Spatial information enhanced by non-spatial information in hippocampal granule cells

Abstract

The hippocampus organizes sequential memory composed of non-spatial information (such as objects and odors) and spatial information (places). The dentate gyrus (DG) in the hippocampus receives two types of information from the lateral and medial entorhinal cortices. Non-spatial and spatial information is delivered respectively to distal and medial dendrites (MDs) of granule cells (GCs) within the molecular layer in the DG. To investigate the role of the association of those two inputs, we measured the response characteristics of distal and MDs of a GC in a rat hippocampal slice and developed a multi-compartment GC model with dynamic synapses; this model reproduces the response characteristics of the dendrites. Upon applying random inputs or input sequences generated by a Markov process to the computational model, it was found that a high-frequency random pulse input to distal dendrites (DDs) and, separately, regular burst inputs to MDs were effective for inducing GC activation. Furthermore, when the random and theta burst inputs were simultaneously applied to the respective dendrites, the pattern discrimination for theta burst input to MDs that caused slight GC activation was enhanced in the presence of random input to DDs. These results suggest that the temporal pattern discrimination of spatial information is originally involved in a synaptic characteristic in GCs and is enhanced by non-spatial information input to DDs. Consequently, the co-activation of two separate inputs may play a crucial role in the information processing on dendrites of GCs by usefully combing each temporal sequence.

On the time course of short-term forgetting: a human experimental model for the sense of balance

“…When considering spatial memory phenomena and the sense of balance a brain region of particular interest is the hippocampal formation. It plays a key role both in spatial orientation (O’Keefe 1990 ) and in the formation and retrieval of memories, including the linkage of episodes to a spatial context (Burgess et al 2002 ; Hayakawa et al 2015 ; Kumaran and Maguire 2007 ; Ventriglia 2008 ; Wagatsuma and Yamaguchi 2007 ). The hippocampus receives input from many areas of the cerebral association cortex, e.g.…”

“…Neurons whose firing rate is a function of the animal’s head direction in the plane of locomotion have been found in several regions of the mammalian brain. These so-called “head direction (HD) cells” are hierarchically organized in a circuit originating in the dorsal tegmental nucleus (Sharp et al 2001b ) and projecting serially via the lateral mammillary nucleus (Blair et al 1998 ; Stackman and Taube 1998 ), anterodorsal thalamus (Taube 1995 ; Blair and Sharp 1995 ) and postsubiculum (Ranck 1984 ; Taube et al 1990a ) to the entorhinal cortex (Sargolini et al 2006 ) the latter being a major link between the hippocampus and other regions of the brain (Hayakawa et al 2015 ; Zhang et al 2013 ). For detailed accounts on the head direction system, the reader should consult reviews by Hartley et al ( 2013 ), Knierim and Hamilton ( 2011 ) or Taube ( 2007 ).…”

An Energy Model of Place Cell Network in Three Dimensional Space

“…Because these cells have remarkable activity patterns in non-sensor systems, their firing structures may reflect the internal operations of the system. The study of place cells, grid cells and the spatial representation system of the brain can provide a deeper understanding of cortical network dynamics (Yates, 2013 ; Hayakawa et al, 2015 ; Pfeiffer and Foster, 2015 ; Bechtel, 2016 ; Geiller et al, 2017 ; Kentros et al, 2017 ; Scaplen et al, 2017 ; Trimper et al, 2017 ).…”

Finite time synchronization of memristor-based Cohen-Grossberg neural networks with mixed delays

“…Although there have been many results about delayed Cohen-Grossberg neural networks [ 31 – 40 ], few of them are related to delayed MCGNNs. Recently, the exponential synchronization of MCGNNs with mixed delays was discussed in [ 41 ], the function projective synchronization of MCGNNs with time-varying discrete delays was studied in [ 42 ]. Obviously, most published results about synchronization control of MCGNNs only consider asymptotical synchronization and exponential synchronization, which mean that the synchronization time is infinite, but in application fields, it is more meaningful that the synchronization can be achieved in finite time.…”