Modification Of Synaptic Weights Research Articles

The representation of priors and decisions in the human parietal cortex.

Animals actively sample their environment through orienting actions such as saccadic eye movements. Saccadic targets are selected based both on sensory evidence immediately preceding the saccade, and a "salience map" or prior built-up over multiple saccades. In the primate cortex, the selection of each individual saccade depends on competition between target-selective cells that ramp up their firing rate to saccade release. However, it is less clear how a cross-saccade prior might be implemented, either in neural firing or through an activity-silent mechanism such as modification of synaptic weights on sensory inputs. Here, we present evidence from magnetoencephalography for 2 distinct processes underlying the selection of the current saccade, and the representation of the prior, in human parietal cortex. While the classic ramping decision process for each saccade was reflected in neural firing rates (measured in the event-related field), a prior built-up over multiple saccades was implemented via modulation of the gain on sensory inputs from the preferred target, as evidenced by rapid frequency tagging. A cascade of computations over time (initial representation of the prior, followed by evidence accumulation and then an integration of prior and evidence) provides a mechanism by which a salience map may be built up across saccades in parietal cortex. It also provides insight into the apparent contradiction that inactivation of parietal cortex has been shown not to affect performance on single-trials, despite the presence of clear evidence accumulation signals in this region.

Dynamical Learning of Dynamics.

The ability of humans and animals to quickly adapt to novel tasks is difficult to reconcile with the standard paradigm of learning by slow synaptic weight modification. Here, we show that fixed-weight neural networks can learn to generate required dynamics by imitation. After appropriate weight pretraining, the networks quickly and dynamically adapt to learn new tasks and thereafter continue to achieve them without further teacher feedback. We explain this ability and illustrate it with a variety of target dynamics, ranging from oscillatory trajectories to driven and chaotic dynamical systems.

Synaptic Suppression Triplet‐STDP Learning Rule Realized in Second‐Order Memristors

AbstractThe synaptic weight modification depends not only on interval of the pre‐/postspike pairs according to spike‐timing dependent plasticity (classical pair‐STDP), but also on the timing of the preceding spike (triplet‐STDP). Triplet‐STDP reflects the unavoidable interaction of spike pairs in natural spike trains through the short‐term suppression effect of preceding spikes. Second‐order memristors with one state variable possessing short‐term dynamics work in a way similar to the biological system. In this work, the suppression triplet‐STDP learning rule is faithfully demonstrated by experiments and simulations using second‐order memristors. Furthermore, a leaky‐integrate‐and‐fire (LIF) neuron is simulated using a circuit constructed with second‐order memristors. Taking the advantage of the LIF neuron, various neuromimetic dynamic processes, including local graded potential leaking out, postsynaptic impulse generation and backpropagation, and synaptic weight modification according to the suppression triplet‐STDP rule, are realized. The realized weight‐dependent pair‐ and triplet‐STDP rules are clearly in line with findings in biology. The physically realized triplet‐STDP rule is powerful in developing direction and speed selectivity for complex pattern recognition and tracking tasks. These scalable artificial synapses and neurons realized in second‐order memristors can intrinsically capture the neuromimetic dynamic processes; they are the promising building blocks for constructing brain‐inspired computation systems.

A Mathematical Model for Storage and Recall of Images using Targeted Synchronization of Coupled Maps

We propose a mathematical model for storage and recall of images using coupled maps. We start by theoretically investigating targeted synchronization in coupled map systems wherein only a desired (partial) subset of the maps is made to synchronize. A simple method is introduced to specify coupling coefficients such that targeted synchronization is ensured. The principle of this method is extended to storage/recall of images using coupled Rulkov maps. The process of adjusting coupling coefficients between Rulkov maps (often used to model neurons) for the purpose of storing a desired image mimics the process of adjusting synaptic strengths between neurons to store memories. Our method uses both synchronisation and synaptic weight modification, as the human brain is thought to do. The stored image can be recalled by providing an initial random pattern to the dynamical system. The storage and recall of the standard image of Lena is explicitly demonstrated.

Intrinsic Plasticity for Natural Competition in Koniocortex-Like Neural Networks.

In this paper, we use the neural property known as intrinsic plasticity to develop neural network models that resemble the koniocortex, the fourth layer of sensory cortices. These models evolved from a very basic two-layered neural network to a complex associative koniocortex network. In the initial network, intrinsic and synaptic plasticity govern the shifting of the activation function, and the modification of synaptic weights, respectively. In this first version, competition is forced, so that the most activated neuron is arbitrarily set to one and the others to zero, while in the second, competition occurs naturally due to inhibition between second layer neurons. In the third version of the network, whose architecture is similar to the koniocortex, competition also occurs naturally owing to the interplay between inhibitory interneurons and synaptic and intrinsic plasticity. A more complex associative neural network was developed based on this basic koniocortex-like neural network, capable of dealing with incomplete patterns and ideally suited to operating similarly to a learning vector quantization network. We also discuss the biological plausibility of the networks and their role in a more complex thalamocortical model.

Adaptive Synaptogenesis Constructs Neural Codes That Benefit Discrimination

Intelligent organisms face a variety of tasks requiring the acquisition of expertise within a specific domain, including the ability to discriminate between a large number of similar patterns. From an energy-efficiency perspective, effective discrimination requires a prudent allocation of neural resources with more frequent patterns and their variants being represented with greater precision. In this work, we demonstrate a biologically plausible means of constructing a single-layer neural network that adaptively (i.e., without supervision) meets this criterion. Specifically, the adaptive algorithm includes synaptogenesis, synaptic shedding, and bi-directional synaptic weight modification to produce a network with outputs (i.e. neural codes) that represent input patterns proportional to the frequency of related patterns. In addition to pattern frequency, the correlational structure of the input environment also affects allocation of neural resources. The combined synaptic modification mechanisms provide an explanation of neuron allocation in the case of self-taught experts.

A compact spike-timing-dependent-plasticity circuit for floating gate weight implementation

Spike timing dependent plasticity (STDP) forms the basis of learning within neural networks. STDP allows for the modification of synaptic weights based upon the relative timing of pre- and post-synaptic spikes. A compact circuit is presented which can implement STDP, including the critical plasticity window, to determine synaptic modification. A physical model to predict the time window for plasticity to occur is formulated and the effects of process variations on the window is analyzed. The STDP circuit is implemented using two dedicated circuit blocks, one for potentiation and one for depression where each block consists of 4 transistors and a polysilicon capacitor. SpectreS simulations of the back-annotated layout of the circuit and experimental results indicate that STDP with biologically plausible critical timing windows over the range from 10µs to 100ms can be implemented. Also a floating gate weight storage capability, with drive circuits, is presented and a detailed analysis correlating weights changes with charging time is given.

Robust Short-Term Memory without Synaptic Learning

Short-term memory in the brain cannot in general be explained the way long-term memory can – as a gradual modification of synaptic weights – since it takes place too quickly. Theories based on some form of cellular bistability, however, do not seem able to account for the fact that noisy neurons can collectively store information in a robust manner. We show how a sufficiently clustered network of simple model neurons can be instantly induced into metastable states capable of retaining information for a short time (a few seconds). The mechanism is robust to different network topologies and kinds of neural model. This could constitute a viable means available to the brain for sensory and/or short-term memory with no need of synaptic learning. Relevant phenomena described by neurobiology and psychology, such as local synchronization of synaptic inputs and power-law statistics of forgetting avalanches, emerge naturally from this mechanism, and we suggest possible experiments to test its viability in more biological settings.

Local learning rules for spiking neurons with dendrite.

Event Abstract Back to Event Local learning rules for spiking neurons with dendrite. Olivier F.L. Manette1* 1 CNRS, France We present in this abstract four local rules to train a network of spiking neurons with dendrite. After training, every neuron of the network becomes specialized for a particular feature of the input signal. This network, with these rules, acts as a features extractor where each neuron contains a vector of TAND. TAND are like logical AND with the information about time between the two events in the input signal. The neuron model used here has one single dendrite. All the synapses are connected at different position along this dendrite. Synapses are only located on the dendrite and never at the soma. When a synapse is activated, it triggers an EPSP that is propagated with attenuation and delay towards the soma. EPSPs amplitude loses as it propagates. Time constants of EPSPs become larger as it spreads. All the different EPSPs from the different synapses are summed and are propagated to the soma. If this sum reaches a threshold then the neuron spikes. Spikes are always and only emitted at the soma and never at the dendrite. The EPSP used here in this model is a particular solution of an alpha function. We have two rules of morphological modifications and two rules of synaptic electrophysiological changes. 1) Create a new connection between two neurons: Every time a neuron is activated i.e it emits an action potential (spike) it will try to create a new connection on the most activated neuron of its pool of neuron. It will create a new synapse on the dendrite of this most active neuron at the exact position where the membrane is most depolarized. 2) Changing the dendrite length: A neuron always creates a new synapse at the max local activation. If this max is located at the soma or at the dendrite but directly next to the soma then, the dendrite is first elongated and the rule #1 or the rule #2 will be applied the next time the local threshold will be reached on this neuron. 3) Modification of synaptic weights: The weight is increased if the neuron is locally depolarized over a synapse position while this synapse is also activated. This weight is decreased if the neuron is locally depolarized after the synapse location relatively to the soma while this synapse is also activated. 4) Changing the time constants: The time constant of the EPSP determine the duration of the EPSP i.e. it determine the accepted temporal precision of the incoming spike. If a synapse is systematically active when the membrane potential is depolarized at the synapse position then the time constant will be reduced. Conversely, the time constant will increase if the synapse is always active before the membrane potential reaches is depolarized over this synapse. Figure 1 Keywords: Electrophysiology Conference: 4th INCF Congress of Neuroinformatics, Boston, United States, 4 Sep - 6 Sep, 2011. Presentation Type: Poster Presentation Topic: Electrophysiology Citation: Manette O (2011). Local learning rules for spiking neurons with dendrite.. Front. Neuroinform. Conference Abstract: 4th INCF Congress of Neuroinformatics. doi: 10.3389/conf.fninf.2011.08.00150 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 17 Oct 2011; Published Online: 19 Oct 2011. * Correspondence: Dr. Olivier F.L. Manette, CNRS, Gif sur Yvette, France, olivier.manette@gmail.com Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Olivier F.L. Manette Google Olivier F.L. Manette Google Scholar Olivier F.L. Manette PubMed Olivier F.L. Manette Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Estimação do volume de árvores utilizando redes neurais artificiais

Rede neural artificial consiste em um conjunto de unidades que contêm funções matemáticas, unidas por pesos. As redes são capazes de aprender, mediante modificação dos pesos sinápticos, e generalizar o aprendizado para outros arquivos desconhecidos. O projeto de redes neurais é composto por três etapas: pré-processamento, processamento e, por fim, pós-processamento dos dados. Um dos problemas clássicos que podem ser abordados por redes é a aproximação de funções. Nesse grupo, pode-se incluir a estimação do volume de árvores. Foram utilizados quatro arquiteturas diferentes, cinco pré-processamentos e duas funções de ativação. As redes que se apresentaram estatisticamente iguais aos dados observados também foram analisadas quanto ao resíduo e à distribuição dos volumes e comparadas com a estimação de volume pelo modelo de Schumacher e Hall. As redes neurais formadas por neurônios, cuja função de ativação era exponencial, apresentaram estimativas estatisticamente iguais aos dados observados. As redes treinadas com os dados normalizados pelo método da interpolação linear e equalizados tiveram melhor desempenho na estimação.

Biologically plausible learning in neural networks: a lesson from bacterial chemotaxis

Learning processes in the brain are usually associated with plastic changes made to optimize the strength of connections between neurons. Although many details related to biophysical mechanisms of synaptic plasticity have been discovered, it is unclear how the concurrent performance of adaptive modifications in a huge number of spatial locations is organized to minimize a given objective function. Since direct experimental observation of even a relatively small subset of such changes is not feasible, computational modeling is an indispensable investigation tool for solving this problem. However, the conventional method of error back-propagation (EBP) employed for optimizing synaptic weights in artificial neural networks is not biologically plausible. This study based on computational experiments demonstrated that such optimization can be performed rather efficiently using the same general method that bacteria employ for moving closer to an attractant or away from a repellent. With regard to neural network optimization, this method consists of regulating the probability of an abrupt change in the direction of synaptic weight modification according to the temporal gradient of the objective function. Neural networks utilizing this method (regulation of modification probability, RMP) can be viewed as analogous to swimming in the multidimensional space of their parameters in the flow of biochemical agents carrying information about the optimality criterion. The efficiency of RMP is comparable to that of EBP, while RMP has several important advantages. Since the biological plausibility of RMP is beyond a reasonable doubt, the RMP concept provides a constructive framework for the experimental analysis of learning in natural neural networks.

Long-Term Plasticity Is Proportional to Theta-Activity

BackgroundTheta rhythm in the hippocampal formation is a main feature of exploratory behaviour and is believed to enable the encoding of new spatial information and the modification of synaptic weights. Cyclic changes of dentate gyrus excitability during theta rhythm are related to its function, but whether theta epochs per se are able to alter network properties of dentate gyrus for long time-periods is still poorly understood.Methodology/Principal FindingsWe used low-frequency stimulation protocols that amplify the power of endogenous theta oscillations, in order to estimate the plasticity effect of endogenous theta oscillations on a population level. We found that stimulation-induced augmentation of the theta rhythm is linked to a subsequent increase of neuronal excitability and decrease of the synaptic response. This EPSP-to-Spike uncoupling is related to an increased postsynaptic spiking on the positive phases of theta frequency oscillations. Parallel increase of the field EPSP slope and the population spike occurs only after concurrent pre- and postsynaptic activation. Furthermore, we observed that long-term potentiation (>24 h) occurs in the dentate gyrus of freely behaving adult rats after phasic activity of entorhinal afferents in the theta-frequency range. This plasticity is proportional to the field bursting activity of granule cells during the stimulation, and may comprise a key step in spatial information transfer. Long-term potentiation of the synaptic component occurs only when the afferent stimulus precedes the evoked population burst, and is input-specific.Conclusions/SignificanceOur data confirm the role of the dentate gyrus in filtering information to the subsequent network during the activated state of the hippocampus.

Contribution of a single CA3 neuron to network synchrony

Oscillations at theta (3–8 Hz) and gamma (30–80 Hz) frequencies co-occur during arousal, exploration, and rapid eye movement sleep and relate to information processing underlying learning and memory within neuronal networks. In hippocampus, gamma and theta frequency oscillations are associated with modification of synaptic weights, spatial learning, and short-term memory. These oscillations are referred to as network phenomena and, thereby, the role of single neuron oscillations in the generation of neuronal networks remains unclear. We report that an individual CA3 pyramidal cell can activate the CA1 neuronal network in vivo in rat hippocampus using electrical stimulations with simultaneous intracellular gamma and extracellular theta and slow (0.5–1 Hz) frequencies. These results suggest that an individual pyramidal cell can contribute to self-organization of a neuronal small-scale network.

Long-term potentiation in visual cortical projections to the medial prefrontal cortex of the rat

In order to investigate neural mechanisms by which the prefrontal cortex adaptively modifies its activities based on past experience, we examined whether or not sensory cortical projections to the medial prefrontal cortex support long-term potentiation (LTP) in rats. Monosynaptic projections from the secondary visual cortex, mediomedial area (V2MM) to the infralimbic cortex were confirmed by orthodromic as well as antidromic activation of single units. High-frequency stimulation (50 Hz, 2 s) induced LTP (approximately 45% increase over the baseline) in the V2MM projection to the infralimbic cortex. LTP induction in this pathway was completely blocked by an injection (i.p.) of CPP, an N-methyl- d-aspartate receptor antagonist. LTP was also induced in the ventral hippocampal projection to the infralimbic cortex by the same high-frequency stimulation. The present results suggest that modification of synaptic weights of afferent sensory cortical projections is one mechanism underlying learning-induced changes in prefrontal cortical neural activities.

Weakly pulse-coupled oscillators, FM interactions, synchronization, and oscillatory associative memory

We study pulse-coupled neural networks that satisfy only two assumptions: each isolated neuron fires periodically, and the neurons are weakly connected. Each such network can be transformed by a piece-wise continuous change of variables into a phase model, whose synchronization behavior and oscillatory associative properties are easier to analyze and understand. Using the phase model, we can predict whether a given pulse-coupled network has oscillatory associative memory, or what minimal adjustments should be made so that it can acquire memory. In the search for such minimal adjustments we obtain a large class of simple pulse-coupled neural networks that can memorize and reproduce synchronized temporal patterns the same way a Hopfield network does with static patterns. The learning occurs via modification of synaptic weights and/or synaptic transmission delays.

Information maintenance and statistical dependence reduction in simple neural networks

This study compares the ability of excitatory, feed-forward neural networks to construct good transformations on their inputs. The quality of such a transformation is judged by the minimization of two information measures: the information loss of the transformation and the statistical dependency of the output. The networks that are compared differ from each other in the parametric properties of their neurons and in their connectivity. The particular network parameters studied are output firing threshold, synaptic connectivity, and associative modification of connection weights. The network parameters that most directly affect firing levels are threshold and connectivity. Networks incorporating neurons with dynamic threshold adjustment produce better transformations. When firing threshold is optimized, sparser synaptic connectivity produces a better transformation than denser connectivity. Associative modification of synaptic weights confers only a slight advantage in the construction of optimal transformations. Additionally, our research shows that some environments are better suited than others for recording. Specifically, input environments high in statistical dependence, i.e. those environments most in need of recoding, are more likely to undergo successful transformations.