3 research outputs found
Error Forward-Propagation: Reusing Feedforward Connections to Propagate Errors in Deep Learning
We introduce Error Forward-Propagation, a biologically plausible mechanism to
propagate error feedback forward through the network. Architectural constraints
on connectivity are virtually eliminated for error feedback in the brain;
systematic backward connectivity is not used or needed to deliver error
feedback. Feedback as a means of assigning credit to neurons earlier in the
forward pathway for their contribution to the final output is thought to be
used in learning in the brain. How the brain solves the credit assignment
problem is unclear. In machine learning, error backpropagation is a highly
successful mechanism for credit assignment in deep multilayered networks.
Backpropagation requires symmetric reciprocal connectivity for every neuron.
From a biological perspective, there is no evidence of such an architectural
constraint, which makes backpropagation implausible for learning in the brain.
This architectural constraint is reduced with the use of random feedback
weights. Models using random feedback weights require backward connectivity
patterns for every neuron, but avoid symmetric weights and reciprocal
connections. In this paper, we practically remove this architectural
constraint, requiring only a backward loop connection for effective error
feedback. We propose reusing the forward connections to deliver the error
feedback by feeding the outputs into the input receiving layer. This mechanism,
Error Forward-Propagation, is a plausible basis for how error feedback occurs
deep in the brain independent of and yet in support of the functionality
underlying intricate network architectures. We show experimentally that
recurrent neural networks with two and three hidden layers can be trained using
Error Forward-Propagation on the MNIST and Fashion MNIST datasets, achieving
and generalization errors respectively
Activation Relaxation: A Local Dynamical Approximation to Backpropagation in the Brain
The backpropagation of error algorithm (backprop) has been instrumental in
the recent success of deep learning. However, a key question remains as to
whether backprop can be formulated in a manner suitable for implementation in
neural circuitry. The primary challenge is to ensure that any candidate
formulation uses only local information, rather than relying on global signals
as in standard backprop. Recently several algorithms for approximating backprop
using only local signals have been proposed. However, these algorithms
typically impose other requirements which challenge biological plausibility:
for example, requiring complex and precise connectivity schemes, or multiple
sequential backwards phases with information being stored across phases. Here,
we propose a novel algorithm, Activation Relaxation (AR), which is motivated by
constructing the backpropagation gradient as the equilibrium point of a
dynamical system. Our algorithm converges rapidly and robustly to the correct
backpropagation gradients, requires only a single type of computational unit,
utilises only a single parallel backwards relaxation phase, and can operate on
arbitrary computation graphs. We illustrate these properties by training deep
neural networks on visual classification tasks, and describe simplifications to
the algorithm which remove further obstacles to neurobiological implementation
(for example, the weight-transport problem, and the use of nonlinear
derivatives), while preserving performance.Comment: initial upload; revised version (updated abstract, related work)
28-09-20; 05/10/20: revised for ICLR submissio
Cognitive Action Laws: The Case of Visual Features
This paper proposes a theory for understanding perceptual learning processes
within the general framework of laws of nature. Neural networks are regarded as
systems whose connections are Lagrangian variables, namely functions depending
on time. They are used to minimize the cognitive action, an appropriate
functional index that measures the agent interactions with the environment. The
cognitive action contains a potential and a kinetic term that nicely resemble
the classic formulation of regularization in machine learning. A special choice
of the functional index, which leads to forth-order differential
equations---Cognitive Action Laws (CAL)---exhibits a structure that mirrors
classic formulation of machine learning. In particular, unlike the action of
mechanics, the stationarity condition corresponds with the global minimum.
Moreover, it is proven that typical asymptotic learning conditions on the
weights can coexist with the initialization provided that the system dynamics
is driven under a policy referred to as information overloading control.
Finally, the theory is experimented for the problem of feature extraction in
computer vision