5 research outputs found
Improving Recall in an Associative Neural Network Model of the Hippocampus
The mammalian hippocampus is involved in auto-association and hetero-association of declarative
memories. We employed a bio-inspired neural model of hippocampal CA1 region to systematically
evaluate its mean recall quality against different number of stored patterns, overlaps and active cells per
pattern. Model consisted of excitatory (pyramidal cells) and four types of inhibitory cells: axo-axonic,
basket, bistratified, and oriens lacunosum-moleculare cells. Cells were simplified compartmental models
with complex ion channel dynamics. Cells’ firing was timed to a theta oscillation paced by two distinct
neuronal populations exhibiting highly regular bursting activity, one tightly coupled to the trough and the
other to the peak of theta. During recall excitatory input to network excitatory cells provided context and
timing information for retrieval of previously stored memory patterns. Dendritic inhibition acted as a nonspecific
global threshold machine that removed spurious activity during recall. Simulations showed recall
quality improved when the network’s memory capacity increased as the number of active cells per pattern
decreased. Furthermore, increased firing rate of a presynaptic inhibitory threshold machine inhibiting a
network of postsynaptic excitatory cells has a better success at removing spurious activity at the network
level and improving recall quality than increased synaptic efficacy of the same threshold machine on the
same network of excitatory cells, while keeping its firing rate fixed
Recall Performance Improvement in a Bio-Inspired Model of the Mammalian Hippocampus
Mammalian hippocampus is involved in short-term formation of declarative memories. We employed a
bio-inspired neural model of hippocampal CA1 region consisting of a zoo of excitatory and inhibitory
cells. Cells’ firing was timed to a theta oscillation paced by two distinct neuronal populations exhibiting
highly regular bursting activity, one tightly coupled to the trough and the other to the peak of theta. To
systematically evaluate the model’s recall performance against number of stored patterns, overlaps and
‘active cells per pattern’, its cells were driven by a non-specific excitatory input to their dendrites. This
excitatory input to model excitatory cells provided context and timing information for retrieval of
previously stored memory patterns. Inhibition to excitatory cells’ dendrites acted as a non-specific global
threshold machine that removed spurious activity during recall. Out of the three models tested, ‘model 1’
recall quality was excellent across all conditions. ‘Model 2’ recall was the worst. The number of ‘active
cells per pattern’ had a massive effect on network recall quality regardless of how many patterns were
stored in it. As ‘active cells per pattern’ decreased, network’s memory capacity increased, interference
effects between stored patterns decreased, and recall quality improved. Key finding was that increased
firing rate of an inhibitory cell inhibiting a network of excitatory cells has a better success at removing
spurious activity at the network level and improving recall quality than increasing the synaptic strength of
the same inhibitory cell inhibiting the same network of excitatory cells, while keeping its firing rate fixed
Benchmarking Hebbian learning rules for associative memory
Associative memory or content addressable memory is an important component
function in computer science and information processing and is a key concept in
cognitive and computational brain science. Many different neural network
architectures and learning rules have been proposed to model associative memory
of the brain while investigating key functions like pattern completion and
rivalry, noise reduction, and storage capacity. A less investigated but
important function is prototype extraction where the training set comprises
pattern instances generated by distorting prototype patterns and the task of
the trained network is to recall the correct prototype pattern given a new
instance. In this paper we characterize these different aspects of associative
memory performance and benchmark six different learning rules on storage
capacity and prototype extraction. We consider only models with Hebbian
plasticity that operate on sparse distributed representations with unit
activities in the interval [0,1]. We evaluate both non-modular and modular
network architectures and compare performance when trained and tested on
different kinds of sparse random binary pattern sets, including correlated
ones. We show that covariance learning has a robust but low storage capacity
under these conditions and that the Bayesian Confidence Propagation learning
rule (BCPNN) is superior with a good margin in all cases except one, reaching a
three times higher composite score than the second best learning rule tested.Comment: 24 pages, 9 figure
Quantitative Investigation Of Memory Recall Performance Of A Computational Microcircuit Model Of The Hippocampus
Memory, the process of encoding, storing, and maintaining information over time in order to influence future actions, is very important in our lives. Losing it, it comes with a great cost. Deciphering the biophysical mechanisms leading to recall improvement should thus be of outmost importance. In this study we embarked on the quest to improve computationally the recall performance of a bio-inspired microcircuit model of the mammalian hippocampus, a brain region responsible for the storage and recall of short-term declarative memories. The model consisted of excitatory and inhibitory cells. The cell properties followed closely what is currently known from the experimental neurosciences. Cells’ firing was timed to a theta oscillation paced by two distinct neuronal populations exhibiting highly regular bursting activity, one tightly coupled to the trough and the other to the peak of theta. An excitatory input provided to excitatory cells context and timing information for retrieval of previously stored memory patterns. Inhibition to excitatory cells acted as a non-specific global threshold machine that removed spurious activity during recall. To systematically evaluate the model’s recall performance against stored patterns, pattern overlap, network size and active cells per pattern, we selectively modulated feedforward and feedback excitatory and inhibitory pathways targeting specific excitatory and inhibitory cells. Of the different model variations (modulated pathways) tested, ‘model 1’ recall quality was excellent across all conditions. ‘Model 2’ recall was the worst. The number of ‘active cells’ representing a memory pattern was the determining factor in improving the model’s recall performance regardless of the number of stored patterns and overlap between them. As ‘active cells per pattern’ decreased, the model’s memory capacity increased, interference effects between stored patterns decreased, and recall quality improved
Improving the Recall Performance of a Brain Mimetic Microcircuit Model
The recall performance of a well-established canonical microcircuit model of the hippocampus, a region of the mammalian brain that acts as a short-term memory, was systematically evaluated. All model cells were simplified compartmental models with complex ion channel dynamics. In addition to excitatory cells (pyramidal cells), four types of inhibitory cells were present: axo-axonic (axonic inhibition), basket (somatic inhibition), bistratified cells (proximal dendritic inhibition) and oriens lacunosum-moleculare (distal dendritic inhibition) cells. All cells’ firing was timed to an external theta rhythm paced into the model by external reciprocally oscillating inhibitory inputs originating from the medial septum. Excitatory input to the model originated from the region CA3 of the hippocampus and provided context and timing information for retrieval of previously stored memory patterns. Model mean recall quality was tested as the number of stored memory patterns was increased against selectively modulated feedforward and feedback excitatory and inhibitory pathways. From all modulated pathways, simulations showed recall performance was best when feedforward inhibition from bistratified cells to pyramidal cell dendrites is dynamically increased as stored memory patterns is increased with or without increased pyramidal cell feedback excitation to bistratified cells. The study furthers our understanding of how memories are retrieved by a brain microcircuit. The findings provide fundamental insights into the inner workings of learning and memory in the brain, which may lead to potential strategies for treatments in memory-related disorders