Asymmetry in neural fields: a spatiotemporal encoding mechanism

Abstract

Neural field models have been successfully applied to model diverse brain mechanisms like visual attention, motor control, and memory. Most theoretical and modeling works have focused on the study of the dynamics of such systems under variations in neural connectivity, mainly symmetric connectivity among neurons. However, less attention has been given to the emerging properties of neuron populations when neural connectivity is asymmetric, although asymmetric activity propagation has been observed in cortical tissue. Here we explore the dynamics of neural fields with asymmetric connectivity and show, in the case of front propagation, that it can bias the population to follow a certain trajectory with higher activation. We find that asymmetry relates linearly to the input speed when the input is spatially localized, and this relation holds for different kernels and input shapes. To illustrate the behavior of asymmetric connectivity, we present an application: standard video sequences of human motion were encoded using the asymmetric neural field and compared to computer vision techniques. Overall, our results indicate that asymmetric neural fields are a competitive approach for spatiotemporal encoding with two main advantages: online classification and distributed operation.

Combining Manifold Learning and Neural Field Dynamics for Multimodal Fusion

“…Then the best-matching unit (BMU) and its direct topological neighbors have their prototype moved towards the stimulus. Connections that have not been updated in a long time are removed, and 1 There have been suggestions to break the symmetry from the DNF side, either through asymmetrical kernels [31] or through distortions of the topology by predictive reinforcements [32], but both require an additional learning step. isolated neurons as well.…”

Neural and Synaptic Array Transceiver: A Brain-Inspired Computing Framework for Embedded Learning

“…Figure 7 The second task involves target tracking. Asymmetric neural fields have been used for solving target tracking [16]. We use the same number of neurons (internal and external) and the same simulation time as above, and modify the kernel to an asymmetric one as Fig 7…”

Neurons and networks organizing and sequencing memories

“…There are several studies which have established the features of pattern identification by brain processes (Kaliukhovich and Vogels, 2013; Beyeler et al, 2013; Safaai et al, 2013; Cerda and Girau, 2013; Brasselet et al, 2012). In order to extract spatiotemporal patterns of multi-neuron firing that have specificity for cognition and memory in primate brain requires that the patterns be obtained within subareas representative of input-output flow of information through the structure which has been previously shown for hippocampus and prefrontal cortex (Hampson et al 2012b.…”

“…This type of condition would promote “generalization” since the firing of TT cells reflects spatiotemporal overlap in the activation of multiple, but different, hierarchical circuits. While it is clear that degree of exposure and similarity of contexts provide a basis for generalization, hierarchical circuitry also provides selective access to “shared” information which would not be available if circuits were more specific such as for sensory detection or response selection (Rotman and Klyachko, 2013; Plakke et al, 2013; Cerda and Girau, 2013; Brasselet et al, 2012). Thus ‘generalization’ is a natural outcome of hierarchical representation because of shared elements encoded and combined in different ways due to different Conjunctive cells activated at the same time.…”