4 research outputs found
Cortical region interactions and the functional role of apical dendrites
The basal and distal apical dendrites of pyramidal cells occupy distinct
cortical layers and are targeted by axons originating in different cortical
regions. Hence, apical and basal dendrites receive information from distinct
sources. Physiological evidence suggests that this anatomically observed
segregation of input sources may have functional significance. This possibility
has been explored in various connectionist models that employ neurons with
functionally distinct apical and basal compartments. A neuron in which separate
sets of inputs can be integrated independently has the potential to operate in a
variety of ways which are not possible for the conventional model of a neuron in
which all inputs are treated equally. This article thus considers how
functionally distinct apical and basal dendrites can contribute to the
information processing capacities of single neurons and, in particular, how
information from different cortical regions could have disparate affects on
neural activity and learning
A balanced memory network
A fundamental problem in neuroscience is understanding how working memory-the ability to store information at intermediate timescales, like tens of seconds-is implemented in realistic neuronal networks. The most likely candidate mechanism is the attractor network, and a great deal of effort has gone toward investigating it theoretically. Yet, despite almost a quarter century of intense work, attractor networks are not fully understood. In particular, there are still two unanswered questions. First, how is it that attractor networks exhibit irregular firing, as is observed experimentally during working memory tasks? And second, how many memories can be stored under biologically realistic conditions? Here we answer both questions by studying an attractor neural network in which inhibition and excitation balance each other. Using mean-field analysis, we derive a three-variable description of attractor networks. From this description it follows that irregular firing can exist only if the number of neurons involved in a memory is large. The same mean-field analysis also shows that the number of memories that can be stored in a network scales with the number of excitatory connections, a result that has been suggested for simple models but never shown for realistic ones. Both of these predictions are verified using simulations with large networks of spiking neurons
The Performance of Associative Memory Models with Biologically Inspired Connectivity
This thesis is concerned with one important question in artificial neural networks, that is, how biologically inspired connectivity of a network affects its associative memory performance.
In recent years, research on the mammalian cerebral cortex, which has the main
responsibility for the associative memory function in the brains, suggests that
the connectivity of this cortical network is far from fully connected, which is
commonly assumed in traditional associative memory models. It is found to
be a sparse network with interesting connectivity characteristics such as the
“small world network” characteristics, represented by short Mean Path Length,
high Clustering Coefficient, and high Global and Local Efficiency. Most of the networks in this thesis are therefore sparsely connected.
There is, however, no conclusive evidence of how these different connectivity
characteristics affect the associative memory performance of a network. This
thesis addresses this question using networks with different types of
connectivity, which are inspired from biological evidences.
The findings of this programme are unexpected and important. Results show
that the performance of a non-spiking associative memory model is found to be
predicted by its linear correlation with the Clustering Coefficient of the network,
regardless of the detailed connectivity patterns. This is particularly important
because the Clustering Coefficient is a static measure of one aspect of
connectivity, whilst the associative memory performance reflects the result of a
complex dynamic process.
On the other hand, this research reveals that improvements in the performance
of a network do not necessarily directly rely on an increase in the network’s
wiring cost. Therefore it is possible to construct networks with high
associative memory performance but relatively low wiring cost. Particularly,
Gaussian distributed connectivity in a network is found to achieve the best
performance with the lowest wiring cost, in all examined connectivity models.
Our results from this programme also suggest that a modular network with an
appropriate configuration of Gaussian distributed connectivity, both internal to
each module and across modules, can perform nearly as well as the Gaussian
distributed non-modular network.
Finally, a comparison between non-spiking and spiking associative memory
models suggests that in terms of associative memory performance, the
implication of connectivity seems to transcend the details of the actual neural
models, that is, whether they are spiking or non-spiking neurons