dendrite itself, only at the soma.
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- Removing bAP attenuation means all spines have equal access to the coincidence signal regardless of distance from the soma. Proximal and distal synapses have identical plasticity thresholds. The functional gradient that makes dendritic location matter is absent.
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- Removing structural plasticity means the geometry of the dendrite is fixed. LTP and LTD change AMPA receptor density at each spine but do not change how well those spines couple electrically to the dendrite. The structural component of long-term potentiation — which in biology is arguably more important than the receptor component for sustained changes — is not captured.
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The only behavior we model:
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- Linear summation of spine EPSPs into V_dend each ms
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- Uniform bAP distribution to all spines on soma firing. In this case the dendrites acts as a cable, relaying the bAp to Postsynapse. No modelling needed.
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---
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**Simplified behaviors**:
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— ms:
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- Each active spine contributes an EPSP to V_dend
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V_dend += receptor_conductance_i * AMPA_weight
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(summed across all spines; no neck attenuation)
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- V_dend decays passively each ms
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V_dend *= (1 - dt / tau_dend)
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tau_dend is the membrane time constant of the branch
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- V_dend passed to soma each ms (read externally)
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— secs:
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- nothing in the simplified model
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— mins:
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- nothing in the simplified model
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(structural remodelling of spine neck geometry
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under LTP/LTD would live here if added later)
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---
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```Gen
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Container: BEH-BD
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include:
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BEH-POST.md
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expansion:
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- BEH-POST ( full: 50x, active: 0x, empty: 10x )
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# managed_by: BEH-EXH or BEH-INH from winnertakeall
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# developed_by: DEV-BD-BEH-POST-TUB from DEV-N
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tub_local:
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- VPost ( full: 50x, active: 0x, empty: 10x )
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tub_intricated:
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- VDB ( contained_by: BEH-SOMA )
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```
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### ms: behaviors BD
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#### CheckVPost:Context
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Contestualizziamo in maniera Fixed?
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```Gen
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context: CheckVPost
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contained_by: BEH-BD
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in_context: Fixed
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rf: ( active: 60x )
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condition: (VPost fullness)
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out_context: VPostMax
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condition: (VPost mediumness)
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out_context: VPostMedium
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condition: (VPost emptiness)
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out_context: VPostLow
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```
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#### VDBMax: Episode
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```Gen
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episode: VDBMax
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contained_by: BEH-BD
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in_context: VPostMax
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rf: ( active: 2x )
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hypothesis: NOT (VDB full)
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action: [VDB increase]
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trace:
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```
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#### VDBMed: Episode
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```Gen
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episode: VDBMed
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contained_by: BEH-BD
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in_context: VPostMed
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rf: ( active: 2x )
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hypothesis: NOT (VDB full)
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action: [VDB increase]
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trace:
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```
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#### VDBLow: Episode
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```Gen
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episode: VDBLow
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contained_by: BEH-BD
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in_context: VPostLow
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rf: ( active: 2x )
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hypothesis: NOT (VDB full)
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action: [VDB increase]
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trace:
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```
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#### VDBlearance: Episode
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Qui facciamo il clearance del VDB. Lo facciamo in questo modo perche' abbiamo semplificato quello che succede fra BD e SOMA.
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```Gen
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episode: VDBClearance
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contained_by: BEH-BD
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in_context: Fixed
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rf: ( active: 2x )
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hypothesis: NOT (VDB empty)
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action: [VDB decrease]
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trace:
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```
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