Index of papers in PLOS Comp. Biol. that mention
  • feedforward
Naoki Hiratani, Tomoki Fukai
Abstract
To explore the answers to these questions, we construct models of feedforward networks with lateral inhibitory circuits and study how propagated correlation influences STDP learning, and what kind of learning algorithm such circuits achieve.
Discussion
By analytically investigating the propagation of input correlations through feedback circuits, we revealed how lateral inhibition influenced plasticity at feedforward connections.
Discussion
Our results also suggested that anti-Hebbian plasticity was helpful for learning from minor sources and implied that different STDP rules at lateral connections induced different algorithms at feedforward connections.
Introduction
We analyzed the propagation of spike correlations through inhibitory circuits, and revealed how such secondary correlations influence STDP learning at both feedforward and feedback connections.
Lateral inhibition enhances minor source detection by STDP
When the network is excited by inputs from external sources, excitatory postsynaptic potential (EPSP) sizes of feedforward connections WX change according to STDP rules.
Lateral inhibition should be strong, fast, and sharp
In this approximation, by inserting Eq (32) into Eq (29), the mean synaptic weight changes of feedforward connections follow
Model
We constructed a network model with three feedforward layers as shown in Fig 1A (see Neural dynamics in Methods for details).
Model
Synaptic weight dynamics by STDP is written as ods for details), the weight change of the feedforward connection WX can be approximated as
Model
Previous simulation studies showed lateral inhibition has critical effects on excitatory STDP learning [17—19]; however, it has not yet been well studied how a secondary correlation generated through the lateral circuits influences STDP at feedforward connections, and it is still largely unknown how lateral inhibition functions with various stimuli in different neural circuits.
pf = 1 — <1 — rsAofi [1 — am: ask/szy] ,qsk = 2; 3 M + 1/2>At12exp[—<k + mam/at].
This result suggests that in the STDP model, eXpected external states are naturally sampled through membrane dynamics that are generated through the interplay of feedforward and feedback inputs.
pf = 1 — <1 — rsAofi [1 — am: ask/szy] ,qsk = 2; 3 M + 1/2>At12exp[—<k + mam/at].
Therefore, STDP rules implemented in a feedforward neural network With lateral inhibition serve as a spike-based solution to the blind source separation or cocktail party effect problem.
feedforward is mentioned in 14 sentences in this paper.
Topics mentioned in this paper:
Jyotika Bahuguna, Ad Aertsen, Arvind Kumar
AMSN
The contribution of FSI inhibition (term 11) could be effectively positive or negative, depending on the ratio of feedforward (I11: and IZF) and recurrent ((IuL + I12) and (I22 + I20) inhibition.
AMSN
In fact, in this scenario, AMSN reverses its sign with respect to XCTX even in the absence of feedforward inhibition.
AMSN
However, for higher cortical driving rates, stronger feedforward inhibition to the D1 MSNs ensures that D2 MSNs exceed the firing rates of D1 MSNs, reversing the sign of AMSN (Fig 3D).
Author Summary
Importantly, DTT can be modulated by input correlations, local connectivity, feedforward inhibition and dopamine.
Effect of GPe induced disinhibition of FSI activity on the DTT
An increase in GPe activity (for instance due to an increase in STN activity) would effectively release D1 and D2 MSNs from the feedforward inhibition.
Effect of symmetrical FSI projections on the DTT
In the multiplicative input scenario, When FSI projections to the D1 and D2 MSN subpopulations are equally strong (IIF 2 I21: 2 IF), the Eqs 13 and 14 reduce to: where commeff is the common FSI feedforward inhibition to both types of MSNs.
Introduction
Moreover, the striatal circuit also shows a highly specific connectivity in terms of the mutual inhibition between the MSN subpopulations [14, 15] and the feedforward inhibition from FSIs [16].
Modulation of the DTT by FSIs
Being the primary source of feedforward inhibition, FSIs play an important role in both the existence and the actual value of the DTT.
Results
Therefore, to understand the effect of the recurrent, mutual connectivity and feedforward inhibition [14—16] on the relative balance of the activities in the direct and indirect pathways we studied the dynamics of the striatal network.
feedforward is mentioned in 15 sentences in this paper.
Topics mentioned in this paper:
Jaldert O. Rombouts, Sander M. Bohte, Pieter R. Roelfsema
Biological plausibility, biological detail and future work
In this context it is of interest that previous studies demonstrated that feedforward propagation of activity to higher cortical areas mainly utilizes the AMPA receptor, whereas feedback effects rely more on the NMDA receptor [85], which plays an important role in synaptic plasticity.
Discussion
Specifically, the learning scheme predicts that feedback connections are important for the induction of tags on feedforward connections from sensory cortices to the association cortex (Fig.
Input layer
At the start of every time step, feedforward connections propagate information from the sensory layer to the association layer through modifiable connections vij.
Learning
follows the same rule so that the strength of feedforward and feedback connections becomes similar during learning, in accordance With neurophysiological findings [33].
Learning
1B) enables the formation of tags on the feedforward connections onto the regular unit.
Learning
In the model, the new stimulus causes feedforward processing on the next time step t, which results in another set of Q-values.
Results
The plasticity of the feedback connections 14/; and 14/2; from the Q-Value layer to the association layer follows the same rule as the updates of connections and wfifk and the feedforward and feedback connections between two units therefore become proportional during learning [14] .
Results
As indicated above, learning makes the strength of feedforward and feedback connections similar so that can be estimated as the amount of feedback w’fj that unit j receives from the seA comparison to Eq.
U
In spite of their simple feedforward structure with only seven units in the association layer, AuGMEnT trained the networks to criterion in all simulations within a median of 11,550 trials.
feedforward is mentioned in 9 sentences in this paper.
Topics mentioned in this paper:
Lorenza A. D’Alessandro, Regina Samaga, Tim Maiwald, Seong-Hwan Rho, Sandra Bonefas, Andreas Raue, Nao Iwamoto, Alexandra Kienast, Katharina Waldow, Rene Meyer, Marcel Schilling, Jens Timmer, Steffen Klamt, Ursula Klingmüller
Discussion
The identification and quantitative description of relevant feedback, feedforward and crosstalk regulation of signaling pathways is an important step towards understanding cellular signaling networks and a key prerequisite for the development of successful drug targeting strategies [41—43]
Discussion
However, if one considers all possible combinations of reported crosstalk, feedback and feedforward mechanisms, these possibilities result in an enormous number of candidate models.
Introduction
However, signaling pathways involve extensive crosstalk and feedforward as well as feedback loops resulting in complex, nonlinear intracellular signaling networks, whose topologies are often context-specific and altered in diseases [1].
Introduction
We started with an interaction graph master model containing previously reported crosstalk, feedback and feedforward mechanisms and selected then minimal model structures of the interaction graph master model that can explain the observed qualitative characteristics of the experimental data.
Negative crosstalk: experimental validation
The candidate mechanisms within model 4_8_12 generate crosstalk as well as feedforward and feedback loops within the network structure leading to robust network behavior.
feedforward is mentioned in 5 sentences in this paper.
Topics mentioned in this paper: