The synaptic weight dynamics of lateral excitatory and inhibitory connections are approximately given as

Initially, in all output neurons, synaptic weights from A-neurons (blue triangles in Fig 2A) become larger because A-neurons are more strongly correlated with one another than B-neurons are.

As a result, synaptic weights from A-neurons to group 1 become weaker, and group 1 neurons eventually become selective for the minor source B (Fig 2C right).

In this approximation, by inserting Eq (32) into Eq (29), the mean synaptic weight changes of feedforward connections follow

For STDP, we used pairwise log-STDP (Fig 1B) [31], which replicates the experimentally observed long-tailed synaptic weight distribution [32,33].

yj(t) The spiking activity of output neuron j uk’(t) Membrane potential of inhibitory neuron k zk(t) The spiking activity of inhibitory neuron k wjix The synaptic weight of a feed-forward excitatory connection from ito j qifl Response probability of input neuron ito external source p 11X, 12X The correlation kernel functions used the gamma distribution With shape parameter kg = 3 in order to reproduce broad spike correlations typically observed in cortical neurons [36,37].

Synaptic weight dynamics by STDP is written as ods for details), the weight change of the feedforward connection WX can be approximated as

Note that because of the mutual inhibition, the synaptic weight from A-neuron is smaller when both groups learn A than it is when only group 1 learns A.

In a fully-defined input paradigm, we than control the synaptic weights and timing of many simulated presynaptic neurons.

Synapses play a central role in neural information processing — weighting individual inputs in different ways allows neurons to perform a range of computations, and the changing of synaptic weights over time allows learning and recovery from injury.

4) Detectability of changes of synaptic weights follows same rules and has same limitations as detection of individual synaptic connections.

Tools for identifying synaptic weights and tracking their changes, thus, play a key role in understanding neural information processing.

Traditionally, synaptic integration and plasticity are studied using intracellular recordings in vitro, where synaptic weights can be directly measured as the amplitude of postsynaptic potentials or currents.

We ask how well synaptic inputs of different amplitudes can be detected, how much data is necessary to reconstruct the amplitudes of excitatory and inhibitory synaptic inputs, and how precisely synaptic weights can be estimated from spikes alone.

As in previous analysis, we use the likelihood ratio to determine whether the synaptic weight has changed.

Detectability of synaptic weight changes in long recordings is comparable to the detectability of connections of constant strength.

In the fully-defined input setting, we can examine, in a single cell, how accurately model estimates of synaptic weights (of different amplitude and sign) capture the actual values.

The increased accuracy in estimating synaptic weights using models of all inputs suggests that postsynaptic spikes might be more readily associated with or disassociated from the spiking of individual presynaptic inputs.

It thereby alleviates a limitation of many previous biologically plausible RL models, which can only train a single layer of modifiable synaptic weights and solve linear tasks [16,21,44,67,70,71,73,76] and binary decisions [21,44,67,70].

Importantly, the synaptic weights from input neurons to memory cells depended on the true weights of the symbols after learning (Fig.

As in other SARSA methods, the updating of synaptic weights is only performed for the transitions that the network actually experiences.

We will first establish the equivalence of online gradient descent defined in Equation (19) and the AuGMEnT learning rule for the synaptic weights wflt) from the regular units onto the time step t-1 should change as: leaving the other weights k7éa unchanged.

We conclude that AuGMEnT causes an online gradient descent on all synaptic weights to minimize the temporal difference error if a = 1.

Dopamine affects striatal function by modulating the intrinsic excitability of the MSNs and synaptic weights [24] and synaptic plasticity [25, 26] of the cortico-striatal projections.

We arrived at this functional description of the striatum as a DTT by considering the striatal network dynamics emerging from including low-level properties such as synaptic weights and connection probabilities.

In such single population models, the striatal output is controlled by the strength of cortico-striatal synaptic weights .