Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity
Anonymous authors
Paper under double-blind review
Abstract
The impressive lifelong learning in animal brains is primarily enabled by plastic changes in synaptic connectivity.
Importantly, these changes are not passive, but are actively controlled by neuromodulation, which is itself under the control of the brain.
The resulting self-modifying abilities of the brain play an important role in learning and adaptation, and are a major basis for biological reinforcement learning.
Here we show for the ﬁrst time that artiﬁcial neural networks with such neuromodulated plasticity can be trained with gradient descent.
Extending previous work on diﬀerentiable
Hebbian plasticity, we propose a diﬀerentiable formulation for the neuromodulation of plasticity.
We show that neuromodulated plasticity improves the performance of neural networks on both reinforcement learning and supervised learning tasks.
In one task, neuromodulated plastic LSTMs with millions of parameters outperform standard LSTMs on a benchmark language modeling task (controlling for the number of parameters).
We conclude that diﬀerentiable neuromodulation of plasticity oﬀers a powerful new framework for training neural networks.
Introduction
Neural networks that deal with temporally extended tasks must be able to store traces of past events.
Often this memory of past events is maintained by neural activity reverberating through recurrent connections; other methods for handling temporal information exist, including memory networks (Sukhbaatar et al., 2015) or temporal convolutions (Mishra et al., 2017).
However, in nature, the primary basis for long-term learning and memory in the brain is synaptic plasticity – the automatic modiﬁcation of synaptic weights as a function of ongoing activity (Martin et al., 2000; Liu et al., 2012).
Plasticity is what enables the brain to store information over the long-term about its environment that would be impossible or impractical for evolution to imprint directly into innate connectivity (e g. things that are diﬀerent within each life, such as the language one speaks).
Importantly, these modiﬁcations are not a passive process, but are actively modulated on a moment-to-moment basis by dedicated systems and mechanisms: the brain can “decide” where and when to modify its own connectivity, as a function of its inputs and computations.
This neuromodulation of plasticity, which involves several chemicals (particularly dopamine;
Calabresi et al.
He et al. 2015; Li et al. 2003; Yagishita et al. 2014), plays an important role in learning and adaptation (Molina-Luna et al., 2009; Smith-Roe & Kelley, 2000; Kreitzer & Malenka, 2008).
By allowing the brain to control its own modiﬁcation as a function of ongoing states and events, the neuromodulation of plasticity can ﬁlter out irrelevant events while selectively incorporating important information, combat catastrophic forgetting of previously acquired knowledge, and implement a self-contained reinforcement learning algorithm by altering its own connectivity in a reward-dependent manner (Schultz et al., 1997; Niv, 2009; Frank et al., 2004; Hoerzer et al., 2014; Miconi, 2017; Ellefsen et al., 2015;
Velez & Clune, 2017).
The complex organization of neuromodulated plasticity is not accidental: it results from a long process of evolutionary optimization.
Evolution has not only designed the general connection pattern of the brain, but has also sculpted the machinery that controls neuromodulation, endowing the brain with carefully tuned self-modifying abilities and enabling eﬃcient lifelong learning.
In eﬀect, this coupling of evolution and plasticity is a meta-learning process (the original and by far most powerful example of meta-learning), whereby a simple but powerful optimization process (evolution guided by natural selection) discovered how to arrange elementary building blocks to produce remarkably eﬃcient learning agents.
Taking inspiration from nature, several authors have shown that evolutionary algorithms can design small neural networks (on the order of hundreds of connections) with neuromodulated plasticity (see the “Related Work” section below).
However, many of the spectacular recent advances in machine learning make use of gradient-based methods (which can directly translate error signals into weight gradients) rather than evolution (which has to discover the gradients through random weight-space exploration).
If we could make plastic, neuromodulated networks amenable to gradient descent, we could leverage gradient-based methods for optimizing and studying neuromodulated plastic networks, expanding the abilities of current deep learning architectures to include these important biologically inspired self-modifying abilities.
Here we build on the diﬀerentiable plasticity framework (Miconi, 2017; Miconi et al., 2018) to implement diﬀerentiable neuromodulated plasticity.
As a result, for the ﬁrst time to our knowledge, we are able to train neuromodulated plastic networks with gradient descent.
We call our framework backpropamine in reference to its ability to emulate the eﬀects of natural neuromodulators (like dopamine) in artiﬁcial neural networks trained by backpropagation.
Our experimental results establish that neuromodulated plastic networks outperform both non-plastic and non-modulated plastic networks, both on simple reinforcement learning tasks and on a complex language modeling task involving a multi-million parameter network.
By showing that neuromodulated plasticity can be optimized through gradient descent, the backpropamine framework potentially provides more powerful types of neural networks, both recurrent and feedforward, for use in all the myriad domains in which neural networks have had tremendous impact.
Related work
Neuromodulated plasticity has long been studied in evolutionary computation.
Evolved networks with neuromodulated plasticity were shown to outperform both non-neuromodulated and non-plastic networks in various tasks (e.g. Soltoggio et al.
2008; Risi & Stanley 2012; see
Soltoggio et al.
2017 for a review).
A key focus of neuromodulation in evolved networks is the mitigation of catastrophic forgetting , that is, allowing neural networks to learn new skills without overwriting previously learned skills.
By activating plasticity only in the subset of neural weights relevant for the task currently being performed, knowledge stored in other weights about diﬀerent tasks is left untouched, alleviating catastrophic forgetting (Ellefsen et al., 2015; Velez & Clune, 2017).
However, evolved networks were historically relatively small and operated on low-dimensional problem spaces.
The diﬀerentiable plasticity framework (Miconi, 2016; Miconi et al., 2018) allows the plasticity of individual synaptic connections to be optimized by gradient descent, in the same way that standard synaptic weights are.
However, while it could improve performance in some tasks over recurrence without plasticity, this method only facilitated passive, non-modulated plasticity, in which weight changes occur automatically as a function of pre- and post-synaptic activity.
Here we extend this framework to implement diﬀerentiable neuromodulated plasticity, in which the plasticity of connections can be modulated moment-to-moment through a signal computed by the network.
This extension allows the network itself to decide over its lifetime where and when to be plastic, endowing the network with true self-modifying abilities.
There are other conceivable though more complex approaches for training self-modifying networks.
For example, the weight modiﬁcations can themselves be computed by a neural network (Schmidhuber, 1993b; Schlag & Schmidhuber, 2017; Munkhdalai & Yu, 2017; Wu et al., 2018).
However, none so far have taken the simple approach of directly optimizing the neuromodulation of plasticity itself within a single network, through gradient descent instead of evolution, as investigated here.
Methods 3. Background: Differentiable Hebbian plasticity
The present work builds upon the existing diﬀerentiable plasticity framework (Miconi, 2016;
Miconi et al., 2018), which allows gradient descent to optimize not just the weights, but also the plasticity of each connection.
In this framework, each connection in the network is augmented with a Hebbian plastic component that grows and decays automatically as a result of ongoing activity.
In eﬀect, each connection contains a ﬁxed and a plastic component:
[Equation]
where x i ( t ) is the output of neuron i at time t , σ is a nonlinearity (we use tanh in all experiments), w i,j is the baseline (non-plastic) weight of the connection between neurons i and j , and α i,j is the plasticity coeﬃcient that scales the magnitude of the plastic component of the connection.
The plastic content is represented by the Hebbian trace Hebb i,j , which accumulates the product of pre- and post-synaptic activity at connection i, j , as shown in
Eq. 2.
Hebb i,j is initialized to zero at the beginning of each episode/lifetime, and is updated automatically according to Eq.
2: it is a purely episodic/intra-life quantity.
By contrast, w i,j , α i,j and η are the structural components of the network, which are optimized by gradient descent between episodes/lifetimes to minimize the expected loss over an episode.
The function Clip( x ) in Eq. 2 is any function or procedure that constrains Hebb i,j to the [ − 1 , 1] range, to negate the inherent instability of Hebbian learning.
In previous work (Miconi et al., 2018), this function was either a simple decay term, or a normalization implementing
Oja’s rule (Oja, 2008).
In the present paper it is simply a hard clip ( x ← 1 if x > 1 ; x ← −if x < − 1 ).
Compared to previously used operations, this simple operation turned out to produce equal or superior performance on the tasks in this paper.
Note the distinction between the η and α i,j parameters: η is the intra-life “learning rate” of plastic connections, which determines how fast new information is incorporated into the plastic component, while α i,j is a scale parameter, which determines the maximum magnitude of the plastic component (since Hebb i,j is constrained to the [-1,1] range).
Importantly, in contrast to other approaches using uniform plasticity (Schmidhuber, 1993a), including “fast weights” (Ba et al., 2016), the amount of plasticity in each connection (represented by α i,j ) is trainable , allowing the meta-optimizer to design complex learning strategies (see Miconi et al.
2018 for a discussion of this point, and experimental comparisons that demonstrate and explain superior performance of diﬀerentiable plasticity over uniformplastic networks).
An important aspect of diﬀerentiable plasticity is extreme ease of implementation: implementing a plastic recurrent network only requires less than four additional lines of code over a standard recurrent network implementation (Miconi et al., 2018).
The Backpropamine framework described below inherits this simplicity; in particular, the “simple neuromodulation” approach does not require any additional code over diﬀerentiable plasticity, but merely a modiﬁcation of it.
3. Backpropamine: Differentiable neuromodulation of plasticity
Two methods are proposed to introduce neuromodulated plasticity within the diﬀerentiable plasticity framework.
In both cases, plasticity is modulated on a moment-to-moment basis by a network-controlled neuromodulatory signal M ( t ) .
The computation of M ( t ) could be done in various ways; at present, it is simply a single scalar output of the network, which is used either directly (for the simple RL tasks) or passed through a meta-learned vector of weights (one for each connection, for the language modeling task).
We now explain how the equations of diﬀerentiable plasticity are modiﬁed to make use of this neuromodulatory signal.
3.2. Simple neuromodulation
The simplest way to introduce neuromodulation of plasticity in this framework is to make the (global) η parameter depend on the output of one or more neurons in the network.
Because η essentially determines the rate of plastic change, placing it under network control allows the network to determine how plastic connections should be at any given time.
Thus, the only modiﬁcation to the equations above in this simple neuromodulation variant is to replace η in Eq.
2 with the network-computed, time-varying neuromodulatory signal M ( t ) .
That is,
Eq. 2 is replaced with
[Equation]
3.2. Retroactive neuromodulation and eligibility traces
More complex schemes are possible.
In particular, we introduce an alternative neuromodulation scheme that takes inspiration from the short-term retroactive eﬀects of neuromodulatory dopamine on Hebbian plasticity in animal brains.
In several experiments, dopamine was shown to retroactively gate the plasticity induced by past activity, within a short time window of about 1s (Yagishita et al., 2014;
He et al., 2015; Fisher et al., 2017; Cassenaer & Laurent, 2012).
Thus, Hebbian plasticity does not directly modify the synaptic weights, but creates a fast-decaying “potential” weight change, which is only incorporated into the actual weights if the synapse receives dopamine within a short time window.
As a result, biological Hebbian traces essentially implement a so-called eligibility trace (Sutton et al., 1998), keeping memory of which synapses contributed to recent activity, while the dopamine signal modulates the transformation of these eligibility traces into actual plastic changes.
Such mechanisms have been modelled in computational neuroscience studies, e.g. (Izhikevich, 2007;
Hoerzer et al., 2014; Fiete et al., 2007; Soltoggio & Steil, 2013; Miconi, 2017)
(see Gerstner et al.
2018 for a recent review of this concept).
Our framework easily accommodates this more reﬁned model of dopamine eﬀects on plasticity.
We simply replace Eq.
2 above with the two equations,
[Equation]
Here E i,j ( t ) (the eligibility trace at connection i, j ) is a simple exponential average of the
Hebbian product of pre- and post-synaptic activity, with trainable decay factor η .
Hebb i,j ( t ) , the actual plastic component of the connection (see Eq. 1), simply accumulates this trace, but gated by the current value of the dopamine signal M ( t ) .
Note that M ( t ) can be positive or negative, approximating the eﬀects of both rises and dips in the baseline dopamine levels (Schultz et al., 1997).
Experiments 4. Task 1: Cue-reward association
Our ﬁrst test task is a simple meta-learning problem that emulates an animal behavioral learning task, as described in Figure 4.1 (Left).
In each episode, one of four input cues is arbitrarily chosen as the Target cue.
Repeatedly, the agent is shown two cues in succession, randomly chosen from the possible four, then a Response cue during which the agent must respond 1 if the Target cue was part of the pair, or 0 otherwise.
A correct response produces a reward of 1.0, while an incorrect response returns reward -1.0 (this is a forced choice task: a response of either 1 or 0 is always produced).
This process iterates for the duration of the episode, which is 200 time steps.
The cues are binary vectors ofbits, randomly generated at the beginning of each episode.
To prevent simple time-locked scheduling strategies, a variable number of zero-input time steps are randomly inserted, including at least one after each presentation of the Go cue; as a result, the length of each trial varies, and the number of trials per episode is somewhat variable (the mean number of trials per episode is 15).
The architecture is a simple recurrent network with 200 neurons in the hidden recurrent layer.
Only the recurrent layer is plastic: input and output weights are non-plastic, having only w i,j coeﬃcients.
There are 24 inputs: 20 binary inputs for the current cue and one input providing the time elapsed since the start of the episode, as well as two binary inputs for the one-hot encoded response at the previous time step and one real-valued channel for the reward received at the previous time step, in accordance with common meta-learning practice (Wang et al., 2016; Duan et al., 2016).
There are four outputs: two binary outputs for the one-hot encoded response, plus an output neuron that predicts the sum of future discounted rewards V ( t ) over the remainder of the episode (as mandated by the A2C algorithm that we use for meta-training, following Wang et al.
(2016)), and the neuromodulatory signal M ( t ) .
The two response outputs undergo a softmax operation to produce probabilities over the response, while the M ( t ) signal is passed through a tanh nonlinearity and the V ( t ) output is a pure linear output.
All gradients are clipped at norm 7.0, which greatly improved stability.
Training curves are shown in Figure 4.1 (Right; each curve shows the median and interquartile range over 10 runs).
Neuromodulatory approaches succeed in learning the task, while non-neuromodulatory networks (Miconi, 2016; Miconi et al., 2018)) and non-plastic, simple recurrent networks fail to learn it.
We hypothesize that this dramatic diﬀerence is related to the relatively high dimensionality of the input cues: just as non-modulated plastic networks seemed to outperform non-plastic networks speciﬁcally when required to memorize arbitrary high-dimensional stimuli (Miconi et al., 2018), neuromodulation seems to speciﬁcally help memorizing reward associations with such arbitrary high-dimensional stimuli (see Appendix). To illustrate the behavior of neuromodulation, we plotted the output of the neuromodulator neuron for several trials. These graphs reveal that neuromodulation reacts to reward in a complex, time-dependent manner (see Appendix).
4. Task 2: Maze navigation task
For a more challenging problem, we also tested the approach on the grid maze exploration task introduced by Miconi et al.
Here, the maze is composed of 9 × 9 squares, surrounded by walls, in which every other square (in either direction) is occupied by a wall.
Thus the maze contains 16 wall squares, arranged in a regular grid except for the center square (Figure 2, left).
The shape of the maze is ﬁxed and unchanging over the whole task.
At each episode, one non-wall square is randomly chosen as the reward location.
When the agent hits this location, it receives a reward and is immediately transported to a random location in the maze.
Each episode lasts 200 time steps, during which the agent must accumulate as much reward as possible.
The reward location is ﬁxed within an episode and randomized across episodes.
Note that the reward is invisible to the agent, and thus the agent only knows it has hit the reward location by the activation of the reward input at the next step (and possibly by the teleportation, if it can detect it).
The architecture is the same as for the previous task, but with only 100 recurrent neurons.
The outputs consist of 4 action channels (i.e. one for each of the possible actions: left, right, up or down) passed through a softmax, as well as the pure linear V ( t ) output and the M ( t ) neuromodulatory signal passed through a tanh nonlinearity.
Inputs to the agent consist of a binary vector describing the 3 × 3 neighborhood centered on the agent (each element being set to 1 or 0 if the corresponding square is or is not a wall), plus four additional inputs for the one-hot encoded action taken at the previous time step, and one input for the reward received at the previous time step, following common practice (Wang et al., 2016).
Again, only recurrent weights are plastic: input-to-recurrent and recurrent-to-output weights are non-plastic.
Results in Figure 2 show that modulatory approaches again outperform non-modulated plasticity.
4. Task 3: Language Modeling
Word-level language modeling is a supervised learning sequence problem, where the goal is to predict the next word in a large language corpus.
Language modeling requires storing long term context, and therefore LSTM models (Hochreiter & Schmidhuber, 1997) generally perform well on this task (Zaremba et al., 2014).
The goal of this experiment is to study the beneﬁts of adding plasticity and neuromodulation to LSTMs.
The Penn Tree Bank corpus (PTB), a well known benchmark for language modeling (Marcus et al., 1993), is used here for comparing diﬀerent models.
The dataset consists of 929k training words, 73k validation words, and 82k, test words, with a vocabulary of 10k words.
During training successive mini-batches of size 20 are sequentially processed.
[Table]
A detailed experimental description is provided in the Appendix, summarized here: Each network consists of an embedding layer, followed by two LSTM layers (approximately of size 200).
The size of the LSTM layers is adjusted to ensure that the total number of trainable parameters (4.8 million) remains constant across all experiments; note that this includes all plasticity-related additional parameters, i.e. α i,j as well as the additional parameters related to neuromodulation (see Appendix).
The ﬁnal layer is a softmax layer of size 10k.
The network is unrolled for 20 time steps during backpropagation through time (Werbos, 1990).
The norm of the gradient is clipped at 5.
This setup is similar to the non-regularized model described by Zaremba et al.
One diﬀerence is that an extra L2 penalty is added to the weights of the network here (adding this penalty consistently improves results for all the models).
Four models are evaluated here (Table 1).
(1) The Baseline LSTM model (described in the previous paragraph) 1 .
(2) LSTM with Diﬀerentiable Plasticity : there are four recurrent connections in each LSTM node and here, plasticity is added to one of them (see A.1 for details) as per equations 1 and 2.
Because the number of plastic connections is large, each plastic connection has its own individual η so that their values can be individually tuned by backpropagation.
(3) LSTM with Simple Neuromodulation: here simple neuromodulation is introduced following equation 3.
The η parameters are replaced by the output of a neuron
M ( t ) .
M ( t ) itself receives as input a weighted combination of the hidden layer’s activations, where the weights are learned in the usual way.
There is one M ( t ) associated with each
LSTM layer.
(4) LSTM with Retroactive Neuromodulation : this model is the same as the
LSTM with Simple Neuromodulation, except it uses the equations that enable eligibility traces (equations 4 and 5).
Additional details for the plastic and neuromodulated plastic
LSTMs are described in the Appendix.
For each of the four models, we separately searched for the best hyperparameters with equally-powered grid-search.
Each model was then run 16 times with its best hyperparameter settings.
The mean test perplexity of these 16 runs along with the 95% conﬁdence interval is presented in Table 1.
Results show that adding diﬀerentiable plasticity to LSTM provides slightly, but statistically signiﬁcantly better results than the Baseline LSTM (Wilcoxon rank-sum test, p = 0 . 0044 ).
Adding neuromodulation further (and statistically signiﬁcantly) lowers the perplexity over and above the LSTM with diﬀerential plasticity ( p = 1 e − 6 ).
Overall, retroactive neuromodulation provides about 1.7 perplexity improvement vs. the Baseline LSTM (statistically signiﬁcant, p = 1 e − 7 ).
Retroactive neuromodulation (i.e. with eligibility traces) does outperform simple neuromodulation, but the improvement is just barely not statistically signiﬁcant at the traditional p < 0 .
05 cutoﬀ ( p = 0 . 066 ).
Note that while these ﬁgures are far from state-of-the-art results (which use considerably larger, more complex architectures), they all still outperform published work using similar architectures (Zaremba et al., 2014).
Discussion and Future Work
This paper introduces a biologically-inspired method for training networks to self-modify their weights.
Building upon the diﬀerentiable plasticity framework, which already improved performance (sometimes dramatically) over non-plastic architectures on various supervised and RL tasks (Miconi, 2016; Miconi et al., 2018), here we introduce neuromodulated plasticity to let the network control its own weight changes.
As a result, for the ﬁrst time, neuromodulated plastic networks can be trained with gradient descent, opening up a new research direction into optimizing large-scale self-modifying neural networks.
As a complement to the beneﬁts in the simple RL domains investigated, our ﬁnding that plastic and neuromodulated LSTMs outperform standard LSTMs on a benchmark language modeling task (importantly, a central domain of application of LSTMs) is potentially of great importance.
LSTMs are used in real-world applications with massive academic and economic impact.
Therefore, if plasticity and neuromodulation consistently improve LSTM performance (for a ﬁxed search space size), the potential beneﬁts could be considerable.
We intend to pursue this line of investigation and test plastic LSTMs (both neuromodulated and non) on other problems for which LSTMs are commonly used, such as forecasting.
Conceptually, an important comparison point is the “Learning to Reinforcement Learn” (L2RL) framework introduced by Wang et al.
In this meta-learning framework, the weights do not change during episodes: all within-episode learning occurs through updates to the activity state of the network.
This framework is explicitly described (Wang et al., 2018) as a model of the slow sculpting of prefrontal cortex by the reward-based dopamine system, an analogy facilitated by the features of the A2C algorithm used for meta-training (such as the use of a value signal and modulation of weight changes by a reward prediction error).
As described in the RL experiments above, our approach adds more ﬂexibility to this model by allowing the system to store state information with weight changes, in addition to hidden state changes.
However, because our framework allows the network to update its own connectivity, we might potentially extend the L2RL model one level higher: rather than using A2C as a hand-designed reward-based weight-modiﬁcation scheme, the system could now determine its own arbitrary weight-modiﬁcation scheme, which might make use of any signal it can compute (reward predictions, surprise, saliency, etc.)
This emergent weight-modifying algorithm (designed over many episodes/lifetimes by the “outer loop” meta-training algorithm) might in turn sculpt network connectivity to implement the meta-learning process described by Wang et al.
Importantly, this additional level of learning (or “meta-meta-learning”) is not just a pure ﬂight of fancy: it has undoubtedly taken place in evolution.
Because humans (and other animals) can perform meta-learning (“learning-to-learn”) during their lifetime (Harlow, 1949; Wang et al., 2018), and because humans are themselves the result of an optimization process (evolution), then meta-meta-learning has not only occurred, but may be the key to some of the most advanced human mental functions.
Our framework opens the tantalizing possibility of studying this process, while allowing us to replace evolution with any gradient-based method in the outermost optimization loop.
To investigate the full potential of our approach, the framework described above requires several improvements.
These include: implementing multiple neuromodulatory signals (each with their own inputs and outputs), as seems to be the case in the brain (Lammel et al., 2014;
Howe & Dombeck, 2016; Saunders et al., 2018); introducing more complex tasks that could make full use of the ﬂexibility of the framework, including the eligibility traces aﬀorded by retroactive modulation and the several levels of learning mentioned above; and addressing the pitfalls in the implementation of reinforcement learning with reward-modulated Hebbian plasticity (e.g. the inherent interference between the unsupervised component of Hebbian learning and reward-based modiﬁcations;
Frémaux et al. 2010; Frémaux & Gerstner 2015), so as to facilitate the automatic design of eﬃcient, self-contained reinforcement learning systems.
Finally, it might be necessary to allow the meta-training algorithm to design the overall architecture of the system, rather than simply the parameters of a ﬁxed, hand-designed architecture.
With such a rich potential for extension, our framework for neuromodulated plastic networks opens many avenues of exciting research.
A Appendix A. Adding plasticity to LSTM
Each LSTM node consists of four weighted recurrent paths through i t , j t , f t and o t as shown in the equations below:
[Equation]
j t , f t and o t are used for controlling the data-ﬂow through the LSTM and i t is the actual data.
Therefore, plasticity is introduced in the path that goes through i t (adding plasticity to the control paths of LSTM is for future-work) .
The corresponding pre-synaptic and post-synaptic activations (denoted by x i ( t − 1) and x j ( t ) respectively in equations 1 and 2) are h t − 1 and i t .
A layer of size 200 has 40k (200 × 200) plastic connections.
Each plastic connection has its own individual η (used in equation 2) that is learned through backpropagation.
The plasticity coeﬃcients ( α i,j ) are used as shown in equation 1.
A. Adding neuromodulation to LSTM
As shown in equation 3, for simple neuromodulation, the η is replaced by the output of a network computed neuron M ( t ) .
For neuromodulated LSTMs, individual η for each plastic connection is replaced by the output of a neuron ( M ( t ) ) that has a fan-out equal to the number of plastic connections.
The input to this neuron is the activations h t − 1 of the layer from the previous time-step.
Each LSTM layer has its dedicated neuromodulatory neuron.
Other variations of this setting include having one dedicated neuromodulatory neuron per node or having one neuromodulatory neuron for the whole network.
Preliminary experiments showed that these variations performed worse and therefore they were not further evaluated.
A. More details for Language Modeling experiment
All the four models presented in Table 1 are trained using SGD.
Initial learning rate was set 1.0.
Each model is trained for 13 epochs.
The hidden states of LSTM are initialized to zero; the ﬁnal hidden states of the current minibatch are used as the initial hidden states of the subsequent minibatch.
Grid-search was performed for four hyperparameters: (1) Learning rate decay factor in the range 0 .
25 to 0 .
4 in steps of 0 .
(2) Epoch at which learning rate decay begins in the range - { 4 , 5 , 6 }.
(3) Initial scale of weights in the range - { 0 . 09 , 0 . 1 , 0 .
(4) L2 penalty constant in the range - { 1 e − 2 , 1 e − 3 , 1 e − 4 , 1 e − 5 , 1 e − 6 }.
The state-of-art results in language modeling domain have been achieved with much larger
LSTM models Melis et al.
In such large models, recurrent dropouts (Gal, 2015) need to be introduced for regularization.
An interesting direction for future-work is to understand the eﬀects of dropout on plastic and neuromodulated connections.
A. Dynamics of neuromodulation
To illustrate the behavior of neuromodulation, we plot the output of the neuromodulator neuron for random trials from several runs of Task 1 (Figure 3). All runs are from well-trained, highly successful network, as seen by the low proportion of negative rewards. For each run, we plot both the value of the neuromodulator output at each time step, and the reward being currently perceived by the network (i.e. the one produced by the response at the previous time step).
The plots reveal rich, complex dynamics that vary greatly between runs.
The modulator neuron clearly reacts to reward; however, this reaction is complex, time-dependent and varies from run to run.
The topmost run for retroactive modulation tends to produce negative neuromodulation in response to positive reward, and vice-versa; while the secondto-last run for simple neuromodulation tends to to the opposite.
A common pattern is to produce negative neuromdulation on the time step just following reward perception (especially for simple neuromodulation).
Two of the runs for retroactive modulation exhibit a pattern where reward perception is followed by highly positive, then highly negative neuromodulation.
Understanding the mechanism by which these complex dynamics perform eﬃcient within-episode learning is an important direction for future work.
A. Cue-reward association task
In the cue-reward association learning task described above, neuromodulated plasticity was able to learn a task that non-modulated plasticity simply could not.
What might be the source of this diﬀerence?
In a previous experiment, we implemented the same task, but using only four ﬁxed 4-bit binary cues for the entire task, namely, ’1000’, ’0100’, ’0010’ and ’0001’.
In this simpliﬁed version of the task, there is no need to memorize the cues for each episode, and the only thing to be learned for each episode is which of the four known cues is associated with reward.
This is in contrast with the version used in the paper above, in which the cues are arbitrary 20-bits vectors randomly generated for each episode.
With the ﬁxed, four-bit cues, non-modulated plasticity was able to learn the task, though somewhat more slowly than neuromodulated plasticity (see Figure 4).
This suggests neuromodulated plasticity could have a stronger advantage over non-modulated plasticity speciﬁcally in situations where the association to be learned involves arbitrary high-dimensional cues, which must be memorized jointly with the association itself.
This echoes the results of Miconi et al.
(2018), who suggest that plastic networks outperform non-plastic ones speciﬁcally on tasks requiring the fast memorization of high-dimensional inputs (e.g. image memorization and reconstruction task in (Miconi et al., 2018)).
Clearly, more work is needed to investigate which problems beneﬁt most from neuromodulated plasticity, over non-modulated or non-plastic approaches.
We intend to pursue this line of research in future work.