Model of memory linking through memory allocation (Kastellakis et al. 2016)

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Accession:206249
Here, we present a simplified, biophysically inspired network model that incorporates multiple plasticity processes and explains linking of information at three different levels: (a) learning of a single associative memory (b) rescuing of a weak memory when paired with a strong one and (c) linking of multiple memories across time. By dissecting synaptic from intrinsic plasticity and neuron-wide from dendritically restricted protein capture, the model reveals a simple, unifying principle: Linked memories share synaptic clusters within the dendrites of overlapping populations of neurons
Reference:
1 . Kastellakis G, Silva AJ, Poirazi P (2016) Linking Memories across Time via Neuronal and Dendritic Overlaps in Model Neurons with Active Dendrites. Cell Rep 17:1491-1504 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism:
Cell Type(s): Abstract integrate-and-fire leaky neuron with dendritic subunits;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: C or C++ program; C or C++ program (web link to model);
Model Concept(s): Active Dendrites;
Implementer(s): Kastellakis, George [gkastel at gmail.com];
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// mtrand.cpp, see include file mtrand.h for information

#include "mtrand.h"
// non-inline function definitions and static member definitions cannot
// reside in header file because of the risk of multiple declarations

// initialization of static private members
unsigned long MTRand_int32::state[n] = {0x0UL};
int MTRand_int32::p = 0;
bool MTRand_int32::init = false;

void MTRand_int32::gen_state() { // generate new state vector
  for (int i = 0; i < (n - m); ++i)
    state[i] = state[i + m] ^ twiddle(state[i], state[i + 1]);
  for (int i = n - m; i < (n - 1); ++i)
    state[i] = state[i + m - n] ^ twiddle(state[i], state[i + 1]);
  state[n - 1] = state[m - 1] ^ twiddle(state[n - 1], state[0]);
  p = 0; // reset position
}

void MTRand_int32::seed(unsigned long s) {  // init by 32 bit seed
  state[0] = s & 0xFFFFFFFFUL; // for > 32 bit machines
  for (int i = 1; i < n; ++i) {
    state[i] = 1812433253UL * (state[i - 1] ^ (state[i - 1] >> 30)) + i;
// see Knuth TAOCP Vol2. 3rd Ed. P.106 for multiplier
// in the previous versions, MSBs of the seed affect only MSBs of the array state
// 2002/01/09 modified by Makoto Matsumoto
    state[i] &= 0xFFFFFFFFUL; // for > 32 bit machines
  }
  p = n; // force gen_state() to be called for next random number
}

void MTRand_int32::seed(const unsigned long* array, int size) { // init by array
  seed(19650218UL);
  int i = 1, j = 0;
  for (int k = ((n > size) ? n : size); k; --k) {
    state[i] = (state[i] ^ ((state[i - 1] ^ (state[i - 1] >> 30)) * 1664525UL))
      + array[j] + j; // non linear
    state[i] &= 0xFFFFFFFFUL; // for > 32 bit machines
    ++j; j %= size;
    if ((++i) == n) { state[0] = state[n - 1]; i = 1; }
  }
  for (int k = n - 1; k; --k) {
    state[i] = (state[i] ^ ((state[i - 1] ^ (state[i - 1] >> 30)) * 1566083941UL)) - i;
    state[i] &= 0xFFFFFFFFUL; // for > 32 bit machines
    if ((++i) == n) { state[0] = state[n - 1]; i = 1; }
  }
  state[0] = 0x80000000UL; // MSB is 1; assuring non-zero initial array
  p = n; // force gen_state() to be called for next random number
}