New paper on learning in spiking neural networks

Chris Kim and I recently published a paper in eLife:

Learning recurrent dynamics in spiking networks.


Spiking activity of neurons engaged in learning and performing a task show complex spatiotemporal dynamics. While the output of recurrent network models can learn to perform various tasks, the possible range of recurrent dynamics that emerge after learning remains unknown. Here we show that modifying the recurrent connectivity with a recursive least squares algorithm provides sufficient flexibility for synaptic and spiking rate dynamics of spiking networks to produce a wide range of spatiotemporal activity. We apply the training method to learn arbitrary firing patterns, stabilize irregular spiking activity in a network of excitatory and inhibitory neurons respecting Dale’s law, and reproduce the heterogeneous spiking rate patterns of cortical neurons engaged in motor planning and movement. We identify sufficient conditions for successful learning, characterize two types of learning errors, and assess the network capacity. Our findings show that synaptically-coupled recurrent spiking networks possess a vast computational capability that can support the diverse activity patterns in the brain.


The ideas that eventually led to this paper were seeded by two events. The first was about five years ago when I heard Dean Buonomano talk about his work with Rodrigo Laje on how to tame chaos in a network of rate neurons. Dean and Rodrigo expanded on the work by Larry Abbott and David Sussillo. The guiding idea from these two influential works stems from the “the echo state machine” or “reservoir computing”. Basically, this idea exploits the inherent chaotic dynamics of a recurrent neural network to project inputs onto diverse trajectories from which a simple learning rule can be deployed to extract a desired output.

To explain the details of this idea and our work, I need to go back to Minsky and Papert and their iconic 1969 book on feedforward neural networks (called perceptrons), who divided learning problems into two types.  The first type is linearly separable, which means that if you want to learn a classifier on some inputs, then a single linear plane can be drawn to separate the two input classes on the space of all inputs. The classic example is the OR function.  When given inputs (x_1,x_2) = (0,1), (1,0), (1,1), it outputs 1 and when given (0,0) it outputs 0. If we consider the inputs on the x-y plane then we can easily draw a line separating point (0,0) from the rest. The classic linearly non-separable problem is exclusive OR or XOR, where (0,0) and (1,1) map to 0, while (0,1) and (1,0) map to 1. In this case, no single straight line can separate the points. Minsky and Papert showed that a single layer perceptron, where the only thing you learn is the connection strengths from input to output, can never learn a linearly inseparable problem. Most interesting and nontrivial problems are inseparable.

Mathematically, we can write a perceptron as x_i^{\alpha+1} = \sum w_{ij}^{\alpha}f^{\alpha}(x_j^{\alpha}), where x_i^{\alpha} is the value of neuron i in layer \alpha and f is a connection or gain function. The inputs are x_i^{0} and the output are x_i^{L}. The perceptron problem is to find a set of w‘s such that the output layer gives you the right value to a task posed in the input layer, e.g. perform XOR. A single layer perceptron is then simply x_i^{1} = \sum w_{ij}^{\alpha}f^{\alpha}(x_j^{0}). Now of course we could always design f to do what you ask but since we are not training f, it needs to be general enough for all problems and is usually chosen to be a monotonic increasing function with a threshold. Minsky and Papert showed that the single layer problem is equivalent to the matrix equation w = M v and this can never solve a linearly inseparable problem, since it defines a plane. If f is a linear function then a multiple layer problem reduces to a single layer problem so what makes perceptron learning and deep learning possible is that there are multiple layers and f is a nonlinear function. Minsky and Paper also claimed that there was no efficient way to train a multi-layered network and this killed the perceptron for more than a decade until backpropagation was discovered and rediscovered in the 1980’s. Backprop rekindled a flurry of neural network activity and then died down because other machine learning methods proved better at that time. The recent ascendancy of deep learning is the third wave of perceptron interest and was spurred by the confluence of 1) more computing power via the GPU, 2) more data, and 3) finding the right parameter tweaks to make perceptron learning work much much better. Perceptrons still can’t solve everything, e.g. NP complete problems are still NP complete, they are still far from being optimal, and they do not discount a resurgence or invention of another method.

The idea of reservoir computing is to make a linearly inseparable problem separable by processing the inputs. The antecedent is the support vector machine or kernel method, which projects the data to a higher dimension such that an inseparable problem is separable. In the XOR example, if we can add a dimension and map (0,0) and (1,1) to (0,0,0) and (1,1,0) and map (1,0) and (0,1) to (1,0,1) and (0,1,1) then the problem is separable. The hard part is finding the mapping or kernels to do this. Reservoir computing uses the orbit of a chaotic system as a kernel. Chaos, by definition, causes initial conditions to diverge exponentially and by following a trajectory for as long as you want you can make as high dimensional a space as you want; in high enough dimensions all points are linearly separable if they are far enough apart. However, the defining feature of chaos is also a bug because any slight error in your input will also diverge exponentially and thus the kernel is inherently unstable. The Sussillo and Abbott breakthrough was that they showed you could have your cake and eat it too. They stabilized the chaos using feedback and/or learning while still preserving the separating property. This then allowed training of the output layer to work extremely efficiently. Laje and Bunomano took this one step further by showing that you could directly train the recurrent network to stabilize chaos. My thought at that time was why are chaotic patterns so special? Why can’t you learn any pattern?

The second pivotal event came in a conversation with the ever so insightful Kechen Zhang when I gave a talk at Hopkins. In that conversation, we discussed how perhaps it was possible that any internal neuron mechanism, such as nonlinear dendrites, could be reproduced by adding more neurons to the network and thus from an operational point of view it didn’t matter if you had the biology correct. There would always exist a recurrent network that could do your job. The problem was to find the properties that make a network “universal” in that it could reproduce the dynamics of any other network or any dynamical system. After this conversation, I was certain that this was true and began spouting this idea to anyone who would listen.

One of the people I mentioned this to was Chris Kim when he contacted me for a job in my lab in 2015. Later Chris told me that he thought my idea was crazy or impossible to prove but he took the job anyway because he wanted to be in Maryland where his family lived. So, upon his arrival in the fall of 2016, I tasked him with training a recurrent neural network to follow arbitrary patterns. I also told him that we should do it on a network of spiking neurons. I thought that doing this on a set of rate neurons would be too easy or already done so we should move to spiking neurons. Michael Buice and I had just recently published our paper on computing finite size corrections to a spiking network of coupled theta neurons with linear synapses. Since we had good control of the dynamics of this network, I thought it would be the ideal system. The network has the form

\dot\theta_i = f(\theta_i, I_i, u_i)

\tau_s \dot u_i= - u_i+ 2 \sum_j w_{ij}\delta(\theta_j-\pi)

Whenever neuron j crosses the angle \pi it gives an impulse to neuron i with weight scaled by w_{ij}, which can be positive or negative. The idea is to train the synaptic drive u_i(t) or the firing rate of neuron i to follow an arbitrary temporal pattern. Despite his initial skepticism, Chris actually got this to work in less than six months. It took us another year or so to understand how and why.

In our paper, we show that if the synapses are fast enough, i.e. \tau_s is small enough, and the patterns are diverse enough, then any set of patterns can be learned. The reason, which is explained in mathematical detail in the paper, is that if the synapses are fast enough, then the synaptic drive acts like a quasi-static function of the inputs and thus the spiking problem reduces to the rate problem

\tau_s \dot u_i= - u_i+ \sum_j w_{ij}g(u_j)

where g is the frequency-input curve of a theta neuron. Then the problem is about satisfying the synaptic drive equation, which given the linearity in the weights, boils down to whether \tau_s \dot u_i + u_i is in the space spanned by \sum w_{ij} g(u_j),  which we show is always possible as long as the desired patterns imposed on u_i(t) are uncorrelated or linearly independent enough. However, there is a limit to how long the patterns can be, which is governed by the number of entries in w_{ij}, which is limited by the number of neurons. The diversity of patterns limitation can also be circumvented by adding auxiliary neurons. If you wanted some number of neurons to do the same thing, you just need to include a lot of other neurons that do different things. A similar argument can be applied to the time averaged firing rate (on any time window) of a given neuron. I now think we have shown that a recurrent network of really simple spiking neurons is dynamically universal. All you need are lots of fast neurons.


Addendum: The dates of events may not all be correct. I think my conversation with Kechen came before Dean’s paper but my recollection is foggy. Memories are not reliable.



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