342 research outputs found
Optimal learning rules for discrete synapses
There is evidence that biological synapses have a limited number of discrete weight states. Memory storage with such synapses behaves quite differently from synapses with unbounded, continuous weights, as old memories are automatically overwritten by new memories. Consequently, there has been substantial discussion about how this affects learning and storage capacity. In this paper, we calculate the storage capacity of discrete, bounded synapses in terms of Shannon information. We use this to optimize the learning rules and investigate how the maximum information capacity depends on the number of synapses, the number of synaptic states, and the coding sparseness. Below a certain critical number of synapses per neuron (comparable to numbers found in biology), we find that storage is similar to unbounded, continuous synapses. Hence, discrete synapses do not necessarily have lower storage capacity
The Effect of Different Forms of Synaptic Plasticity on Pattern Recognition in the Cerebellar Cortex
“The original publication is available at www.springerlink.com”. Copyright Springer.Many cerebellar learning theories assume that long-term depression (LTD) of synapses between parallel fibres (PFs) and Purkinje cells (PCs) provides the basis for pattern recognition in the cerebellum. Previous work has suggested that PCs can use a novel neural code based on the duration of silent periods. These simulations have used a simplified learning rule, where the synaptic conductance was halved each time a pattern was learned. However, experimental studies in cerebellar slices show that the synaptic conductance saturates and is rarely reduced to less than 50% of its baseline value. Moreover, the previous simulations did not include plasticity of the synapses between inhibitory interneurons and PCs. Here we study the effect of LTD saturation and inhibitory synaptic plasticity on pattern recognition in a complex PC model. We find that the PC model is very sensitive to the value at which LTD saturates, but is unaffected by inhibitory synaptic plasticity.Peer reviewe
Binary Willshaw learning yields high synaptic capacity for long-term familiarity memory
We investigate from a computational perspective the efficiency of the
Willshaw synaptic update rule in the context of familiarity discrimination, a
binary-answer, memory-related task that has been linked through psychophysical
experiments with modified neural activity patterns in the prefrontal and
perirhinal cortex regions. Our motivation for recovering this well-known
learning prescription is two-fold: first, the switch-like nature of the induced
synaptic bonds, as there is evidence that biological synaptic transitions might
occur in a discrete stepwise fashion. Second, the possibility that in the
mammalian brain, unused, silent synapses might be pruned in the long-term.
Besides the usual pattern and network capacities, we calculate the synaptic
capacity of the model, a recently proposed measure where only the functional
subset of synapses is taken into account. We find that in terms of network
capacity, Willshaw learning is strongly affected by the pattern coding rates,
which have to be kept fixed and very low at any time to achieve a non-zero
capacity in the large network limit. The information carried per functional
synapse, however, diverges and is comparable to that of the pattern association
case, even for more realistic moderately low activity levels that are a
function of network size.Comment: 20 pages, 4 figure
Nonspecific synaptic plasticity improves the recognition of sparse patterns degraded by local noise
Safaryan, K. et al. Nonspecific synaptic plasticity improves the recognition of sparse patterns degraded by local noise. Sci. Rep. 7, 46550; doi: 10.1038/srep46550 (2017). This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ © The Author(s) 2017.Many forms of synaptic plasticity require the local production of volatile or rapidly diffusing substances such as nitric oxide. The nonspecific plasticity these neuromodulators may induce at neighboring non-active synapses is thought to be detrimental for the specificity of memory storage. We show here that memory retrieval may benefit from this non-specific plasticity when the applied sparse binary input patterns are degraded by local noise. Simulations of a biophysically realistic model of a cerebellar Purkinje cell in a pattern recognition task show that, in the absence of noise, leakage of plasticity to adjacent synapses degrades the recognition of sparse static patterns. However, above a local noise level of 20 %, the model with nonspecific plasticity outperforms the standard, specific model. The gain in performance is greatest when the spatial distribution of noise in the input matches the range of diffusion-induced plasticity. Hence non-specific plasticity may offer a benefit in noisy environments or when the pressure to generalize is strong.Peer reviewe
Learning flexible sensori-motor mappings in a complex network
Given the complex structure of the brain, how can synaptic plasticity explain the learning and forgetting of associations when these are continuously changing? We address this question by studying different reinforcement learning rules in a multilayer network in order to reproduce monkey behavior in a visuomotor association task. Our model can only reproduce the learning performance of the monkey if the synaptic modifications depend on the pre- and postsynaptic activity, and if the intrinsic level of stochasticity is low. This favored learning rule is based on reward modulated Hebbian synaptic plasticity and shows the interesting feature that the learning performance does not substantially degrade when adding layers to the network, even for a complex proble
How much of the Hippocampus can be Explained by Functional Constraints?
In the spirit of Marr, we discuss an information-theoretic approach that derives, from the role of the hippocampus in memory, constraints on its anatomical and physiological structure. The observed structure is consistent with such constraints, and, further, we relate the quantitative arguments developed in earlier analytical studies to experimental measures extracted from neuronal recordings in the behaving rat
The Importance of Forgetting: Limiting Memory Improves Recovery of Topological Characteristics from Neural Data
We develop of a line of work initiated by Curto and Itskov towards
understanding the amount of information contained in the spike trains of
hippocampal place cells via topology considerations. Previously, it was
established that simply knowing which groups of place cells fire together in an
animal's hippocampus is sufficient to extract the global topology of the
animal's physical environment. We model a system where collections of place
cells group and ungroup according to short-term plasticity rules. In
particular, we obtain the surprising result that in experiments with spurious
firing, the accuracy of the extracted topological information decreases with
the persistence (beyond a certain regime) of the cell groups. This suggests
that synaptic transience, or forgetting, is a mechanism by which the brain
counteracts the effects of spurious place cell activity
Structural Plasticity and Associative Memory in Balanced Neural Networks With Spike-Time Dependent Inhibitory Plasticity
Several homeostatic mechanisms enable the brain to maintain desired
levels of neuronal activity. One of these, homeostatic structural plasticity,
has been reported to restore activity in networks disrupted
by peripheral lesions by altering their neuronal connectivity. While
multiple lesion experiments have studied the changes in neurite morphology
that underlie modifications of synapses in these networks,
the underlying mechanisms that drive these changes and the effects of
the altered connectivity on network function are yet to be explained.
Experimental evidence suggests that neuronal activity modulates
neurite morphology and that it may stimulate neurites to selectively
sprout or retract to restore network activity levels. In this study, a new
spiking network model was developed to investigate these activity
dependent growth regimes of neurites. Simulations of the model accurately
reproduce network rewiring after peripheral lesions as reported
in experiments. To ensure that these simulations closely resembled
the behaviour of networks in the brain, a biologically realistic network
model that exhibits low frequency Asynchronous Irregular (AI) activity
as observed in cerebral cortex was deafferented. Furthermore, to
study the functional effects of peripheral lesioning and subsequent
network repair by homeostatic structural plasticity, associative memories
were stored in the network and their recall performances before
deafferentation and after, during the repair process, were compared.
The simulation results indicate that the re-establishment of activity
in neurons both within and outside the deprived region, the Lesion
Projection Zone (LPZ), requires opposite activity dependent growth
rules for excitatory and inhibitory post-synaptic elements. Analysis of
these growth regimes indicates that they also contribute to the maintenance
of activity levels in individual neurons. In this model, the
directional formation of synapses that is observed in experiments requires
that pre-synaptic excitatory and inhibitory elements also follow
opposite growth rules. Furthermore, it was observed that the proposed
model of homeostatic structural plasticity and the inhibitory synaptic
plasticity mechanism that also balances the AI network are both
necessary for successful rewiring. Next, even though average activity
was restored to deprived neurons, these neurons did not retain their
AI firing characteristics after repair. Finally, the recall performance of
associative memories, which deteriorated after deafferentation, was
not restored after network reorganisation
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